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We propose a minimand perturbation method to derive the confidence regions for the regularized estimators for the Cox’s proportional hazards model. Although the regularized estimation procedure produces a more stable point estimate, it remains challenging to provide an interval estimator or an analytic variance estimator for the associated point estimate. Based on the sandwich formula, the current variance estimator provides a simple approximation, but its finite-sample performance is not entirely satisfactory. Besides, the sandwich formula can only provide variance estimates for the non zero coefficients. In this article, we present a generic description for the perturbation method and then introduce a computation algorithm using the adaptive least absolute shrinkage and selection operator (LASSO) penalty. Through simulation studies, we demonstrate that our method can better approximate the limiting distribution of the adaptive LASSO estimator and produces more accurate inference compared with the sandwich formula. The simulation results also indicate the possibility of extending the applications to the adaptive elastic-net penalty. We further demonstrate our method using data from a Phase III clinical trial in prostate cancer. [ABSTRACT FROM AUTHOR]

In this paper, we develop the methodology for designing clinical trials with any factorial arrangement when the primary outcome is time to event. We provide a matrix formulation for calculating the sample size and study duration necessary to test any effect with a prespecified type I error rate and power. Assuming that a time to event follows an exponential distribution, we describe the relationships between the effect size, the power, and the sample size. We present examples for illustration purposes. We provide a simulation study to verify the numerical calculations of the expected number of events and the duration of the trial. The change in the power produced by a reduced number of observations or by accruing no patients to certain factorial combinations is also described. [ABSTRACT FROM AUTHOR]

We propose two innovations in statistical sampling for controls to enable better design of population-based case-control studies. The main innovation leads to novel solutions, without using weights, of the difficult and long-standing problem of selecting a control from persons in a household. Another advance concerns the drawing (at the outset) of the households them- selves and involves random-digit dialing with atypical use of list-assisted sampling. A common element throughout is that one capitalizes on flexibility (not broadly available in usual survey settings) in choosing the frame, which specifies the population of persons from which both cases and controls come. [ABSTRACT FROM AUTHOR]

The statistical methodology under order restriction is very mathematical and complex. Thus, we provide a brief methodological background of order-restricted likelihood ratio tests for the normal theoretical case for the basic understanding of its applications, and relegate more technical details to the appendices. For data analysis, algorithms for computing the order-restricted estimates and computation of p-values are described. A two-step procedure is presented for obtaining the sample size in clinical trials when the minimum power, say 0.80 or 0.90 is specified, and the normal means satisfy an order restriction. Using this approach will result in reduction of 14-24% in the sample size required when one-sided ordered alternatives are used, as illustrated by several examples. [ABSTRACT FROM AUTHOR]

The objective of this study was to investigate the influence of the number and timing of a binary time-dependent covariate on the bias using the last-observation carried forward in the proportional hazards model. Under various assumptions of censoring rates, transition probabilities of the time-dependent covariate, sample size, and the log hazard-ratio for the covariate, we empirically examined the impact that the number and timing have on the bias of the estimator of the covariate. An example from the Systolic Hypertension in the Elderly Program was used. Inference on the effect of systolic blood pressure on survival is strongly affected by the number and timing of systolic blood measurements. [ABSTRACT FROM AUTHOR]

This two-by-two factorially designed study evaluate approaches for communicating feedback of lung cancer susceptibility to smokers as a method for motivating smoking cessation. The study factors were: method of communicating feedback (by mail with telephone follow-up or in-person) and carbon monoxide feedback (yes or no). One-hundred-forty-four smokers were stratified on race and randomized to one of four conditions. Participants were surveyed at baseline and 2-month follow-up. Polymerase chain reaction (PCR) testing for the absence of the glutathione S transferase mu (GSTM1) gene was the susceptibility marker. Regardless of counseling method or carbon monoxide (CO)feedback, the majority (90%) of smokers accurately recalled the test result and 66% accurately interpreted the meaning of the test result. Smokers who received their result in person were significantly less likely to have read the result booklet than those in the telephone counseling group (OR = .28, 95%; CI .12-.62; p < .05). Neither counseling method nor CO feedback increased smokers' perceived risks for lung cancer. However, at the counseling session those who received in-person counseling were significantly less frightened by the test result than those who received telephone counseling (OR = .42, 95%; CI .20-86; p < .05) and at the 2-month follow-up those who received a CO test were significantly less frightened by their susceptibility result (OR = .40, 95%; CI .17-.92; p < .05) than those who did not have a CO test. Evaluation of further refinements in communicating the meaning of susceptibility results to motivate smoking cessation is warranted. [ABSTRACT FROM AUTHOR]