Exploration of the prognostic value of the resection of adult brainstem high-grade glioma based on competing risk model, propensity score matching, and conditional survival rate

Zhang, Dainan; Li, Haiming; Wang, Jia

doi:10.1007/s10072-022-06557-z

Cited by 5 publications

(1 citation statement)

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“…The propensity weighting methodology was proposed as a novel method of causal inference that aims at reducing imbalances between arms [17][18][19]. This technique is based on the calculation of propensity, de ned as the individuals' probability of showing PE, given observed baseline and pre-randomization response [20].…”

Section: Introductionmentioning

confidence: 99%

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

Preprint

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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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Section: Introductionmentioning

confidence: 99%