1
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Mütze T, Bell J, Englert S, Hougaard P, Jackson D, Lanius V, Ravn H. Principles for Defining Estimands in Clinical Trials-A Proposal. Pharm Stat 2024. [PMID: 39138846 DOI: 10.1002/pst.2432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 07/02/2024] [Accepted: 07/21/2024] [Indexed: 08/15/2024]
Abstract
The ICH E9(R1) guideline outlines the estimand framework, which aligns planning, design, conduct, analysis, and interpretation of a clinical trial. The benefits and value of using this framework in clinical trials have been outlined in the literature, and guidance has been provided on how to choose the estimand and define the estimand attributes. Although progress has been made in the implementation of estimands in clinical trials, to the best of our knowledge, there is no published discussion on the basic principles that estimands in clinical trials should fulfill to be well defined and consistent with the ideas presented in the ICH E9(R1) guideline. Therefore, in this Viewpoint article, we propose four key principles for defining an estimand. These principles form a basis for well-defined treatment effects that reflect the estimand thinking process. We hope that this Viewpoint will complement ICH E9(R1) and stimulate a discussion on which fundamental properties an estimand in a clinical trial should have and that such discussions will eventually lead to an improved clarity and precision for defining estimands in clinical trials.
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Affiliation(s)
- Tobias Mütze
- Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - James Bell
- Clinical Operations, Elderbrook Solutions GmbH, Buckinghamshire, UK
| | - Stefan Englert
- Statistical Modeling and Methodology, Janssen-Cilag GmbH, Johnson & Johnson Company, Neuss, Germany
| | - Philip Hougaard
- Biostatistics and Data Science, H. Lundbeck A/S, Valby, Denmark
| | - Dan Jackson
- Statistical Innovation, AstraZeneca, Cambridge, UK
| | - Vivian Lanius
- Clinical Statistics and Analytics, Bayer AG, Wuppertal, Germany
| | - Henrik Ravn
- Biostatistics Methods and Outreach, Novo Nordisk A/S, Søborg, Denmark
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2
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Liu S, Yang S, Zhang Y, Liu G(F. Multiply robust estimators in longitudinal studies with missing data under control-based imputation. Biometrics 2024; 80:ujad036. [PMID: 38393335 PMCID: PMC10885818 DOI: 10.1093/biomtc/ujad036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/05/2023] [Accepted: 12/19/2023] [Indexed: 02/25/2024]
Abstract
Longitudinal studies are often subject to missing data. The recent guidance from regulatory agencies, such as the ICH E9(R1) addendum addresses the importance of defining a treatment effect estimand with the consideration of intercurrent events. Jump-to-reference (J2R) is one classical control-based scenario for the treatment effect evaluation, where the participants in the treatment group after intercurrent events are assumed to have the same disease progress as those with identical covariates in the control group. We establish new estimators to assess the average treatment effect based on a proposed potential outcomes framework under J2R. Various identification formulas are constructed, motivating estimators that rely on different parts of the observed data distribution. Moreover, we obtain a novel estimator inspired by the efficient influence function, with multiple robustness in the sense that it achieves n1/2-consistency if any pairs of multiple nuisance functions are correctly specified, or if the nuisance functions converge at a rate not slower than n-1/4 when using flexible modeling approaches. The finite-sample performance of the proposed estimators is validated in simulation studies and an antidepressant clinical trial.
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Affiliation(s)
- Siyi Liu
- Department of Statistics, North Carolina State University, Raleigh, NC 27607, United States
| | - Shu Yang
- Department of Statistics, North Carolina State University, Raleigh, NC 27607, United States
| | - Yilong Zhang
- Merck & Co., Inc., Kenilworth, NJ 07033, United States
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3
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Wahab AHA, Qu Y, Michiels H, Luo J, Zhuang R, McDaniel D, Xi D, Polverejan E, Gilbert S, Ruberg S, Sabbaghi A. CITIES: Clinical trials with intercurrent events simulator. Biom J 2024; 66:e2200103. [PMID: 37740165 DOI: 10.1002/bimj.202200103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/30/2023] [Accepted: 07/17/2023] [Indexed: 09/24/2023]
Abstract
Although clinical trials are often designed with randomization and well-controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real-life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data-generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real-life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real-life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real-life trials.
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Affiliation(s)
| | - Yongming Qu
- Department of Statistics, Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Hege Michiels
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Junxiang Luo
- Department of Biostatistics and Programming, Moderna, Cambridge, Massachusetts, USA
| | - Run Zhuang
- Department of Statistics, Purdue University, West Lafayette, Indiana, USA
| | - Dominique McDaniel
- Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, Pennsylvania, USA
| | - Dong Xi
- Department of Biostatistics, Gilead Sciences, Foster City, California, USA
| | - Elena Polverejan
- Statistics and Decision Sciences, Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Steven Gilbert
- Global Product Development, Pfizer, Cambridge, Massachusetts, USA
| | | | - Arman Sabbaghi
- Department of Statistics, Purdue University, West Lafayette, Indiana, USA
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4
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Jin M. Imputation methods for informative censoring in survival analysis with time dependent covariates. Contemp Clin Trials 2024; 136:107401. [PMID: 37995968 DOI: 10.1016/j.cct.2023.107401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 10/16/2023] [Accepted: 11/18/2023] [Indexed: 11/25/2023]
Abstract
Cox proportional hazards model has been an established model for survival analysis. The flexibility of incorporating time dependent covariates has made the analysis more suitable in many clinical trials when the time dependent covariates may be predictive factors for the events. Subjects are censored for various reasons, but they are usually nonnormatively censored in the analysis. Methods for informative censoring are not well studied for settings with time dependent covariates. In this paper, we propose a few methods for informative censoring in survival analysis by Cox model with time dependent covariates, including tipping point method and Reference Based Imputation (Jump to Reference and Copy Reference). The implementation of these methods by multiple imputation is described and illustrated with two data examples.
