Propensity score‐based methods for causal inference and external data leveraging in regulatory settings: From basic ideas to implementation

Li, Heng; Yue, Lilly Q.

doi:10.1002/pst.2294

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…The PSW methodology currently used in epidemiological and social science studies has been proposed as a novel approach to better control the potential negative effect of unbalanced distribution of PE in the assessment of the TE and the effect size in RCTs. Recently, it was adopted in a regulatory setting by the FDA, where it was used in observational studies to support marketing applications for medical devices [18,19,20,21].…”

Section: Discussionmentioning

confidence: 99%

Interpreting clinical trial outcomes marred by placebo response with an assessment of false-negative and true-negative clinical trials in depression using propensity-weighting

Gomeni,

Hopkins,

Bressolle-Gomeni

et al. 2023

Preprint

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The objective of this study was to evaluate the performances of the propensity score weighted (PSW) methodology in a post-hoc re-analysis of a failed and a negative RCTs in major depressive disorders. The conventional study designs, randomizations, and statistical approaches do not account for the baseline distribution of major non-specific prognostic and confounding factors such as the individual propensity to show a placebo effect (PE). Therefore, the conventional analysis approaches implicitly assume that the baseline PE is the same for all subjects in the trial even if this assumption is not supported by our knowledge on the impact of PE on the estimated treatment effect (TE). The consequence of this assumption is an inflation of false negative results (type II error) in presence of a high proportion of subjects with high PE and an inflation of false positive (type I error) in presence of a high proportion of subjects with low PE value. Differently from the conventional approach, the inverse of the estimated PE probability was used as weight in the mixed-effects analysis to assess TE in the PSW analysis. The results of this analysis indicated an enhanced signal of drug response in the failed trial (ClinicalTrials.gov NCT00584974) and confirmed the absence of drug effect in the negative trial (ClinicalTrials.gov NCT0058497). This approach can be considered as a reference approach that minimize the risk of inflating either type I or type II error in contrast to what happens in the analyses of RCT studies conducted with the conventional statistical methodology.

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Section: Discussionmentioning

confidence: 99%

Interpreting clinical trial outcomes marred by placebo response with an assessment of false-negative and true-negative clinical trials in depression using propensity-weighting

Gomeni,

Hopkins,

Bressolle-Gomeni

et al. 2023

Preprint

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A propensity score‐integrated approach for leveraging external data in a randomized controlled trial with time‐to‐event endpoints

Chen,

Lu,

Wang

et al. 2024

Pharmaceutical Statistics

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In a randomized controlled trial with time‐to‐event endpoint, some commonly used statistical tests to test for various aspects of survival differences, such as survival probability at a fixed time point, survival function up to a specific time point, and restricted mean survival time, may not be directly applicable when external data are leveraged to augment an arm (or both arms) of an RCT. In this paper, we propose a propensity score‐integrated approach to extend such tests when external data are leveraged. Simulation studies are conducted to evaluate the operating characteristics of three propensity score‐integrated statistical tests, and an illustrative example is given to demonstrate how these proposed procedures can be implemented.

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Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

Goméni¹,

Bressolle-Gomeni²,

Fava

2023

Transl Psychiatry

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Treatment effect in clinical trials for major depressive disorders (RCT) can be viewed as the resultant of treatment specific and non-specific effects. Baseline individual propensity to respond non-specifically to any treatment or intervention can be considered as a major non-specific confounding effect. The greater is the baseline propensity, the lower will be the chance to detect any treatment-specific effect. The statistical methodologies currently applied for analyzing RCTs doesn’t account for potential unbalance in the allocation of subjects to treatment arms due to heterogenous distributions of propensity. Hence, the groups to be compared may be imbalanced, and thus incomparable. Propensity weighting methodology was used to reduce baseline imbalances between arms. A randomized, double-blind, placebo controlled, three arms, parallel group, 8-week, fixed-dose study to evaluate efficacy of paroxetine CR 12.5 and 25 mg/day is presented as a cases study. An artificial intelligence model was developed to predict placebo response at week 8 in subjects assigned to placebo arm using changes from screening to baseline of individual Hamilton Depression Rating Scale items. This model was used to predict the probability to respond to placebo in each subject. The inverse of the probability was used as weight in the mixed-effects model applied to assess treatment effect. The analysis with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect-size about twice larger than the non-weighted analysis. Propensity weighting provides an unbiased strategy to account for heterogeneous and uncontrolled placebo effect making patients’ data comparable across treatment arms.

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Propensity score‐based methods for causal inference and external data leveraging in regulatory settings: From basic ideas to implementation

Cited by 5 publications

References 35 publications

Interpreting clinical trial outcomes marred by placebo response with an assessment of false-negative and true-negative clinical trials in depression using propensity-weighting

Interpreting clinical trial outcomes marred by placebo response with an assessment of false-negative and true-negative clinical trials in depression using propensity-weighting

A propensity score‐integrated approach for leveraging external data in a randomized controlled trial with time‐to‐event endpoints

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo

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