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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  [1175 journals]
  • The population-wise error rate for clinical trials with overlapping
           populations

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      Authors: Werner Brannath, Charlie Hillner, Kornelius Rohmeyer
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      We introduce a new multiple type I error criterion for clinical trials with multiple, overlapping populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type I error rate, i.e. the probability that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted [math]-values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggested multiple-testing approach for umbrella trials.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-12-01T08:07:58Z
      DOI: 10.1177/09622802221135249
       
  • Additive hazards model with time-varying coefficients and imaging
           predictors

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      Authors: Qi Yang, Chuchu Wang, Haijin He, Xiaoxiao Zhou, Xinyuan Song
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Conventional hazard regression analyses frequently assume constant regression coefficients and scalar covariates. However, some covariate effects may vary with time. Moreover, medical imaging has become an increasingly important tool in screening, diagnosis, and prognosis of various diseases, given its information visualization and quantitative assessment. This study considers an additive hazards model with time-varying coefficients and imaging predictors to examine the dynamic effects of potential scalar and imaging risk factors for the failure of interest. We develop a two-stage approach that comprises the high-dimensional functional principal component analysis technique in the first stage and the counting process-based estimating equation approach in the second stage. In addition, we construct the pointwise confidence intervals for the proposed estimators and provide a significance test for the effects of scalar and imaging covariates. Simulation studies demonstrate the satisfactory performance of the proposed method. An application to the Alzheimer’s disease neuroimaging initiative study further illustrates the utility of the methodology.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-12-01T07:20:20Z
      DOI: 10.1177/09622802221137746
       
  • An overview of propensity score matching methods for clustered data

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      Authors: Benjamin Langworthy, Yujie Wu, Molin Wang
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Propensity score matching is commonly used in observational studies to control for confounding and estimate the causal effects of a treatment or exposure. Frequently, in observational studies data are clustered, which adds to the complexity of using propensity score techniques. In this article, we give an overview of propensity score matching methods for clustered data, and highlight how propensity score matching can be used to account for not just measured confounders, but also unmeasured cluster level confounders. We also consider using machine learning methods such as generalized boosted models to estimate the propensity score and show that accounting for clustering when using these methods can greatly reduce the performance, particularly when there are a large number of clusters and a small number of subjects per cluster. In order to get around this we highlight scenarios where it may be possible to control for measured covariates using propensity score matching, while using fixed effects regression in the outcome model to control for cluster level covariates. Using simulation studies we compare the performance of different propensity score matching methods for clustered data across a number of different settings. Finally, as an illustrative example we apply propensity score matching methods for clustered data to study the causal effect of aspirin on hearing deterioration using data from the conservation of hearing study.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-25T08:59:02Z
      DOI: 10.1177/09622802221133556
       
  • Simulating time-to-event data subject to competing risks and clustering: A
           review and synthesis

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      Authors: Can Meng, Denise Esserman, Fan Li, Yize Zhao, Ondrej Blaha, Wenhan Lu, Yuxuan Wang, Peter Peduzzi, Erich J. Greene
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Simulation studies play an important role in evaluating the performance of statistical models developed for analyzing complex survival data such as those with competing risks and clustering. This article aims to provide researchers with a basic understanding of competing risks data generation, techniques for inducing cluster-level correlation, and ways to combine them together in simulation studies, in the context of randomized clinical trials with a binary exposure or treatment. We review data generation with competing and semi-competing risks and three approaches of inducing cluster-level correlation for time-to-event data: the frailty model framework, the probability transform, and Moran’s algorithm. Using exponentially distributed event times as an example, we discuss how to introduce cluster-level correlation into generating complex survival outcomes, and illustrate multiple ways of combining these methods to simulate clustered, competing and semi-competing risks data with pre-specified correlation values or degree of clustering.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-22T09:01:32Z
      DOI: 10.1177/09622802221136067
       
