In analyses of time-to-event outcomes, we may utilize efficient sampling designs to save effort, cost, and valuable specimens. The originally proposed case-cohort design is one such design, and performs analysis on a random sample selected from the full cohort supplemented with unsampled individuals who experienced an event. In Alzheimer’s disease biomarker discovery, such cost-savings is important as samples that contain biomarker measurements can be very limited due to high burden and cost. Furthermore, quantifying differences across subgroups and ensuring that diagnostic biomarker effects can be well powered and well understood across subpopulations is an increasingly important goal. This ability to quantify subgroup differences is required in efforts to minimize health disparities and achieve precision medicine. In this paper, we emphasize the importance of such considerations and propose using a stratified case-cohort estimator to sample a subcohort with balanced subgroups across the proposed effect modifier. We provide inverse-probability-of-sampling-based estimates that consistently estimate covariate effects in the population while improving precision for assessing differences across rare subpopulations. Simulation results are presented to illustrate power gains for testing effect modification, especially as a population subgroup becomes increasingly rare, and show minimal bias for marginal covariate effect estimates. We apply this method to data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to better assess potential heterogeneity of phosphorylated tau as a biomarker for AD progression across ethnoracial and genetic status subpopulations.
Reliable estimation of population health indicators is essential for evidence-based public health planning and policy formulation. This study applies a ranked set sampling approach with auxiliary health information to improve the estimation of population means in large-scale health surveys. Using data from the fifth round of the National Family Health Survey (NFHS-5) for the Varanasi district of Uttar Pradesh, India, two empirical applications related to maternal and child health are examined. In the first application, maternal body mass index is used as auxiliary information to estimate infant birth weight, while in the second, maternal hemoglobin level is utilized to improve estimation of child stunting prevalence. New classes of estimators incorporating logarithmic transformations of auxiliary variables are employed and evaluated in terms of mean squared error. The empirical findings demonstrate that the proposed estimators provide consistently more precise and reliable estimates than existing methods across different sample sizes. These results highlight the practical value of ranked set sampling and auxiliary information in enhancing the quality of health estimates from national surveys, with direct implications for public health monitoring and policy evaluation.
In this research, we follow the late Professor Tze Lai’s inspiration to propose a new Kiefer–Weiss–Lorden–Lai (KWLL) framework that minimizes the expected number of samples to evaluate efficacy (rejecting the null hypothesis) or futility (accepting the null hypothesis) subject to the maximum sample size and Type I error probability constraints. Compared to the traditional sample size calculation, our proposed KWLL framework structures the parameter of the alternative hypothesis as a function of design decisions, instead of a pre-specified value, while making a quick and accurate decision whether to accept or reject the null hypothesis. Moreover, we use the so-called 2-Sequential Probability Ratio Test (2-SPRT) as the foundation to develop reasonable algorithms that might provide a useful approximate solution to the KWLL framework. Finally, a case study using correlation as the test statistic is presented to illustrate the usefulness of our proposed framework and algorithm.
In this paper, we develop bivariate stochastic functional linear models (BSFLM) for gene-based association analysis of quantitative traits with high-dimensional sequencing genetic data in longitudinal studies. In longitudinal studies, the traits of different subjects are assumed to be independent for population data. The two traits of one subject, however, are correlated with each other. In addition, the multiple measurements of one trait of a subject are also correlated. To build valid models to analyze bivariate quantitative traits with sequencing genetic data in longitudinal studies, a variance-covariance structure is constructed to describe (a) variation accounting for the correlation between the two traits and (b) variation accounting for the correlation within multiple measurements of a trait on the same subject. Functional data analysis techniques are utilized to reduce high dimensionality of sequence data and draw useful genetic information. Spline models are used to approximate temporal mean functions and genetic effect functions. By intensive simulation studies, it is shown that the proposed BSFLM control type I errors well and have good power levels. We test and refine the models and related software using real datasets of multi-ethnic study of atherosclerosis.
