Publications by authors named "Alex Dmitrienko"

59 Publications

Data-Driven Subgroup Identification in Confirmatory Clinical Trials.

Ther Innov Regul Sci 2021 Jul 29. Epub 2021 Jul 29.

Eli Lilly, Indianapolis, IND, USA.

Data-driven subgroup analysis plays an important role in clinical trials. This paper focuses on practical considerations in post-hoc subgroup investigations in the context of confirmatory clinical trials. The analysis is aimed at assessing the heterogeneity of treatment effects across the trial population and identifying patient subgroups with enhanced treatment benefit. The subgroups are defined using baseline patient characteristics, including demographic and clinical factors. Much progress has been made in the development of reliable statistical methods for subgroup investigation, including methods based on global models and recursive partitioning. The paper provides a review of principled approaches to data-driven subgroup identification and illustrates subgroup analysis strategies using a family of recursive partitioning methods known as the SIDES (subgroup identification based on differential effect search) methods. These methods are applied to a Phase III trial in patients with metastatic colorectal cancer. The paper discusses key considerations in subgroup exploration, including the role of covariate adjustment, subgroup analysis at early decision points and interpretation of subgroup search results in trials with a positive overall effect.
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http://dx.doi.org/10.1007/s43441-021-00329-1DOI Listing
July 2021

Hydroxychloroquine/Azithromycin Therapy and QT Prolongation in Hospitalized Patients With COVID-19.

JACC Clin Electrophysiol 2021 01 5;7(1):16-25. Epub 2020 Aug 5.

Department of Cardiovascular Medicine, Oakland University William Beaumont School of Medicine and Beaumont Hospital, Royal Oak, Michigan, USA. Electronic address:

Objectives: This study aimed to characterize corrected QT (QTc) prolongation in a cohort of hospitalized patients with coronavirus disease-2019 (COVID-19) who were treated with hydroxychloroquine and azithromycin (HCQ/AZM).

Background: HCQ/AZM is being widely used to treat COVID-19 despite the known risk of QT interval prolongation and the unknown risk of arrhythmogenesis in this population.

Methods: A retrospective cohort of COVID-19 hospitalized patients treated with HCQ/AZM was reviewed. The QTc interval was calculated before drug administration and for the first 5 days following initiation. The primary endpoint was the magnitude of QTc prolongation, and factors associated with QTc prolongation. Secondary endpoints were incidences of sustained ventricular tachycardia or ventricular fibrillation and all-cause mortality.

Results: Among 415 patients who received concomitant HCQ/AZM, the mean QTc increased from 443 ± 25 ms to a maximum of 473 ± 40 ms (87 [21%] patients had a QTc ≥500 ms). Factors associated with QTc prolongation ≥500 ms were age (p < 0.001), body mass index <30 kg/m (p = 0.005), heart failure (p < 0.001), elevated creatinine (p = 0.005), and peak troponin (p < 0.001). The change in QTc was not associated with death over the short period of the study in a population in which mortality was already high (hazard ratio: 0.998; p = 0.607). No primary high-grade ventricular arrhythmias were observed.

Conclusions: An increase in QTc was seen in hospitalized patients with COVID-19 treated with HCQ/AZM. Several clinical factors were associated with greater QTc prolongation. Changes in QTc were not associated with increased risk of death.
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http://dx.doi.org/10.1016/j.jacep.2020.07.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7406234PMC
January 2021

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

Ther Innov Regul Sci 2020 05 6;54(3):507-518. Epub 2020 Jan 6.

Institute for Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin, Berlin, Germany.

Background: The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective.

Methods: The software application subscreen (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as P values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The subscreen application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness.

Results: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials.

Conclusion: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.
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http://dx.doi.org/10.1007/s43441-019-00082-6DOI Listing
May 2020

Optimized multiple testing procedures for nested sub-populations based on a continuous biomarker.

Stat Methods Med Res 2020 10 30;29(10):2945-2957. Epub 2020 Mar 30.

Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

An important step in the development of targeted therapies is the identification and confirmation of sub-populations where the treatment has a positive treatment effect compared to a control. These sub-populations are often based on continuous biomarkers, measured at baseline. For example, patients can be classified into biomarker low and biomarker high subgroups, which are defined via a threshold on the continuous biomarker. However, if insufficient information on the biomarker is available, the a priori choice of the threshold can be challenging and it has been proposed to consider several thresholds and to apply appropriate multiple testing procedures to test for a treatment effect in the corresponding subgroups controlling the family-wise type 1 error rate. In this manuscript we propose a framework to select optimal thresholds and corresponding optimized multiple testing procedures that maximize the expected power to identify at least one subgroup with a positive treatment effect. Optimization is performed over a prior on a family of models, modelling the relation of the biomarker with the expected outcome under treatment and under control. We find that for the considered scenarios 3 to 4 thresholds give the optimal power. If there is a prior belief on a small subgroup where the treatment has a positive effect, additional optimization of the spacing of thresholds may result in a large benefit. The procedure is illustrated with a clinical trial example in depression.
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http://dx.doi.org/10.1177/0962280220913071DOI Listing
October 2020

A Systematic Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies.

Ther Innov Regul Sci 2019 Jun 16:2168479019853782. Epub 2019 Jun 16.

5 Institute for Social Medicine, Epidemiology and Health Economics, Charité-Universitätsmedizin, Berlin, Germany.

Background: The analysis of subgroups in clinical trials is essential to assess differences in treatment effects for distinct patient clusters, that is, to detect patients with greater treatment benefit or patients where the treatment seems to be ineffective.

Methods: The software application (R package) has been developed to analyze the population of clinical trials in minute detail. The aim was to efficiently calculate point estimates (eg, hazard ratios) for multiple subgroups to identify groups that potentially differ from the overall trial result. The approach intentionally avoids inferential statistics such as values or confidence intervals but intends to encourage discussions enriched with external evidence (eg, from other studies) about the exploratory results, which can be accompanied by further statistical methods in subsequent analyses. The application was applied to 2 clinical study data sets and used in a simulation study to demonstrate its usefulness.

Results: The visualization of numerous combined subgroups illustrates the homogeneity or heterogeneity of potentially all subgroup estimates with the overall result. With this, the application leads to more targeted planning of future trials.

Conclusion: This described approach supports the current trend and requirements for the investigation of subgroup effects as discussed in the EMA draft guidance for subgroup analyses in confirmatory clinical trials (EMA 2014). The lack of a convenient tool to answer spontaneous questions from different perspectives can hinder an efficient discussion, especially in joint interdisciplinary study teams. With the new application, an easily executed but powerful tool is provided to fill this gap.
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http://dx.doi.org/10.1177/2168479019853782DOI Listing
June 2019

Recent advances in methodology for clinical trials in small populations: the InSPiRe project.

Orphanet J Rare Dis 2018 10 25;13(1):186. Epub 2018 Oct 25.

Warwick Medical School, University of Warwick, Coventry, UK.

Where there are a limited number of patients, such as in a rare disease, clinical trials in these small populations present several challenges, including statistical issues. This led to an EU FP7 call for proposals in 2013. One of the three projects funded was the Innovative Methodology for Small Populations Research (InSPiRe) project. This paper summarizes the main results of the project, which was completed in 2017.The InSPiRe project has led to development of novel statistical methodology for clinical trials in small populations in four areas. We have explored new decision-making methods for small population clinical trials using a Bayesian decision-theoretic framework to compare costs with potential benefits, developed approaches for targeted treatment trials, enabling simultaneous identification of subgroups and confirmation of treatment effect for these patients, worked on early phase clinical trial design and on extrapolation from adult to pediatric studies, developing methods to enable use of pharmacokinetics and pharmacodynamics data, and also developed improved robust meta-analysis methods for a small number of trials to support the planning, analysis and interpretation of a trial as well as enabling extrapolation between patient groups. In addition to scientific publications, we have contributed to regulatory guidance and produced free software in order to facilitate implementation of the novel methods.
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http://dx.doi.org/10.1186/s13023-018-0919-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6203217PMC
October 2018

Multiplicity Considerations in Clinical Trials.

N Engl J Med 2018 05;378(22):2115-2122

From Mediana, Overland Park, KS (A.D.); and Boston University, Boston (R.B.D.).

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http://dx.doi.org/10.1056/NEJMra1709701DOI Listing
May 2018

Rethinking the Clinically Based Thresholds of TransCelerate BioPharma for Risk-Based Monitoring.

Ther Innov Regul Sci 2018 09 1;52(5):560-571. Epub 2018 Feb 1.