  • Standard error estimation in meta-analysis of studies reporting medians

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      Authors: Sean McGrath, Stephan Katzenschlager, Alexandra J Zimmer, Alexander Seitel, Russell Steele, Andrea Benedetti
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-22T08:56:28Z
      DOI: 10.1177/09622802221139233
       
  • Point estimation following a two-stage group sequential trial

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      Authors: Michael J Grayling, James MS Wason
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Repeated testing in a group sequential trial can result in bias in the maximum likelihood estimate of the unknown parameter of interest. Many authors have therefore proposed adjusted point estimation procedures, which attempt to reduce such bias. Here, we describe nine possible point estimators within a common general framework for a two-stage group sequential trial. We then contrast their performance in five example trial settings, examining their conditional and marginal biases and residual mean square error. By focusing on the case of a trial with a single interim analysis, additional new results aiding the determination of the estimators are given. Our findings demonstrate that the uniform minimum variance unbiased estimator, whilst being marginally unbiased, often has large conditional bias and residual mean square error. If one is concerned solely about inference on progression to the second trial stage, the conditional uniform minimum variance unbiased estimator may be preferred. Two estimators, termed mean adjusted estimators, which attempt to reduce the marginal bias, arguably perform best in terms of the marginal residual mean square error. In all, one should choose an estimator accounting for its conditional and marginal biases and residual mean square error; the most suitable estimator will depend on relative desires to minimise each of these factors. If one cares solely about the conditional and marginal biases, the conditional maximum likelihood estimate may be preferred provided lower and upper stopping boundaries are included. If the conditional and marginal residual mean square error are also of concern, two mean adjusted estimators perform well.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-17T07:26:19Z
      DOI: 10.1177/09622802221137745
       
  • Regularization approaches in clinical biostatistics: A review of methods
           and their applications

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      Authors: Sarah Friedrich, Andreas Groll, Katja Ickstadt, Thomas Kneib, Markus Pauly, Jörg Rahnenführer, Tim Friede
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-17T07:24:48Z
      DOI: 10.1177/09622802221133557
       
  • A distribution-free smoothed combination method to improve discrimination
           accuracy in multi-category classification

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      Authors: Raju Maiti, Jialiang Li, Priyam Das, Xueqing Liu, Lei Feng, Derek J Hausenloy, Bibhas Chakraborty
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Results from multiple diagnostic tests are combined in many ways to improve the overall diagnostic accuracy. For binary classification, maximization of the empirical estimate of the area under the receiver operating characteristic curve has widely been used to produce an optimal linear combination of multiple biomarkers. However, in the presence of a large number of biomarkers, this method proves to be computationally expensive and difficult to implement since it involves maximization of a discontinuous, non-smooth function for which gradient-based methods cannot be used directly. The complexity of this problem further increases when the classification problem becomes multi-category. In this article, we develop a linear combination method that maximizes a smooth approximation of the empirical Hyper-volume Under Manifolds for the multi-category outcome. We approximate HUM by replacing the indicator function with the sigmoid function and normal cumulative distribution function. With such smooth approximations, efficient gradient-based algorithms are employed to obtain better solutions with less computing time. We show that under some regularity conditions, the proposed method yields consistent estimates of the coefficient parameters. We derive the asymptotic normality of the coefficient estimates. A simulation study is performed to study the effectiveness of our proposed method as compared to other existing methods. The method is illustrated using two real medical data sets.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-17T06:51:40Z
      DOI: 10.1177/09622802221137742
       
