Sample size and robust marginal methods for cluster-randomized trials with censored event times

This is the peer reviewed version of the following article: Zhong Yujie, and Cook Richard J. (2015), Sample size and robust marginal methods for cluster-randomized trials with censored event times, Statist. Med., 34, pages 901–923. doi: 10.1002/sim.6395, which has been published in final form at http://dx.doi.org/10.1002/sim.6395. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.In cluster-randomized trials, intervention effects are often formulated by specifying marginal models, fitting them under a working independence assumption, and using robust variance estimates to address the association in the responses within clusters. We develop sample size criteria within this framework, with analyses based on semiparametric Cox regression models fitted with event times subject to right censoring. At the design stage, copula models are specified to enable derivation of the asymptotic variance of estimators from a marginal Cox regression model and to compute the number of clusters necessary to satisfy power requirements. Simulation studies demonstrate the validity of the sample size formula in finite samples for a range of cluster sizes, censoring rates and degrees of within-cluster association among event times. The power and relative efficiency implications of copula misspecification is studied, as well as the effect of within-cluster dependence in the censoring times. Sample size criteria and other design issues are also addressed for the setting where the event status is only ascertained at periodic assessments and times are interval censored.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626

On the estimation of intracluster correlation for time-to-event outcomes in cluster randomized trials

Cluster randomized trials (CRTs) involve the random assignment of intact social units rather than independent subjects to intervention groups. Time-to-event outcomes often are endpoints in CRTs where the intracluster correlation coefficient (ICC) serves as a descriptive parameter to assess the similarity among outcomes in a cluster. However, estimating the ICC in CRTs with time-to-event outcomes is a challenge due to the presence of censored observations. The ICC is estimated for two CRTs using the censoring indicators and observed outcomes. A simulation study explores the effect of administrative censoring on estimating the ICC. Results show that the ICC estimators derived from censoring indicators and observed outcomes are negatively biased for positively correlated outcomes. Analytic work further supports these results. Censoring indicators may be preferred to estimate the ICC under moderate frequency of administrative censoring while the observed outcomes may be preferred under minimal frequency of administrative censoring

Some contributions to nonparametric and semiparametric inference for clustered and multistate data.

This dissertation is composed of research projects that involve methods which can be broadly classified as either nonparametric or semiparametric. Chapter 1 provides an introduction of the problems addressed in these projects, a brief review of the related works that have done so far, and an outline of the methods developed in this dissertation. Chapter 2 describes in details the first project which aims at developing a rank-sum test for clustered data where an outcome from group in a cluster is associated with the number of observations belonging to that group in that cluster. Chapter 3 proposes the use of pseudo-value regression (Andersen, Klein, and Rosthøj, 2003) in combination with penalized and latent factor regression techniques for prediction of future state occupation in a multistate model based on high dimensional baseline covariates. Chapter 4 describes the development of an R package involving various rank based tests for clustered data which are useful in situations where the number of outcomes in a cluster or in a particular group within a cluster is informative. Chapter 5 explains the fouth project which aims at developing a covariate-adjusted rank-sum test for clustered data through alingned rank transformation

Methods for Clustered Competing Risks Data and Causal Inference using Instrumental Variables for Censored Time-to-event Data

In this dissertation, we propose new methods for analysis of clustered competing risks data (Chapters 1 and 2) and for instrumental variable (IV) analysis of univariate censored time-to-event data and competing risks data (Chapters 3 and 4). In Chapter 1, we propose estimating center effects through cause-specific proportional hazards frailty models that allow correlation among a center’s cause-specific effects. To evaluate center performance, we propose a directly standardized excess cumulative incidence (ECI) measure. We apply our methods to evaluate Organ Procurement Organizations with respect to (i) receipt of a kidney transplant and (ii) death on the wait-list. In Chapter 2, we propose to model the effects of cluster and individual-level covariates directly on the cumulative incidence functions of each risk through a semiparametric mixture component model with cluster-specific random effects. Our model permits joint inference on all competing events and provides estimates of the effects of clustering. We apply our method to multicenter competing risks data. In Chapter 3, we turn our focus to causal inference in the censored time-to-event setting in the presence of unmeasured confounders. We develop weighted IV estimators of the complier average causal effect on the restricted mean survival time. Our method accommodates instrument-outcome confounding and covariate dependent censoring. We establish the asymptotic properties, derive easily implementable variance estimators, and apply our method to compare modalities for end stage renal disease (ESRD) patients using national registry data. In Chapter 4, we develop IV analysis methods for competing risks data. Our method permits simultaneous inference of exposure effects on the absolute risk of all competing events and accommodates exposure dependent censoring. We apply the methods to compare dialytic modalities for ESRD patients with respect to risk of death from (i) cardiovascular diseases and (ii) other causes.PHDBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144110/1/shdharma_1.pd

Accelerated failure tima models for multivariate interval-censored data with flexible distributional assumptions

Department of Probability and Mathematical StatisticsKatedra pravděpodobnosti a matematické statistikyFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult