Author
Michael Wulfsohn
Bio: Michael Wulfsohn is an academic researcher from North Carolina State University. The author has contributed to research in topics: Covariate & Random effects model. The author has an hindex of 3, co-authored 3 publications receiving 1342 citations.
Papers
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TL;DR: This work argues that the Cox proportional hazards regression model method is superior to naive methods where one maximizes the partial likelihood of the Cox model using the observed covariate values and improves on two-stage methods where empirical Bayes estimates of the covariate process are computed and then used as time-dependent covariates.
Abstract: 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
911 citations
TL;DR: A randomized controlled trial in 524 subjects who had had a first episode of Pneumocystis carinii pneumonia found that the efficacy and safety of a reduced dose of zidovudine were superior to the standard-treatment group and the low-dose group.
Abstract: Background. The initially tested dose of zidovudine for the treatment of patients with advanced disease caused by the human immunodeficiency virus type 1 (HIV) was 1500 mg. Although this dose is effective, it is associated with substantial toxicity. Methods. To evaluate the efficacy and safety of a reduced dose, we conducted a randomized controlled trial in 524 subjects who had had a first episode of Pneumocystis carinii pneumonia. The subjects were assigned to receive zidovudine in either a dose of 250 mg taken orally every four hours (the standard-treatment group, n = 262) or a dose of 200 mg taken orally every four hours for four weeks and thereafter 100 mg taken every four hours (the low-dose group, n = 262). Results. The median length of follow-up was 25.6 months. At 18 months the estimated survival rates were 52 percent for the standard-treatment group and 63 percent for the low-dose group (P = 0.012 by the log-rank test). At 24 months the estimated survival rates were 27 percent for the st...
313 citations
TL;DR: Zidovudine in a dose of 180 mg per square meter every six hours can be safely administered to children with advanced HIV disease, and there was marked improvement in weight gain, cognitive function, serum and cerebrospinal fluid concentrations of p24 antigen, and the proportion of cerebro Spinal fluid cultures negative for HIV.
Abstract: Background and Methods. Zidovudine has been shown to be an effective antiretroviral treatment in adults with human immunodeficiency virus (HIV) infection. We examined the safety of zidovudine and the tolerance of and therapeutic response to the drug in 88 children with advanced HIV disease. During a 24-week outpatient trial, zidovudine (180 mg per square meter of body-surface area per dose) was given by mouth every six hours and serial measurements were made of clinical, immunologic, and virologic indexes. Children who completed 24 weeks of treatment were permitted to continue receiving zidovudine. Results. Of the 88 children (mean age, 3.9 years; range, 4 months to 11 years), 61 completed the initial 24-week trial, and 49 continued to receive zidovudine for up to 90 weeks (median follow-up, 56 weeks). The patients generally tolerated zidovudine well. One or more episodes of hematologic toxicity occurred in 54 children (61 percent) — anemia (hemoglobin level, <75 g per liter) in 23 children (26 p...
197 citations
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TL;DR: In this article, the authors proposed a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on the vector of explanatory variables in the presence of missing response data.
Abstract: We propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data. The proposed estimators do not require full specification of the likelihood. They can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random. These estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable. The likelihood-based parametric G-computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance. But because of possible model misspecification, the parametric G-computation algorithm estimator, in contrast with the proposed w...
1,510 citations
TL;DR: This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data, using results from a clinical trial into the treatment of schizophrenia.
Abstract: This paper formulates a class of models for the joint behaviour of a sequence of longitudinal measurements and an associated sequence of event times, including single-event survival data. This class includes and extends a number of specific models which have been proposed recently, and, in the absence of association, reduces to separate models for the measurements and events based, respectively, on a normal linear model with correlated errors and a semi-parametric proportional hazards or intensity model with frailty. Special cases of the model class are discussed in detail and an estimation procedure which allows the two components to be linked through a latent stochastic process is described. Methods are illustrated using results from a clinical trial into the treatment of schizophrenia.