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Affiliation(s)
- Man Jin
- Data and Statistical Sciences, AbbVie Inc., North Chicago 60064, IL, USA.
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5
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Michoel T, Zhang JD. Causal inference in drug discovery and development. Drug Discov Today 2023; 28:103737. [PMID: 37591410 DOI: 10.1016/j.drudis.2023.103737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 07/31/2023] [Accepted: 08/10/2023] [Indexed: 08/19/2023]
Abstract
To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.
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Affiliation(s)
- Tom Michoel
- Computational Biology Unit, Department of Informatics, University of Bergen, Postboks 7803, 5020 Bergen, Norway
| | - Jitao David Zhang
- Pharma Early Research and Development, Roche Innovation Centre Basel, F. Hoffmann-La Roche, Grenzacherstrasse 124, 4070 Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Spiegelgasse 1, 4051 Basel, Switzerland.
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6
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Ocampo A, Bather JR. Single-world intervention graphs for defining, identifying, and communicating estimands in clinical trials. Stat Med 2023; 42:3892-3902. [PMID: 37340887 DOI: 10.1002/sim.9833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 05/17/2023] [Accepted: 06/01/2023] [Indexed: 06/22/2023]
Abstract
Confusion often arises when attempting to articulate target estimand(s) of a clinical trial in plain language. We aim to rectify this confusion by using a type of causal graph called the Single-World Intervention Graph (SWIG) to provide a visual representation of the estimand that can be effectively communicated to interdisciplinary stakeholders. These graphs not only display estimands, but also illustrate the assumptions under which a causal estimand is identifiable by presenting the graphical relationships between the treatment, intercurrent events, and clinical outcomes. To demonstrate its usefulness in pharmaceutical research, we present examples of SWIGs for various intercurrent event strategies specified in the ICH E9(R1) addendum, as well as an example from a real-world clinical trial for chronic pain. code to generate all the SWIGs shown is this paper is made available. We advocate clinical trialists adopt the use of SWIGs in their estimand discussions during the planning stages of their studies.
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Affiliation(s)
- Alex Ocampo
- Neuroscience Biostatistics Division, Novartis Pharma AG, Basel, Switzerland
| | - Jemar R Bather
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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7
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Bigirumurame T, Hiu SKW, Teare MD, Wason JMS, Bryant A, Breckons M. Current practices in studies applying the target trial emulation framework: a protocol for a systematic review. BMJ Open 2023; 13:e070963. [PMID: 37369393 PMCID: PMC10410979 DOI: 10.1136/bmjopen-2022-070963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/06/2023] [Indexed: 06/29/2023] Open
Abstract
INTRODUCTION Observational studies represent an alternative to estimate real-world causal effects in the absence of available randomised controlled trials (RCTs). Target trial emulation is a framework for the application of RCT design principles to emulate a hypothetical open-label RCT (the hypothetical target trial) using existing observational data as the primary data source as opposed to the prospective recruitment and measurement of randomised units. The aim of this systematic review is to investigate the practices of studies applying the target trial emulation framework to evaluate the effectiveness of interventions. METHODS AND ANALYSIS We will systematically search in Medline (via Ovid), Embase (via Ovid, entries from medRxiv are included), PsycINFO (via Ovid), SCOPUS, Web of Science, Cochrane Library, the ISRCTN registry and ClinicalTrials.gov for all study reports and protocols which used the trial emulation framework (without time restriction). We will extract information concerning study design, data source, analysis, results, interpretation and dissemination. Two reviewers will perform study selection, data extraction and quality assessment. Disagreements between reviewers will be resolved by a third reviewer. A narrative approach will be used to synthesise and report qualitative and quantitative data. Reporting of the review will be informed by Preferred Reporting Items for Systematic Review and Meta-Analysis guidance (PRISMA). ETHICS AND DISSEMINATION Ethical approval is not required as it is a protocol for a systematic review. Findings will be disseminated through peer-reviewed publications and conference presentations.
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Affiliation(s)
| | - Shaun Kuan Wei Hiu
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - M Dawn Teare
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Andrew Bryant
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Matthew Breckons
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
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8
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Whitehead LE, Sailer O, Witham MD, Wason JMS. Bayesian borrowing for basket trials with longitudinal outcomes. Stat Med 2023. [PMID: 37120858 DOI: 10.1002/sim.9751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/28/2023] [Accepted: 04/16/2023] [Indexed: 05/02/2023]
Abstract
Basket trials are a novel clinical trial design in which a single intervention is investigated in multiple patient subgroups, or "baskets." They offer the opportunity to share information between subgroups, potentially increasing power to detect treatment effects. Basket trials offer several advantages over running a series of separate trials, including reduced sample sizes, increased efficiency, and reduced costs. Primarily, basket trials have been undertaken in Phase II oncology settings, but could be a promising design in other areas where a shared underlying biological mechanism drives different diseases. One such area is chronic aging-related diseases. However, trials in this area frequently have longitudinal outcomes, and therefore suitable methods are needed to share information in this setting. In this paper, we extend three Bayesian borrowing methods for a basket design with continuous longitudinal endpoints. We demonstrate our methods on a real-world dataset and in a simulation study where the aim is to detect positive basketwise treatment effects. Methods are compared with standalone analysis of each basket without borrowing. Our results confirm that methods that share information can improve power to detect positive treatment effects and increase precision over independent analysis in many scenarios. In highly heterogeneous scenarios, there is a trade-off between increased power and increased risk of type I errors. Our proposed methods for basket trials with continuous longitudinal outcomes aim to facilitate their applicability in the area of aging related diseases. Choice of method should be made based on trial priorities and the expected basketwise distribution of treatment effects.
