1
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Mizuma K, Hashimoto T, Sakui S, Kuroda S. Principal quantile treatment effect estimation using principal scores. Stat Med 2024. [PMID: 39155816 DOI: 10.1002/sim.10178] [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: 02/14/2023] [Revised: 04/25/2024] [Accepted: 07/11/2024] [Indexed: 08/20/2024]
Abstract
Intercurrent events and estimands play a key role in defining the treatment effects of interest precisely. Sometimes the median or other quantiles of outcomes in a principal stratum according to potential occurrence of intercurrent events are of interest in randomized clinical trials. Naïve analyses such as those based on the observed occurrence of the intercurrent events lead to biased results. Therefore, we propose principal quantile treatment effect estimators that can nonparametrically estimate the distribution of potential outcomes by principal score weighting without relying on the exclusion restriction assumption. Our simulation studies show that the proposed method works in situations where the median or quantiles may be regarded as the preferred population-level summary over the mean. We illustrate our proposed method by using data from a randomized controlled trial conducted on patients with nonerosive reflux disease.
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Affiliation(s)
- Kotaro Mizuma
- Statistical & Quantitative Sciences, Data Science Institute, Takeda Pharmaceutical Company Limited, Osaka, Japan
| | - Takamasa Hashimoto
- Statistical & Quantitative Sciences, Data Science Institute, Takeda Pharmaceutical Company Limited, Osaka, Japan
| | - Sho Sakui
- Statistical & Quantitative Sciences, Data Science Institute, Takeda Pharmaceutical Company Limited, Osaka, Japan
| | - Shingo Kuroda
- Statistical & Quantitative Sciences, Data Science Institute, Takeda Pharmaceutical Company Limited, Osaka, Japan
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2
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Lyu T, Bornkamp B, Mueller-Velten G, Schmidli H. Bayesian inference for a principal stratum estimand on recurrent events truncated by death. Biometrics 2023; 79:3792-3802. [PMID: 36647690 DOI: 10.1111/biom.13831] [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: 01/12/2022] [Accepted: 01/05/2023] [Indexed: 01/18/2023]
Abstract
Recurrent events are often important endpoints in randomized clinical trials. For example, the number of recurrent disease-related hospitalizations may be considered as a clinically meaningful endpoint in cardiovascular studies. In some settings, the recurrent event process may be terminated by an event such as death, which makes it more challenging to define and estimate a causal treatment effect on recurrent event endpoints. In this paper, we focus on the principal stratum estimand, where the treatment effect of interest on recurrent events is defined among subjects who would be alive regardless of the assigned treatment. For the estimation of the principal stratum effect in randomized clinical trials, we propose a Bayesian approach based on a joint model of the recurrent event and death processes with a frailty term accounting for within-subject correlation. We also present Bayesian posterior predictive check procedures for assessing the model fit. The proposed approaches are demonstrated in the randomized Phase III chronic heart failure trial PARAGON-HF (NCT01920711).
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Affiliation(s)
- Tianmeng Lyu
- Novartis Pharmaceuticals Corporation, East Hanover, New Jersey, USA
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3
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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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4
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A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development. J Pharmacokinet Pharmacodyn 2023; 50:147-172. [PMID: 36870005 PMCID: PMC10169901 DOI: 10.1007/s10928-023-09850-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023]
Abstract
Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.
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5
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Qu Y, Lipkovich I, Ruberg SJ. Assessing the commonly used assumptions in estimating the principal causal effect in clinical trials. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2166097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Yongming Qu
- Department of Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, 46285, USA
| | - Ilya Lipkovich
- Department of Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, 46285, USA
| | - Stephen J. Ruberg
- Analytix Thinking, LCC, 11121 Bentgrass Court, Indianapolis, IN 46236, USA
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6
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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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7
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Lipkovich I, Ratitch B, Qu Y, Zhang X, Shan M, Mallinckrodt C. Using principal stratification in analysis of clinical trials. Stat Med 2022; 41:3837-3877. [PMID: 35851717 DOI: 10.1002/sim.9439] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 03/06/2022] [Accepted: 05/03/2022] [Indexed: 11/08/2022]
Abstract
The ICH E9(R1) addendum (2019) proposed principal stratification (PS) as one of five strategies for dealing with intercurrent events. Therefore, understanding the strengths, limitations, and assumptions of PS is important for the broad community of clinical trialists. Many approaches have been developed under the general framework of PS in different areas of research, including experimental and observational studies. These diverse applications have utilized a diverse set of tools and assumptions. Thus, need exists to present these approaches in a unifying manner. The goal of this tutorial is threefold. First, we provide a coherent and unifying description of PS. Second, we emphasize that estimation of effects within PS relies on strong assumptions and we thoroughly examine the consequences of these assumptions to understand in which situations certain assumptions are reasonable. Finally, we provide an overview of a variety of key methods for PS analysis and use a real clinical trial example to illustrate them. Examples of code for implementation of some of these approaches are given in Supplemental Materials.
