Exploratory Subgroup Analyses in Clinical Research

Gerd Rosenkranz

• Publisher: Wiley
• Year: 2020
• Print ISBN: 9781119536970
• eBook ISBN: 9781119537007
• Edition: 1
• Topic: Medicine
• Subtopic: Pharmacology

This essential guide on subgroup analyses in the emerging area of personalized medicine covers the issues of subgroup analyses from a practical and a theoretical/methodological point of view. The practical part introduces the issues using examples from the literature where subgroup analyses led to unexpected or difficult-to-interpret results, which have been interpreted differently by different stakeholders. On the technical side, the book addresses selection and selection bias variance reduction by borrowing information from the full population in estimating a subgroup effect. To this end, subgroup analysis will be linked to statistical modelling, and subgroup selection to model selection. This connection makes the techniques developed for model selection applicable to subgroup analysis.

Beginning with a history of subgroup analysis, Exploratory Subgroup Analyses in Clinical Research offers chapters that cover: objectives and current practice of subgroup analyses; pitfalls of subgroup analyses; subgroup analysis and modeling; hierarchical models in subgroup analysis; and selection bias in regression. It also looks at the predicted individual treatment effect and offers an outlook of the topic in its final chapter.

• Focuses on the statistical aspects of subgroup analysis
• Filled with classroom and conference-workshop tested material
• Written by a leading expert in the field of subgroup analysis
• Complemented with a companion website featuring downloadable datasets and examples for teaching use

Exploratory Subgroup Analyses in Clinical Research is an ideal book for medical statisticians and biostatisticians and will greatly benefit physicians and researchers interested in personalized medicine.

1
Some History of Subgroup Analysis

The essence of tragedy has been described as the destructive collision of two sets of protagonists, both of whom are correct. The statisticians are right in denouncing subgroups that are formed post hoc from exercises in pure data dredging. The clinicians are also right, however, in insisting that a subgroup is respectable and worthwhile when established a priori from pathophysiological principles. (Feinstein, 1998)

Alvan R Feinstein (1925–2001)

The Problem of Cogent Subgroups: A Clinicostatistical Tragedy.

1.1 Introduction

The history of subgroup analysis is characterized by a strong difference in opinions about its value. One group of scientists has a skeptical attitude towards the topic warning of the risks of subgroup analysis and other attempts to target treatments. For example, Yusuf et al. (1984) stated that “…it would be unfortunate if desire for the perfect (i.e. knowledge of exactly who will benefit from treatment) were to become the enemy of the possible (i.e. knowledge of the direction and approximate size of the effects of treatment of wide categories of patients).” Many clinicians are afraid of applying the overall results of large trials to individual patients without consideration of determinants of individual responses (Rothwell, 2005) while most prominently statisticians have raised concerns (Assmann et al., 2000, Sleight, 2000, Lagakos, 2006, Guillemin, 2007, Lonergan et al., 2017) and requested that:

• Investigators should be cautious when undertaking subgroup analyses.
• Subgroup findings should be exploratory, and only exceptionally should they affect the conclusions from trials.
• Editors and reviewers of journals need to correct any inappropriate, over‐enthusiastic uses of subgroup analyses.

The statement “subgroups kill people” was attributed – rightly or wrongly – to statistician Sir Richard Peto in van Gijn and Algra (1994). In fact, Peto commented on subgroup analyses undertaken on the GISSI1 study (GISSI Study Group, 1986): “The GISSI study…is one of the most important randomized trials ever conducted and when it was published provided the best evidence then available that thrombolytic therapy reduced mortality. But the ability of the GISSI report to save lives could be substantially compromised by misinterpretation by clinicians of some of the data‐dependent subset analyses that it contained.” (Peto, 1990)

A second camp of scientists and pharmaceutical executives is more attracted by the opportunities than by the risks of subgroup analysis driven by the vision of “personalized” medicine. In 1977, Sir Richard Sykes, at the time chief executive officer of Glaxo‐Wellcome, later chairman of GlaxoSmith‐Kline and rector of Imperial College London, wrote:

“It will soon be possible for patients in clinical trials to undergo ...

Table of contents

• Cover
• Table of Contents
• Preface
• Acknowledgments
• Acronyms
• About the Companion Website
• Introduction
• 1 Some History of Subgroup Analysis
• 2 Objectives and Current Practice of Subgroup Analyses
• 3 Pitfalls of Subgroup Analyses
• 4 Subgroup Analysis and Modeling
• 5 Hierarchical Models in Subgroup Analysis
• 6 Selection Bias in Regression
• 7 The Predicted Individual Treatment Effect
• 8 Prediction models
• 9 Outlook
• Bibliography
• Index
• End User License Agreement