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  Subjects -> STATISTICS (Total: 130 journals)
Showing 1 - 151 of 151 Journals sorted by number of followers
Review of Economics and Statistics     Hybrid Journal   (Followers: 150)
Statistics in Medicine     Hybrid Journal   (Followers: 136)
Journal of Econometrics     Hybrid Journal   (Followers: 83)
Journal of the American Statistical Association     Full-text available via subscription   (Followers: 72, SJR: 3.746, CiteScore: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Biometrics     Hybrid Journal   (Followers: 50)
Sociological Methods & Research     Hybrid Journal   (Followers: 43)
Journal of the Royal Statistical Society, Series B (Statistical Methodology)     Hybrid Journal   (Followers: 41)
Journal of Business & Economic Statistics     Full-text available via subscription   (Followers: 39, SJR: 3.664, CiteScore: 2)
Journal of the Royal Statistical Society Series C (Applied Statistics)     Hybrid Journal   (Followers: 37)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 35)
Oxford Bulletin of Economics and Statistics     Hybrid Journal   (Followers: 33)
Journal of Risk and Uncertainty     Hybrid Journal   (Followers: 33)
Journal of the Royal Statistical Society, Series A (Statistics in Society)     Hybrid Journal   (Followers: 28)
Statistical Methods in Medical Research     Hybrid Journal   (Followers: 28)
The American Statistician     Full-text available via subscription   (Followers: 26)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of Biopharmaceutical Statistics     Hybrid Journal   (Followers: 23)
Journal of Computational & Graphical Statistics     Full-text available via subscription   (Followers: 21)
Journal of Applied Statistics     Hybrid Journal   (Followers: 20)
Journal of Forecasting     Hybrid Journal   (Followers: 20)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 18)
Statistical Modelling     Hybrid Journal   (Followers: 18)
International Journal of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Journal of Statistical Software     Open Access   (Followers: 16, SJR: 13.802, CiteScore: 16)
Journal of Time Series Analysis     Hybrid Journal   (Followers: 16)
Risk Management     Hybrid Journal   (Followers: 16)
Pharmaceutical Statistics     Hybrid Journal   (Followers: 15)
Computational Statistics     Hybrid Journal   (Followers: 15)
Statistics and Computing     Hybrid Journal   (Followers: 14)
Demographic Research     Open Access   (Followers: 14)
Statistics & Probability Letters     Hybrid Journal   (Followers: 13)
Journal of Statistical Physics     Hybrid Journal   (Followers: 13)
Australian & New Zealand Journal of Statistics     Hybrid Journal   (Followers: 12)
International Statistical Review     Hybrid Journal   (Followers: 12)
Decisions in Economics and Finance     Hybrid Journal   (Followers: 12)
Structural and Multidisciplinary Optimization     Hybrid Journal   (Followers: 12)
Statistics: A Journal of Theoretical and Applied Statistics     Hybrid Journal   (Followers: 12)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 11)
Communications in Statistics - Theory and Methods     Hybrid Journal   (Followers: 11)
Advances in Complex Systems     Hybrid Journal   (Followers: 10)
Journal of Probability and Statistics     Open Access   (Followers: 10)
The Canadian Journal of Statistics / La Revue Canadienne de Statistique     Hybrid Journal   (Followers: 10)
Biometrical Journal     Hybrid Journal   (Followers: 9)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Scandinavian Journal of Statistics     Hybrid Journal   (Followers: 9)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Fuzzy Optimization and Decision Making     Hybrid Journal   (Followers: 8)
Current Research in Biostatistics     Open Access   (Followers: 8)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Multivariate Behavioral Research     Hybrid Journal   (Followers: 8)
Stata Journal     Full-text available via subscription   (Followers: 8)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Journal of Statistical Planning and Inference     Hybrid Journal   (Followers: 7)
Handbook of Statistics     Full-text available via subscription   (Followers: 7)
Journal of Combinatorial Optimization     Hybrid Journal   (Followers: 7)
Journal of Educational and Behavioral Statistics     Hybrid Journal   (Followers: 7)
Lifetime Data Analysis     Hybrid Journal   (Followers: 7)
Queueing Systems     Hybrid Journal   (Followers: 7)
Research Synthesis Methods     Hybrid Journal   (Followers: 7)
Significance     Hybrid Journal   (Followers: 7)
Environmental and Ecological Statistics     Hybrid Journal   (Followers: 7)
International Journal of Computational Economics and Econometrics     Hybrid Journal   (Followers: 6)
Journal of Mathematics and Statistics     Open Access   (Followers: 6)
Journal of Global Optimization     Hybrid Journal   (Followers: 6)
Journal of Nonparametric Statistics     Hybrid Journal   (Followers: 6)
Statistical Methods and Applications     Hybrid Journal   (Followers: 6)
Law, Probability and Risk     Hybrid Journal   (Followers: 6)
Engineering With Computers     Hybrid Journal   (Followers: 5)
Optimization Methods and Software     Hybrid Journal   (Followers: 5)
CHANCE     Hybrid Journal   (Followers: 5)
Handbook of Numerical Analysis     Full-text available via subscription   (Followers: 4)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Mathematical Methods of Statistics     Hybrid Journal   (Followers: 4)
ESAIM: Probability and Statistics     Open Access   (Followers: 4)
Metrika     Hybrid Journal   (Followers: 4)
Statistical Papers     Hybrid Journal   (Followers: 4)
Monthly Statistics of International Trade - Statistiques mensuelles du commerce international     Full-text available via subscription   (Followers: 3)
Sankhya A     Hybrid Journal   (Followers: 3)
Journal of Statistical and Econometric Methods     Open Access   (Followers: 3)
Journal of Theoretical Probability     Hybrid Journal   (Followers: 3)
Statistical Inference for Stochastic Processes     Hybrid Journal   (Followers: 3)
Journal of Algebraic Combinatorics     Hybrid Journal   (Followers: 3)
Stochastic Models     Hybrid Journal   (Followers: 2)
Building Simulation     Hybrid Journal   (Followers: 2)
Stochastics An International Journal of Probability and Stochastic Processes: formerly Stochastics and Stochastics Reports     Hybrid Journal   (Followers: 2)
IEA World Energy Statistics and Balances -     Full-text available via subscription   (Followers: 2)
Optimization Letters     Hybrid Journal   (Followers: 2)
TEST     Hybrid Journal   (Followers: 2)
Technology Innovations in Statistics Education (TISE)     Open Access   (Followers: 2)
Extremes     Hybrid Journal   (Followers: 2)
AStA Advances in Statistical Analysis     Hybrid Journal   (Followers: 2)
International Journal of Stochastic Analysis     Open Access   (Followers: 2)
Statistica Neerlandica     Hybrid Journal   (Followers: 1)
Wiley Interdisciplinary Reviews - Computational Statistics     Hybrid Journal   (Followers: 1)
Measurement Interdisciplinary Research and Perspectives     Hybrid Journal   (Followers: 1)
Statistics and Economics     Open Access  
Review of Socionetwork Strategies     Hybrid Journal  
SourceOECD Measuring Globalisation Statistics - SourceOCDE Mesurer la mondialisation - Base de donnees statistiques     Full-text available via subscription  
Journal of the Korean Statistical Society     Hybrid Journal  
Sequential Analysis: Design Methods and Applications     Hybrid Journal  

