A Hybrid Newton-Type Method for the Linear Regression in Case-cohort Studies

Case-cohort designs are increasingly commonly used in large epidemiological cohort studies. Nan, Yu, and Kalbeisch (2004) provided the asymptotic results for censored linear regression models in case-cohort studies. In this article, we consider computational aspects of their proposed rank based estimating methods. We show that the rank based discontinuous estimating functions for case-cohort studies are monotone, a property established for cohort data in the literature, when generalized Gehan type of weights are used. Though the estimating problem can be formulated to a linear programming problem as that for cohort data, due to its easily uncontrollable large scale even for a moderate sample size, we instead propose a Newton-type iterated method to search for an approximate root for the discontinuous monotone estimating function. Simulation results provide a good demonstration of the proposed method

Regression Calibration in Semiparametric Accelerated Failure Time Models

In large cohort studies, it often happens that some covariates are expensive to measure and hence only measured on a validation set. On the other hand, relatively cheap but error-prone measurements of the covariates are available for all subjects. Regression calibration (RC) estimation method ( Prentice, 1982 ,  Biometrika   69 , 331–342) is a popular method for analyzing such data and has been applied to the Cox model by Wang et al. (1997,  Biometrics   53 , 131–145) under normal measurement error and rare disease assumptions. In this article, we consider the RC estimation method for the semiparametric accelerated failure time model with covariates subject to measurement error. Asymptotic properties of the proposed method are investigated under a two-phase sampling scheme for validation data that are selected via stratified random sampling, resulting in neither independent nor identically distributed observations. We show that the estimates converge to some well-defined parameters. In particular, unbiased estimation is feasible under additive normal measurement error models for normal covariates and under Berkson error models. The proposed method performs well in finite-sample simulation studies. We also apply the proposed method to a depression mortality study.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79345/1/j.1541-0420.2009.01295.x.pd

Subgroup Identification Using the personalized Package

A plethora of disparate statistical methods have been proposed for subgroup identification to help tailor treatment decisions for patients. However a majority of them do not have corresponding R packages and the few that do pertain to particular statistical methods or provide little means of evaluating whether meaningful subgroups have been found. Recently, the work of Chen, Tian, Cai, and Yu (2017) unified many of these subgroup identification methods into one general, consistent framework. The goal of the personalized package is to provide a corresponding unified software framework for subgroup identification analyses that provides not only estimation of subgroups, but evaluation of treatment effects within estimated subgroups. The personalized package allows for a variety of subgroup identification methods for many types of outcomes commonly encountered in medical settings. The package is built to incorporate the entire subgroup identification analysis pipeline including propensity score diagnostics, subgroup estimation, analysis of the treatment effects within subgroups, and evaluation of identified subgroups. In this framework, different methods can be accessed with little change in the analysis code. Similarly, new methods can easily be incorporated into the package. Besides familiar statistical models, the package also allows flexible machine learning tools to be leveraged in subgroup identification. Further estimation improvements can be obtained via efficiency augmentation

Prediction of Coronary Artery Disease Risk Based on Multiple Longitudinal Biomarkers

In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well-documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort

Entropy Balancing for Causal Generalization with Target Sample Summary Information

In this paper, we focus on estimating the average treatment effect (ATE) of a target population when individual-level data from a source population and summary-level data (e.g., first or second moments of certain covariates) from the target population are available. In the presence of heterogeneous treatment effect, the ATE of the target population can be different from that of the source population when distributions of treatment effect modifiers are dissimilar in these two populations, a phenomenon also known as covariate shift. Many methods have been developed to adjust for covariate shift, but most require individual covariates from a representative target sample. We develop a weighting approach based on summary-level information from the target sample to adjust for possible covariate shift in effect modifiers. In particular, weights of the treated and control groups within a source sample are calibrated by the summary-level information of the target sample. Our approach also seeks additional covariate balance between the treated and control groups in the source sample. We study the asymptotic behavior of the corresponding weighted estimator for the target population ATE under a wide range of conditions. The theoretical implications are confirmed in simulation studies and a real data application