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Statistical Methods in Medical Research
Journal Prestige (SJR): 1.402
Citation Impact (citeScore): 2
Number of Followers: 30  
 
  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]
  • Editorial

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      Authors: Daniela De Angelis, Paul Birrell, Sebastian Funk, Thomas House
      Pages: 1639 - 1640
      Abstract: Statistical Methods in Medical Research, Volume 31, Issue 9, Page 1639-1640, September 2022.

      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-10T11:31:30Z
      DOI: 10.1177/09622802221118613
      Issue No: Vol. 31, No. 9 (2022)
       
  • Inferring risks of coronavirus transmission from community household data

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      Authors: Thomas House, Heather Riley, Lorenzo Pellis, Koen B Pouwels, Sebastian Bacon, Arturas Eidukas, Kaveh Jahanshahi, Rosalind M Eggo, A. Sarah Walker
      Pages: 1738 - 1756
      Abstract: Statistical Methods in Medical Research, Volume 31, Issue 9, Page 1738-1756, September 2022.
      The response of many governments to the COVID-19 pandemic has involved measures to control within- and between-household transmission, providing motivation to improve understanding of the absolute and relative risks in these contexts. Here, we perform exploratory, residual-based, and transmission-dynamic household analysis of the Office for National Statistics COVID-19 Infection Survey data from 26 April 2020 to 15 July 2021 in England. This provides evidence for: (i) temporally varying rates of introduction of infection into households broadly following the trajectory of the overall epidemic and vaccination programme; (ii) susceptible-Infectious transmission probabilities of within-household transmission in the 15–35% range; (iii) the emergence of the Alpha and Delta variants, with the former being around 50% more infectious than wildtype and 35% less infectious than Delta within households; (iv) significantly (in the range of 25–300%) more risk of bringing infection into the household for workers in patient-facing roles pre-vaccine; (v) increased risk for secondary school-age children of bringing the infection into the household when schools are open; (vi) increased risk for primary school-age children of bringing the infection into the household when schools were open since the emergence of new variants.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-10T11:30:56Z
      DOI: 10.1177/09622802211055853
      Issue No: Vol. 31, No. 9 (2022)
       
  • A new cure rate regression framework for bivariate data based on the Chen
           distribution

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      Authors: Ricardo Puziol de Oliveira, Marcos Vinicius de Oliveira Peres, Edson Z Martinez, Jorge Alberto Achcar
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The present study introduces a new multivariate mixture cure rate model based on the Chen probability distribution to model recurrent event data in the presence of cure fraction. In this context, we provide an alternative for the use of some usual modeling approaches as the semiparametric Cox proportional hazards model commonly used in lifetime data analysis, considering a new bivariate parametric model to be used in the data analysis of bivariate lifetime data assuming a mixture structure for the bivariate data in presence of covariates, censored data and cure fraction. Under a Bayesian setting, the proposed methodology was considered to analyze two real medical datasets from a retrospective cohort study related to leukemia and diabetic retinopathy diseases. The model validation process was addressed by using the Cox-Snell residuals, which allowed us to identify the suitability of the new proposed mixture cure rate model.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-21T09:00:42Z
      DOI: 10.1177/09622802221122418
       
  • Bayesian analysis of longitudinal binary responses based on the
           multivariate probit model: A comparison of five methods

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      Authors: Kaifeng Lu, Fang Chen
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Dichotomous response data observed over multiple time points, especially data that exhibit longitudinal structures, are important in many applied fields. The multivariate probit model has been an attractive tool in such situations for its ability to handle correlations among the outcomes, typically by modeling the covariance (correlation) structure of the latent variables. In addition, a multivariate probit model facilitates controlled imputations for nonignorable dropout, a phenomenon commonly observed in clinical trials of experimental drugs or biologic products. While the model is relatively simple to specify, estimation, particularly from a Bayesian perspective that relies on Markov chain Monte Carlo sampling, is not as straightforward. Here we compare five sampling algorithms for the correlation matrix and discuss their merits: a parameter-expanded Metropolis-Hastings algorithm (Zhang et al., 2006), a parameter-expanded Gibbs sampling algorithm (Talhouk et al., 2012), a parameter-expanded Gibbs sampling algorithm with unit constraints on conditional variances (Tang, 2018), a partial autocorrelation parameterization approach (Gaskins et al., 2014), and a semi-partial correlation parameterization approach (Ghosh et al., 2021). We describe each algorithm, use simulation studies to evaluate their performance, and focus on comparison criteria such as computational cost, convergence time, robustness, and ease of implementations. We find that the parameter-expanded Gibbs sampling algorithm by Talhouk et al. (2012) often has the most efficient convergence with relatively low computational complexity, while the partial autocorrelation parameterization approach is more flexible for estimating the correlation matrix of latent variables for typical late phase longitudinal studies.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-21T09:00:22Z
      DOI: 10.1177/09622802221122403
       
