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Englert S, Mercier F, Pilling EA, Homer V, Habermehl C, Zimmermann S, Kan-Dobrosky N. Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development. Pharm Stat 2023; 22:921-937. [PMID: 37403434 DOI: 10.1002/pst.2319] [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: 08/05/2022] [Revised: 06/07/2023] [Accepted: 06/20/2023] [Indexed: 07/06/2023]
Abstract
The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross-industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment-related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population-level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early-stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow-up is suspended, a while-on-treatment strategy is implied.
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Affiliation(s)
- Stefan Englert
- Statistical Modeling & Methodology, Janssen R&D, Janssen-Cilag GmbH, Neuss, Germany
| | - François Mercier
- Biostatistics, Roche Innovation Center Basel, F Hoffmann-La Roche AG, Basel, Switzerland
| | | | - Victoria Homer
- Cancer Research (UK) Clinical Trials Unit, University of Birmingham, Birmingham, UK
| | - Christina Habermehl
- Global Biostatistics, The healthcare Business of Merck KgaA, Darmstadt, Germany
| | - Stefan Zimmermann
- Early Clinical Development Oncology, Roche Innovation Center Zurich, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Natalia Kan-Dobrosky
- Statistical Science, PPD, Part of Thermo Fisher Scientific, Wilmington, North Carolina, USA
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Mitroiu M, Teerenstra S, Oude Rengerink K, Pétavy F, Roes KCB. Estimation of treatment effects in short-term depression studies. An evaluation based on the ICH E9(R1) estimands framework. Pharm Stat 2022; 21:1037-1057. [PMID: 35678545 PMCID: PMC9543408 DOI: 10.1002/pst.2214] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 02/18/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022]
Abstract
Estimands aim to incorporate intercurrent events in design, data collection and estimation of treatment effects in clinical trials. Our aim was to understand what estimands may correspond to efficacy analyses commonly employed in clinical trials conducted before publication of ICH E9(R1). We re‐analysed six clinical trials evaluating a new anti‐depression treatment. We selected the following analysis methods—ANCOVA on complete cases, following last observation carried forward (LOCF) imputation and following multiple imputation; mixed‐models for repeated measurements without imputation (MMRM), MMRM following LOCF imputation and following jump‐to‐reference imputation; and pattern‐mixture mixed models. We included a principal stratum analysis based on the predicted subset of the study population who would not discontinue due to adverse events or lack of efficacy. We translated each analysis into the implicitly targeted estimand, and formulated corresponding clinical questions. We could map six estimands to analysis methods. The same analysis method could be mapped to more than one estimand. The major difference between estimands was the strategy for intercurrent events, with other attributes mostly the same across mapped estimands. The quantitative differences in MADRS10 population‐level summaries between the estimands were 4–8 points. Not all six estimands had a clinically meaningful interpretation. Only a few analyses would target the same estimand, hence only few could be used as sensitivity analyses. The fact that an analysis could estimate different estimands emphasises the importance of prospectively defining the estimands targeting the primary objective of a trial. The fact that an estimand can be targeted by different analyses emphasises the importance of prespecifying precisely the estimator for the targeted estimand.
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Affiliation(s)
- Marian Mitroiu
- Methodology Working Group, College ter Beoordeling van Geneesmiddelen - Medicines Evaluation Board, Utrecht, The Netherlands.,Clinical Trial Methodology Department, Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Steven Teerenstra
- Methodology Working Group, College ter Beoordeling van Geneesmiddelen - Medicines Evaluation Board, Utrecht, The Netherlands.,Department for Health Evidence, Section Biostatistics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Katrien Oude Rengerink
- Methodology Working Group, College ter Beoordeling van Geneesmiddelen - Medicines Evaluation Board, Utrecht, The Netherlands.,Clinical Trial Methodology Department, Biostatistics and Research Support, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Frank Pétavy
- Data Analytics and Methods Taskforce, European Medicines Agency, Amsterdam, The Netherlands
| | - Kit C B Roes
- Methodology Working Group, College ter Beoordeling van Geneesmiddelen - Medicines Evaluation Board, Utrecht, The Netherlands.,Department for Health Evidence, Section Biostatistics, Radboud University Medical Center, Nijmegen, The Netherlands
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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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Larsen KG, Josiassen MK. Rejoinder to Letter to the Editor on “A New Principal Stratum Estimand Investigating the Treatment Effect in Patients Who Would Comply, If Treated With a Specific Treatment”. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1918237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Klaus Groes Larsen
- Department of Biostatistics & Programming, H. Lundbeck A/S, Valby, Denmark
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Dukes O, Van Lancker K, Bornkamp B, Heinzmann D, Rufibach K, Wolbers M. On Identification of the Principal Stratum Effect in Patients Who Would Comply If Treated. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2021.1872697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Oliver Dukes
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, Belgium
| | - 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
| | - Marcel Wolbers
- Methods, Collaboration, and Outreach Group (MCO), Department of Biostatistics, Hoffmann-La Roche Ltd, Basel, Switzerland
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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: 16] [Impact Index Per Article: 5.3] [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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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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