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Statistical Methods in Medical Research
Journal Prestige (SJR): 1.402
Citation Impact (citeScore): 2
Number of Followers: 28  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0962-2802 - ISSN (Online) 1477-0334
Published by Sage Publications Homepage  [1174 journals]
  • Nonparametric kernel estimation of the probability of cure in a mixture
           cure model when the cure status is partially observed

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      Authors: Wende Clarence Safari, Ignacio López-de-Ullibarri, María Amalia Jácome
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Cure models are a class of time-to-event models where a proportion of individuals will never experience the event of interest. The lifetimes of these so-called cured individuals are always censored. It is usually assumed that one never knows which censored observation is cured and which is uncured, so the cure status is unknown for censored times. In this paper, we develop a method to estimate the probability of cure in the mixture cure model when some censored individuals are known to be cured. A cure probability estimator that incorporates the cure status information is introduced. This estimator is shown to be strongly consistent and asymptotically normally distributed. Two alternative estimators are also presented. The first one considers a competing risks approach with two types of competing events, the event of interest and the cure. The second alternative estimator is based on the fact that the probability of cure can be written as the conditional mean of the cure status. Hence, nonparametric regression methods can be applied to estimate this conditional mean. However, the cure status remains unknown for some censored individuals. Consequently, the application of regression methods in this context requires handling missing data in the response variable (cure status). Simulations are performed to evaluate the finite sample performance of the estimators, and we apply them to the analysis of two datasets related to survival of breast cancer patients and length of hospital stay of COVID-19 patients requiring intensive care.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-01T08:01:22Z
      DOI: 10.1177/09622802221115880
       
  • An efficient approach for optimizing the cost-effective individualized
           treatment rule using conditional random forest

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      Authors: Yizhe Xu, Tom H. Greene, Adam P. Bress, Brandon K. Bellows, Yue Zhang, Zugui Zhang, Paul Kolm, William S. Weintraub, Andrew S. Moran, Jincheng Shen
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective individualized treatment rule under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit to assess the trade-off between health benefits and related costs. We estimate cost-effective individualized treatment rule as a function of patients’ characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal cost-effective individualized treatment rule using net-monetary-benefit-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial to illustrate the cost-effectiveness gains of assigning customized intensive blood pressure therapy.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-01T08:00:56Z
      DOI: 10.1177/09622802221115876
       
  • Testing latent class of subjects with structural zeros in negative
           binomial models with applications to gut microbiome data

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      Authors: Peng Ye, Xinhui Qiao, Wan Tang, Chunyi Wang, Hua He
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Human microbiome research has become a hot-spot in health and medical research in the past decade due to the rapid development of modern high-throughput. Typical data in a microbiome study consisting of the operational taxonomic unit counts may have over-dispersion and/or structural zero issues. In such cases, negative binomial models can be applied to address the over-dispersion issue, while zero-inflated negative binomial models can be applied to address both issues. In practice, it is essential to know if there is zero-inflation in the data before applying negative binomial or zero-inflated negative binomial models because zero-inflated negative binomial models may be unnecessarily complex and difficult to interpret, or may even suffer from convergence issues if there is no zero-inflation in the data. On the other hand, negative binomial models may yield invalid inferences if the data does exhibit excessive zeros. In this paper, we develop a new test for detecting zero-inflation resulting from a latent class of subjects with structural zeros in a negative binomial regression model by directly comparing the amount of observed zeros with what would be expected under the negative binomial regression model. A closed form of the test statistic as well as its asymptotic properties are derived based on estimating equations. Intensive simulation studies are conducted to investigate the performance of the new test and compare it with the classical Wald, likelihood ratio, and score tests. The tests are also applied to human gut microbiome data to test latent class in microbial genera.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-28T06:16:04Z
      DOI: 10.1177/09622802221115881
       
  • Analysis of hospital readmissions with competing risks

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      Authors: Wenbo Wu, Kevin He, Xu Shi, Douglas E Schaubel, John D Kalbfleisch
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The 30-day hospital readmission rate has been used in provider profiling for evaluating inter-provider care coordination, medical cost effectiveness, and patient quality of life. Current profiling analyzes use logistic regression to model 30-day readmission as a binary outcome, but one disadvantage of this approach is that this outcome is strongly affected by competing risks (e.g., death). Thus, one, perhaps unintended, consequence is that if two facilities have the same rates of readmission, the one with the higher rate of competing risks will have the lower 30-day readmission rate. We propose a discrete time competing risk model wherein the cause-specific readmission hazard is used to assess provider-level effects. This approach takes account of the timing of events and focuses on the readmission rates which are of primary interest. The quality measure, then is a standardized readmission ratio, akin to a standardized mortality ratio. This measure is not systematically affected by the rate of competing risks. To facilitate the estimation and inference of a large number of provider effects, we develop an efficient Blockwise Inversion Newton algorithm, and a stabilized robust score test that overcomes the conservative nature of the classical robust score test. An application to dialysis patients demonstrates improved profiling, model fitting, and outlier detection over existing methods.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-28T06:15:39Z
      DOI: 10.1177/09622802221115879
       
  • Ensemble methods for survival function estimation with time-varying
           covariates

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      Authors: Weichi Yao, Halina Frydman, Denis Larocque, Jeffrey S Simonoff
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Survival data with time-varying covariates are common in practice. If relevant, they can improve on the estimation of a survival function. However, the traditional survival forests—conditional inference forest, relative risk forest and random survival forest—have accommodated only time-invariant covariates. We generalize the conditional inference and relative risk forests to allow time-varying covariates. We also propose a general framework for estimation of a survival function in the presence of time-varying covariates. We compare their performance with that of the Cox model and transformation forest, adapted here to accommodate time-varying covariates, through a comprehensive simulation study in which the Kaplan-Meier estimate serves as a benchmark, and performance is compared using the integrated [math] difference between the true and estimated survival functions. In general, the performance of the two proposed forests substantially improves over the Kaplan-Meier estimate. Taking into account all other factors, under the proportional hazard setting, the best method is always one of the two proposed forests, while under the non-proportional hazard setting, it is the adapted transformation forest. [math]-fold cross-validation is used as an effective tool to choose between the methods in practice.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-27T04:56:41Z
      DOI: 10.1177/09622802221111549
       
  • The benefits of covariate adjustment for adaptive multi-arm designs

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      Authors: Kim May Lee, David S. Robertson, Thomas Jaki, Richard Emsley
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-25T12:06:58Z
      DOI: 10.1177/09622802221114544
       
  • Reference ranges: Why tolerance intervals should not be used. Comment on
           Liu, Bretz and Cortina-Borja, Reference range: Which statistical intervals
           to use' SMMR, 2021,Vol. 30(2) 523–534

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      Authors: Stefan Wellek, Christine Jennen-Steinmetz
      Abstract: Statistical Methods in Medical Research, Ahead of Print.