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Affiliation(s)
- Lou E Whitehead
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Oliver Sailer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Miles D Witham
- AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, UK
| | - James M S Wason
- Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
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9
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Han S, Zhou XH. Defining estimands in clinical trials: A unified procedure. Stat Med 2023; 42:1869-1887. [PMID: 36883638 DOI: 10.1002/sim.9702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/09/2023] [Accepted: 02/17/2023] [Indexed: 03/09/2023]
Abstract
The ICH E9 (R1) addendum proposes five strategies to define estimands by addressing intercurrent events. However, mathematical forms of these targeted quantities are lacking, which might lead to discordance between statisticians who estimate these quantities and clinicians, drug sponsors, and regulators who interpret them. To improve the concordance, we provide a unified four-step procedure for constructing the mathematical estimands. We apply the procedure for each strategy to derive the mathematical estimands and compare the five strategies in practical interpretations, data collection, and analytical methods. Finally, we show that the procedure can help ease tasks of defining estimands in settings with multiple types of intercurrent events using two real clinical trials.
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Affiliation(s)
- Shasha Han
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.,Beijing International Center for Mathematical Research, Peking University, Beijing, China
| | - Xiao-Hua Zhou
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.,Department of Biostatistics, School of Public Health, Peking University, Beijing, China.,National Engineering Laboratory of Big Data Analysis and Applied Technology, Peking University, Beijing, China
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10
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Jin M, Fang Y. Methods for Informative Censoring in Time-to-Event Data Analysis. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2182355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Man Jin
- Data and Statistical Sciences, AbbVie Inc
| | - Yixin Fang
- Data and Statistical Sciences, AbbVie Inc
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11
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Erlendsdottir M, Crawford FW. Randomized controlled trials of biomarker targets. Clin Trials 2023; 20:47-58. [PMID: 36373783 PMCID: PMC9974557 DOI: 10.1177/17407745221131820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Randomized controlled trials are used to estimate the causal effect of a treatment on a health outcome of interest in a patient population. Often the specified treatment in a randomized controlled trial is a medical intervention-such as a drug or procedure-experienced directly by the patient. Sometimes the "treatment" in a randomized controlled trial is a target-such as a goal biomarker measurement-that the patient's physician attempts to reach using available medications or procedures. Large randomized controlled trials of biomarker targets are common in clinical research, and trials have been conducted to compare targets in the management of hypertension, diabetes, anemia, and acute respiratory distress syndrome. However, different randomized controlled trials intended to evaluate the same biomarker targets have produced conflicting recommendations, and meta-analyses that aggregate results of trials of biomarker targets have been inconclusive. METHODS We use causal reasoning to explain why randomized controlled trials of biomarker targets can arrive at conflicting or misleading conclusions. We describe four key threats to the validity of trials of targets: (1) intention-to-treat analysis can be misleading when a direct effect of target assignment on the outcome exists due to lack of blinding; (2) incomparability in results across trials of targets; (3) time-varying adaptive treatment strategies; and (4) Goodhart's law, "when a measure becomes a target, it ceases to be a good measure." RESULTS We illustrate these findings using evidence from 15 randomized controlled trials of blood pressure targets for management of hypertension. Randomized trials of blood pressure targets exhibit substantial variation in the trial patient populations and antihypertensives used to achieve the blood pressure targets assigned in the trials. The trials did not compare or account for time-varying treatment strategies used to reach the randomized targets. Possible "off-target" effects of antihypertensive medications needed to reach lower blood pressure targets may explain the absence of a clear benefit from intensive blood pressure control. DISCUSSION Researchers should critically assess meta-analyses of trials of targets for variation in the types, distributions, and off-target effects of therapies studied. Trial investigators should release detailed information about the biomarker targets compared in new randomized trials, as well as confounders, treatments delivered, and outcomes. New randomized controlled trials should experimentally compare treatment algorithms incorporating biomarkers, rather than targets alone. Causal inference methodology that adjusts for time-varying confounding should be used to compare time-varying treatment strategies in observational settings.
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Affiliation(s)
| | - Forrest W. Crawford
- Department of Biostatistics, Yale School of Public Health,Department of Statistics & Data Science, Yale University,Department of Ecology & Evolutionary Biology, Yale University,Yale School of Management
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12
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Wang C, Zhang Y, Mealli F, Bornkamp B. Sensitivity analyses for the principal ignorability assumption using multiple imputation. Pharm Stat 2023; 22:64-78. [PMID: 36053974 DOI: 10.1002/pst.2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/03/2022] [Accepted: 07/27/2022] [Indexed: 02/01/2023]
Abstract
In the context of clinical trials, there is interest in the treatment effect for subpopulations of patients defined by intercurrent events, namely disease-related events occurring after treatment initiation that affect either the interpretation or the existence of endpoints. With the principal stratum strategy, the ICH E9(R1) guideline introduces a formal framework in drug development for defining treatment effects in such subpopulations. Statistical estimation of the treatment effect can be performed based on the principal ignorability assumption using multiple imputation approaches. Principal ignorability is a conditional independence assumption that cannot be directly verified; therefore, it is crucial to evaluate the robustness of results to deviations from this assumption. As a sensitivity analysis, we propose a joint model that multiply imputes the principal stratum membership and the outcome variable while allowing different levels of violation of the principal ignorability assumption. We illustrate with a simulation study that the joint imputation model-based approaches are superior to naive subpopulation analyses. Motivated by an oncology clinical trial, we implement the sensitivity analysis on a time-to-event outcome to assess the treatment effect in the subpopulation of patients who discontinued due to adverse events using a synthetic dataset. Finally, we explore the potential usage and provide interpretation of such analyses in clinical settings, as well as possible extension of such models in more general cases.