  • Dirichlet process mixture models for regression discontinuity designs

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      Authors: Federico Ricciardi, Silvia Liverani, Gianluca Baio
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the threshold can be seen as a randomising device assigning treatment to those falling just above the threshold and withholding it from those who fall below. Bandwidth selection represents a compelling decision for the regression discontinuity design analysis as results may be highly sensitive to its choice. A few methods to select the optimal bandwidth, mainly from the econometric literature, have been proposed. However, their use in practice is limited. We propose a methodology that, tackling the problem from an applied point of view, considers units’ exchangeability, that is, their similarity with respect to measured covariates, as the main criteria to select subjects for the analysis, irrespectively of their distance from the threshold. We cluster the sample using a Dirichlet process mixture model to identify balanced and homogeneous clusters. Our proposal exploits the posterior similarity matrix, which contains the pairwise probabilities that two observations are allocated to the same cluster in the Markov chain Monte Carlo sample. Thus we include in the regression discontinuity design analysis only those clusters for which we have stronger evidence of exchangeability. We illustrate the validity of our methodology with both a simulated experiment and a motivating example on the effect of statins on cholesterol levels.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-11T06:14:46Z
      DOI: 10.1177/09622802221129044
       
  • Use of clinical tolerance limits for assessing agreement

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      Authors: Taffé Patrick
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In this study, we have further extended the methodology proposed, first, by Lin et al. (2002) and, later, extended by Stevens et al. (2017, 2018), on the coverage probability/probability of agreement, by relaxing the strong parametric assumptions regarding the distribution of the latent trait and developing inference methods allowing to compute both pointwise and simultaneous confidence bands. The methodology requires repeated measurements by at least one of the two measurement methods and accommodates heteroscedastic measurement errors. It performs often very well even when one has only one measurement by one of the two measurement methods and at least five repeated measurements from the other. It circumvents some of the deficiencies of the Bland & Altman limits of agreement method and provides a more direct assessment of the agreement level.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-10T05:54:40Z
      DOI: 10.1177/09622802221137743
       
  • Sensitivity analyses in longitudinal clinical trials via distributional
           imputation

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      Authors: Siyi Liu, Shu Yang, Yilong Zhang, Guanghan (Frank) Liu
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Missing data is inevitable in longitudinal clinical trials. Conventionally, the missing at random assumption is assumed to handle missingness, which however is unverifiable empirically. Thus, sensitivity analyses are critically important to assess the robustness of the study conclusions against untestable assumptions. Toward this end, regulatory agencies and the pharmaceutical industry use sensitivity models such as return-to-baseline, control-based, and washout imputation, following the ICH E9(R1) guidance. Multiple imputation is popular in sensitivity analyses; however, it may be inefficient and result in an unsatisfying interval estimation by Rubin’s combining rule. We propose distributional imputation in sensitivity analysis, which imputes each missing value by samples from its target imputation model given the observed data. Drawn on the idea of Monte Carlo integration, the distributional imputation estimator solves the mean estimating equations of the imputed dataset. It is fully efficient with theoretical guarantees. Moreover, we propose weighted bootstrap to obtain a consistent variance estimator, taking into account the variabilities due to model parameter estimation and target parameter estimation. The superiority of the distributional imputation framework is validated in the simulation study and an antidepressant longitudinal clinical trial.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-07T07:10:49Z
      DOI: 10.1177/09622802221135251
       
  • Complete effect decomposition for an arbitrary number of multiple ordered
           mediators with time-varying confounders: A method for generalized causal
           multi-mediation analysis

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      Authors: An-Shun Tai, Sheng-Hsuan Lin
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Causal mediation analysis is advantageous for mechanism investigation. In settings with multiple causally ordered mediators, path-specific effects have been introduced to specify the effects of certain combinations of mediators. However, most path-specific effects are unidentifiable. An interventional analog of path-specific effects is adapted to address the non-identifiability problem. Moreover, previous studies only focused on cases with two or three mediators due to the complexity of the mediation formula in a large number of mediators. In this study, we provide a generalized definition of traditional path-specific effects and interventional path-specific effects with a recursive formula, along with the required assumptions for nonparametric identification. Subsequently, a general approach is developed with an arbitrary number of multiple ordered mediators and with time-varying confounders. All methods and software proposed in this study contribute to comprehensively decomposing a causal effect confirmed by data science and help disentangling causal mechanisms in the presence of complicated causal structures among multiple mediators.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-02T06:08:42Z
      DOI: 10.1177/09622802221130580
       