  • A nonparametric test for equality of survival medians using right-censored
           prevalent cohort survival data

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      Authors: James Hugh McVittie, Masoud Asgharian
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The median is a robust summary commonly used for comparison between populations. The existing literature falls short in testing for equality of survival medians when the collected data do not form representative samples from their respective target populations and are subject to right censoring. Such data commonly occur in prevalent cohort studies with follow-up. We consider a particular case where the disease under study is stable, that is, the incidence rate of the disease is stable. It is known that survival data collected on diseased cases, when the disease under study is stable, form a length-biased sample from the target population. We fill the gap for the particular case of length-biased right-censored survival data by proposing a large-sample test using the nonparametric maximum likelihood estimator of the survivor function in the target population. The small sample performance of the proposed test statistic is studied via simulation. We apply the proposed method to test for differences in survival medians of Alzheimer’s disease and dementia groups using the survival data collected as part of the Canadian Study of Health and Aging.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-21T08:09:32Z
      DOI: 10.1177/09622802221125912
       
  • A natural history and copula-based joint model for regional and distant
           breast cancer metastasis

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      Authors: Alessandro Gasparini, Keith Humphreys
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding – potentially latent – disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-19T05:10:08Z
      DOI: 10.1177/09622802221122410
       
  • Bounded-width confidence interval following optimal sequential analysis of
           adverse events with binary data

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      Authors: Ivair R Silva, Yan Zhuang
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In sequential testing with binary data, sample size and time to detect a signal are the key performance measures to optimize. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-market drug and vaccine safety surveillance of adverse events. The precision of the relative risk estimator on termination of the analysis is a meaningful design criterion as well. This paper presents a linear programming framework to find the optimal alpha spending that minimizes expected time to signal, or expected sample size as needed. The solution enables (a) to bound the width of the confidence interval following the end of the analysis, (b) designs with outer signaling thresholds and inner non-signaling thresholds, and (c) sequential designs with variable Bernoulli probabilities. To illustrate, we use real data on the monitoring of adverse events following the H1N1 vaccination. The numerical results are obtained using the R Sequential package.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-19T05:09:49Z
      DOI: 10.1177/09622802221122383
       
  • Robust integrative biclustering for multi-view data

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      Authors: Weijie Zhang, Christine Wendt, Russel Bowler, Craig P Hersh, Sandra E Safo
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In many biomedical research, multiple views of data (e.g. genomics, proteomics) are available, and a particular interest might be the detection of sample subgroups characterized by specific groups of variables. Biclustering methods are well-suited for this problem as they assume that specific groups of variables might be relevant only to specific groups of samples. Many biclustering methods exist for detecting row–column clusters in a view but few methods exist for data from multiple views. The few existing algorithms are heavily dependent on regularization parameters for getting row–column clusters, and they impose unnecessary burden on users thus limiting their use in practice. We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views. Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster, finds stable biclusters, and results in interpretable row–column associations. Simulations and real data analyses show that integrative sparse singular value decomposition outperforms several other single- and multi-view biclustering methods and is able to detect meaningful biclusters. iSSVD is a user-friendly, computationally efficient algorithm that will be useful in many disease subtyping applications.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-14T05:18:07Z
      DOI: 10.1177/09622802221122427
       
  • Flexible semiparametric mode regression for time-to-event data

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      Authors: Alexander Seipp, Verena Uslar, Dirk Weyhe, Antje Timmer, Fabian Otto-Sobotka
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The distribution of time-to-event outcomes is usually right-skewed. While for symmetric and moderately skewed data the mean and median are appropriate location measures, the mode is preferable for heavily skewed data as it better represents the center of the distribution. Mode regression has been introduced for uncensored data to model the relationship between covariates and the mode of the outcome. Starting from nonparametric kernel density based mode regression, we examine the use of inverse probability of censoring weights to extend mode regression to handle right-censored data. We add a semiparametric predictor to add further flexibility to the model and we construct a pseudo Akaike’s information criterion to select the bandwidth and smoothing parameters. We use simulations to evaluate the performance of our proposed approach. We demonstrate the benefit of adding mode regression to one’s toolbox for analyzing survival data on a pancreatic cancer data set from a prospectively maintained cancer registry.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-14T05:17:49Z
      DOI: 10.1177/09622802221122406
       