883 citations
01 Dec 2010
TL;DR: This chapter gives an overview of frequently used mixed models for continuous as well as discrete longitudinal data, with emphasis on model formulation and parameter interpretation.
Abstract: Mixed models have become very popular for the analysis of longitudinal data, partly because they are flexible and widely applicable, partly also because many commercially available software packages offer procedures to fit them. They assume that measurements from a single subject share a set of latent, unobserved, random effects which are used to generate an association structure between the repeated measurements. In this chapter, we give an overview of frequently used mixed models for continuous as well as discrete longitudinal data, with emphasis on model formulation and parameter interpretation. The fact that the latent structures generate associations implies that mixed models are also extremely convenient for the joint analysis of longitudinal data with other outcomes such as dropout time or some time-to-event outcome, or for the analysis of multiple longitudinally measured outcomes. All models will be extensively illustrated with the analysis of real data.
603 citations
17 Apr 1998
TL;DR: This research highlights the need to understand more fully the rationale behind the continued use of condoms and how they can be used to reduce the risk of infection and increase the likelihood of survival.
Abstract: Elaine Abrams, M.D. Harlem Hospital Center, New York, NY Arthur Ammann, M.D. Global Strategies of HIV Prevention, San Rafael, CA Martin Anderson, M.D., M.P.H. University of California at Los Angeles, Los Angeles, CA Carol Baker, M.D. Baylor College of Medicine, Houston, TX Lawrence Bernstein, M.D. Albert Einstein College of Medicine, Bronx, NY Michael Brady, M.D. Columbus Children's Hospital, Columbus, OH Kathleen Brooke Family Representative Sandra Burchett, M.D. Children's Hospital, Boston, MA Carolyn Burr, R.N, Ed.D. NPHRC, Newark, NJ Joseph Cervia, M.D. Long Island Jewish Medical Center, New Hyde Park, NY Diana Clarke, Pharm.D. Boston Medical Center, Boston, MA Daniel Collado Family Representative Ellen Cooper, M.D. Boston University School of Medicine, Boston, MA Marilyn Crain, M.D., M.P.H. University of Alabama at Birmingham, Birmingham, AL Barry Dashefsky, M.D. NPHRC and UMDNJ-New Jersey Medical School, Newark, NJ Carol DiPaolo Family Representative Diane Donovan Family Representative Janet A. Englund, M.D. University of Chicago Hospitals, Chicago, IL Mary Glenn Fowler, M.D., M.P.H. Centers for Disease Control and Prevention, Atlanta, GA Lisa M. Frenkel, M.D. University of Washington, Seattle, WA Donna Futterman, M.D. Montefiore Medical Center, Bronx, NY Anne Gershon, M.D. Columbia University, New York, NY Samuel Grubman, M.D. St. Vincent's Hospital and Medical Center of New York, NY Peter Havens, M.D. Children's Hospital of Wisconsin, Milwaukee, WI Karen Hench, M.S., HRSA, Rockville, MD Neal Hoffman, M.D. Montefiore Medical Center, Bronx, NY Walter Hughes, M.D. St. Jude Children's Research Hospital, Memphis, TN Nancy Hutton, M.D. Johns Hopkins School of Medicine, Baltimore, MD George Johnson, M.D. Medical University of South Carolina, Charleston, SC Mark Kline, M.D. Baylor College of Medicine, Houston, TX Andrea Kovacs, M.D. LAC USC Medical Center, Los Angeles, CA
573 citations
TL;DR: An introduction to event history analysis via multi-state models is given, including the two-state model for survival analysis, the competing risks and illness-death models, and models for bone marrow transplantation.
Abstract: An introduction to event history analysis via multi-state models is given. Examples include the two-state model for survival analysis, the competing risks and illness-death models, and models for bone marrow transplantation. Statistical model specification via transition intensities and likelihood inference is introduced. Consequences of observational patterns are discussed, and a real example concerning mortality and bleeding episodes in a liver cirrhosis trial is discussed.
553 citations