      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-15T07:24:53Z
      DOI: 10.1177/09622802221114538
       
  • Optimal single-arm two-stage designs with consideration of dependency on
           efficacy and safety

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      Authors: Huan Yin, Weizhen Wang, Zhongzhan Zhang
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In phase II clinical trials on cancer, it is of great interest to establish the efficacy and safety of a new treatment simultaneously. Existing hypotheses may not achieve this goal effectively. We introduce two new sets of hypotheses that consider the association between the two factors, then construct the optimal two-stage designs for the hypotheses. The proposed designs strictly control the maximum type I error rate at the given nominal level [math], maintain the minimum power at least the given [math], and have the smallest expected total sample size under the null hypothesis. Furthermore, an algorithm is provided to compute these designs. R-codes are given in the Supplemental Material.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-15T07:24:33Z
      DOI: 10.1177/09622802221111553
       
  • A comparison of two frameworks for multi-state modelling, applied to
           outcomes after hospital admissions with COVID-19

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      Authors: Christopher H Jackson, Brian DM Tom, Peter D Kirwan, Sema Mandal, Shaun R Seaman, Kevin Kunzmann, Anne M Presanis, Daniela De Angelis
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using ‘cure-rate’ models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-15T07:24:13Z
      DOI: 10.1177/09622802221106720
       
  • Hunting for protective drugs at the break of a pandemic: Causal inference
           from hospital data

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      Authors: Carlo Berzuini, Luisa Bernardinelli
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      At the break of a pandemic, the protective efficacy of therapeutic interventions needs rapid evaluation. An experimental approach to the problem will not always be appropriate. An alternative route are observational studies, whether based on regional health service data or hospital records. In this paper, we discuss the use of methods of causal inference for the analysis of such data, with special reference to causal questions that may arise in a pandemic. We apply the methods by using the aid of a directed acyclic graph (DAG) representation of the problem, to encode our causal assumptions and to logically connect the scientific questions. We illustrate the usefulness of DAGs in the context of a controversy over the effects of renin aldosterone system inhibitors (RASIs) in hypertensive individuals at risk of (or affected by) severe acute respiratory syndrome coronavirus 2 disease. We consider questions concerning the existence and the directions of those effects, their underlying mechanisms, and the possible dependence of the effects on context variables. This paper describes the cognitive steps that led to a DAG representation of the problem, based on background knowledge and evidence from past studies, and the use of the DAG to analyze our hospital data and assess the interpretive limits of the results. Our study contributed to subverting early opinions about RASIs, by suggesting that these drugs may indeed protect the older hypertensive Covid-19 patients from the consequences of the disease. Mechanistic interaction methods revealed that the benefit may be greater (in a sense to be made clear) in the older stratum of the population.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-15T07:24:04Z
      DOI: 10.1177/09622802221098428
       
  • Joint analysis of informatively interval-censored failure time and panel
           count data

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      Authors: Shuying Wang, Chunjie Wang, Xinyuan Song, Da Xu
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Interval-censored failure time and panel count data, which frequently arise in medical studies and social sciences, are two types of important incomplete data. Although methods for their joint analysis have been available in the literature, they did not consider the observation process, which may depend on the failure time and/or panel count of interest. This study considers a three-component joint model to analyze interval-censored failure time, panel counts, and the observation process within a unique framework. Gamma and distribution-free frailties are introduced to jointly model the interdependency among the interval-censored data, panel count data, and the observation process. We propose a sieve maximum likelihood approach coupled with Bernstein polynomial approximation to estimate the unknown parameters and baseline hazard function. The asymptotic properties of the resulting estimators are established. An extensive simulation study suggests that the proposed procedure works well for practical situations. An application of the method to a real-life dataset collected from a cardiac allograft vasculopathy study is presented.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-12T07:11:27Z
      DOI: 10.1177/09622802221111559
       
  • Unified approach to optimal estimation of mean and standard deviation from
           sample summaries

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      Authors: Narayanaswamy Balakrishnan, Jan Rychtář, Dewey Taylor, Stephen D Walter
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Recently, various methods have been developed to estimate the sample mean and standard deviation when only the sample size, and other selected sample summaries are reported. In this paper, we provide a unified approach to optimal estimation that can be easily adopted when only some summary statistics are reported. We show that the proposed estimators have the lowest variance among linear unbiased estimators. We also show that in the most commonly reported cases, that is, when only a three-number or five-number summary is reported, the newly proposed estimators match the previously developed estimators. Finally, we demonstrate the performance of the estimators numerically.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-12T07:11:07Z
      DOI: 10.1177/09622802221111546
       
  • Uncertainty quantification for epidemiological forecasts of COVID-19
           through combinations of model predictions

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      Authors: D. S. Silk, V. E. Bowman, D. Semochkina, U. Dalrymple, D. C. Woods
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Scientific advice to the UK government throughout the COVID-19 pandemic has been informed by ensembles of epidemiological models provided by members of the Scientific Pandemic Influenza group on Modelling. Among other applications, the model ensembles have been used to forecast daily incidence, deaths and hospitalizations. The models differ in approach (e.g. deterministic or agent-based) and in assumptions made about the disease and population. These differences capture genuine uncertainty in the understanding of disease dynamics and in the choice of simplifying assumptions underpinning the model. Although analyses of multi-model ensembles can be logistically challenging when time-frames are short, accounting for structural uncertainty can improve accuracy and reduce the risk of over-confidence in predictions. In this study, we compare the performance of various ensemble methods to combine short-term (14-day) COVID-19 forecasts within the context of the pandemic response. We address practical issues around the availability of model predictions and make some initial proposals to address the shortcomings of standard methods in this challenging situation.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-08T06:10:29Z
      DOI: 10.1177/09622802221109523
       
  • Single reader between-cases AUC estimator with nested data

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      Authors: Hongfei Du, Si Wen, Yufei Guo, Fang Jin, Brandon D Gallas
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The area under the receiver operating characteristic curve (AUC) is widely used in evaluating diagnostic performance for many clinical tasks. It is still challenging to evaluate the reading performance of distinguishing between positive and negative regions of interest (ROIs) in the nested-data problem, where multiple ROIs are nested within the cases. To address this issue, we identify two kinds of AUC estimators, within-cases AUC and between-cases AUC. We focus on the between-cases AUC estimator, since our main research interest is in patient-level diagnostic performance rather than location-level performance (the ability to separate ROIs with and without disease within each patient). Another reason is that as the case number increases, the number of between-cases paired ROIs is much larger than the number of within-cases ROIs. We provide estimators for the variance of the between-cases AUC and for the covariance when there are two readers. We derive and prove the above estimators’ theoretical values based on a simulation model and characterize their behavior using Monte Carlo simulation results. We also provide a real-data example. Moreover, we connect the distribution-based simulation model with the simulation model based on the linear mixed-effect model, which helps better understand the sources of variation in the simulated dataset.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-06T01:48:19Z
      DOI: 10.1177/09622802221111539
       