  • Power prior models for estimating response rates in a small n, sequential,
           multiple assignment, randomized trial

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      Authors: Yan-Cheng Chao, Thomas M Braun, Roy N Tamura, Kelley M Kidwell
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      A small n, sequential, multiple assignment, randomized trial (snSMART) is a small sample, two-stage design where participants receive up to two treatments sequentially, but the second treatment depends on response to the first treatment. The parameters of interest in an snSMART are the first-stage response rates of the treatments, but outcomes from both stages can be used to obtain more information from a small sample. A novel way to incorporate the outcomes from both stages uses power prior models, in which first stage outcomes from an snSMART are regarded as the primary (internal) data and second stage outcomes are regarded as supplemental data (co-data). We apply existing power prior models to snSMART data, and we also develop new extensions of power prior models. All methods are compared to each other and to the Bayesian joint stage model (BJSM) via simulation studies. By comparing the biases and the efficiency of the response rate estimates among all proposed power prior methods, we suggest application of Fisher’s Exact Test or the Bhattacharyya’s overlap measure to an snSMART to estimate the response rates in an snSMART, which both have performance mostly as good or better than the BJSM. We describe the situations where each of these suggested approaches is preferred.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-09T11:54:39Z
      DOI: 10.1177/09622802221122795
       
  • Estimation of the treatment effect following a clinical trial that stopped
           early for benefit

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      Authors: Ian C Marschner, Manjula Schou, Andrew J Martin
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      When a clinical trial stops early for benefit, the maximum likelihood estimate (MLE) of the treatment effect may be subject to overestimation bias. Several authors have proposed adjusting for this bias using the conditional MLE, which is obtained by conditioning on early stopping. However, this approach has a fundamental problem in that the adjusted estimate may not be in the direction of benefit, even though the study has stopped early due to benefit. In this paper, we address this problem by embedding both the MLE and the conditional MLE within a broader class of penalised likelihood estimates, and choosing a member of the class that is a favourable compromise between the two. This penalised MLE, and its associated confidence interval, always lie in the direction of benefit when the study stops early for benefit. We study its properties using both simulations and analyses of the ENZAMET trial in metastatic prostate cancer. Conditional on stopping early for benefit, the method is found to have good unbiasedness and coverage properties, along with very favourable efficiency at earlier interim analyses. We recommend the penalised MLE as a supplementary analysis to a conventional primary analysis when a clinical trial stops early for benefit.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-06T07:09:26Z
      DOI: 10.1177/09622802221122445
       
  • Optimal sampling allocation for outcome-dependent designs in
           cluster-correlated data settings

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      Authors: Claudia Rivera-Rodriguez, Sebastien Haneuse, Sara Sauer
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In clinical and public health studies, it is often the case that some variables relevant to the analysis are too difficult or costly to measure for all individuals in the population of interest. Rather, a subsample of these individuals must be identified for additional data collection. A sampling scheme that incorporates readily-available information for the entire target population at the design stage can increase the statistical efficiency of the intended analysis. While there is no universally optimal sampling design, under certain principles and restrictions, a well-designed and efficient sampling strategy can be implemented. In two-phase designs, efficiency can be gained by stratifying on the outcome and/or auxiliary information that is known at phase I. Additional gains in efficiency can be obtained by determining the optimal allocation of the sample sizes across the strata, which depends on the quantity that is being estimated. In this paper, the inference is concerned with one or multiple regression parameter(s) where the study units are naturally clustered and, thus, exhibit correlation in outcomes. We propose several allocation strategies within the framework of two-phase designs for the estimation of the regression parameter(s) obtained from weighted generalized estimating equations. The proposed methods extend existing theory to address the objective of the estimating regression parameters in cluster-correlated data settings by minimizing the asymptotic variance of the estimator subject to a fixed sample size. Through a comprehensive simulation study, we show that the proposed allocation schemes have the potential to yield substantial efficiency gains over alternative strategies.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-30T07:06:26Z
      DOI: 10.1177/09622802221122423
       
  • Hierarchical continuous-time inhomogeneous hidden Markov model for cancer
           screening with extensive followup data