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Affiliation(s)
| | | | - Yongming Qu
- Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Xiang Zhang
- CSL Behring, King of Prussia, Pennsylvania, USA
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8
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Fletcher C, Hefting N, Wright M, Bell J, Anzures-Cabrera J, Wright D, Lynggaard H, Schueler A. Marking 2-Years of New Thinking in Clinical Trials: The Estimand Journey. Ther Innov Regul Sci 2022; 56:637-650. [PMID: 35462609 PMCID: PMC9035309 DOI: 10.1007/s43441-022-00402-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/04/2022] [Indexed: 11/30/2022]
Abstract
The ICH E9(R1) addendum on Estimands and Sensitivity Analyses in Clinical Trials has introduced a new estimand framework for the design, conduct, analysis, and interpretation of clinical trials. We share Pharmaceutical Industry experiences of implementing the estimand framework in the first two years since the final guidance became available with key lessons learned and highlight what else needs to be done to continue the journey in embedding the estimand framework in clinical trials. Emerging best practices and points to consider on strategies for implementing a new estimand thinking process are provided. Whilst much of the focus of implementing ICH E9(R1) to date has been on defining estimands, we highlight some of the important aspects relating to the choice of statistical analysis methods and sensitivity analyses to ensure estimands can be estimated robustly with minimal bias. In particular, we discuss the implications if complete follow-up is not possible when the treatment policy strategy is being used to handle intercurrent events. ICH E9(R1) was introduced just before the start of the COVID-19 pandemic, but a positive outcome from the pandemic has been an acceleration in the adoption of the estimand framework, including differentiating intercurrent events related or not related to the pandemic. In summary, much has been learned on the estimand journey and continued sharing of case studies will help to further advance the understanding and increase awareness across all clinical researchers of the estimand framework.
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Affiliation(s)
- C Fletcher
- Biostatistics, GlaxoSmithKline Plc, Stevenage, United Kingdom.
| | - N Hefting
- Clinical Development, Psychiatry, H. Lundbeck A/S, Valby, Denmark
| | - M Wright
- Analytics, Novartis Pharma AG, Basel, Switzerland
| | - J Bell
- Clinical Operations, Elderbrook Solutions GmbH, High Wycombe, United Kingdom
| | - J Anzures-Cabrera
- Data Sciences, Roche Products Ltd, Welywn Garden City, United Kingdom
| | - D Wright
- Statistical Innovation, DS&AI, BioPharma R&D, AstraZeneca, Cambridge, United Kingdom
| | - H Lynggaard
- Biostatistics, Data Science, Novo Nordisk A/S, Bagsværd, Denmark
| | - A Schueler
- Biostatistics, Epidemiology & Medical Writing, Merck Healthcare KGaA, Darmstadt, Germany
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9
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Sun C, Zhong L, Wu Y, Cao C, Guo D, Liu J, Gong L, Zhang S, Sun J, Yu Y, Tong W, Yang J. Incorporation of Laboratory Test Biomarkers Into Dual Antiplatelet Therapy Score Improves Prediction of Ischemic and Bleeding Events in Post-percutaneous Coronary Intervention Patients. Front Cardiovasc Med 2022; 9:834975. [PMID: 35651911 PMCID: PMC9148992 DOI: 10.3389/fcvm.2022.834975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
This study aimed to examine the performance of the dual antiplatelet therapy (DAPT) score in two retrospective cohorts of post-percutaneous coronary intervention (PCI) patients and to explore whether incorporating additional biomarkers could further improve the predictive power of the DAPT score. In a retrospective derivation cohort of 4,798 PCI patients, the validity of DAPT score for stratifying ischemic/bleeding risks was explored. Then, the association between the baseline status of 54 laboratory test biomarkers and ischemic/bleeding events was revealed while adjusting for the DAPT score. Combinations of individual laboratory test biomarkers that were significantly associated with ischemic/bleeding events were explored to identify the ones that improved discrimination of ischemic and bleeding events when incorporated into DAPT score. Finally, the impact of the combination of biomarkers with DAPT score was validated in an independent retrospective validation cohort of 1,916 PCI patients. Patients with a high DAPT score (DAPT score ≥ 2) had significantly higher risk of ischemic events and significantly lower risk of bleeding than patients with a low DAPT score (DAPT score < 2). Moreover, the addition of aspartate aminotransferase (AST) and red cell distribution width CV (RDW-CV) into the DAPT score further improved discrimination of ischemia and bleeding. Furthermore, the incremental predictive value of AST + RDW-CV maintained with measurements was updated at post-baseline time points. DAPT score successfully stratified the risks of ischemia/bleeding post PCI in the current cohorts. Incorporation of AST + RDW-CV into the DAPT score further improved prediction for both ischemic and bleeding events.
