The Use of a Variable Representing Compliance Improves Accuracy of Estimation of the Effect of Treatment Allocation Regardless of Discontinuation in Trials with Incomplete Follow-up

Guizzaro, Lorenzo; Pétavy, Frank; Ristl, Robin; Gallo, Ciro

doi:10.1080/19466315.2020.1736141

Cited by 15 publications

(18 citation statements)

References 26 publications

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“…Such methods have been informally referred to as ‘retrieved dropout’ approaches. Unfortunately, such approaches remain poorly studied in the literature, with only a few, recent, publications on the topic [ 30 – 32 ]. Much work therefore remains to be done to describe the characteristics, strengths, and weaknesses of the various methods to do this.…”

Section: The Implementation Experience So Farmentioning

confidence: 99%

Marking 2-Years of New Thinking in Clinical Trials: The Estimand Journey

Fletcher

Hefting

Wright

et al. 2022

Ther Innov Regul Sci

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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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Section: The Implementation Experience So Farmentioning

confidence: 99%

Marking 2-Years of New Thinking in Clinical Trials: The Estimand Journey

Fletcher

Hefting

Wright

et al. 2022

Ther Innov Regul Sci

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“…Imputation under a MAR assumption after inclusion of appropriate internal time-varying covariates associated with the ICE (e.g. time-varying indicators of treatment compliance, discontinuation or initiation of rescue treatment) have been proposed by Guizzaro et al [2021]. In this approach, post-ICE outcomes are included in every step of the analysis, including in the fitting of the imputation model.…”

Section: Alignment Between the Estimand And The Missing Data Strategymentioning

confidence: 99%

“…calendar time of the visit) as well as internal time-varying covariates (e.g. time-varying indicators of treatment discontinuation or initiation of rescue treatment) may in principle also be included if indicated [Guizzaro et al, 2021]. Missing covariate values are not allowed.…”

Section: Imputation Modelmentioning

confidence: 99%

Standard and reference-based conditional mean imputation

Wolbers¹,

Noci²,

Delmar³

et al. 2021

Preprint

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Clinical trials with longitudinal outcomes typically include missing data due to missed assessments or structural missingness of outcomes after intercurrent events handled with a hypothetical strategy. Approaches based on Bayesian random multiple imputation and Rubin's rule for pooling results across multiple imputed datasets are increasingly used in order to align the analysis of these trials with the targeted estimand. We propose and justify deterministic conditional mean imputation combined with the jackknife for inference as an alternative approach. The method is applicable to imputations under a missing-at-random assumption as well as for reference-based imputation approaches. In an application and a simulation study, we demonstrate that it provides consistent treatment effect estimates with the Bayesian approach and reliable frequentist inference with accurate standard error estimation and type I error control. A further advantage of the method is that it does not rely on random sampling and is therefore replicable and unaffected by Monte Carlo error.

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“…It stresses the need to pro-actively address the issue of intercurrent events in the design, analysis and reporting of studies. The working principles described in the E9 addendum are referred to as the 'Estimand Framework', 12,13 because it accentuates the importance of defining the specific target (or estimand) that is to be estimated, see also Reference 14,15. Despite early work on the application of Instrumental Variable (IV) methods to RCTs 16,17 they have been seldom used in practice. In this article we consider the application of IV methods for pursuing three distinct estimand strategies described in the E9 Addendum:…”

Section: Introductionmentioning

confidence: 99%

Connecting Instrumental Variable methods for causal inference to the Estimand Framework

Bowden

Bornkamp

Glimm

et al. 2021

Statistics in Medicine

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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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The Use of a Variable Representing Compliance Improves Accuracy of Estimation of the Effect of Treatment Allocation Regardless of Discontinuation in Trials with Incomplete Follow-up

Cited by 15 publications

References 26 publications

Marking 2-Years of New Thinking in Clinical Trials: The Estimand Journey

Marking 2-Years of New Thinking in Clinical Trials: The Estimand Journey

Standard and reference-based conditional mean imputation

Connecting Instrumental Variable methods for causal inference to the Estimand Framework

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