Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials

• URL: http://arxiv.org/abs/2411.09635v1
• Date: Thu, 14 Nov 2024 18:01:02 GMT
• Title: Counterfactual Uncertainty Quantification of Factual Estimand of Efficacy from Before-and-After Treatment Repeated Measures Randomized Controlled Trials
• Authors: Xingya Wang, Yang Han, Yushi Liu, Szu-Yu Tang, Jason C. Hsu,
• Abstract summary: This article shows $textitcounterfactual$ uncertainty quantification (CUQ), quantifying uncertainty for factual point estimates but in a counterfactual setting, is surprisingly achievable. We urge caution when estimate of the unobservable true condition of a patient before treatment has measurement error, because that violation of standard regression assumption can cause attenuation in estimating treatment effects.
• Score: 1.3461364647443341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ideal estimand for comparing a new treatment $Rx$ with a control $C$ is the $\textit{counterfactual}$ efficacy $Rx:C$, the expected differential outcome between $Rx$ and $C$ if each patient were given $\textit{both}$. While counterfactual $\textit{point estimation}$ from $\textit{factual}$ Randomized Controlled Trials (RCTs) has been available, this article shows $\textit{counterfactual}$ uncertainty quantification (CUQ), quantifying uncertainty for factual point estimates but in a counterfactual setting, is surprisingly achievable. We achieve CUQ whose variability is typically smaller than factual UQ, by creating a new statistical modeling principle called ETZ which is applicable to RCTs with $\textit{Before-and-After}$ treatment Repeated Measures, common in many therapeutic areas. We urge caution when estimate of the unobservable true condition of a patient before treatment has measurement error, because that violation of standard regression assumption can cause attenuation in estimating treatment effects. Fortunately, we prove that, for traditional medicine in general, and for targeted therapy with efficacy defined as averaged over the population, counterfactual point estimation is unbiased. However, for targeted therapy, both Real Human and Digital Twins approaches should respect this limitation, lest predicted treatment effect in $\textit{subgroups}$ will have bias.

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