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Affiliation(s)
- Chengming Sun
- Department of Clinical Laboratory, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Lin Zhong
- Department of Cardiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Yanqiu Wu
- Medical Information Center, Peking University People’s Hospital, Beijing, China
| | - Chengfu Cao
- Department of Cardiology, Peking University People’s Hospital, Beijing, China
| | - Danjie Guo
- Department of Cardiology, Peking University People’s Hospital, Beijing, China
| | - Jie Liu
- Biochip Laboratory, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Lei Gong
- Biochip Laboratory, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Shouxin Zhang
- Biochip Laboratory, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Jun Sun
- Biochip Laboratory, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Yingqi Yu
- Gennlife (Beijing) Technology Co., Ltd., Beijing, China
| | - Weiwei Tong
- Gennlife (Beijing) Technology Co., Ltd., Beijing, China
- *Correspondence: Weiwei Tong,
| | - Jun Yang
- Department of Cardiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
- Jun Yang,
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10
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Kong S, Heinzmann D, Lauer S, Tian L. Weighted Approach for Estimating Effects in Principal Strata With Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2021.2009020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
| | | | | | - Lu Tian
- Stanford University, Palo Alto, CA
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11
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Luo J, Ruberg SJ, Qu Y. Estimating the treatment effect for adherers using multiple imputation. Pharm Stat 2021; 21:525-534. [PMID: 34927339 DOI: 10.1002/pst.2184] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 11/23/2021] [Accepted: 11/30/2021] [Indexed: 11/07/2022]
Abstract
Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonization (ICH)-E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal-inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high-dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructed confidence intervals (CIs) through bootstrapping. A simulation study showed that the MI-based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing two types of basal insulin for patients with type 1 diabetes.
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Affiliation(s)
- Junxiang Luo
- Department of Biostatistics and Programming, Moderna, Inc., Cambridge, Massachusetts, USA
| | | | - Yongming Qu
- Department of Statistics, Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
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12
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Manitz J, Kan-Dobrosky N, Buchner H, Casadebaig ML, Degtyarev E, Dey J, Haddad V, Jie F, Martin E, Mo M, Rufibach K, Shentu Y, Stalbovskaya V, Sammi Tang R, Yung G, Zhou J. Estimands for overall survival in clinical trials with treatment switching in oncology. Pharm Stat 2021; 21:150-162. [PMID: 34605168 DOI: 10.1002/pst.2158] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 04/28/2021] [Accepted: 07/10/2021] [Indexed: 11/09/2022]
Abstract
An addendum of the ICH E9 guideline on Statistical Principles for Clinical Trials was released in November 2019 introducing the estimand framework. This new framework aims to align trial objectives and statistical analyses by requiring a precise definition of the inferential quantity of interest, that is, the estimand. This definition explicitly accounts for intercurrent events, such as switching to new anticancer therapies for the analysis of overall survival (OS), the gold standard in oncology. Traditionally, OS in confirmatory studies is analyzed using the intention-to-treat (ITT) approach comparing treatment groups as they were initially randomized regardless of whether treatment switching occurred and regardless of any subsequent therapy (treatment-policy strategy). Regulatory authorities and other stakeholders often consider ITT results as most relevant. However, the respective estimand only yields a clinically meaningful comparison of two treatment arms if subsequent therapies are already approved and reflect clinical practice. We illustrate different scenarios where subsequent therapies are not yet approved drugs and thus do not reflect clinical practice. In such situations the hypothetical strategy could be more meaningful from patient's and prescriber's perspective. The cross-industry Oncology Estimand Working Group (www.oncoestimand.org) was initiated to foster a common understanding and consistent implementation of the estimand framework in oncology clinical trials. This paper summarizes the group's recommendations for appropriate estimands in the presence of treatment switching, one of the key intercurrent events in oncology clinical trials. We also discuss how different choices of estimands may impact study design, data collection, trial conduct, analysis, and interpretation.
