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M S Wulfsohn

Publications and source records attributed to M S Wulfsohn.

3 recordsLinked to original sources

Phase II double-blind, placebo-controlled study of the safety and efficacy of cidofovir topical gel for the treatment of patients with human papillomavirus infection.

Genital condylomata acuminata are nonmalignant human papillomavirus (HPV)-induced tumors in which HPV types 6 and 11 are most commonly found. Usual treatments for condylomata acuminata are nonspecific and are based on the destruction or removal of infected tissue. These procedures are often painful and are characterized by a high relapse rate. We report here what is to our knowledge the first double-blind, placebo-controlled study of the use of cidofovir, a nucleotide analogue, for the treatment of genital papillomavirus infections. Thirty patients were enrolled in the study; 19 received cidofovir, and 11 received placebo. The median number of warts and the median baseline wart area were comparable for both groups. Nine (47%) of 19 patients in the cidofovir group had a complete response (total healing), compared with 0 of the patients in the placebo group (P=.006). None of the patients in the cidofovir group experienced progression of the disease, compared with 5 (45%) of 11 patients in the placebo group. The side effects recorded for both groups were comparable.

Adult↗

A joint model for survival and longitudinal data measured with error.

The relationship between a longitudinal covariate and a failure time process can be assessed using the Cox proportional hazards regression model. We consider the problem of estimating the parameters in the Cox model when the longitudinal covariate is measured infrequently and with measurement error. We assume a repeated measures random effects model for the covariate process. Estimates of the parameters are obtained by maximizing the joint likelihood for the covariate process and the failure time process. This approach uses the available information optimally because we use both the covariate and survival data simultaneously. Parameters are estimated using the expectation-maximization algorithm. We argue that such a method is superior to naive methods where one maximizes the partial likelihood of the Cox model using the observed covariate values. It also improves on two-stage methods where, in the first stage, empirical Bayes estimates of the covariate process are computed and then used as time-dependent covariates in a second stage to find the parameters in the Cox model that maximize the partial likelihood.

Algorithms↗