arXiv subjects

Jason C. Hsu

Publications and source records attributed to Jason C. Hsu.

6 recordsLinked to original sources

arXiv2025

ETZ: A Modeling Principle for Confirmability of Drug-Development Studies

Transitioning from Phase 2 to Phase 3 in drug development, at a rate of $\approx$40%, is the most stringent among phase transitions (Hay et al. (2014)). Yet, success rate at Phase 3 leading to approval is only $\approx$50% (Arrowsmith (2011b)). To improve Confirmability, we propose a methodological shift: replacing multiple hypothesis testing with inference based on confidence sets, and substituting conventional power and sample size calculations with a Confidently Bounded Quantile (CBQ) framework. Our confidence set inferences to answer the questions of whether to transition to a Confirmatory study as well as what to designate as the endpoint in that study. Construction of our directed confidence sets follows the Partitioning Principle, taking the best of each of Pivoting and Neyman Confidence Set Construction. Rooted in Tukey's Confidently Bounded Allowance (CBA) (Tukey (1994a)), our proposed CBQ makes the transitioning decision following the Correct and Useful Inference principle in Hsu (1996). CBQ removes from "power" the probability of rejecting for wrong reasons, eliminating the need for informal discounting in power calculation that has existed in the biopharmaceutical industry. ETZ, the modeling principle proposed in Wang et al. (2025), quantifies the impact of three variability components on confirmability. In repeated-measures RCTs, it separates within-subject and between-subject variability, further dividing the latter into baseline and trajectory components. This enables informed investment decisions for the sponsors on targeting variability reduction to improve confirmability. A Shiny-based Confirmability App supports all computations.

stat.ME↗

arXiv2025

On Assessing Overall Survival (OS) in Oncology Studies

In assessing Overall Survival (OS) in oncology studies, it is essential for the efficacy measure to be Logic-respecting, for otherwise patients may be incorrectly targeted. This paper explains, while Time Ratio (TR) is Logic-respecting, Hazard Ratio (HR) is not Logic-respecting. With Time Ratio (TR) being recommended, a smooth transitioning strategy is suggested. The conclusion states: Logicality requires, and Subgroup Mixable Estimation (SME) delivers, an efficacy assessment for the overall population within the range of minimum and maximum efficacy in the subgroups, no matter how outcome is measured, whichever logic-respecting efficacy measure is chosen, the same efficacy assessment regardless of how subgroups are stratified.

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arXiv2024

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

This article quantifies the uncertainty reduction achievable for \textit{counterfactual} estimand, and cautions against potential bias when the estimand uses Digital Twins. Posed by Neyman (1923a) who showed unbiased \textit{point estimation} from designed \textit{factual} experiments is possible, \textit{counterfactual} uncertainty quantification (CUQ) remained an open challenge for about one hundred years. The $Rx: C$ \textit{counterfactual} efficacy we focus on is the ideal estimand for comparing treatment $Rx$ with control $C$, the expected outcome differential if each patient received \textit{both} $Rx$ and $C$. Enabled by our new statistical modeling principle called ETZ, we show CUQ is achievable in Randomized Controlled Trials (RCTs) with \textit{Before-and-After} Repeated Measures, common in many therapeutic areas. The CUQ we are able to achieve typically has lower variability than factual UQ. We caution against using predictors with measurement error, which violates regression assumptions and can cause \textit{attenuation} bias in estimating treatment effects. For traditional medicine and population-averaged targeted therapy, counterfactual point estimation remains unbiased. However, in both Real Human and Digital Twin approaches, estimating effects in \emph{subgroups} may suffer attenuation bias.

stat.ML↗

arXiv2023

Principles of Conditionality and Layering of Error Rates with Application to Platform Trials

There has been a misconception that only one type of error rate control is necessary in clinical trials, leading to debates over whether to prioritize Familywise Error Rate (FWER) or False Discovery Rate (FDR). This misconception has led to misleading statements about FWER control and proposals to shift towards FDR control, which could be manipulated by the industry. In reality, since the early 2000s, biopharmaceutical statistics have implicitly applied two layers of Type I error rate control. This aligns with Tukey's 1953 invention of Error Rate per Family (ERpF) for controlling error across studies, while FWER applies within each study. Our paper clarifies this layering, using Platform trials to demonstrate the verifiable conditions needed across studies for the FDA to fulfill its regulatory mission. We show that controlling FWER within a study at $5\%$ inherently controls ERpF across studies at 5-per-100, regardless of study correlations. This supports current regulatory practices that protect public health while fostering innovation. We also address concerns about ERpF stability in Platform trials, where shared controls introduce dependencies. By applying the Conditionality Principle and utilizing an innovative Shiny app, we explore how correlations impact ERpF variability, providing deeper insights for informed decision-making. Our findings, supported by principles like Layering of Error Rate Controls and the Conditionality Principle, are particularly relevant as Platform trials gain popularity for their efficiency in testing multiple treatments simultaneously.

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arXiv2020

A Simultaneous Inference Procedure to Identify Subgroups from RCTs with Survival Outcomes: Application to Analysis of AMD Progression Studies

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized clinical trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with survival outcome. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which control both within- and across- marker multiplicity appropriately. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups been identified from AREDS. This simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.

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arXiv2014

Subgroup Mixable Inference in Personalized Medicine, with an Application to Time-to-Event Outcomes

Measuring treatment efficacy in mixture of subgroups from a randomized clinical trial is a fundamental problem in personalized medicine development, in deciding whether to treat the entire patient population or to target a subgroup. We show that some commonly used efficacy measures are not suitable for a mixture population. We also show that, while it is important to adjust for imbalance in the data using least squares means (LSmeans) (not marginal means) estimation, the current practice of applying LSmeans to directly estimate the efficacy in a mixture population for any type of outcome is inappropriate. Proposing a new principle called {\em subgroup mixable estimation}, we establish the logical relationship among parameters that represent efficacy and develop a general inference procedure to confidently infer efficacy in subgroups and their mixtures. Using oncology studies with time-to-event outcomes as an example, we show that Hazard Ratio is not suitable for measuring efficacy in a mixture population, and provide alternative efficacy measures with a valid inference procedure.

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