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      Authors: Rui Meng, Braden Soper, Herbert KH Lee, Jan F Nygård, Mari Nygård
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Continuous-time hidden Markov models are an attractive approach for disease modeling because they are explainable and capable of handling both irregularly sampled, skewed and sparse data arising from real-world medical practice, in particular to screening data with extensive followup. Most applications in this context consider time-homogeneous models due to their relative computational simplicity. However, the time homogeneous assumption is too strong to accurately model the natural history of many diseases including cancer. Moreover, cancer risk across the population is not homogeneous either, since exposure to disease risk factors can vary considerably between individuals. This is important when analyzing longitudinal datasets and different birth cohorts. We model the heterogeneity of disease progression and regression using piece-wise constant intensity functions and model the heterogeneity of risks in the population using a latent mixture structure. Different submodels under the mixture structure employ the same types of Markov states reflecting disease progression and allowing both clinical interpretation and model parsimony. We also consider flexible observational models dealing with model over-dispersion in real data. An efficient, scalable Expectation-Maximization algorithm for inference is proposed with the theoretical guaranteed convergence property. We demonstrate our method’s superior performance compared to other state-of-the-art methods using synthetic data and a real-world cervical cancer screening dataset from the Cancer Registry of Norway. Moreover, we present two model-based risk stratification methods that identify the risk levels of individuals.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-30T07:05:52Z
      DOI: 10.1177/09622802221122390
       
  • Approximate Bayesian computation design for phase I clinical trials

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      Authors: Huaqing Jin, Wenbin Du, Guosheng Yin
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In the development of new cancer treatment, an essential step is to determine the maximum tolerated dose in a phase I clinical trial. In general, phase I trial designs can be classified as either model-based or algorithm-based approaches. Model-based phase I designs are typically more efficient by using all observed data, while there is a potential risk of model misspecification that may lead to unreliable dose assignment and incorrect maximum tolerated dose identification. In contrast, most of the algorithm-based designs are less efficient in using cumulative information, because they tend to focus on the observed data in the neighborhood of the current dose level for dose movement. To use the data more efficiently yet without any model assumption, we propose a novel approximate Bayesian computation approach to phase I trial design. Not only is the approximate Bayesian computation design free of any dose–toxicity curve assumption, but it can also aggregate all the available information accrued in the trial for dose assignment. Extensive simulation studies demonstrate its robustness and efficiency compared with other phase I trial designs. We apply the approximate Bayesian computation design to the MEK inhibitor selumetinib trial to demonstrate its satisfactory performance. The proposed design can be a useful addition to the family of phase I clinical trial designs due to its simplicity, efficiency and robustness.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-29T07:15:06Z
      DOI: 10.1177/09622802221122402
       
  • Modified Brier score for evaluating prediction accuracy for binary
           outcomes

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      Authors: Wei Yang, Jiakun Jiang, Erin M Schnellinger, Stephen E Kimmel, Wensheng Guo
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer Surveillance Consortium and compare the performance of predicting breast cancer risk using the models with and without its most important predictor.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-29T07:14:49Z
      DOI: 10.1177/09622802221122391
       
  • A generalized epidemiological model with dynamic and asymptomatic
           population

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      Authors: Anirban Ghatak, Shivshanker Singh Patel, Soham Bonnerjee, Subhrajyoty Roy
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In this paper, we develop an extension of compartmental epidemiological models which is suitable for COVID-19. The model presented in this paper comprises seven compartments in the progression of the disease. This model, named as the SINTRUE (Susceptible, Infected and pre-symptomatic, Infected and Symptomatic but Not Tested, Tested Positive, Recorded Recovered, Unrecorded Recovered, and Expired) model. The proposed model incorporates transmission due to asymptomatic carriers and captures the spread of the disease due to the movement of people to/from different administrative boundaries within a country. In addition, the model allows estimating the number of undocumented infections in the population and the number of unrecorded recoveries. The associated parameters in the model can help architect the public health policy and operational management of the pandemic. The results show that the testing rate of the asymptomatic patients is a crucial parameter to fight against the pandemic. The model is also shown to have a better predictive capability than the other epidemiological models.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-08-18T03:21:01Z
      DOI: 10.1177/09622802221115877
       
  • 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
       
  • 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
       
  • 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
       
  • 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
       
  • 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
      First page: 1656
      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
       
  • 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
      First page: 1716
      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
       
  • 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
      First page: 1757
      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
       
  • 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
      First page: 1778
      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
       
  • Hunting for protective drugs at the break of a pandemic: Causal inference
           from hospital data

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      Authors: Carlo Berzuini, Luisa Bernardinelli
      First page: 1803
      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
       
 
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