  • Statistical methods used to combine the effective reproduction number,
           [math], and other related measures of COVID-19 in the UK

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      Authors: Thomas Maishman, Stephanie Schaap, Daniel S Silk, Sarah J Nevitt, David C Woods, Veronica E Bowman
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-04T06:09:57Z
      DOI: 10.1177/09622802221109506
       
  • Authors’ response

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      Authors: Wei Liu, Frank Bretz, Mario Cortina-Borja
      Abstract: Statistical Methods in Medical Research, Ahead of Print.

      Citation: Statistical Methods in Medical Research
      PubDate: 2022-07-02T02:51:22Z
      DOI: 10.1177/09622802221111376
       
  • Analysis of survival data with cure fraction and variable selection: A
           pseudo-observations approach

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      Authors: Chien-Lin Su, Sy Han Chiou, Feng-Chang Lin, Robert W Platt
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In biomedical studies, survival data with a cure fraction (the proportion of subjects cured of disease) are commonly encountered. The mixture cure and bounded cumulative hazard models are two main types of cure fraction models when analyzing survival data with long-term survivors. In this article, in the framework of the Cox proportional hazards mixture cure model and bounded cumulative hazard model, we propose several estimators utilizing pseudo-observations to assess the effects of covariates on the cure rate and the risk of having the event of interest for survival data with a cure fraction. A variable selection procedure is also presented based on the pseudo-observations using penalized generalized estimating equations for proportional hazards mixture cure and bounded cumulative hazard models. Extensive simulation studies are conducted to examine the proposed methods. The proposed technique is demonstrated through applications to a melanoma study and a dental data set with high-dimensional covariates.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-27T07:11:43Z
      DOI: 10.1177/09622802221108579
       
  • Methods of analysis for survival outcomes with time-updated mediators,
           with application to longitudinal disease registry data

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      Authors: Kamaryn T Tanner, Linda D Sharples, Rhian M Daniel, Ruth H Keogh
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Mediation analysis is a useful tool to illuminate the mechanisms through which an exposure affects an outcome but statistical challenges exist with time-to-event outcomes and longitudinal observational data. Natural direct and indirect effects cannot be identified when there are exposure-induced confounders of the mediator-outcome relationship. Previous measurements of a repeatedly-measured mediator may themselves confound the relationship between the mediator and the outcome. To overcome these obstacles, two recent methods have been proposed, one based on path-specific effects and one based on an additive hazards model and the concept of exposure splitting. We investigate these techniques, focusing on their application to observational datasets. We apply both methods to an analysis of the UK Cystic Fibrosis Registry dataset to identify how much of the relationship between onset of cystic fibrosis-related diabetes and subsequent survival acts through pulmonary function. Statistical properties of the methods are investigated using simulation. Both methods produce unbiased estimates of indirect and direct effects in scenarios consistent with their stated assumptions but, if the data are measured infrequently, estimates may be biased. Findings are used to highlight considerations in the interpretation of the observational data analysis.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-17T06:20:28Z
      DOI: 10.1177/09622802221107104
       
  • The analysis of COVID-19 in-hospital mortality: A competing risk approach
           or a cure model'

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      Authors: Xiaonan Xue, Omar Saeed, Francesco Castagna, Ulrich P Jorde, Ilir Agalliu
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed “cure” of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-17T06:20:21Z
      DOI: 10.1177/09622802221106300
       
  • Group sequential methods for the Mann-Whitney parameter

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      Authors: Claus P Nowak, Tobias Mütze, Frank Konietschke
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Late phase clinical trials are occasionally planned with one or more interim analyses to allow for early termination or adaptation of the study. While extensive theory has been developed for the analysis of ordered categorical data in terms of the Wilcoxon-Mann-Whitney test, there has been comparatively little discussion in the group sequential literature on how to provide repeated confidence intervals and simple power formulas to ease sample size determination. Dealing more broadly with the nonparametric Behrens-Fisher problem, we focus on the comparison of two parallel treatment arms and show that the Wilcoxon-Mann-Whitney test, the Brunner-Munzel test, as well as a test procedure based on the log win odds, a modification of the win ratio, asymptotically follow the canonical joint distribution. In addition to developing power formulas based on these results, simulations confirm the adequacy of the proposed methods for a range of scenarios. Lastly, we apply our methodology to the FREEDOMS clinical trial (ClinicalTrials.gov Identifier: NCT00289978) in patients with relapse-remitting multiple sclerosis.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-14T06:12:31Z
      DOI: 10.1177/09622802221107103
       
  • Analyzing the overall effects of the microbiome abundance data with a
           Bayesian predictive value approach

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      Authors: Xinyan Zhang, Nengjun Yi
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The microbiome abundance data is known to be over-dispersed and sparse count data. Among various zero-inflated models, zero-inflated negative binomial (ZINB) model and zero-inflated beta binomial (ZIBB) model are the methods to analyze the microbiome abundance data. ZINB and ZIBB have two sets of parameters, which are for modeling the zero-inflation part and the count part separately. Most previous methods have focused on making inferences in terms of separate case-control effect for the zero-inflation part and the count part. However, in a case-control study, the primary interest is normally focused on the inference and a single interpretation of the overall unconditional mean (also known as the overall effect) of the microbiome abundance in microbiome studies. Here, we propose a Bayesian predictive value (BPV) approach to estimate the overall effect of the microbiome abundance. This approach is implemented based on R package brms. Hence, the parameters in the models will be estimated with two Markov chain Monte Carlo (MCMC) algorithms used in Stan. We performed simulations and real data applications to compare the proposed approach and R package glmmTMB with simulation method in the estimation and inference in terms of the ratio function between the overall effects from two groups in a case-control study. The results show that the performance of the BPV approach is better than R package glmmTMB with the simulation method in terms of lower absolute biases and relative absolute biases, and coverage probability being closer to the nominal level especially when the sample size is small and zero-inflation rate is high.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-13T06:44:15Z
      DOI: 10.1177/09622802221107106
       
  • Adjusting for time of infection or positive test when estimating the risk
           of a post-infection outcome in an epidemic