  • Meta-analysis methods for risk difference: A comparison of different
           models

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      Authors: Juanru Guo, Mengli Xiao, Haitao Chu, Lifeng Lin
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Risk difference is a frequently-used effect measure for binary outcomes. In a meta-analysis, commonly-used methods to synthesize risk differences include: (1) the two-step methods that estimate study-specific risk differences first, then followed by the univariate common-effect model, fixed-effects model, or random-effects models; and (2) the one-step methods using bivariate random-effects models to estimate the summary risk difference from study-specific risks. These methods are expected to have similar performance when the number of studies is large and the event rate is not rare. However, studies with zero events are common in meta-analyses, and bias may occur with the conventional two-step methods from excluding zero-event studies or using an artificial continuity correction to zero events. In contrast, zero-event studies can be included and modeled by bivariate random-effects models in a single step. This article compares various methods to estimate risk differences in meta-analyses. Specifically, we present two case studies and three simulation studies to compare the performance of conventional two-step methods and bivariate random-effects models in the presence or absence of zero-event studies. In conclusion, we recommend researchers using bivariate random-effects models to estimate risk differences in meta-analyses, particularly in the presence of zero events.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-02T03:41:35Z
      DOI: 10.1177/09622802221125913
       
  • Bayesian inference for Cox proportional hazard models with partial
           likelihoods, nonlinear covariate effects and correlated observations

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      Authors: Ziang Zhang, Alex Stringer, Patrick Brown, Jamie Stafford
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      We propose a flexible and scalable approximate Bayesian inference methodology for the Cox Proportional Hazards model with partial likelihood. The model we consider includes nonlinear covariate effects and correlated survival times. The proposed method is based on nested approximations and adaptive quadrature, and the computational burden of working with the log-partial likelihood is mitigated through automatic differentiation and Laplace approximation. We provide two simulation studies to show the accuracy of the proposed approach, compared with the existing methods. We demonstrate the practical utility of our method and its computational advantages over Markov Chain Monte Carlo methods through the analysis of Kidney infection times, which are paired, and the analysis of Leukemia survival times with a semi-parametric covariate effect and spatial variation.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-01T07:22:49Z
      DOI: 10.1177/09622802221134172
       
  • Efficient algorithms for survival data with multiple outcomes using the
           frailty model

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      Authors: Xifen Huang, Jinfeng Xu, Yunpeng Zhou
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Survival data with multiple outcomes are frequently encountered in biomedical investigations. An illustrative example comes from Alzheimer’s Disease Neuroimaging Initiative study where the cognitively normal subjects may clinically progress to mild cognitive impairment and/or Alzheimer’s disease dementia. Transition time from normal cognition to mild cognitive impairment and that from mild cognitive impairment to Alzheimer’s disease are expected to be correlated within subjects and the dependence is often accommodated by the frailty (random effects). Estimation in the frailty model unavoidably involves multiple integrations which may be intractable and hence leads to severe computational challenges, especially in the presence of high-dimensional covariates. In this paper, we propose efficient minorization–maximization algorithms in the frailty model for survival data with multiple outcomes. The alternating direction method of multipliers is further incorporated for simultaneous variable selection and homogeneity pursuit via regularization and fusion. Extensive simulation studies are conducted to assess the performance of the proposed algorithms. An application to the Alzheimer’s Disease Neuroimaging Initiative data is also provided to illustrate their practical utilities.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-01T07:21:49Z
      DOI: 10.1177/09622802221133554
       