4 Mediana Inc, Overland Park, KS, USA.

Background: The quality of data from clinical trials has received a great deal of attention in recent years. Of central importance is the need to protect the well-being of study participants and maintain the integrity of final analysis results. However, traditional approaches to assess data quality have come under increased scrutiny as providing little benefit for the substantial cost. Numerous regulatory guidance documents and industry position papers have described risk-based approaches to identify quality and safety issues. In particular, the position paper of TransCelerate BioPharma recommends defining risk thresholds to assess safety and quality risks based on past clinical experience. This exercise can be extremely time-consuming, and the resulting thresholds may only be relevant to a particular therapeutic area, patient or clinical site population. In addition, predefined thresholds cannot account for safety or quality issues where the underlying rate of observing a particular problem may change over the course of a clinical trial, and often do not consider varying patient exposure.

Methods: In this manuscript, we appropriate rules commonly utilized for funnel plots to define a traffic-light system for risk indicators based on statistical criteria that consider the duration of patient follow-up. Further, we describe how these methods can be adapted to assess changing risk over time. Finally, we illustrate numerous graphical approaches to summarize and communicate risk, and discuss hybrid clinical-statistical approaches to allow for the assessment of risk at sites with low patient enrollment.

Results: We illustrate the aforementioned methodologies for a clinical trial in patients with schizophrenia.

Conclusions: Funnel plots are a flexible graphical technique that can form the basis for a risk-based strategy to assess data integrity, while considering site sample size, patient exposure, and changing risk across time.
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http://dx.doi.org/10.1177/2168479017738981DOI Listing
September 2018

Special Issue: Multiplicity Issues in Clinical Trials.

Authors:
Alex Dmitrienko

J Biopharm Stat 2018 ;28(1):1-2

a (Mediana Inc).

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http://dx.doi.org/10.1080/10543406.2017.1397013DOI Listing
July 2019

Mixture-based gatekeeping procedures in adaptive clinical trials.

J Biopharm Stat 2018 28;28(1):129-145. Epub 2017 Dec 28.

a U.S. Food and Drug Administration , Silver Spring , Maryland , USA.

Clinical trials with data-driven decision rules often pursue multiple clinical objectives such as the evaluation of several endpoints or several doses of an experimental treatment. These complex analysis strategies give rise to "multivariate" multiplicity problems with several components or sources of multiplicity. A general framework for defining gatekeeping procedures in clinical trials with adaptive multistage designs is proposed in this paper. The mixture method is applied to build a gatekeeping procedure at each stage and inferences at each decision point (interim or final analysis) are performed using the combination function approach. An advantage of utilizing the mixture method is that it enables powerful gatekeeping procedures applicable to a broad class of settings with complex logical relationships among the hypotheses of interest. Further, the combination function approach supports flexible data-driven decisions such as a decision to increase the sample size or remove a treatment arm. The paper concludes with a clinical trial example that illustrates the methodology by applying it to develop an adaptive two-stage design with a mixture-based gatekeeping procedure.
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http://dx.doi.org/10.1080/10543406.2017.1399901DOI Listing
July 2019

An enhanced mixture method for constructing gatekeeping procedures in clinical trials.

J Biopharm Stat 2018 14;28(1):113-128. Epub 2017 Dec 14.

e Department of Statistics, George Washington University , Washington , DC , USA.

It is increasingly common to encounter complex multiplicity problems with several multiplicity components in confirmatory Phase III clinical trials. These components are often based on several endpoints (primary and secondary endpoints) and several dose-control comparisons. When constructing a multiplicity adjustment in these settings, it is important to control the Type I error rate over all multiplicity components. An important class of multiple testing procedures, known as gatekeeping procedures, was derived using the mixture method that enables clinical trial sponsors to set up efficient multiplicity adjustments that account for clinically relevant logical relationships among the hypotheses of interest. An enhanced version of this mixture method is introduced in this paper to construct more powerful gatekeeping procedures for a specific type of logical relationships that rely on transitive serial restrictions. Restrictions of this kind are very common in Phase III clinical trials and the proposed method is applicable to a broad class of multiplicity problems. Several examples are provided to illustrate the new method and results of simulation trials are presented to compare the performance of gatekeeping procedures derived using this method and other available methods.
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http://dx.doi.org/10.1080/10543406.2017.1399900DOI Listing
July 2019

Multiplicity issues in exploratory subgroup analysis.

J Biopharm Stat 2018 27;28(1):63-81. Epub 2017 Nov 27.

d QuintilesIMS, Advisory Analytics , Montreal , Québec , Canada.