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      Authors: Shaun R Seaman, Tommy Nyberg, Christopher E Overton, David J Pascall, Anne M Presanis, Daniela De Angelis
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      When comparing the risk of a post-infection binary outcome, for example, hospitalisation, for two variants of an infectious pathogen, it is important to adjust for calendar time of infection. Typically, the infection time is unknown and positive test time used as a proxy for it. Positive test time may also be used when assessing how risk of the outcome changes over calendar time. We show that if time from infection to positive test is correlated with the outcome, the risk conditional on positive test time is a function of the trajectory of infection incidence. Hence, a risk ratio adjusted for positive test time can be quite different from the risk ratio adjusted for infection time. We propose a simple sensitivity analysis that indicates how risk ratios adjusted for positive test time and infection time may differ. This involves adjusting for a shifted positive test time, shifted to make the difference between it and infection time uncorrelated with the outcome. We illustrate this method by reanalysing published results on the relative risk of hospitalisation following infection with the Alpha versus pre-existing variants of SARS-CoV-2. Results indicate the relative risk adjusted for infection time may be lower than that adjusted for positive test time.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-13T06:44:04Z
      DOI: 10.1177/09622802221107105
       
  • MISL: Multiple imputation by super learning

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      Authors: Thomas Carpenito, Justin Manjourides
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Multiple imputation techniques are commonly used when data are missing, however, there are many options one can consider. Multivariate imputation by chained equations is a popular method for generating imputations but relies on specifying models when imputing missing values. In this work, we introduce multiple imputation by super learning, an update to the multivariate imputation by chained equations method to generate imputations with ensemble learning. Ensemble methodologies have recently gained attention for use in inference and prediction as they optimally combine a variety of user-specified parametric and non-parametric models and perform well when estimating complex functions, including those with interaction terms. Through two simulations we compare inferences made using the multiple imputation by super learning approach to those made with other commonly used multiple imputation methods and demonstrate multiple imputation by super learning as a superior option when considering characteristics such as bias, confidence interval coverage rate, and confidence interval width.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-06T05:14:45Z
      DOI: 10.1177/09622802221104238
       
  • Multiple imputation for cause-specific Cox models: Assessing methods for
           estimation and prediction

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      Authors: Edouard F Bonneville, Matthieu Resche-Rigon, Johannes Schetelig, Hein Putter, Liesbeth C de Wreede
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In studies analyzing competing time-to-event outcomes, interest often lies in both estimating the effects of baseline covariates on the cause-specific hazards and predicting cumulative incidence functions. When missing values occur in these baseline covariates, they may be discarded as part of a complete-case analysis or multiply imputed. In the latter case, the imputations may be performed either compatibly with a substantive model pre-specified as a cause-specific Cox model [substantive model compatible fully conditional specification (SMC-FCS)], or approximately so [multivariate imputation by chained equations (MICE)]. In a large simulation study, we assessed the performance of these three different methods in terms of estimating cause-specific regression coefficients and predicting cumulative incidence functions. Concerning regression coefficients, results provide further support for use of SMC-FCS over MICE, particularly when covariate effects are large and the baseline hazards of the competing events are substantially different. Complete-case analysis also shows adequate performance in settings where missingness is not outcome dependent. With regard to cumulative incidence prediction, SMC-FCS and MICE are performed more similarly, as also evidenced in the illustrative analysis of competing outcomes following a hematopoietic stem cell transplantation. The findings are discussed alongside recommendations for practising statisticians.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-06T05:14:09Z
      DOI: 10.1177/09622802221102623
       
  • Conversion of non-inferiority margin from hazard ratio to restricted mean
           survival time difference using data from multiple historical trials

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      Authors: Ruizhe Chen, Sanjib Basu, Jeffrey P Meyers, Qian Shi
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The restricted mean survival time measure has gained a lot of interests for designing and analyzing oncology trials with time-to-event endpoints due to its intuitive clinical interpretation and potentially high statistical power. In the non-inferiority trial literature, restricted mean survival time has been used as an alternative measure for reanalyzing a completed trial, which was originally designed and analyzed based on traditional proportional hazard model. However, the reanalysis procedure requires a conversion from the non-inferiority margin measured in hazard ratio to a non-inferiority margin measured by restricted mean survival time difference. An existing conversion method assumes a Weibull distribution for the population survival time of the historical active control group under the proportional hazard assumption using data from a single trial. In this article, we develop a methodology for non-inferiority margin conversion when data from multiple historical active control studies are available, and introduce a Kaplan-Meier estimator-based method for the non-inferiority margin conversion to relax the parametric assumption. We report extensive simulation studies to examine the performances of proposed methods under the Weibull data generative models and a piecewise-exponential data generative model that mimic the tumor recurrence and survival characteristics of advanced colon cancer. This work is motivated to achieve non-inferiority margin conversion, using historical patient-level data from a large colon cancer clinical database, to reanalyze an internationally collaborated non-inferiority study that evaluates 6-month versus 3-month duration of adjuvant chemotherapy in stage III colon cancer patients.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-01T06:00:01Z
      DOI: 10.1177/09622802221102621
       
  • Fitting joint models of longitudinal observations and time to event by
           sequential Bayesian updating

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      Authors: Paul McKeigue
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Joint modelling of longitudinal measurements and time to event, with longitudinal and event submodels coupled by latent state variables, has wide application in biostatistics. Standard methods for fitting these models require numerical integration to marginalize over the trajectories of the latent states, which is computationally prohibitive for high-dimensional data and for the large data sets that are generated from electronic health records. This paper describes an alternative model-fitting approach based on sequential Bayesian updating, which allows the likelihood to be factorized as the product of the likelihoods of a state-space model and a Poisson regression model. Updates for linear Gaussian state-space models can be efficiently generated with a Kalman filter and the approach can be implemented with existing software. An application to a publicly available data set is demonstrated.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-01T05:50:24Z
      DOI: 10.1177/09622802221104241
       
  • Path-specific effects in the presence of a survival outcome and causally
           ordered multiple mediators with application to genomic data

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      Authors: An-Shun Tai, Pei-Hsuan Lin, Yen-Tsung Huang, Sheng-Hsuan Lin
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Causal multimediation analysis (i.e. the causal mediation analysis with multiple mediators) is critical for understanding the effectiveness of interventions, especially in medical research. Deriving the path-specific effects of exposure on the outcome through a set of mediators can provide detail about the causal mechanism of interest However, existing models are usually restricted to partial decomposition, which can only be used to evaluate the cumulative effect of several paths. In genetics studies, partial decomposition fails to reflect the real causal effects mediated by genes, especially in complex gene regulatory networks. Moreover, because of the lack of a generalized identification procedure, the current multimediation analysis cannot be applied to the estimation of path-specific effects for any number of mediators. In this study, we derive the interventional analogs of path-specific effect for complete decomposition to address the difficulty of nonidentifiability. On the basis of two survival models of the outcome, we derive the generalized analytic forms for interventional analogs of path-specific effects by assuming the normal distributions of mediators. We apply the new methodology to investigate the causal mechanism of signature genes in lung cancer based on the cell cycle pathway, and the results clarify the gene pathway in cancer.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-30T06:45:10Z
      DOI: 10.1177/09622802221104239
       
