Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time‐to‐event outcomes in randomized controlled trials

Weir, Isabelle R; Rider, Jennifer R.; Trinquart, Ludovic

doi:10.1002/pst.2159

Cited by 7 publications

(6 citation statements)

References 44 publications

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“…However, the natural direct and indirect effects redefined under a randomized interventional analogue are different from the classical NDE and NIE. 10 Although there is continued interest in developing mediation methods in the natural effects framework in recent years, [27][28][29][30][31][32][33][34][35] some have also criticized the use of natural effects because the cross-world independency assumptions do not have corresponding real-world implications, [36][37][38][39][40][41][42] though these views are not universal. 7,43 A new approach called "separable treatment effect approach" (or "interventionist approach") has been proposed that could mitigate the concerns around implausible cross-world assumptions by decomposing the treatment to two components to make the natural direct and indirect paths have an empirical meaning from the interventional perspective in the real world.…”

Section: Discussionmentioning

confidence: 99%

Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis

Li,

Mathur,

Solomon

et al. 2023

Epidemiology

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Existing methods for regression-based mediation analysis assume that the exposure-mediator effect, exposure-outcome effect, and mediator-outcome effect are constant across levels of the baseline characteristics of patients. However, investigators often have insight into how these underlying effects may be modified by baseline characteristics and are interested in how the resulting mediation effects, such as the natural direct effect (NDE), the natural indirect effect. (NIE), and the proportion mediated, are modified by these baseline characteristics. Motivated by an empirical example of anti-interleukin-1 therapy’s benefit on incident anemia reduction and its mediation by an early change in an inflammatory biomarker, we extended the closed-form regression-based causal mediation analysis with effect measure modification (EMM). Using a simulated numerical example, we demonstrated that naive analysis without considering EMM can give biased estimates of NDE and NIE and visually illustrated how baseline characteristics affect the presence and magnitude of EMM of NDE and NIE. We then applied the extended method to the empirical example informed by pathophysiologic insights into potential EMM by age, diabetes, and baseline inflammation. We found that the proportion modified through the early post-treatment inflammatory biomarker was greater for younger, nondiabetic patients with lower baseline level of inflammation, suggesting differential usefulness of the early post-treatment inflammatory biomarker in monitoring patients depending on baseline characteristics. To facilitate the adoption of EMM considerations in causal mediation analysis by the wider clinical and epidemiologic research communities, we developed a free- and open-source R package, regmedint.

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Section: Discussionmentioning

confidence: 99%

Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis

Li,

Mathur,

Solomon

et al. 2023

Epidemiology

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“…Li et al (2011) addressed some practical aspects of implementation 19 and further solutions are outlined in the Bayesian approaches section below. An extension from binary to time‐to‐event outcomes has been derived 20 . Joffe and Greene 14 consider principal surrogacy to belong within the CA class of methods.…”

Section: Statistical Methods For Surrogate Evaluationmentioning

confidence: 99%

“…An extension from binary to time-to-event outcomes has been derived. 20 Joffe and Greene 14 consider principal surrogacy to belong within the CA class of methods.…”

Section: Principal Surrogacymentioning

confidence: 99%

Informed decision‐making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments

Weir

Taylor

2022

Pharmaceutical Statistics

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The desire, by patients and society, for faster access to therapies has driven a long tradition of the use of surrogate endpoints in the evaluation of pharmaceuticals and, more recently, biologics and other innovative medical technologies. The consequent need for statistical validation of potential surrogate outcome measures is a prime example on the theme of statistical support for decision-making in health technology assessment (HTA). Following the pioneering methodology based on hypothesis testing that Prentice presented in 1989, a host of further methods, both frequentist and Bayesian, have been developed to enable the value of a putative surrogate outcome to be determined. This rich methodological seam has generated practical methods for surrogate evaluation, the most recent of which are based on the principles of information theory and bring together ideas from the causal effects and causal association paradigms. Following our synopsis of statistical methods, we then consider how regulatory authorities (on licensing) and payer and HTA agencies (on reimbursement) use clinical trial evidence based on surrogate outcomes. We review existing HTA surrogate outcome evaluative frameworks.We conclude with recommendations for further steps: (1) prioritisation by regulators and payers of the application of formal surrogate outcome evaluative frameworks, (2) application of formal Bayesian decision-analytic methods to support reimbursement decisions, and (3) greater utilization of conditional surrogate-based licensing and reimbursement approvals, with subsequent reassessment of treatments in confirmatory trials based on final patientrelevant outcomes.

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“…Causal solutions for validation become more challenging when the surrogate is also subject to censoring. Instead, others such as Parast et al (2017) rely on measures such as proportion explained, and Hsu et al (2015); Vandenberghe et al (2018), and Weir et al (2021) address time-varying surrogates using mediation approaches and proportion mediated metrics within the causal effects paradigm.…”

Section: Current Limitations For Validation With Time-to-event Datamentioning

confidence: 99%

“…(2018), and Weir et al. (2021) address time‐varying surrogates using mediation approaches and proportion mediated metrics within the causal effects paradigm.…”

Section: Introductionmentioning

confidence: 99%

Surrogacy validation for time‐to‐event outcomes with illness‐death frailty models

Roberts,

Elliott,

Taylor

2023

Biometrical J

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A common practice in clinical trials is to evaluate a treatment effect on an intermediate outcome when the true outcome of interest would be difficult or costly to measure. We consider how to validate intermediate outcomes in a causally‐valid way when the trial outcomes are time‐to‐event. Using counterfactual outcomes, those that would be observed if the counterfactual treatment had been given, the causal association paradigm assesses the relationship of the treatment effect on the surrogate outcome with the treatment effect on the true, primary outcome. In particular, we propose illness‐death models to accommodate the censored and semicompeting risk structure of survival data. The proposed causal version of these models involves estimable and counterfactual frailty terms. Via these multistate models, we characterize what a valid surrogate would look like using a causal effect predictiveness plot. We evaluate the estimation properties of a Bayesian method using Markov chain Monte Carlo and assess the sensitivity of our model assumptions. Our motivating data source is a localized prostate cancer clinical trial where the two survival outcomes are time to distant metastasis and time to death.

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Counterfactual mediation analysis in the multistate model framework for surrogate and clinical time‐to‐event outcomes in randomized controlled trials

Cited by 7 publications

References 44 publications

Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis

Effect Measure Modification by Covariates in Mediation: Extending Regression-based Causal Mediation Analysis

Informed decision‐making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments

Surrogacy validation for time‐to‐event outcomes with illness‐death frailty models

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