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Affiliation(s)
- Craig Wang
- Department of Analytics, Novartis Pharma AG, Basel, Switzerland
| | - Yufen Zhang
- Department of Analytics, Novartis Pharmaceuticals Corp, East Hanover, New Jersey, USA
| | - Fabrizia Mealli
- Department of Statistics, Computer Science and Applications, Florence Center for Data Science, University of Florence, Florence, Italy.,Economics Department, European University Institute, Florence, Italy
| | - Björn Bornkamp
- Department of Analytics, Novartis Pharma AG, Basel, Switzerland
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13
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Rogers JA, Maas H, Pitarch AP. An introduction to causal inference for pharmacometricians. CPT Pharmacometrics Syst Pharmacol 2022; 12:27-40. [PMID: 36385744 PMCID: PMC9835139 DOI: 10.1002/psp4.12894] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 11/18/2022] Open
Abstract
As formal causal inference begins to play a greater role in disciplines that intersect with pharmacometrics, such as biostatistics, epidemiology, and artificial intelligence/machine learning, pharmacometricians may increasingly benefit from a basic fluency in foundational causal inference concepts. This tutorial seeks to orient pharmacometricians to three such fundamental concepts: potential outcomes, g-formula, and directed acyclic graphs (DAGs).
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Affiliation(s)
| | - Hugo Maas
- Boehringer Ingelheim Pharma GmbH & Co. KGIngelheim am RheinGermany
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14
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Wang SJ, Huang Z, Zhu H. Performance of LTMLE in the presence of missing data in control-matched longitudinal studies. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2108136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Sue-Jane Wang
- Division of Biometrics I, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Zhipeng Huang
- Division of Biometrics I, Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Hai Zhu
- Department of Biometrics and Clinical Development, SystImmune, Inc
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15
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Ionan AC, Paterniti M, Mehrotra DV, Scott J, Ratitch B, Collins S, Gomatam S, Nie L, Rufibach K, Bretz F. Clinical and Statistical Perspectives on the ICH E9(R1) Estimand Framework Implementation. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2081601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Alexei C. Ionan
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | - Miya Paterniti
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | - Devan V. Mehrotra
- Biostatistics and Research Decision Sciences, Merck & Co., Inc, Kenilworth, NJ
| | - John Scott
- Center for Biologics Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | | | - Sylva Collins
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | - Shanti Gomatam
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | - Lei Nie
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
| | - Kaspar Rufibach
- Product Development Data Sciences, F. Hoffmann-La Roche, Basel, Switzerland
| | - Frank Bretz
- Analytics, Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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16
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Van Lancker K, Tarima S, Bartlett J, Bauer M, Bharani-Dharan B, Bretz F, Flournoy N, Michiels H, Olarte Parra C, Rosenberger JL, Cro S. Estimands and their Estimators for Clinical Trials Impacted by the COVID-19 Pandemic: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2094459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Kelly Van Lancker
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, U.S.A.
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Sergey Tarima
- Division of Biostatistics, Medical College of Wisconsin, U.S.A.
| | | | - Madeline Bauer
- Division of Infectious Diseases, Keck School of Medicine, University of Southern California (ret), Los Angeles, U.S.A.
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Nancy Flournoy
- Department of Statistics, University of Missouri (emerita), Columbia, U.S.A.
| | - Hege Michiels
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | | | - James L Rosenberger
- National Institute of Statistical Sciences, and Department of Statistics, Penn State University, University Park, PA 16802-2111 U.S.A.
| | - Suzie Cro
- Imperial Clinical Trials Unit, Imperial College London, U.K
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17
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Palin V, Van Staa TP, Steels S, Troxel AB, Groenwold RHH, MacDonald TM, Torgerson D, Faries D, Mancini P, Ouwens M, Frith LJ, Tsirtsonis K, MacLennan G, Nordon C. A first step towards best practice recommendations for the design and statistical analyses of pragmatic clinical trials: a modified Delphi approach. Br J Clin Pharmacol 2022; 88:5183-5201. [PMID: 35701368 DOI: 10.1111/bcp.15441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 04/29/2022] [Accepted: 05/22/2022] [Indexed: 11/30/2022] Open
Abstract
AIM Pragmatic clinical trials (PCTs) are randomised trials implemented through routine clinical practice, where design parameters of traditional randomised controlled trials are modified to increase generalizability. However, this may introduce statistical challenges. We aimed to identify these challenges and discuss possible solutions leading to best practice recommendations for the design and analysis of PCTs. METHODS A modified Delphi method was used to reach consensus among a panel of 11 experts in clinical trials and statistics. Statistical issues were identified in a focused literature review and aggregated with insights and possible solutions from expert collected through a series of survey iterations. Issues were ranked according to their importance. RESULTS 27 articles were included and combined with experts' insight to generate a list of issues categorized into: participants; recruiting sites; randomisation, blinding and intervention; outcome (selection and measurement); and data analysis. Consensus was reached about the most important issues: risk of participants' attrition; heterogeneity of "usual care" across sites; absence of blinding; use of a subjective endpoint; and data analysis aligned with the trial estimand. Potential issues should be anticipated and preferably be addressed in the trial protocol. The experts provided solutions regarding data collection and data analysis, which were considered of equal importance. DISCUSSION A set of important statistical issues in PCTs was identified and approaches were suggested to anticipate and/or minimize these through data analysis. Any impact of choosing a pragmatic design feature should be gauged in the light of the trial estimand.