  • Revisiting Gaussian Markov random fields and Bayesian disease mapping

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      Authors: Ying C MacNab
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      We revisit several conditionally formulated Gaussian Markov random fields, known as the intrinsic conditional autoregressive model, the proper conditional autoregressive model, and the Leroux et al. conditional autoregressive model, as well as convolution models such as the well known Besag, York and Mollie model, its (adaptive) re-parameterization, and its scaled alternatives, for their roles of modelling underlying spatial risks in Bayesian disease mapping. Analytic and simulation studies, with graphic visualizations, and disease mapping case studies, present insights and critique on these models for their nature and capacities in characterizing spatial dependencies, local influences, and spatial covariance and correlation functions, and in facilitating stabilized and efficient posterior risk prediction and inference. It is illustrated that these models are Gaussian (Markov) random fields of different spatial dependence, local influence, and (covariance) correlation functions and can play different and complementary roles in Bayesian disease mapping applications.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-11-01T07:20:09Z
      DOI: 10.1177/09622802221129040
       
  • Nonparametric Bayesian functional selection in 1-M matched case-crossover
           studies

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      Authors: Wenyu Gao, Inyoung Kim, Eun Sug Park
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      The matched case-crossover study has been used in many areas such as public health, biomedical, and epidemiological research for humans, animals, and other subjects with clustered binary outcomes. The control information for each stratum is based on the subject’s exposure experience, and the stratifying variable is the individual subject. It is generally accepted that any effects associated with the matching covariates by stratum can be removed in the conditional logistic regression model. However, when there are numerous covariates, it is important to perform variable selection to study the functional association between the variables and the relative risk of diseases or clustered binary outcomes by simultaneously adjusting effect modifications. The methods for simultaneously evaluating effect modifications by matching covariates such as time, as well as performing automatic variable and functional selections under semiparametric model frameworks, are quite limited. In this article, we propose a unified Bayesian approach due to its ability to detect both parametric and nonparametric relationships between the predictors and the relative risk of diseases or binary outcomes, accounting for potential effect modifications by matching covariates such as time, and perform automatic variable and functional selections. We demonstrate the advantages of our approach using simulation study and an epidemiological example of a 1-4 bidirectional case-crossover study.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-21T06:14:31Z
      DOI: 10.1177/09622802221133553
       
  • Resampling-based inferences for compositional regression with application
           to beef cattle microbiomes

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      Authors: Sujin Lee, Sungkyu Jung, Jeferson Lourenco, Dean Pringle, Jeongyoun Ahn
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Gut microbiomes are increasingly found to be associated with many health-related characteristics of humans as well as animals. Regression with compositional microbiomes covariates is commonly used to identify important bacterial taxa that are related to various phenotype responses. Often the dimension of microbiome taxa easily exceeds the number of available samples, which creates a serious challenge in the estimation and inference of the model. The sparse log-contrast regression method is useful for such cases as it can yield a model estimate that depends on only a small number of taxa. However, a formal statistical inference procedure for individual regression coefficients has not been properly established yet. We propose a new estimation and inference procedure for linear regression models with extremely low-sample-sized compositional predictors. Under the compositional log-contrast regression framework, the proposed approach consists of two steps. The first step is to screen relevant predictors by fitting a log-contrast model with a sparse penalty. The screened-in variables are used as predictors in the non-sparse log-contrast model in the second step, where each of the regression coefficients is tested using nonparametric, resampling-based methods such as permutation and bootstrap. The performances of the proposed methods are evaluated by a simulation study, which shows they outperform traditional approaches based on normal assumptions or large sample asymptotics. Application to steer microbiome data successfully identifies key bacterial taxa that are related to important cattle quality measures.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-21T06:13:12Z
      DOI: 10.1177/09622802221133550
       
  • Multi-arm covariate adjusted response adaptive designs for ordinal outcome
           clinical trials