  • Model-free screening for variables with treatment interaction

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      Authors: Shiferaw B Bizuayehu, Jin Xu
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Precision medicine is a medical paradigm that focuses on making effective treatment decision based on individual patient characteristics. When there are a large amount of patient information, such as patient’s genetic information, medical records and clinical measurements, available, it is of interest to select the covariates which have interactions with the treatment, for example, in determining the individualized treatment regime where only a subset of covariates with treatment interactions involves in decision making. We propose a marginal feature ranking and screening procedure for measuring interactions between the treatment and covariates. The method does not require imposing a specific model structure on the regression model and is applicable in a high dimensional setting. Theoretical properties in terms of consistency in ranking and selection are established. We demonstrate the finite sample performance of the proposed method by simulation and illustrate the applications with two real data examples from clinical trials.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-30T06:45:03Z
      DOI: 10.1177/09622802221102624
       
  • Doubly-robust estimator of the difference in restricted mean times lost
           with competing risks data

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      Authors: Jingyi Lin, Ludovic Trinquart
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In the context of competing risks data, the subdistribution hazard ratio has limited clinical interpretability to measure treatment effects. An alternative is the difference in restricted mean times lost (RMTL), which gives the mean time lost to a specific cause of failure between treatment groups. In non-randomized studies, the average causal effect is conventionally used for decision-making about treatment and public health policies. We show how the difference in RMTL can be estimated by contrasting the integrated cumulative incidence functions from a Fine-Gray model. We also show how the difference in RMTL can be estimated by using inverse probability of treatment weighting and contrasts between weighted non-parametric estimators of the area below the cumulative incidence. We use pseudo-observation approaches to estimate both component models and we integrate them into a doubly-robust estimator. We demonstrate that this estimator is consistent when either component is correctly specified. We conduct simulation studies to assess its finite-sample performance and demonstrate its inherited consistency property from its component models. We also examine the performance of this estimator under varying degrees of covariate overlap and under a model misspecification of nonlinearity. We apply the proposed method to assess biomarker-treatment interaction in subpopulations of the POPLAR and OAK randomized controlled trials of second-line therapy for advanced non-small-cell lung cancer.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-24T05:45:01Z
      DOI: 10.1177/09622802221102625
       
  • A dose–effect network meta-analysis model with application in
           antidepressants using restricted cubic splines

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      Authors: Tasnim Hamza, Toshi A Furukawa, Nicola Orsini, Andrea Cipriani, Cynthia P Iglesias, Georgia Salanti
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Network meta-analysis has been used to answer a range of clinical questions about the preferred intervention for a given condition. Although the effectiveness and safety of pharmacological agents depend on the dose administered, network meta-analysis applications typically ignore the role that drugs dosage plays in the results. This leads to more heterogeneity in the network. In this paper, we present a suite of network meta-analysis models that incorporate the dose–effect relationship using restricted cubic splines. We extend existing models into a dose–effect network meta-regression to account for study-level covariates and for groups of agents in a class-effect dose–effect network meta-analysis model. We apply our models to a network of aggregate data about the efficacy of 21 antidepressants and placebo for depression. We find that all antidepressants are more efficacious than placebo after a certain dose. Also, we identify the dose level at which each antidepressant's effect exceeds that of placebo and estimate the dose beyond which the effect of antidepressants no longer increases. When covariates were introduced to the model, we find that studies with small sample size tend to exaggerate antidepressants efficacy for several of the drugs. Our dose–effect network meta-analysis model with restricted cubic splines provides a flexible approach to modelling the dose–effect relationship in multiple interventions. Decision-makers can use our model to inform treatment choice.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-02-24T04:44:34Z
      DOI: 10.1177/09622802211070256
       
  • Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating
           the reproductive number

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      Authors: Matt J. Keeling, Louise Dyson, Glen Guyver-Fletcher, Alex Holmes, Malcolm G Semple, Michael J. Tildesley, Edward M. Hill
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, [math], has taken on special significance in terms of the general understanding of whether the epidemic is under control ([math]). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-01-17T04:12:50Z
      DOI: 10.1177/09622802211070257
       
  • Robust statistical inference for matched win statistics

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      Authors: Roland A. Matsouaka
      First page: 1423
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      As alternatives to the time-to-first-event analysis of composite endpoints, the win statistics, that is, the net benefit, the win ratio, and the win odds have been proposed to assess treatment effects, using a hierarchy of prioritized component outcomes based on clinical relevance or severity. Whether we are using paired organs of a human body or pair-matching patients by risk profiles or propensity scores, we can leverage the level of granularity of matched win statistics to assess the treatment effect. However, inference for the matched win statistics (net benefit, win ratio, and win odds)—quantities related to proportions—is either not available or unsatisfactory, especially in samples of small to moderate size or when the proportion of wins (or losses) is near 0 or 1. In this paper, we present methods to address these limitations. First, we introduce a different statistic to test for the null hypothesis of no treatment effect and provided a sample size formula. Then, we use the method of variance estimates recovery to derive reliable, boundary-respecting confidence intervals for the matched net benefit, win ratio, and win odds. Finally, a simulation study demonstrates the performance of the proposed methods. We illustrate the proposed methods with two data examples.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-17T05:25:42Z
      DOI: 10.1177/09622802221090761
       
  • Combined statistical decision limits based on two GH-2000 scores for the
           detection of growth hormone misuse

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      Authors: Wei Liu, Frank Bretz, Dankmar Böhning, Richard I.G. Holt, Yang Han, Walailuck Böhning, Nishan Guha, David A. Cowan
      First page: 1439
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The growth hormone-2000 biomarker method, based on the measurements of insulin-like growth factor-I and the amino-terminal pro-peptide of type III collagen, has been developed as a powerful technique for the detection of growth hormone misuse by athletes. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen are combined in gender-specific formulas to create the growth hormone-2000 score, which is used to determine whether growth hormone has been administered. To comply with World Anti-Doping Agency regulations, each analyte must be measured by two methods. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen can be measured by a number of approved methods, each leading to its own growth hormone-2000 score. Single decision limits for each growth hormone-2000 score have been introduced and developed by Bassett, Erotokritou-Mulligan, Holt, Böhning and their co-authors in a series of papers. These have been incorporated into the guidelines of the World Anti-Doping Agency. A joint decision limit was constructed based on the sample correlation between the two growth hormone-2000 scores generated from an available sample to increase the sensitivity of the biomarker method. This paper takes this idea further into a fully developed statistical approach. It constructs combined decision limits when two growth hormone-2000 scores from different assay combinations are used to decide whether an athlete has been misusing growth hormone. The combined decision limits are directly related to tolerance regions and constructed using a Bayesian approach. It is also shown to have highly satisfactory frequentist properties. The new approach meets the required false-positive rate with a pre-specified level of certainty.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-25T11:42:36Z
      DOI: 10.1177/09622802221093730
       