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Affiliation(s)
- Victoria Palin
- Division of Informatics, Imaging & Data Sciences, Manchester Environmental Research Institute, University of Manchester, United Kingdom
| | - Tjeerd P Van Staa
- Division of Informatics, Imaging & Data Sciences, Manchester Environmental Research Institute, University of Manchester, United Kingdom
| | - Stephanie Steels
- Department of Social Care and Social Work, Manchester Metropolitan University, Manchester, United Kingdom
| | - Andrea B Troxel
- Division of Biostatistics, Department of Population Health, NYU Grossman School of Medicine, NYU, USA
| | - Rolf H H Groenwold
- Department of Clinical Epidemiology, Leiden University Medical Centre, The Netherlands
| | - Tom M MacDonald
- MEMO Research, University of Dundee, Ninewells Hospital & Medical School, Dundee, United Kingdom
| | - David Torgerson
- Department of Health Sciences, University of York, United Kingdom
| | - Douglas Faries
- Global Statistical Sciences, Eli Lilly & Co., Indianapolis, IN, USA
| | | | | | | | | | - Graham MacLennan
- The Centre for Healthcare Randomised Trials, University of Aberdeen, United Kingdom
| | - Clementine Nordon
- formally LASER Research, Paris, France; currently AstraZeneca, Cambridge, United Kingdom
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18
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Olarte Parra C, Daniel RM, Bartlett JW. Hypothetical estimands in clinical trials: a unification of causal inference and missing data methods. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2081599] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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19
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Gabriel EE, Sachs MC, Sjölander A. Sharp nonparametric bounds for decomposition effects with two binary mediators. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2057316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Erin E Gabriel
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Denmark
| | - Michael C Sachs
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Denmark
| | - Arvid Sjölander
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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20
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Ma C, Shen X, Qu Y, Du Y. Analysis of an incomplete binary outcome dichotomized from an underlying continuous variable in clinical trials. Pharm Stat 2022; 21:907-918. [PMID: 35277928 DOI: 10.1002/pst.2204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 11/22/2021] [Accepted: 02/27/2022] [Indexed: 11/11/2022]
Abstract
In many clinical trials, outcomes of interest are binary-valued. It is not uncommon that a binary-valued outcome is dichotomized from a continuous outcome at a threshold of clinical interest. To analyze such data, common approaches include (a) fitting a generalized linear mixed model (GLMM) to the dichotomized longitudinal binary outcome; and (b) the multiple imputation (MI) based method: imputing missing values in the continuous outcome, dichotomizing it into a binary outcome, and then fitting a generalized linear model to the "complete" data. We conducted comprehensive simulation studies to compare the performance of the GLMM versus the MI-based method for estimating the risk difference and the logarithm of odds ratio between two treatment arms at the end of study. In those simulation studies, we considered a range of multivariate distribution options for the continuous outcome (including a multivariate normal distribution, a multivariate t-distribution, a multivariate log-normal distribution, and the empirical distribution from a real clinical trial data) to evaluate the robustness of the estimators to various data-generating models. Simulation results demonstrate that both methods work well under those considered distribution options, but the MI-based method is more efficient with smaller mean squared errors compared to the GLMM. We further applied both the GLMM and the MI-based method to 29 phase 3 diabetes clinical trials, and found that the MI-based method generally led to smaller variance estimates compared to the GLMM.
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Affiliation(s)
- Chenchen Ma
- Statistics, Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Xin Shen
- SAS Programming, Brightech International, Somerset, New Jersey, USA
| | - Yongming Qu
- Statistics, Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Yu Du
- Statistics, Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
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21
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Grøvle L, Hasvik E, Haugen AJ. Impact of rescue medication in placebo-controlled trials of pharmacotherapy for neuropathic pain and low back pain. Pain 2022; 163:e417-e425. [PMID: 34407031 DOI: 10.1097/j.pain.0000000000002380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/14/2021] [Indexed: 11/26/2022]
Abstract
ABSTRACT Rescue medication (RM) consumption is commonly used as a secondary outcome in placebo-controlled trials of chronic pain, but its validity has yet to be established. If participants randomized to placebo take more RM than those randomized to an active drug, the difference in pain between the 2 groups may be reduced, potentially masking effects of the active drug. This study assessed proportional RM consumption in the placebo and active groups according to results of 42 randomized controlled trials of neuropathic pain (NeP), and 29 trials of low back pain, which were included in 2 systematic reviews and meta-analyses. Trial results were assessed based on effect size, statistical significance, and whether the drug was recommended as first-line treatment by the systematic reviews. In trials indicating effect of the investigational drug, RM consumption was generally higher in the placebo groups than in the active groups. In trials reporting a small or a medium effect size of the investigational drug, subjects receiving placebo consumed 17% to 30% more RM than subjects receiving active drug, potentially leading to underestimation of the effects of the investigational drugs. Few trials reported a large effect size. Differences in RM consumption between participants receiving placebo and those receiving active drug were seldom taken in account by the individual trials and not at all by the systemic reviews when making treatment recommendations for NeP or low back pain. Elaboration on analytical methods to assess treatment effects in chronic pain trials using RM is warranted.
