Min Zhang

The estimation of points at which regression coefficients change has attracted considerable interest across many research fields. As in other regression contexts, missing data are ubiquitous. However, while much of the existing literature on missing data focuses on estimating regression coefficients, very few studies address missing data in the context of change point estimation. Linear spline models are powerful tools for studying change points; however, as we demonstrate in simulations, improperly handled missing outcomes can lead to biased estimates of change points. To address this, we propose two novel estimators for change points in linear spline models with potentially missing outcomes: an inverse probability weighting (IPW) estimator and a doubly robust augmented inverse probability weighting (DR‐AIPW) estimator, and study an imputation‐based outcome regression (OR) estimator. We establish the consistency and asymptotic normality of these estimators. Moreover, the DR‐AIPW estimator provides dual protections for consistency, and its optimality is also verified using semi‐parametric theory. We develop two‐step IPW, DR‐AIPW, and OR algorithms for implementation. Simulation studies and real‐data applications demonstrate the strong numerical performance of the proposed estimators.

Stroke is a major global cause of death, disability, and socioeconomic burden. This study analyzed long-term trends in burden of stroke attributable to high systolic blood pressure (HSBP), to evaluate the effect of HSBP control in stroke prevention. The Global Burden of Disease (GBD) 2021 database was utilized. Crude and age-standardized incidence, prevalence, mortality, and disability-adjusted life-year (DALY) rates were employed as disease burden indicators. The Global and Different Socio-demographic Index (SDI) region trends in stroke burden were analyzed using Joinpoint analysis. Subgroup analyses were conducted by gender and age. The age-standardized incidence rate (AAPC=-0.81, 95%CI:-0.86 to -0.75), mortality rate (AAPC=-1.60, 95%CI: -1.77 to -1.43), prevalence rate (AAPC= -0.29, 95%CI: -0.30 to -0.28), and DALY rate (AAPC=-1.57, 95%CI:-1.67 to -1.47) exhibited a consistent downward trend from 1990 to 2021, but the absolute burden of stroke increased, especially in China. HSBP as the leading risk factor for stroke-related DALYs globally, accounting for 58.08% of stroke deaths and 57.25% of DALYs in 2021. Regional disparities were evident, lower SDI regions face disproportionate burden versus higher SDI regions Men exhibited higher HSBP-related stroke burden than women. Despite global improvements in hypertension management and a concomitant decline in HSBP-related stroke burden, disparities persist across SDI regions. Targeted interventions, including improved access to affordable antihypertensive medications, public health education, and lifestyle modifications, are crucial to reducing the burden of stroke.

In many clinical trials, one is interested in evaluating the treatment effect based on different types of outcomes, including recurrent and terminal events. The most popular approach is the time-to-first-event analysis (TTFE), based on the composite outcome of the time to the first event among all events of interest. The motivation for the composite outcome approach is to increase the number of events and potentially increase power. Other composite outcome or composite analysis methods are also studied in the literature, but are less adopted in practice. In this article, we first review the mainstream composite analysis methods and classify them into three categories: (A) Composite-outcome Methods, which combine multiple events into a composite outcome before analysis, e.g., combining events into a time-to-event outcome in TTFE and into a single recurrent event process in the combined-recurrent-event analysis (CRE); (B) Joint-analysis Methods, which test for the recurrent event process and the terminal event jointly, e.g., Joint Frailty Model (JFM), Ghosh-Lin Method (GL), and Nelsen-Aalen Method (NA); (C) Win-ratio type Methods that account for the ordering of two types of events, e.g., Win-fraction Regression (WR). We conduct comprehensive simulation studies to evaluate the performance of various types of methods in terms of type I error control and power under a wide range of scenarios. We found that the non-parametric joint testing approach (GL/NA) and CRE have overall the best performance. However, TTFE and WR exhibit relatively low power. Also, adding events that have no or weak association with treatment usually decreases power.

