Causal Inference and Estimands in Clinical Trials

Lipkovich, Ilya; Ratitch, Bohdana; Mallinckrodt, Craig

doi:10.1080/19466315.2019.1697739

Cited by 54 publications

(61 citation statements)

References 62 publications

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“…Incorporating the intercurrent events terminology in ICH E9 (R1) and the principal stratum strategies 7‐14 for the estimand, a set of tripartite estimands is defined as: For all patients, the treatment difference in the probability of patients with the first ICE due to adverse events (AE) For all patients, the treatment difference in the probability of patients with the first ICE due to lack of efficacy (LoE) For patients who are in a principal stratum defined by treatment adherence, the difference in the primary efficacy outcome. …”

Section: Methodsmentioning

confidence: 99%

“…The estimation of the treatment effect for such a principal stratum was first proposed by Frangakis and Rubin, 8 and there have been subsequent overviews and discussions of estimation for principal stratum 10,12,15 . Generally, there are 4 approaches in estimating e 3 in (5): Methods based on monotonicity assumption 9,13,16‐19 Methods that estimate the boundary, assume some sensitivity parameter(s), or use the Bayesian method to pose restriction on the sensitivity parameters 13,14,20,21 Methods based on estimating the principal score (the probability belonging to a principal stratum) based on baseline covariates 18,22,23 Methods based on the estimation of potential outcome and/or principal score via baseline covariates and potential post‐baseline intermediate measurements 7,11 …”

Section: Methodsmentioning

confidence: 99%

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Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Luo

Ruberg³

2020

Pharmaceutical Statistics

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Luo

Ruberg³

2020

Pharmaceutical Statistics

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“…The potential outcome (PO) framework [ 4 , 5 ] was used by Lipkovich et al [ 6 ] to define causal estimands. Let denote the outcome of interest and denote the PO with assigned treatment regimen , but actually taking treatment regimen during the study.…”

Section: Lack Of Generalizability In “Treatment Policy” Strategymentioning

confidence: 99%

Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic

Lipkovich

2021

Ther Innov Regul Sci

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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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“…Generally, the intercurrent events due to COVID-19 can be categorized into those that are of administrative or operational nature (e.g., treatment discontinuation due to drug supply issues), and those that are directly related to the effect of COVID-19 on the health status of subjects (e.g., treatment discontinuation due to COVID-19 symptoms), see Akacha et al ( 2020 ) and Meyer et al ( 2020 ). However, the additional intercurrent events are introducing ambiguity to the original research question and teams need to discuss how to account for them (Akacha et al 2017 ; Akacha, Bretz, and Ruberg 2017 ; Lipkovich, Ratitch, and Mallinckrodt 2020 ). Care therefore also has to be taken if the pattern of intercurrent events is different before and after an interim analysis, in line with the usual recommendations to assess consistency across trial stages in an adaptive design.…”

Section: Issues In Adapting a Running Trial In The Covid-19 Pandemicmentioning

confidence: 99%

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

Kunz

Jörgens

Bretz

et al. 2020

Statistics in Biopharmaceutical Research

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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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Causal Inference and Estimands in Clinical Trials

Cited by 54 publications

References 62 publications

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework

Implementation of ICH E9 (R1): A Few Points Learned During the COVID-19 Pandemic

Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue?

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