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      Authors: Soumyadeep Das, Rahul Bhattacharya, Atanu Biswas
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Covariate adjusted response adaptive designs are developed with ordinal categorical responses for phase III clinical trial involving multiple treatments. Stochastic ordering principle is used to order the treatments according to effectiveness and consequently allocation functions are developed by combining the cumulative odds ratios suitably. The performance of the proposed designs is investigated through relevant exact as well as large sample measures. To investigate the performance in a real situation, a real clinical trial involving lung cancer patients is further redesigned using the proposed allocation design.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-21T05:49:02Z
      DOI: 10.1177/09622802221133558
       
  • A general method for calculating power for GEE analysis of complete and
           incomplete stepped wedge cluster randomized trials

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      Authors: Ying Zhang, John S Preisser, Elizabeth L Turner, Paul J Rathouz, Mark Toles, Fan Li
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Stepped wedge designs have uni-directional crossovers at randomly assigned time points (steps) where clusters switch from control to intervention condition. Incomplete stepped wedge designs are increasingly used in cluster randomized trials of health care interventions and have periods without data collection due to logistical, resource and patient-centered considerations. The development of sample size formulae for stepped wedge trials has primarily focused on complete designs and continuous responses. Addressing this gap, a general, fast, non-simulation based power procedure is proposed for generalized estimating equations analysis of complete and incomplete stepped wedge designs and its predicted power is compared to simulated power for binary and continuous responses. An extensive set of simulations for six and twelve clusters is based upon the Connect-Home trial with an incomplete stepped wedge design. Results show that empirical test size is well controlled using a t-test with bias-corrected sandwich variance estimator for as few as six clusters. Analytical power agrees well with a simulated power in scenarios with twelve clusters. For six clusters, analytical power is similar to simulated power with estimation using the correctly specified model-based variance estimator. To explore the impact of study design choice on power, the proposed fast GEE power method is applied to the Connect-Home trial design, four alternative incomplete stepped wedge designs and one complete design.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-18T01:21:35Z
      DOI: 10.1177/09622802221129861
       
  • Shotgun-2: A Bayesian phase I/II basket trial design to identify
           indication-specific optimal biological doses

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      Authors: Xin Chen, Jingyi Zhang, Liyun Jianga, Fangrong Yan
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      For novel molecularly targeted agents and immunotherapies, the objective of dose-finding is often to identify the optimal biological dose, rather than the maximum tolerated dose. However, optimal biological doses may not be the same for different indications, challenging the traditional dose-finding framework. Therefore, we proposed a Bayesian phase I/II basket trial design, named “shotgun-2,” to identify indication-specific optimal biological doses. A dose-escalation part is conducted in stage I to identify the maximum tolerated dose and admissible dose sets. In stage II, dose optimization is performed incorporating both toxicity and efficacy for each indication. Simulation studies under both fixed and random scenarios show that, compared with the traditional “phase I  +  cohort expansion” design, the shotgun-2 design is robust and can improve the probability of correctly selecting the optimal biological doses. Furthermore, this study provides a useful tool for identifying indication-specific optimal biological doses and accelerating drug development.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-11T07:56:42Z
      DOI: 10.1177/09622802221129049
       
  • Standardization of continuous and categorical covariates in sparse
           penalized regressions

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      Authors: Xiang Li, Yong Ma, Qing Pan
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      In sparse penalized regressions, candidate covariates of different units need to be standardized beforehand so that the coefficient sizes are directly comparable and reflect their relative impacts, which leads to fairer variable selection. However, when covariates of mixed data types (e.g. continuous, binary or categorical) exist in the same dataset, the commonly used standardization methods may lead to different selection probabilities even when the covariates have the same impact on or level of association with the outcome. In this paper, we propose a novel standardization method that targets at generating comparable selection probabilities in sparse penalized regressions for continuous, binary or categorical covariates with the same impact. We illustrate the advantages of the proposed method in simulation studies, and apply it to the National Ambulatory Medical Care Survey data to select factors related to the opioid prescription in the US.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-03T08:05:27Z
      DOI: 10.1177/09622802221129042
       
  • A screening method for ultra-high dimensional features with overlapped
           partition structures

