Illustrating the Impact of a Time-Varying Covariate With an Extended Kaplan-Meier Estimator
ABSTRACT In clinical endpoint trials, the association between a baseline covariate and the risk of an endpoint is often measured by the hazard ratio as calculated by a Cox regression model, and illustrated by Kaplan-Meier curves comparing cohorts defined by levels of the covariate. The Cox regression model is easily extended to the case of time-varying covariates; however, there is no clear approach for similarly extending the standard Kaplan-Meier estimator. Various ad hoc procedures that have been used in the medical literature are seriously flawed. This article discusses an extended Kaplan-Meier estimator that can be used with time-varying covariates and illustrates this method using data from a long-term clinical trial.
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- "The Cox model calculates a hazard ratio associated with noncompliance under the assumption that this hazard ratio is constant over time. In order to explore visually whether or not this assumption is true, we create extended Kaplan-Meier curves that compare endpoint rates for cohorts defined by time-varying covariates . These curves are somewhat difficult to interpret but are consistent with the Cox regression method. "
ABSTRACT: Noncompliance with study medications is an important issue in the design of endpoint clinical trials. Including noncompliant patient data in an intention-to-treat analysis could seriously decrease study power. Standard methods for calculating sample size account for noncompliance, but all assume that noncompliance is noninformative, i.e., that the risk of discontinuation is independent of the risk of experiencing a study endpoint. Using data from several published clinical trials (OPTIMAAL, LIFE, RENAAL, SOLVD-Prevention and SOLVD-Treatment), we demonstrate that this assumption is often untrue, and we discuss the effect of informative noncompliance on power and sample size.Current controlled trials in cardiovascular medicine 08/2004; 5(1):5. DOI:10.1186/1468-6708-5-5 · 2.33 Impact Factor
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ABSTRACT: Taking advantage of the proximity between Temple's Statistics Department and many large pharmaceutical companies, a number of successful professional relationships developed and grew. A special partnership was formed between Temple's Biostatistics Research Center and Merck's Research Laboratories. This led to a number of summer graduate student internships that resulted in the identification of interesting dissertation topics and numerous award winning Ph.D. dissertations, most leading to successful publication in statistics journals. It also led to the creation of the very successful annual Merck- Temple Conferences, the 12th having been run in October 2004. These conferences attracted leading statisticians as speakers and were well attended. We feel that such partnerships will be of increased importance in the future due to the increasing complexity of drug and vaccine development requiring collaboration in development and application of novel experimental designs and methods of analysis. We will provide an overview of this successful partnership and illustrate, with cases, the mutual benefits of this professional relationship.
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ABSTRACT: Presents a new technique for designing single stage fully complementary metal oxide semiconductor (FCMOS) and CMOS domino logic circuits so that they are totally self checking for all single s-off and s-on faults. It involves the encoding of the output of the circuit in an error detecting code. CMOS circuits designed using the technique have two outputs. Two of the combinations (01,10) are considered to be valid code-words. The circuit is augmented such that any stuck-off (stuck-on) fault in the modified circuit produces a non-valid output 11(00), thus ensuring automatic fault detectionVLSI Test Symposium, 1992. '10th Anniversary. Design, Test and Application: ASICs and Systems-on-a-Chip', Digest of Papers., 1992 IEEE; 05/1992