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Affiliation(s)
- Lars Grøvle
- Department of Rheumatology, Østfold Hospital Trust, Grålum, Norway
| | - Eivind Hasvik
- Department of Physical Medicine and Rehabilitation, Østfold Hospital Trust, Grålum, Norway
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22
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Assessing treatment benefit in the presence of placebo response using the sequential parallel comparison design. Stat Med 2022; 41:2166-2190. [DOI: 10.1002/sim.9349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/30/2021] [Accepted: 01/05/2022] [Indexed: 11/07/2022]
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23
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Qu Y, Dai B. Return-to-baseline multiple imputation for missing values in clinical trials. Pharm Stat 2022; 21:641-653. [PMID: 34985825 DOI: 10.1002/pst.2191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 11/17/2021] [Accepted: 12/20/2021] [Indexed: 11/07/2022]
Abstract
Return-to-baseline is an important method to impute missing values or unobserved potential outcomes when certain hypothetical strategies are used to handle intercurrent events in clinical trials. Current return-to-baseline approaches seen in literature and in practice inflate the variability of the "complete" dataset after imputation and lead to biased mean estimators when the probability of missingness depends on the observed baseline and/or postbaseline intermediate outcomes. In this article, we first provide a set of criteria a return-to-baseline imputation method should satisfy. Under this framework, we propose a novel return-to-baseline imputation method. Simulations show the completed data after the new imputation approach have the proper distribution, and the estimators based on the new imputation method outperform the traditional method in terms of both bias and variance, when missingness depends on the observed values. The new method can be implemented easily with the existing multiple imputation procedures in commonly used statistical packages.
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Affiliation(s)
- Yongming Qu
- Department of Statistics, Data and Analytics, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Biyue Dai
- Department of Statistics, Data and Analytics, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, Indiana, USA
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24
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Keele L, Grieve R. Contrasting approaches for addressing non-adherence in randomized controlled trials: An illustration from the REFLUX trial. Clin Trials 2021; 19:97-106. [PMID: 34949104 DOI: 10.1177/17407745211056881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND In many randomized controlled trials, a substantial proportion of patients do not comply with the treatment protocol to which they have been randomly assigned. Randomized controlled trials are required to report results according to the intention-to-treat estimand, but recent methodological guidance recognizes the importance of estimating other causal quantities. METHODS This article outlines an analytical framework for randomized controlled trials with non-compliance. We apply the ICH E9 (R1) addendum and combine it with the potential outcomes framework to define key estimands, outline the major assumptions for identification of each estimand, and highlight the assumptions that cannot be verified from the randomized controlled trial data. We contrast the assumptions and estimates in a re-analysis of the REFLUX trial. We report alternative estimates for the effectiveness of receipt of laparoscopic surgery versus medical management for patients with gastro-intestinal reflux disease. RESULTS The article finds that adjusted as-treated and per-protocol estimates were similar in magnitude to those based intention-to-treat methods. Instrumental variable estimates of the complier average causal effect were larger, with wider confidence intervals. CONCLUSION We recommend that in randomized controlled trials with non-compliance, studies should outline which estimand is most relevant to the study context, evaluate key assumptions, and present estimates from a range of methods as a sensitivity analysis.
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Affiliation(s)
- Luke Keele
- University of Pennsylvania, Philadelphia, PA, USA
| | - Richard Grieve
- London School of Hygiene and Tropical Medicine, London, UK
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25
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Groenwold RHH. Grand Challenge-Crossing Borders to Develop Epidemiologic Methods. FRONTIERS IN EPIDEMIOLOGY 2021; 1:786988. [PMID: 38455239 PMCID: PMC10910928 DOI: 10.3389/fepid.2021.786988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/26/2021] [Indexed: 03/09/2024]
Affiliation(s)
- Rolf H. H. Groenwold
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
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26
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Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic. Ther Innov Regul Sci 2021; 55:984-988. [PMID: 33983621 PMCID: PMC8117454 DOI: 10.1007/s43441-021-00297-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/09/2021] [Indexed: 11/17/2022]
Abstract
The current COVID-19 pandemic poses numerous challenges for ongoing clinical trials and provides a stress-testing environment for the existing principles and practice of estimands in clinical trials. The pandemic may increase the rate of intercurrent events (ICEs) and missing values, spurring a great deal of discussion on amending protocols and statistical analysis plans to address these issues. In this article, we revisit recent research on estimands and handling of missing values, especially the ICH E9 (R1) Addendum on Estimands and Sensitivity Analysis in Clinical Trials. Based on an in-depth discussion of the strategies for handling ICEs using a causal inference framework, we suggest some improvements in applying the estimand and estimation framework in ICH E9 (R1). Specifically, we discuss a mix of strategies allowing us to handle ICEs differentially based on reasons for ICEs. We also suggest ICEs should be handled primarily by hypothetical strategies and provide examples of different hypothetical strategies for different types of ICEs as well as a road map for estimation and sensitivity analyses. We conclude that the proposed framework helps streamline translating clinical objectives into targets of statistical inference and automatically resolves many issues with defining estimands and choosing estimation procedures arising from events such as the pandemic.