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      Authors: Jie He, Jiali Song, Xiao-hua Zhou, Yan Hou
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Ultra-high dimensional data, such as gene and neuroimaging data, are becoming increasingly important in biomedical science. Identifying important biomarkers from the huge number of features can help us gain better insights into further researches. Variable screening is an efficient tool to achieve this goal under the large scale cases, which reduces the dimension of features into a moderate size by removing the major part of inactive ones. Developing novel variable screening methods for high-dimensional features with group structures is challenging, especially under the overlapped cases. For example, the huge-scaled genes usually can be partitioned into hundreds of pathways according to background knowledge. One primary characteristic for this type of data is that many genes may appear across more than one pathway, which means that different pathways are overlapped. However, existing variable screening methods only could deal with disjoint group structure cases. To fill this gap, we propose a novel variable screening method for the generalized linear model by incorporating overlapped partition structures with theoretical guarantee. Besides the sure screening property, we also test the performance of the proposed method through a series of numerical studies and apply it to statistical analysis of a breast cancer data.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-30T06:58:30Z
      DOI: 10.1177/09622802221129043
       
  • 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
       
  • 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
      First page: 2261
      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
       
  • 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
      First page: 2287
      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
       
  • 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
      First page: 2297
      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
       
  • Approximate Bayesian computation design for phase I clinical trials

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      Authors: Huaqing Jin, Wenbin Du, Guosheng Yin
      First page: 2310
      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
       
  • Bounded-width confidence interval following optimal sequential analysis of
           adverse events with binary data

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      Authors: Ivair R Silva, Yan Zhuang
      First page: 2323
      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
       
  • Penalized Spline-Involved Tree-based (PenSIT) Learning for estimating an
           optimal dynamic treatment regime using observational data

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      Authors: Kelly A Speth, Michael R Elliott, Juan L Marquez, Lu Wang
      First page: 2338
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Dynamic treatment regimes are a set of time-adaptive decision rules that can be used to personalize treatment across multiple stages of care. Grounded in causal inference methods, dynamic treatment regimes identify variables that differentiate the treatment effect and may be used to tailor treatments across individuals based on the patient’s own characteristics – thereby representing an important step toward personalized medicine. In this manuscript we introduce Penalized Spline-Involved Tree-based Learning, which seeks to improve upon existing tree-based approaches to estimating an optimal dynamic treatment regime. Instead of using weights determined from the estimated propensity scores, which may result in unstable estimates when weights are highly variable, we predict missing counterfactual outcomes using regression models that incorporate a penalized spline of the propensity score and other covariates predictive of the outcome. We further develop a novel purity measure applied within a decision tree framework to produce a flexible yet interpretable method for estimating an optimal multi-stage multi-treatment dynamic treatment regime. In simulation experiments we demonstrate good performance of Penalized Spline-Involved Tree-based Learning relative to competing methods and, in particular, we show that Penalized Spline-Involved Tree-based Learning may be advantageous when the sample size is small and/or when the level of confounding of the outcome is high. We apply Penalized Spline-Involved Tree-based Learning to the retrospectively-collected Medical Information Mart for Intensive Care dataset to identify variables that may be used to tailor early fluid resuscitation strategies in septic patients.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-03T08:07:08Z
      DOI: 10.1177/09622802221122397
       
  • 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
      First page: 2352
      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
       
  • Bayesian tensor logistic regression with applications to neuroimaging data
           analysis of Alzheimer’sdisease