The general topic of subgroup identification has attracted much attention in the clinical trial literature due to its important role in the development of tailored therapies and personalized medicine. Subgroup search methods are commonly used in late-phase clinical trials to identify subsets of the trial population with certain desirable characteristics. Post-hoc or exploratory subgroup exploration has been criticized for being extremely unreliable. Principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining have been developed to address this criticism. These approaches emphasize fundamental statistical principles, including the importance of performing multiplicity adjustments to account for selection bias inherent in subgroup search. This article provides a detailed review of multiplicity issues arising in exploratory subgroup analysis. Multiplicity corrections in the context of principled subgroup search will be illustrated using the family of SIDES (subgroup identification based on differential effect search) methods. A case study based on a Phase III oncology trial will be presented to discuss the details of subgroup search algorithms with resampling-based multiplicity adjustment procedures.
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http://dx.doi.org/10.1080/10543406.2017.1397009DOI Listing
July 2019

Penalty-based approaches to evaluating multiplicity adjustments in clinical trials: Traditional multiplicity problems.

J Biopharm Stat 2018 27;28(1):146-168. Epub 2017 Nov 27.

b Mediana Inc., Kansas, USA.

Given the importance of addressing multiplicity issues in confirmatory clinical trials, several recent publications focused on the general goal of identifying most appropriate methods for multiplicity adjustment in each individual setting. This goal can be accomplished using the Clinical Scenario Evaluation approach. This approach encourages trial sponsors to perform comprehensive assessments of applicable analysis strategies such as multiplicity adjustments under all plausible sets of statistical assumptions using relevant evaluation criteria. This two-part paper applies a novel class of criteria, known as criteria based on multiplicity penalties, to the problem of evaluating the performance of several candidate multiplicity adjustments. The ultimate goal of this evaluation is to identify efficient and robust adjustments for each individual trial and optimally select parameters of these adjustments. Part I deals with traditional problems with a single source of multiplicity. Two case studies based on recently conducted Phase III trials are used to illustrate penalty-based approaches to evaluating candidate multiple testing methods and constructing optimization algorithms.
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http://dx.doi.org/10.1080/10543406.2017.1397010DOI Listing
July 2019

Editorial: Multiplicity issues in clinical trials.

Stat Med 2017 12;36(28):4423-4426

Boston University, Boston, MA, U.S.A.

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http://dx.doi.org/10.1002/sim.7506DOI Listing
December 2017

Penalty-based approaches to evaluating multiplicity adjustments in clinical trials: Advanced multiplicity problems.

J Biopharm Stat 2018 10;28(1):169-188. Epub 2017 Nov 10.

b Mediana Inc., Kansas, USA.

Given the importance of addressing multiplicity issues in confirmatory clinical trials, several recent publications focused on the general goal of identifying most appropriate methods for multiplicity adjustment in each individual setting. This goal can be accomplished using the Clinical Scenario Evaluation approach. This approach encourages trial sponsors to perform comprehensive assessments of applicable analysis strategies such as multiplicity adjustments under all plausible sets of statistical assumptions using relevant evaluation criteria. This two-part paper applies a novel class of criteria, known as criteria based on multiplicity penalties, to the problem of evaluating the performance of several candidate multiplicity adjustments. The ultimate goal of this evaluation is to identify efficient and robust adjustments for each individual trial and optimally select parameters of these adjustments. Part II focuses on advanced settings with several sources of multiplicity, for example, clinical trials with several endpoints evaluated at two or more doses of an experimental treatment. A case study is given to illustrate a penalty-based approach to evaluating candidate multiple testing procedures in advanced multiplicity problems.
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http://dx.doi.org/10.1080/10543406.2017.1397011DOI Listing
July 2019

Multiplicity considerations in subgroup analysis.

Stat Med 2017 Dec 1;36(28):4446-4454. Epub 2017 Aug 1.

QuintilesIMS, Inc., Durham, NC, U.S.A.

