Statistical Considerations on Subgroup Analysis in Clinical Trials

Alosh, Mohamed; Fritsch, Kathleen; Huque, Mohammad F.; Mahjoob, Kooros; Pennello, Gene; Rothmann, Mark D.; Russek‐Cohen, Estelle; Smith, Fraser; Wilson, Stephen; Yue, Lilly Q.

doi:10.1080/19466315.2015.1077726

Cited by 58 publications

(40 citation statements)

References 22 publications

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“…Firstly, univariate interaction tests are not, in general, able to determine the direction or the magnitude of the treatment interaction of interest (see e.g., Alosh et al. 2015). When the variable in question has more than two levels, an interaction test can only lead to the conclusion that the treatment effects are not the same in all the subgroups but cannot detect the direction of treatment effect changes.…”

Section: Frequentist Methods For Subgroup Analysismentioning

confidence: 99%

“…Existence of subgroups that appear to respond differently to treatment can affect inclusion criteria in later clinical trials or in labeling decisions for approved drugs (Alosh et al. 2015). Though subgroup analyses are often recommended and routinely performed, there are a number of concerns which lead many to interpret the results of subgroup analyses with caution.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Henderson

Louis

Wang

et al. 2016

Health Serv Outcomes Res Method

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Evaluation of heterogeneity of treatment effect (HTE) is an essential aspect of personalized medicine and patient-centered outcomes research. Our goal in this article is to promote the use of Bayesian methods for subgroup analysis and to lower the barriers to their implementation by describing the ways in which the companion software beanz can facilitate these types of analyses. To advance this goal, we describe several key Bayesian models for investigating HTE and outline the ways in which they are well-suited to address many of the commonly cited challenges in the study of HTE. Topics highlighted include shrinkage estimation, model choice, sensitivity analysis, and posterior predictive checking. A case study is presented in which we demonstrate the use of the methods discussed.

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Section: Frequentist Methods For Subgroup Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Henderson

Louis

Wang

et al. 2016

Health Serv Outcomes Res Method

View full text Add to dashboard Cite

show abstract

“…18,65 Closely related to the evaluation of a prognostic test for added value is its evaluation for consistency across subgroups defined by standard variables such as age, race, and sex, and by variables particular to the clinical context. For a review of subgroup analysis from a regulatory perspective, see Alosh et al 66 …”

Section: Tang and Pennellomentioning

confidence: 99%

Validation of Prognostic Marker Tests: Statistical Lessons Learned From Regulatory Experience

Tang

Pennello

2016

Ther Innov Regul Sci

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Despite concerted efforts to discover and validate prognostic biomarkers or signatures, few medical tests indicated for prognostic uses have been widely accepted by the clinical community. Even fewer, perhaps, are covered by public or private health plans. We were able to identify 6 prognostic marker tests that have been approved or cleared by the US Food and Drug Administration. The pivotal clinical studies for these prognostic marker tests exhibited a wide variety of designs and statistical analyses. From these experiences, we develop statistical points to consider for design, conduct, and analysis of successful clinical validation studies of prognostic tests. In particular, we review broad themes regarding prospective and retrospective study designs, sample size, clinical performance evaluation, and handling of missing data. Our review emphasizes the distinction between a prognostic biomarker and the medical test used to measure it. For this purpose, a section on test measurement validation is also provided.

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“…Testing each hypothesis in a multi-group study inflates the overall Type I error rate. Multiplicity adjustment is required to preserve the overall Type I error rate [5]. We calibrated [6] the operating characteristics in simulations to ensure the overall Type I error rate was close to 0.05 (one-sided) in all designs that used different statistical models.…”

Section: Introductionmentioning

confidence: 99%

“…Approaches to improve statistical power in subgroup analysis include: using available information from previous studies [5] and borrowing information across subgroups [3]. We did a meta-analysis that contained data from nine DHA supplementation trials across the world to obtain informative priors.…”

Section: Introductionmentioning

confidence: 99%

Personalized medicine enrichment design for DHA supplementation clinical trial

Lei

Mayo

Carlson

et al. 2017

Contemporary Clinical Trials Communications

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Personalized medicine aims to match patient subpopulation to the most beneficial treatment. The purpose of this study is to design a prospective clinical trial in which we hope to achieve the highest level of confirmation in identifying and making treatment recommendations for subgroups, when the risk levels in the control arm can be ordered. This study was motivated by our goal to identify subgroups in a DHA (docosahexaenoic acid) supplementation trial to reduce preterm birth (gestational age<37 weeks) rate. We performed a meta-analysis to obtain informative prior distributions and simulated operating characteristics to ensure that overall Type I error rate was close to 0.05 in designs with three different models: independent, hierarchical, and dynamic linear models. We performed simulations and sensitivity analysis to examine the subgroup power of models and compared results to a chi-square test. We performed simulations under two hypotheses: a large overall treatment effect and a small overall treatment effect. Within each hypothesis, we designed three different subgroup effects scenarios where resulting subgroup rates are linear, flat, or nonlinear. When the resulting subgroup rates are linear or flat, dynamic linear model appeared to be the most powerful method to identify the subgroups with a treatment effect. It also outperformed other methods when resulting subgroup rates are nonlinear and the overall treatment effect is big. When the resulting subgroup rates are nonlinear and the overall treatment effect is small, hierarchical model and chi-square test did better. Compared to independent and hierarchical models, dynamic linear model tends to be relatively robust and powerful when the control arm has ordinal risk subgroups.

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Statistical Considerations on Subgroup Analysis in Clinical Trials

Cited by 58 publications

References 22 publications

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Bayesian analysis of heterogeneous treatment effects for patient-centered outcomes research

Validation of Prognostic Marker Tests: Statistical Lessons Learned From Regulatory Experience

Personalized medicine enrichment design for DHA supplementation clinical trial

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