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27
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Bowden J, Bornkamp B, Glimm E, Bretz F. Connecting Instrumental Variable methods for causal inference to the Estimand Framework. Stat Med 2021; 40:5605-5627. [PMID: 34288021 DOI: 10.1002/sim.9143] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 11/09/2022]
Abstract
Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in clinical trials to the E9 guideline of the International Council for Harmonisation. The E9 addendum emphasises the need to account for post-randomization or 'intercurrent' events that can potentially influence the interpretation of a treatment effect estimate at a trial's conclusion. Instrumental Variables (IV) methods have been used extensively in economics, epidemiology, and academic clinical studies for 'causal inference,' but less so in the pharmaceutical industry setting until now. In this tutorial article we review the basic tools for causal inference, including graphical diagrams and potential outcomes, as well as several conceptual frameworks that an IV analysis can sit within. We discuss in detail how to map these approaches to the Treatment Policy, Principal Stratum and Hypothetical 'estimand strategies' introduced in the E9 addendum, and provide details of their implementation using standard regression models. Specific attention is given to discussing the assumptions each estimation strategy relies on in order to be consistent, the extent to which they can be empirically tested and sensitivity analyses in which specific assumptions can be relaxed. We finish by applying the methods described to simulated data closely matching two recent pharmaceutical trials to further motivate and clarify the ideas.
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Affiliation(s)
- Jack Bowden
- Exeter Diabetes Group (ExCEED), College of Medicine and Health, University of Exeter, Exeter, UK.,MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | | | - Ekkehard Glimm
- Novartis Pharma AG, Basel, Switzerland.,Institute for Biometry and Medical Informatics, Medical Faculty, University of Magdeburg, Magdeburg, Germany
| | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland.,Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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28
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Ocampo A, Schmidli H, Quarg P, Callegari F, Pagano M. Identifying treatment effects using trimmed means when data are missing not at random. Pharm Stat 2021; 20:1265-1277. [PMID: 34169641 DOI: 10.1002/pst.2147] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 05/28/2021] [Accepted: 06/01/2021] [Indexed: 11/06/2022]
Abstract
Patients often discontinue from a clinical trial because their health condition is not improving or they cannot tolerate the assigned treatment. Consequently, the observed clinical outcomes in the trial are likely better on average than if every patient had completed the trial. If these differences between trial completers and non-completers cannot be explained by the observed data, then the study outcomes are missing not at random (MNAR). One way to overcome this problem-the trimmed means approach for missing data due to study discontinuation-sets missing values as the worst observed outcome and then trims away a fraction of the distribution from each treatment arm before calculating differences in treatment efficacy (Permutt T, Li F. Trimmed means for symptom trials with dropouts. Pharm Stat. 2017;16(1):20-28). In this paper, we derive sufficient and necessary conditions for when this approach can identify the average population treatment effect. Simulation studies show the trimmed means approach's ability to effectively estimate treatment efficacy when data are MNAR and missingness due to study discontinuation is strongly associated with an unfavorable outcome, but trimmed means fail when data are missing at random. If the reasons for study discontinuation in a clinical trial are known, analysts can improve estimates with a combination of multiple imputation and the trimmed means approach when the assumptions of each hold. We compare the methodology to existing approaches using data from a clinical trial for chronic pain. An R package trim implements the method. When the assumptions are justifiable, using trimmed means can help identify treatment effects notwithstanding MNAR data.
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Affiliation(s)
- Alex Ocampo
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | | | | | - Marcello Pagano
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
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29
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Ren T, Shen W, Zhang L, Zhao H. Bayesian phase II clinical trial design with noncompliance. Stat Med 2021; 40:4457-4472. [PMID: 34050539 DOI: 10.1002/sim.9041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 02/27/2021] [Accepted: 04/15/2021] [Indexed: 11/08/2022]
Abstract
Noncompliance issue is common in early phase clinical trials; and may lead to biased estimation of the intent-to-treat effect and incorrect conclusions for the clinical trial. In this work, we propose a Bayesian approach for sequentially monitoring the phase II randomized clinical trials that takes account for the noncompliance information. We adopt the principal stratification framework and propose to use Bayesian additive regression trees for selecting useful baseline covariates and estimating the complier average causal effect (CACE) for both efficacy and toxicity outcomes. The decision of early termination or not is then made adaptively based on the estimated CACE from the accumulated data. Simulation studies have confirmed the excellent performance of the proposed design in the presence of noncompliance.
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Affiliation(s)
- Tingyang Ren
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Weining Shen
- Department of Statistics, University of California, Irvine, California, USA
| | - Liwen Zhang
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
| | - Haibing Zhao
- School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China
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30
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Schmidli H, Roger JH, Akacha M. Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1895883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
| | - James H. Roger
- Medical Statistics Department, London School of Hygiene & Tropical Medicine, London, UK
| | - Mouna Akacha
- Statistical Methodology, Novartis, Basel, Switzerland, on behalf of the Recurrent Event Qualification Opinion Consortium
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31
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Hamasaki T, Bretz F. Statistics in Biopharmaceutical Research Best Papers Award. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1912479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
| | - Frank Bretz
- Clinical Development & Analytics, Novartis Pharma, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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32
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Bornkamp B, Rufibach K, Lin J, Liu Y, Mehrotra DV, Roychoudhury S, Schmidli H, Shentu Y, Wolbers M. Principal stratum strategy: Potential role in drug development. Pharm Stat 2021; 20:737-751. [PMID: 33624407 DOI: 10.1002/pst.2104] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 12/01/2020] [Accepted: 02/05/2021] [Indexed: 12/12/2022]
Abstract
A randomized trial allows estimation of the causal effect of an intervention compared to a control in the overall population and in subpopulations defined by baseline characteristics. Often, however, clinical questions also arise regarding the treatment effect in subpopulations of patients, which would experience clinical or disease related events post-randomization. Events that occur after treatment initiation and potentially affect the interpretation or the existence of the measurements are called intercurrent events in the ICH E9(R1) guideline. If the intercurrent event is a consequence of treatment, randomization alone is no longer sufficient to meaningfully estimate the treatment effect. Analyses comparing the subgroups of patients without the intercurrent events for intervention and control will not estimate a causal effect. This is well known, but post-hoc analyses of this kind are commonly performed in drug development. An alternative approach is the principal stratum strategy, which classifies subjects according to their potential occurrence of an intercurrent event on both study arms. We illustrate with examples that questions formulated through principal strata occur naturally in drug development and argue that approaching these questions with the ICH E9(R1) estimand framework has the potential to lead to more transparent assumptions as well as more adequate analyses and conclusions. In addition, we provide an overview of assumptions required for estimation of effects in principal strata. Most of these assumptions are unverifiable and should hence be based on solid scientific understanding. Sensitivity analyses are needed to assess robustness of conclusions.