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      Authors: Ying Wu, Dan Chen, Chaoqian Li, Niansheng Tang
      First page: 2368
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Alzheimer’s disease (AD) can be diagnosed by utilizing traditional logistic regression models to fit magnetic resonance imaging (MRI) data of brain, which is regarded as a vector of covariates. But its parameter estimation is inefficient and computationally extensive due to ultrahigh dimensionality and complicated structure of MRI data. To overcome this deficiency, this paper proposes a tensor logistic regression model (TLRM) for AD’s MRI data by regarding MRI tensor as covariates. Under this framework, a tensor candecomp/parafac (CP) decomposition tool is employed to reduce ultrahigh dimensional tensor to a high dimensional level, a novel Bayesian adaptive Lasso method is developed to simultaneously select important components of tensor and estimate model parameters by incorporating the P[math]lya-Gamma method leading a closed-form likelihood and avoiding the usage of the Metropolis-Hastings algorithm, and Gibbs sampler technique in Markov chain Monte Carlo (MCMC). A tensor’s product technique is utilized to optimize the calculation program and speed up the calculation of MCMC. Bayes factor together with the path sampling approach is presented to select tensor rank in CP decomposition. Effectiveness of the proposed method is illustrated on simulation studies and an MRI data analysis.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-09-26T05:57:02Z
      DOI: 10.1177/09622802221122409
       
  • 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
      First page: 2383
      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
       
  • Optimal sampling allocation for outcome-dependent designs in
           cluster-correlated data settings

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      Authors: Claudia Rivera-Rodriguez, Sebastien Haneuse, Sara Sauer
      First page: 2400
      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
       
  • A natural history and copula-based joint model for regional and distant
           breast cancer metastasis

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      Authors: Alessandro Gasparini, Keith Humphreys
      First page: 2415
      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
       
  • 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
      First page: 2431
      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 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
      First page: 2442
      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
       
  • 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
      First page: 2456
      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
       
  • Online control of the False Discovery Rate in group-sequential platform
           trials

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      Authors: Sonja Zehetmayer, Martin Posch, Franz Koenig
      First page: 2470
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      When testing multiple hypotheses, a suitable error rate should be controlled even in exploratory trials. Conventional methods to control the False Discovery Rate assume that all p-values are available at the time point of test decision. In platform trials, however, treatment arms enter and leave the trial at different times during its conduct. Therefore, the actual number of treatments and hypothesis tests is not fixed in advance and hypotheses are not tested at once, but sequentially. Recently, for such a setting the concept of online control of the False Discovery Rate was introduced. We propose several heuristic variations of the LOND procedure (significance Levels based On Number of Discoveries) that incorporate interim analyses for platform trials, and study their online False Discovery Rate via simulations. To adjust for the interim looks spending functions are applied with O’Brien-Fleming or Pocock type group-sequential boundaries. The power depends on the prior distribution of effect sizes, for example, whether true alternatives are uniformly distributed over time or not. We consider the choice of design parameters for the LOND procedure to maximize the overall power and investigate the impact on the False Discovery Rate by including both concurrent and non-concurrent control data.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-03T08:03:17Z
      DOI: 10.1177/09622802221129051
       
  • Data-driven clustering of infectious disease incidence into age groups

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      Authors: Rami Yaari, Amit Huppert, Itai Dattner
      First page: 2486
      Abstract: Statistical Methods in Medical Research, Ahead of Print.
      Understanding the patterns of infectious diseases spread in the population is an important element of mitigation and vaccination programs. A major and common characteristic of most infectious diseases is age-related heterogeneity in the transmission, which potentially can affect the dynamics of an epidemic as manifested by the pattern of disease incidence in different age groups. Currently there are no statistical criteria of how to partition the disease incidence data into clusters. We develop the first data-driven methodology for deciding on the best partition of incidence data into age-groups, in a well defined statistical sense. The method employs a top-down hierarchical partitioning algorithm, with a stopping criteria based on multiple hypotheses significance testing controlling the family wise error rate. The type one error and statistical power of the method are tested using simulations. The method is then applied to Covid-19 incidence data in Israel, in order to extract the significant age-group clusters in each wave of the epidemic.
      Citation: Statistical Methods in Medical Research
      PubDate: 2022-10-11T07:55:03Z
      DOI: 10.1177/09622802221129041
       
 
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