  • A Bayesian approach to simultaneous adjustment of misclassification and
           missingness in categorical covariates

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      Authors: Michelle Xia, Rexford M. Akakpo
      First page: 1449
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      This study considers concurrent adjustment of misclassification and missingness in categorical covariates in regression models. Under various misclassification and missingness mechanisms, we derive a general mixture regression structure for regression models that can incorporate multiple surrogates of categorical covariates that are subject to misclassification and missingness. In simulation studies, we demonstrate that including observations with missingness and/or multiple surrogates of the covariate helps alleviate the efficiency loss caused by misclassification. In addition, we study the efficacy of misclassification adjustment when the number of categories increases for the covariate of interest. Using data from the Longitudinal Studies of HIV-Associated Lung Infections and Complications, we perform simultaneous adjustment of misclassification and missingness in the self-reported cocaine and heroin use variable when assessing its association with lung density measures.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-04-27T08:32:50Z
      DOI: 10.1177/09622802221094941
       
  • Estimation of multiple ordered ROC curves using placement values

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      Authors: Soutik Ghosal, Katherine L Grantz, Zhen Chen
      First page: 1470
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In many diagnostic accuracy studies, a priori orders may be available on multiple receiver operating characteristic curves. For example, being closer to delivery, fetal ultrasound measures in the third trimester should be no less accurate than those in the second trimester in predicting small-for-gestational-age births. Such an a priori order should be incorporated in estimating receiver operating characteristic curves and associated summary accuracy statistics, as it can potentially improve statistical efficiency of these estimates. Early work in the literature has mainly taken an indirect approach to this task and has induced the desired a priori order through modeling test score distributions. We instead propose a new strategy that incorporates the order directly through the modeling of receiver operating characteristic curves. We achieve this by exploiting the link between placement value (the relative position of a diseased test score in the healthy score distribution), the cumulative distribution function of placement value, and receiver operating characteristic curve, and by building stochastically ordered random variables through mixture distributions. We take a Bayesian semiparametric approach in using Dirichlet process mixture models so that the placement values can be flexibly modeled. We conduct extensive simulation studies to examine the performance of the proposed methodology and apply the new framework to data from obstetrics and women’s health studies.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-04-22T06:54:08Z
      DOI: 10.1177/09622802221094940
       
  • Unbiased and robust analysis of co-localization in super-resolution images

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      Authors: Xueyan Liu, Clifford S. Guy, Emilio Boada-Romero, Douglas R. Green, Margaret E. Flanagan, Cheng Cheng, Hui Zhang
      First page: 1484
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Spatial data from high-resolution images abound in many scientific disciplines. For example, single-molecule localization microscopy, such as stochastic optical reconstruction microscopy, provides super-resolution images to help scientists investigate co-localization of proteins and hence their interactions inside cells, which are key events in living cells. However, there are few accurate methods for analyzing co-localization in super-resolution images. The current methods and software are prone to produce false-positive errors and are restricted to only 2-dimensional images. In this paper, we develop a novel statistical method to effectively address the problems of unbiased and robust quantification and comparison of protein co-localization for multiple 2- and 3-dimensional image datasets. This method significantly improves the analysis of protein co-localization using super-resolution image data, as shown by its excellent performance in simulation studies and an analysis of co-localization of protein light chain 3 and lysosomal-associated membrane protein 1 in cell autophagy. Moreover, this method is directly applicable to co-localization analyses in other disciplines, such as diagnostic imaging, epidemiology, environmental science, and ecology.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-04-22T06:53:51Z
      DOI: 10.1177/09622802221094133
       
  • On estimating the area under the ROC curve in ranked set sampling

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      Authors: M. Mahdizadeh, Ehsan Zamanzade
      First page: 1500
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In medical research, the receiver operating characteristic curve is widely used to evaluate accuracy of a continuous biomarker. The area under this curve is known as an index for overall performance of the biomarker. This article develops three new estimators of the area under the receiver operating characteristic curve in ranked set sampling. The first estimator is obtained under normality assumption. The two other estimators are constructed by applying a Box–Cox transformation on data, and then using either a parametric estimator or a kernel-density-based estimator. A simulation study is carried out to compare the proposed estimators with those available in the literature. It emerges that the new estimators offer some advantages in specific situations. Application of the methods is demonstrated using real data in the context of medicine.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-12T01:25:51Z
      DOI: 10.1177/09622802221097211
       
  • Design and analysis of partially randomized preference trials with
           propensity score stratification

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      Authors: Yumin Wang, Fan Li, Ondrej Blaha, Can Meng, Denise Esserman
      First page: 1515
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      While the two-stage randomized design allows us to unbiasedly evaluate the impact of patients’ treatment preference on the outcome of interest, it may not always be practical to implement in clinical practice; patients with a strong preference may not be willing to be randomized. The more pragmatic, partially randomized preference design (PRPD) allows patients who are unwilling to be randomized, but willing to state their preference, to receive their preferred treatment in lieu of the first-stage randomization in the two-stage design, at the cost of potentially introducing bias in estimating the effects of interest. In this article, we consider the application of propensity score stratification (PSS) in a PRPD to recreate a conditional first-stage randomization based on observed covariates, enabling the estimation and inference of the overall treatment, selection and preference effects with minimum bias. We additionally derive a set of closed-form sample size formulas for detecting all three effects of interest in a PSS-PRPD. Simulation studies demonstrate the bias reduction properties of the PSS-PRPD, and validate the accuracy of the proposed sample size formulas. Our results show that 5 to 10 propensity score strata may be needed to correct for biases in effect estimates, and the exact number of strata needed to achieve the best match between the empirical power and formula prediction may depend on the degree of effect heterogeneity. Finally, we demonstrate our proposed formulas by estimating the required sample sizes to detect treatment, selection and preference effects in the context of the Harapan Study.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-04-26T07:29:05Z
      DOI: 10.1177/09622802221095673
       
  • Estimating and testing the influence of early diagnosis on cancer survival
           via point effects of diagnoses and treatments

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      Authors: Xiaoqin Wang, Johannes Blom, Weimin Ye, Li Yin
      First page: 1538
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      A cancer diagnosis is part of a complex stochastic process, which involves patient's characteristics, diagnosing methods, an initial assessment of cancer progression, treatments and a certain outcome of interest. To evaluate the performance of diagnoses, one needs not only a consistent estimation of the causal effect under a specified regime of diagnoses and treatments but also reliable confidence interval, P-value and hypothesis testing of the causal effect. In this article, we identify causal effects under various regimes of diagnoses and treatments by the point effects of diagnoses and treatments and thus are able to estimate and test these causal effects by estimating and testing point effects in the familiar framework of single-point causal inference. Specifically, using data from a Swedish prognosis study of stomach cancer, we estimate and test the causal effects on cancer survival under various regimes of diagnosing and treating hospitals including the optimal regime. We also estimate and test the modification of the causal effect by age. With its simple setting, one can readily extend the example to a large variety of settings in the area of cancer diagnosis: different personal characteristics such as family history, different diagnosing procedures such as multistage screening, and different cancer outcomes such as cancer progression.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-05T05:27:42Z
      DOI: 10.1177/09622802221098429
       
