arXiv subjects

Yongqi Zhong

Publications and source records attributed to Yongqi Zhong.

3 recordsLinked to original sources

arXiv2026

From Test Performance to Risk-Based Effect Sizes: A Unified Wald-Type Framework to Design Clinical Validation Studies for Binary and Survival Outcomes

Clinical validation studies of predictive tests are usually designed to focus on sensitivity ($Se$) and specificity ($Sp$), while statistical power is often calculated on regression-effect scales (e.g., risk ratio, hazard ratio). However, these quantities are statistically connected. Here, we provide closed-form links from sensitivity, specificity, and disease prevalence ($π$) to predictive risks, risk contrasts, and Wald-type variance, power, and sample-size formulas for binary and fixed-horizon survival outcomes. Analyses of statistical efficiency via C- and D-optimal principles demonstrate how prevalence and threshold choices affect study efficiency, supporting rapid decisions in preliminary studies and informing the design of subsequent, larger studies. Simulations show good calibration across most realistic scenarios; when events are rare and test effects are simultaneously very large, continuity and minimum-event corrections are needed to stabilize the approximation. We illustrate the framework with a case study describing use of the coronary artery calcium score for predicting incident cardiovascular disease in patients with type 2 diabetes mellitus. The formulas let investigators check power and required enrollment directly from $(Se,Sp,π)$, without running a separate simulation for each design candidate.

stat.ME↗

arXiv2022

What does it mean to be "representative"?

Medical and population health science researchers frequently make ambiguous statements about whether they believe their study sample or results are "representative" of some (implicit or explicit) target population. Here, we provide a comprehensive definition of representativeness, with the goal of capturing the different ways in which a study can be representative of a target population. We propose that a study is representative if the estimate obtained in the study sample is generalizable to the target population (either due to representative sampling, estimation of stratum specific effects, or quantitative methods to generalize or transport estimates) or the interpretation of the results is generalizable to the target population (based on fundamental scientific premises and substantive background knowledge). We explore this definition in the context of four COVID-19 studies, ranging from laboratory science to descriptive epidemiology. All statements regarding representativeness should make clear the way in which the study results generalize, the target population the results are being generalized to, and the assumptions that must hold for that generalization to be scientifically or statistically justifiable.

stat.AP↗

arXiv2022

Missing data interpolation in integrative multi-cohort analysis with disparate covariate information

Integrative analysis of datasets generated by multiple cohorts is a widely-used approach for increasing sample size, precision of population estimators, and generalizability of analysis results in epidemiological studies. However, often each individual cohort dataset does not have all variables of interest for an integrative analysis collected as a part of an original study. Such cohort-level missingness poses methodological challenges to the integrative analysis since missing variables have traditionally: (1) been removed from the data for complete case analysis; or (2) been completed by missing data interpolation techniques using data with the same covariate distribution from other studies. In most integrative-analysis studies, neither approach is optimal as it leads to either loosing the majority of study covariates or challenges in specifying the cohorts following the same distributions. We propose a novel approach to identify the studies with same distributions that could be used for completing the cohort-level missing information. Our methodology relies on (1) identifying sub-groups of cohorts with similar covariate distributions using cohort identity random forest prediction models followed by clustering; and then (2) applying a recursive pairwise distribution test for high dimensional data to these sub-groups. Extensive simulation studies show that cohorts with the same distribution are correctly grouped together in almost all simulation settings. Our methods' application to two ECHO-wide Cohort Studies reveals that the cohorts grouped together reflect the similarities in study design. The methods are implemented in R software package relate.

stat.ME↗