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Affiliation(s)
- Björn Bornkamp
- Clinical Development and Analytics, Novartis, Basel, Switzerland
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach Group (MCO), Department of Biostatistics, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Jianchang Lin
- Statistical & Quantitative Sciences (SQS), Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Yi Liu
- Nektar Therapeutics, San Francisco, California, USA
| | - Devan V Mehrotra
- Clinical Biostatistics, Merck & Co., Inc., North Wales, Pennsylvania, USA
| | | | - Heinz Schmidli
- Clinical Development and Analytics, Novartis, Basel, Switzerland
| | - Yue Shentu
- Merck & Co., Inc., Rahway, New Jersey, USA
| | - Marcel Wolbers
- Methods, Collaboration, and Outreach Group (MCO), Department of Biostatistics, Hoffmann-La Roche Ltd, Basel, Switzerland
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33
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Qu Y, Shurzinske L, Sethuraman S. Defining estimands using a mix of strategies to handle intercurrent events in clinical trials. Pharm Stat 2020; 20:314-323. [PMID: 33098267 DOI: 10.1002/pst.2078] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 08/06/2020] [Accepted: 10/06/2020] [Indexed: 10/23/2022]
Abstract
Randomized controlled trials (RCTs) are the gold standard for evaluation of the efficacy and safety of investigational interventions. If every patient in an RCT were to adhere to the randomized treatment, one could simply analyze the complete data to infer the treatment effect. However, intercurrent events (ICEs) including the use of concomitant medication for unsatisfactory efficacy, treatment discontinuation due to adverse events, or lack of efficacy may lead to interventions that deviate from the original treatment assignment. Therefore, defining the appropriate estimand (the appropriate parameter to be estimated) based on the primary objective of the study is critical prior to determining the statistical analysis method and analyzing the data. The International Council for Harmonisation (ICH) E9 (R1), adopted on November 20, 2019, provided five strategies to define the estimand: treatment policy, hypothetical, composite variable, while on treatment, and principal stratum. In this article, we propose an estimand using a mix of strategies in handling ICEs. This estimand is an average of the "null" treatment difference for those with ICEs potentially related to safety and the treatment difference for the other patients if they would complete the assigned treatments. Two examples from clinical trials evaluating antidiabetes treatments are provided to illustrate the estimation of this proposed estimand and to compare it with the estimates for estimands using hypothetical and treatment policy strategies in handling ICEs.
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Affiliation(s)
- Yongming Qu
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
| | - Linda Shurzinske
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
| | - Shanthi Sethuraman
- Global Statistical Sciences, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, USA
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34
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Kunz CU, Jörgens S, Bretz F, Stallard N, Van Lancker K, Xi D, Zohar S, Gerlinger C, Friede T. Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue? Stat Biopharm Res 2020; 12:461-477. [PMID: 34191979 PMCID: PMC8011492 DOI: 10.1080/19466315.2020.1799857] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 01/09/2023]
Abstract
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
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Affiliation(s)
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, The University of Warwick, Coventry, UK
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Dong Xi
- Novartis Pharmaceuticals, East Hanover, NJ
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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Jin M, Liu G. Estimand framework: Delineating what to be estimated with clinical questions of interest in clinical trials. Contemp Clin Trials 2020; 96:106093. [PMID: 32777382 DOI: 10.1016/j.cct.2020.106093] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/21/2020] [Accepted: 07/26/2020] [Indexed: 12/01/2022]
Abstract
ICH (International Council for Harmonization) E9 R1 (2019) proposes a framework to define estimands in clinical trials. Although the concept of estimand was proposed previously when US Food and Drug Administration (FDA) issued the panel report on handling missing data in clinical trials, many details including attributes and different strategies have not been developed until the recent ICH E9 (R1) addendum. A clearly defined estimand should include considerations of five attributes including patient population, treatment regimen of interest, endpoint/variables, handling of intercurrent events (IEs), and summary measures for assessing treatment effect. To evaluate the underlying treatment effects of a new investigational drug or biologic product, it is desirable to consider estimands that are aligned with the objectives of the study and that are meaningful to the stakeholders such as physicians or patients, health authority administration, and payers, etc.. In this paper, the concepts, attributes and strategies of the estimand framework will be reviewed and illustrated with clinical trial examples. Some common estimands and their associated scientific questions are discussed within a causal inference framework for longitudinal clinical trials.
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Affiliation(s)
- Man Jin
- AbbVie Inc., North Chicago, IL, USA.
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Qu Y, Luo J, Ruberg SJ. Implementation of tripartite estimands using adherence causal estimators under the causal inference framework. Pharm Stat 2020; 20:55-67. [PMID: 33442928 DOI: 10.1002/pst.2054] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 05/28/2020] [Accepted: 07/01/2020] [Indexed: 11/06/2022]
Abstract
Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes.
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Affiliation(s)
- Yongming Qu
- Department of Biometrics, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Junxiang Luo
- Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
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