  • Response-adaptive treatment randomization for multiple comparisons of
           treatments with recurrentevent responses

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      Authors: Jingya Gao, Feifang Hu, Siu Hung Cheung, Pei-Fang Su
      First page: 1549
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Recurrent event responses are frequently encountered during clinical trials of treatments for certain diseases, such as asthma. The recurrence rates of different treatments are often compared by applying the negative binomial model. In addition, a balanced treatment-allocation procedure that assigns the same number of patients to each treatment is often applied. Recently, a response-adaptive treatment-allocation procedure has been developed for trials with recurrent event data, and has been shown to be superior to balanced treatment allocation. However, this response-adaptive treatment allocation procedure is only applicable for the comparison of two treatments. In this paper, we derive response-adaptive treatment-allocation procedures for trials which comprise several treatments. As pairwise comparisons and multiple comparisons with a control are two common multiple-testing scenarios in trials with more than two treatments, corresponding treatment-allocation procedures for these scenarios are also investigated. The redesign of two clinical studies illustrates the clinical benefits that would be obtained from our proposed response-adaptive treatment-allocation procedures.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-04-29T05:20:52Z
      DOI: 10.1177/09622802221095244
       
  • Variance partitioning in spatio-temporal disease mapping models

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      Authors: Maria Franco-Villoria, Massimo Ventrucci, Håvard Rue
      First page: 1566
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov random fields, that we name the variance partitioning model. The variance partitioning model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability. The use of a penalized complexity prior on the mixing parameter aids in coding prior information in an intuitive way. We illustrate the advantages of the variance partitioning model using two case studies.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-19T04:45:46Z
      DOI: 10.1177/09622802221099642
       
  • An objective bayesian approach to estimation in multistage experiments

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      Authors: Pierre Bunouf
      First page: 1579
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      This article presents a Bayesian approach to estimation in multistage experiments based on the reference prior theory. The idea of deriving design-dependent priors was first introduced using Jeffreys’ criterion. A theoretical framework was then established by showing that explicit reference to the design is fully Bayesian justified and Bayesian objectivity cannot ignore such information. Extending the work to multi-parameter problems, a general form of priors was derived from the reference prior theory. In this article, I evidence the good frequentist properties of the reference posterior estimators with normally distributed data. As a notable advance, I address the issue of the point and the interval estimations upon experiment termination. The approach is applied to a data set collected in a clinical trial in schizophrenia with the possibility to stop the trial early if interim results provide sufficient evidence of efficacy or futility. Finally, I discuss the idea of using the reference posterior estimators as a default choice for objective estimation in multistage experiment.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-11T07:51:48Z
      DOI: 10.1177/09622802221099640
       
  • A poisson-multinomial spatial model for simultaneous outbreaks with
           application to arboviral diseases

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      Authors: Alexandra M. Schmidt, Laís P. Freitas, Oswaldo G. Cruz, Marilia S. Carvalho
      First page: 1590
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Dengue, Zika, and chikungunya are arboviral diseases (AVD) transmitted mainly by Aedes aegypti. Rio de Janeiro city, Brazil, has been endemic for dengue for over 30 years, and experienced the first joint epidemic of the three diseases between 2015-2016. They present similar symptoms and only a small proportion of cases are laboratory-confirmed. These facts lead to potential misdiagnosis and, consequently, uncertainty in the registration of the cases. We have available the number of cases of each disease for the [math] neighborhoods of Rio de Janeiro. We propose a Poisson model for the total number of cases of Aedes-borne diseases and, conditioned on the total, we assume a multinomial model for the allocation of the number of cases of each of the diseases across the neighborhoods. This provides simultaneously the estimation of the associations of the relative risk of the total cases of AVD with environmental and socioeconomic variables; and the estimation of the probability of presence of each disease as a function of available covariates. Our findings suggest that a one standard deviation increase in the social development index decreases the relative risk of the total cases of AVD by 28%. Neighborhoods with smaller proportion of green area had greater odds of having chikungunya in comparison to dengue and Zika. A one standard deviation increase in population density decreases the odds of a neighborhood having Zika instead of dengue by 18% but increases the odds of chikungunya in comparison to dengue by 18% and by 43% in comparison to Zika.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-06T05:14:24Z
      DOI: 10.1177/09622802221102628
       
  • MEGH: A parametric class of general hazard models for clustered survival
           data

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      Authors: Francisco Javier Rubio, Reza Drikvandi
      First page: 1603
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative regions. The analysis of such data requires accounting for between-cluster variability. Ignoring such variability would impose unrealistic assumptions in the analysis and could affect the inference on the statistical models. We develop a novel parametric mixed-effects general hazard (MEGH) model that is particularly suitable for the analysis of clustered survival data. The proposed structure generalises the mixed-effects proportional hazards and mixed-effects accelerated failure time structures, among other structures, which are obtained as special cases of the MEGH structure. We develop a likelihood-based algorithm for parameter estimation in general subclasses of the MEGH model, which is implemented in our R package MEGH. We propose diagnostic tools for assessing the random effects and their distributional assumption in the proposed MEGH model. We investigate the performance of the MEGH model using theoretical and simulation studies, as well as a real data application on leukaemia.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-06-07T05:47:42Z
      DOI: 10.1177/09622802221102620
       
  • Simulation extrapolation method for measurement error: A review

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      Authors: Varadan Sevilimedu, Lili Yu
      First page: 1617
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Measurement error is pervasive in statistics due to the non-availability of authentic data. The reasons for measurement error mainly relate to cost, convenience, and human error. Measurement error can result in non-negligible bias due to attenuated estimates, reduced power of statistical tests, and lower coverage probabilities of the coefficient estimators in a regression model. Several methods have been proposed to correct for measurement error, all of which can be grouped into two broad categories based on the underlying model—functional and structural. Functional models provide flexibility and robustness to estimators by placing minimal or no assumptions on the distribution of the mismeasured covariate or by treating them as a fixed entity, as opposed to a structural model which treats the underlying mismeasured covariates as random with a specified structure. The simulation extrapolation method is one method that is used for the partial correction of measurement error in both structural and functional models. Reviews of measurement error correction techniques are available in the literature. However, none of the previously conducted reviews has exclusively focused on simulation extrapolation and its application in continuous measurement error models, despite its widespread use and ease of application. We attempt to close this gap in the literature by highlighting its development over the past two and a half decades.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-05-24T05:44:44Z
      DOI: 10.1177/09622802221102619
       
 
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