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Affiliation(s)
- Juliane Manitz
- Global Biostatistics, EMD Serono, Billerica, Massachusetts, USA
| | | | - Hannes Buchner
- Biostatistics and Data Science, Staburo GmbH, Munich, Germany
| | | | - Evgeny Degtyarev
- Clinical Development and Analytics, Novartis, Basel, Switzerland
| | - Jyotirmoy Dey
- Data and Statistical Sciences, AbbVie Inc., North Chicago, Illinois, USA
| | | | - Fei Jie
- Biostatistics and Data Management, Daiichi Sankyo Inc, Basking Ridge, New Jersey, USA
| | - Emily Martin
- Global Biostatistics, EMD Serono, Billerica, Massachusetts, USA
| | - Mindy Mo
- Oncology Clinical Statistics US, Bayer, Whippany, New Jersey, USA
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Yue Shentu
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, New Jersey, USA
| | | | - Rui Sammi Tang
- Global Biometric, Servier Pharmaceuticals, Boston, Massachusetts, USA
| | - Godwin Yung
- Methods, Collaboration, and Outreach, Genentech, San Francisco, California, USA
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13
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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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14
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Zhang Y, Fu H, Ruberg SJ, Qu Y. Statistical Inference on the Estimators of the Adherer Average Causal Effect. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1891965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Ying Zhang
- Department of Data and Analytics, Eli Lilly and Company, Indianapolis, IN
| | - Haoda Fu
- Department of Advanced Analytics and Data Sciences, Eli Lilly and Company, Indianapolis, IN
| | | | - Yongming Qu
- Department of Data and Analytics, Eli Lilly and Company, Indianapolis, IN
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15
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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: 30] [Impact Index Per Article: 10.0] [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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16
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Lipkovich I, Ratitch B, Mallinckrodt CH. Causal Inference and Estimands in Clinical Trials. Stat Biopharm Res 2020. [DOI: 10.1080/19466315.2019.1697739] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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17
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Qu Y, Fu H, Luo J, Ruberg SJ. A General Framework for Treatment Effect Estimators Considering Patient Adherence. Stat Biopharm Res 2020. [DOI: 10.1080/19466315.2019.1700157] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
| | - Haoda Fu
- Eli Lilly and Company, Indianapolis, IN
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18
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Polverejan E, Dragalin V. Aligning Treatment Policy Estimands and Estimators—A Simulation Study in Alzheimer’s Disease. Stat Biopharm Res 2019. [DOI: 10.1080/19466315.2019.1689845] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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19
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Larsen KG, Josiassen MK. A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment. Stat Biopharm Res 2019. [DOI: 10.1080/19466315.2019.1689847] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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20
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Affiliation(s)
- Thomas Permutt
- Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD
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21
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Magnusson BP, Schmidli H, Rouyrre N, Scharfstein DO. Bayesian inference for a principal stratum estimand to assess the treatment effect in a subgroup characterized by postrandomization event occurrence. Stat Med 2019; 38:4761-4771. [DOI: 10.1002/sim.8333] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 06/14/2019] [Accepted: 07/02/2019] [Indexed: 01/08/2023]
Affiliation(s)
| | - Heinz Schmidli
- Biostatistics and PharmacometricsNovartis Pharma AG Basel Switzerland
| | - Nicolas Rouyrre
- Biostatistics and PharmacometricsNovartis Pharma AG Basel Switzerland
| | - Daniel O. Scharfstein
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health Baltimore Maryland
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