Propensity scores are commonly used to reduce the confounding bias in non-randomized observational studies for estimating the average treatment effect. An important assumption underlying this approach is that all confounders that are associated with both the treatment and the outcome of interest are measured and included in the propensity score model. In the absence of strong prior knowledge about potential confounders, researchers may agnostically want to adjust for a high-dimensional set of pre-treatment variables. As such, variable selection procedure is needed for propensity score estimation. In addition, studies show that including variables related to treatment only in the propensity score model may inflate the variance of the treatment effect estimators, while including variables that are predictive of only the outcome can improve efficiency. In this article, we propose to incorporate outcome-covariate relationship in the propensity score model by including the predicted binary outcome probability as a covariate. Our approach can be easily adapted to an ensemble of variable selection methods, including regularization methods and modern machine-learning tools based on classification and regression trees. We evaluate our method to estimate the treatment effects on a binary outcome, which is possibly censored, across multiple treatment groups. Simulation studies indicate that incorporating outcome probability for estimating the propensity scores can improve statistical efficiency and protect against model misspecification. The proposed methods are applied to a cohort of advanced-stage prostate cancer patients identified from a private insurance claims database for comparing the adverse effects of four commonly used drugs for treating castration-resistant prostate cancer.

The segmented model has significant applications in scientific research when the change-point effect exists. In this article, we propose a comprehensive semiparametric framework in segmented models to test the existence and estimate the location of change points in the generalized outcome setting. The proposed framework is based on a semismooth estimating equation for the change-point estimation and an average score-type test for hypothesis testing. The root-n consistency, asymptotic normality, and asymptotic efficiency of estimators for all parameters in the segmented model are rigorously studied. The distribution of the average score-type test statistics under the null hypothesis is rigorously derived. Extensive simulation studies are conducted to assess the numerical performance of the proposed change-point estimation method and the average score-type test. We investigate change-point effects of baseline glomerular filtration rate and body mass index on bleeding after intervention using data from Blue Cross Blue Shield. This application study successfully identifies statistically significant change-point effects, with the estimated values providing clinically meaningful insights.

While mixed methods research is increasingly used to examine determinants of unwarranted variability in healthcare delivery and outcomes, novel integrative approaches are required to meet the needs of mixed methods healthcare delivery research. This article describes novel refining strategies that enhance the linkage between qualitative and quantitative dimensions of a mixed methods healthcare delivery research study. Leveraging our study experiences, this paper demonstrates several refining strategies: (1) using mediated allocation concealment to facilitate qualitative sampling; (2) informing qualitative inquiry through quantitative analytics; and (3) training and immersing multidisciplinary researchers in qualitative data collection and analysis. Developing and implementing strategies in mixed methods healthcare delivery research could advance methodological rigor and strengthen multidisciplinary collaboration.

Estimating thresholds when a threshold effect exists has important applications in biomedical research. However, models/methods commonly used in the biomedical literature may lead to a biased estimate. For patients undergoing coronary artery bypass grafting (CABG), it is thought that exposure to low oxygen delivery (DO2) contributes to an increased risk of avoidable acute kidney injury. This research is motivated by estimating the threshold of nadir DO2 for CABG patients to help develop an evidence-based guideline for improving cardiac surgery practices. We review several models (sudden-jump model, broken-stick model, and the constrained broken-stick model) that can be adopted to estimate the threshold and discuss modeling assumptions, scientific plausibility, and implications in estimating the threshold. Under each model, various estimation methods are studied and compared. In particular, under a constrained broken-stick model, a modified two-step Newton–Raphson algorithm is introduced. Through comprehensive simulation studies and an application to data on CABG patients from the University of Michigan, we show that the constrained broken-stick model is flexible, more robust, and able to incorporate scientific knowledge to improve efficiency. The two-step Newton–Raphson algorithm has good computational performances relative to existing methods.

Summary: Comparative effectiveness research often involves evaluating the differences in the risks of an event of interest between two or more treatments using observational data. Often, the post‐treatment outcome of interest is whether the event happens within a pre‐specified time window, which leads to a binary outcome. One source of bias for estimating the causal treatment effect is the presence of confounders, which are usually controlled using propensity score‐based methods. An additional source of bias is right‐censoring, which occurs when the information on the outcome of interest is not completely available due to dropout, study termination, or treatment switch before the event of interest. We propose an inverse probability weighted regression‐based estimator that can simultaneously handle both confounding and right‐censoring, calling the method CIPWR, with the letter C highlighting the censoring component. CIPWR estimates the average treatment effects by averaging the predicted outcomes obtained from a logistic regression model that is fitted using a weighted score function. The CIPWR estimator has a double robustness property such that estimation consistency can be achieved when either the model for the outcome or the models for both treatment and censoring are correctly specified. We establish the asymptotic properties of the CIPWR estimator for conducting inference, and compare its finite sample performance with that of several alternatives through simulation studies. The methods under comparison are applied to a cohort of prostate cancer patients from an insurance claims database for comparing the adverse effects of four candidate drugs for advanced stage prostate cancer.