This paper deals with the general topic of subgroup analysis in late-stage clinical trials with emphasis on multiplicity considerations. The discussion begins with multiplicity issues arising in the context of exploratory subgroup analysis, including principled approaches to subgroup search that are applied as part of subgroup exploration exercises as well as in adaptive biomarker-driven designs. Key considerations in confirmatory subgroup analysis based on one or more pre-specified patient populations are reviewed, including a survey of multiplicity adjustment methods recommended in multi-population phase III clinical trials. Guidelines for interpretation of significant findings in several patient populations are introduced to facilitate the decision-making process and achieve consistent labeling across development programs. Copyright © 2017 John Wiley & Sons, Ltd.
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http://dx.doi.org/10.1002/sim.7416DOI Listing
December 2017

Tutorial in biostatistics: data-driven subgroup identification and analysis in clinical trials.

Stat Med 2017 01 3;36(1):136-196. Epub 2016 Aug 3.

Boston University, Boston, MA, U.S.A.

It is well known that both the direction and magnitude of the treatment effect in clinical trials are often affected by baseline patient characteristics (generally referred to as biomarkers). Characterization of treatment effect heterogeneity plays a central role in the field of personalized medicine and facilitates the development of tailored therapies. This tutorial focuses on a general class of problems arising in data-driven subgroup analysis, namely, identification of biomarkers with strong predictive properties and patient subgroups with desirable characteristics such as improved benefit and/or safety. Limitations of ad-hoc approaches to biomarker exploration and subgroup identification in clinical trials are discussed, and the ad-hoc approaches are contrasted with principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining. A general framework for evaluating predictive biomarkers and identification of associated subgroups is introduced. The tutorial provides a review of a broad class of statistical methods used in subgroup discovery, including global outcome modeling methods, global treatment effect modeling methods, optimal treatment regimes, and local modeling methods. Commonly used subgroup identification methods are illustrated using two case studies based on clinical trials with binary and survival endpoints. Copyright © 2016 John Wiley & Sons, Ltd.
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http://dx.doi.org/10.1002/sim.7064DOI Listing
January 2017

Statistical Considerations for an Adaptive Design for a Serious Rare Disease.

Ther Innov Regul Sci 2016 May;50(3):375-384

4 University of Alabama, Birmingham, AL, USA.

Background: Neuromyelitis optica spectrum disorder (NMOSD) is a rare, disabling autoimmune disorder of the central nervous system. Clinical trials in NMOSD present unique design and statistical challenges to adequately determine treatment effect and to minimize risk.

Methods: The N-MOmentum trial (NCT02200770) is evaluating the efficacy and safety of MEDI-551, an anti-CD 19 B-cell depleting monoclonal antibody, in patients with NMOSD and employs a number of unique design features. Patients are randomized (3:1) to receive MEDI-551 or placebo for up to 197 days. NMOSD attacks are evaluated by the investigator and confirmed by an independent adjudication committee. The primary endpoint is time to first relapse as determined by adjudication committee. Sample size re-estimation and futility analyses are planned interim analyses. Novel multiplicity adjustment methods are developed to control the study-wise type I error. Methods for assessing inter- and intrarater reliability are proposed.

Conclusions: The N-MOmentum study minimizes exposure to placebo for individual patients. The application of several statistical methods in the N-MOmentum trial is novel in NMOSD and aims to achieve a balance between minimizing risk and maintaining scientific integrity.
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http://dx.doi.org/10.1177/2168479015619203DOI Listing
May 2016

Trends and innovations in clinical trial statistics.

J Biopharm Stat 2016 ;26(1):1-2

a Quantitative Decision Strategies and Analytics, Quintiles Advisory Services.

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http://dx.doi.org/10.1080/10543406.2015.1092036DOI Listing
October 2016

Tradeoff-based optimization criteria in clinical trials with multiple objectives and adaptive designs.

J Biopharm Stat 2016 ;26(1):120-40

c Biostatistics, MedImmune , Gaithersburg , Maryland , USA.

The article discusses clinical trial optimization problems in the context of mid- to late-stage drug development. Using the Clinical Scenario Evaluation approach, main objectives of clinical trial optimization are formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. The paper focuses on a class of optimization criteria arising in clinical trials with several competing goals, termed tradeoff-based optimization criteria, and discusses key considerations in constructing and applying tradeoff-based criteria. The clinical trial optimization framework considered in the paper is illustrated using two case studies based on a clinical trial with multiple objectives and a two-stage clinical trial which utilizes adaptive decision rules.
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http://dx.doi.org/10.1080/10543406.2015.1092032DOI Listing
October 2016

Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review.

J Biopharm Stat 2016 ;26(1):99-119

a Center for Medical Statistics and Informatics, Medizinische Universität Wien , Vienna , Austria.

Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.
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http://dx.doi.org/10.1080/10543406.2015.1092034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4732423PMC
October 2016

General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials.

J Biopharm Stat 2016 ;26(1):71-98

a Center for Statistics in Drug Development, Quintiles , Overland Park , Kansas , USA.

This article focuses on a broad class of statistical and clinical considerations related to the assessment of treatment effects across patient subgroups in late-stage clinical trials. This article begins with a comprehensive review of clinical trial literature and regulatory guidelines to help define scientifically sound approaches to evaluating subgroup effects in clinical trials. All commonly used types of subgroup analysis are considered in the article, including different variations of prospectively defined and post-hoc subgroup investigations. In the context of confirmatory subgroup analysis, key design and analysis options are presented, which includes conventional and innovative trial designs that support multi-population tailoring approaches. A detailed summary of exploratory subgroup analysis (with the purpose of either consistency assessment or subgroup identification) is also provided. The article promotes a more disciplined approach to post-hoc subgroup identification and formulates key principles that support reliable evaluation of subgroup effects in this setting.
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http://dx.doi.org/10.1080/10543406.2015.1092033DOI Listing
October 2016

Mixture-based gatekeeping procedures for multiplicity problems with multiple sequences of hypotheses.

J Biopharm Stat 2016 6;26(4):758-80. Epub 2015 Aug 6.

c Novartis , Paris , France.

Complex multiplicity problems arise in drug development programs with several sets of clinical objectives. This article considers a common setting with two sources of multiplicity induced by the analysis of multiple dose levels based on ordered endpoints. This results in multiplicity problems with multiple sequences of null hypotheses of no effect. Type I error rate inflation in problems of this type is typically addressed by using gatekeeping procedures that account for the hierarchical structure of the trial objectives. A general method for building gatekeeping procedures, known as the mixture method, tends to be conservative in problems with several sequences of hypotheses. This article defines a modified mixture method and shows that this method provides a power advantage over the standard mixture method. In addition, it is demonstrated that in special cases the modified mixture method allows for a stepwise testing algorithm, which facilities the implementation of gatekeeping procedures and general decision making. The new methodology is illustrated using two clinical trial examples.
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http://dx.doi.org/10.1080/10543406.2015.1074917DOI Listing
November 2017

Multiplicity in confirmatory clinical trials: a case study with discussion from a JSM panel.

Stat Med 2015 Nov 25;34(26):3461-80. Epub 2015 Jun 25.

US Food and Drug Administration, U.S.A.

An invited panel session was conducted in the 2012 Joint Statistical Meetings, San Diego, California, USA, to stimulate the discussion on multiplicity issues in confirmatory clinical trials for drug development. A total of 11 expert panel members were invited and 9 participated. Prior to the session, a case study was previously provided to the panel members to facilitate the discussion, focusing on the key components of the study design and multiplicity. The Phase 3 development program for this new experimental treatment was based on a single randomized controlled trial alone. Each panelist was asked to clarify if he or she responded as if he or she were a pharmaceutical drug sponsor, an academic panelist or a health regulatory scientist.
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http://dx.doi.org/10.1002/sim.6561DOI Listing
November 2015

Multipopulation Tailoring Clinical Trials: Design, Analysis, and Inference Considerations.

Ther Innov Regul Sci 2014 Jul;48(4):453-462

1 Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA.

Several recent publications have focused on statistical considerations that arise in multipopulation tailoring clinical trials that evaluate treatment effect in an overall patient population as well as one or more predefined subpopulations. This paper presents a decision-making framework applicable to these trials and evaluates the operating characteristics of this framework versus one based solely on the results of primary hypothesis tests. The operating characteristics are presented as rates of applicable errors, known as influence errors and interaction errors.
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http://dx.doi.org/10.1177/2168479013519630DOI Listing
July 2014

Assessment of type I error rate associated with dose-group switching in a longitudinal Alzheimer trial.

J Biopharm Stat 2014 ;24(3):660-84

a I-BioStat, Universiteit Hasselt , Diepenbeek , Belgium.

In clinical trials, there always is the possibility to use data-driven adaptation at the end of a study. There prevails, however, concern on whether the type I error rate of the trial could be inflated with such design, thus, necessitating multiplicity adjustment. In this project, a simulation experiment was set up to assess type I error rate inflation associated with switching dose group as a function of dropout rate at the end of the study, where the primary analysis is in terms of a longitudinal outcome. This simulation is inspired by a clinical trial in Alzheimer's disease. The type I error rate was assessed under a number of scenarios, in terms of differing correlations between efficacy and tolerance, different missingness mechanisms, and different probabilities of switching. A collection of parameter values was used to assess sensitivity of the analysis. Results from ignorable likelihood analysis show that the type I error rate with and without switching was approximately the posited error rate for the various scenarios. Under last observation carried forward (LOCF), the type I error rate blew up both with and without switching. The type I error inflation is clearly connected to the criterion used for switching. While in general switching, in a way related to the primary endpoint, may impact the type I error, this was not the case for most scenarios in the longitudinal Alzheimer trial setting under consideration, where patients are expected to worsen over time.
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http://dx.doi.org/10.1080/10543406.2014.888570DOI Listing
December 2014

Strategies for identifying predictive biomarkers and subgroups with enhanced treatment effect in clinical trials using SIDES.

J Biopharm Stat 2014 ;24(1):130-53

a Quintiles , Durham , North Carolina , USA.

Several approaches to identification of predictive biomarkers and subgroups of patients with enhanced treatment effect have been proposed in the literature. The SIDES method introduced in Lipkovich et al. (2011) adopts a recursive partitioning algorithm for screening treatment-by-biomarker interactions. This article introduces an improved biomarker discovery/subgroup search method (SIDEScreen). The SIDEScreen method relies on a two-stage procedure that first selects a small number of biomarkers with the highest predictive ability based on an appropriate variable importance score and then identifies subgroups with enhanced treatment effect based on the selected biomarkers. The two-stage approach helps increase the signal-to-noise ratio by screening out noninformative biomarkers. We evaluate operating characteristics of the standard SIDES method and two SIDEScreen procedures based on fixed and adaptive screens. Our main finding is that the adaptive SIDEScreen method is a more flexible biomarker discovery tool than SIDES and it better handles multiplicity in complex subgroup search problems. The methods presented in the article are illustrated using a clinical trial example.
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http://dx.doi.org/10.1080/10543406.2013.856024DOI Listing
August 2014

Bayesian assessment of the influence and interaction conditions in multipopulation tailoring clinical trials.

J Biopharm Stat 2014 ;24(1):94-109

a Lilly Research Laboratories, Eli Lilly and Company , Indianapolis , Indiana , USA.

Multipopulation tailoring trials provide a trial design option that supports the realization of tailored therapeutics or personalized medicine. Several recent publications have focused on statistical and clinical considerations that arise in these trials that are designed to study the overall treatment effect in a population of interest as well as one or more prospectively defined subpopulations. Millen et al. (2012) introduced the influence and interaction conditions as part of a general framework to facilitate decision making in multipopulation trials. This article provides Bayesian methods for assessing the influence and interaction conditions. The methods introduced are illustrated using case studies based on clinical trials with biomarker-driven designs.
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http://dx.doi.org/10.1080/10543406.2013.856025DOI Listing
August 2014

Guest editors' note: Special issue on subgroup analysis in clinical trials.

J Biopharm Stat 2014 ;24(1):1-3

a US Food and Drug Administration , Silver Spring , Maryland , USA.

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http://dx.doi.org/10.1080/10543406.2014.858958DOI Listing
August 2014

Selection of hypothesis weights and ordering when testing multiple hypotheses in clinical trials.

J Biopharm Stat 2013 ;23(6):1403-19

a Alcon Laboratories , Fort Worth Texas , USA.

This article discusses the problem of selecting free parameters of multiple testing procedures in confirmatory Phase III clinical trials with multiple objectives, including hypothesis weights and hypothesis ordering. We identify classes of multiple testing procedures that provide different interpretations of these parameters. This includes basic single-step procedures (Bonferroni procedure) that employ fixed hypothesis weights, as well as more powerful stepwise procedures (Holm, fallback, and chain procedures) that reweight the hypotheses during the testing process. We examine the behavior of different classes of multiple testing procedures in problems with unequally weighted hypotheses and a priori ordered hypotheses and provide practical guidelines for the choice of hypothesis weights and hypothesis ordering. The concepts discussed in the article are illustrated using case studies based on clinical trials with multiple endpoints, multiple dose-placebo comparisons, and multiple patient populations.
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http://dx.doi.org/10.1080/10543406.2013.834920DOI Listing
June 2014
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