Copyright (c) 2008 All rights reserved. http://works.bepress.com/nicholas_jewell Recent documents in Nicholas P. Jewell en-us Mon, 07 Jan 2008 14:04:23 PST 3600 http://works.bepress.com/nicholas_jewell/53 http://works.bepress.com/nicholas_jewell/53 Thu, 18 Oct 2007 11:15:32 PDT The Methods for Improving Reproductive Health in Africa (MIRA) trial is a recently completed randomized trial that investigated the effect of diaphragm and lubricant gel use in reducing HIV infection among susceptible women. 5,045 women were randomly assigned to either the active treatment arm or not. Additionally, all subjects in both arms received intensive condom counselling and provision, the "gold standard" HIV prevention barrier method. There was much lower reported condom use in the intervention arm than in the control arm, making it difficult to answer important public health questions based solely on the intention-to-treat analysis. We adapt an analysis technique from causal inference to estimate the "direct effects" of assignment to the diaphragm arm, adjusting for condom use in an appropriate sense. Issues raised in the MIRA trial apply to other trials of HIV prevention methods, some of which are currently being conducted or designed. Michael Rosenblum Clinical Trials Epidemiology Statistical Theory and Methods Intervention & Safety Trials http://works.bepress.com/nicholas_jewell/52 http://works.bepress.com/nicholas_jewell/52 Fri, 25 Aug 2006 13:31:53 PDT Nicholas P. Jewell General Biostatistics http://works.bepress.com/nicholas_jewell/51 http://works.bepress.com/nicholas_jewell/51 Tue, 22 Aug 2006 12:13:22 PDT We address the problem of providing variances for parameter estimates obtained under a penalized likelihood formulation through use of the EM algorithm. The proposed solution represents a synthesis of two existent techniques. Firstly, we exploit the supplemented EM algorithm developed in Meng and Rubin (1991) that provides variance estimates for maximum likelihood estimates obtained via the EM algorithm. Their procedure relies on evaluating the Jacobian of the mapping induced by the EM algorithm. Secondly, we utilize a result from Green (1990) that provides an expression for the Jacobian of the mapping induced by the EM algorithm applied to a penalized likelihood. The resultant procedure requires no additional code to that needed for the penalized EM algorithm itself. Mark R. Segal Computation Statistical Theory and Methods http://works.bepress.com/nicholas_jewell/50 http://works.bepress.com/nicholas_jewell/50 Tue, 22 Aug 2006 12:13:18 PDT We analyze three sets of doubly-censored cohort data on incubation times, estimating incubation distributions using semi-parametric methods and assessing the comparability of the estimates. Weibull models appear to be inappropriate for at least one of the cohorts, and the estimates for the different cohorts are substantially different. We use these estimates as inputs for backcalculation, using a nonparametric method based on maximum penalized likelihood. The different incubations all produce fits to the reported AIDS counts that are as good as the fit from a nonstationary incubation distribution that models treatment effects, but the estimated infection curves are very different. We also develop a method for estimating nonstationarity as part of the backcalculation procedure and find that such estimates also depend very heavily on the assumed incubation distribution. We conclude that incubation distributions are so uncertain that meaningful error bounds are difficult to place on backcalculated estimates and that backcalculation may be too unreliable to be used without being supplemented by other sources of information in HIV prevalence and incidence. Peter R. Bacchetti Epidemiology Statistical Theory and Methods http://works.bepress.com/nicholas_jewell/49 http://works.bepress.com/nicholas_jewell/49 Tue, 22 Aug 2006 12:13:13 PDT In biostatistical applications interest often focuses on the estimation of the distribution of time T between two consecutive events. If the initial event time is observed and the subsequent event time is only known to be larger or smaller than an observed point in time, then the data is described by the well understood singly censored current status model, also known as interval censored data, case I. Jewell, Malani and Vittinghoff (1994) extended this current status model by allowing the initial time to be unobserved, but with its distribution over an observed interval [A,B] known to be uniformly distributed; the data is referred to as doubly censored current status data. These authors used this model to handle applications in AIDS partner studies focusing on the nonparametirc maximum likelihood estimate (NPMLE) of the distribution function, G, of T. The model is a submodel of the current status model, but G is essentially the derivative of the distribution function of interest, F, in the current status model. In this paper we establish that the NPMLE of G is uniformly consistent and that the resulting estimators for square root n estimable parameters are efficient. We propose an iterative weighted Pool-Adjacent-Violator-Algorithm to compute the NPMLE of G. The rate of convergence of the NPMLE of F is also established. Mark J. van der Laan Survival Analysis http://works.bepress.com/nicholas_jewell/48 http://works.bepress.com/nicholas_jewell/48 Tue, 22 Aug 2006 12:13:09 PDT Backcalculation is the primary method used to reconstruct past human immunodeficiency virus (HIV) infection rates, to estimate current prevalence of HIV infection, and to project future incidence of acquired immunodeficiency syndrome (AIDS). The method is very sensitive to uncertainty about the incubation period. We estimate incubation distributions from three sets of cohort data and find that the estimates for the cohorts are substantially different. Backcalculations employing the different estimates produce equally good fits to reported AIDS counts but quite different estimates of cumulative infections. These results suggest that the incubation distribution is likely to differ for different populations and that the differences are large enough to have a big impact on the resulting estimates of HIV infection rates. This seriously limits the usefulness of backcalculation for populations (such as intravenous drug users, heterosexuals, and women) that lack precise information on incubation times. Peter Bacchetti Epidemiology Statistical Theory and Methods http://works.bepress.com/nicholas_jewell/47 http://works.bepress.com/nicholas_jewell/47 Tue, 22 Aug 2006 12:13:05 PDT Recently a great deal of attention has been given to binary regression models for clustered or correlated observations. The data of interest are of the form of a binary dependent or response variable, together with independent variables, where sets of observations are grouped together into clusters. A number of models and methods of analysis have been suggested to study such data. Many of these are extensions in some way of the familiar logistic regression model for binary data which are not grouped (i.e., each cluster is of size one). In general, the analyses of these clustered data models proceed by assuming that the observed clusters are a simple random sample of clusters selected from a population of clusters. In this paper, we consider the application of these procedures to the case where the clusters are selected randomly in a manner which depends on the pattern of responses in the cluster. For example, we show that ignoring the retrospective nature of the sample design, by fitting standard logistic regression models for clustered binary data, may result in misleading estimates of the effects of covariates and the precision of estimated regression coefficients. John M. Neuhaus Categorical Data Analysis http://works.bepress.com/nicholas_jewell/46 http://works.bepress.com/nicholas_jewell/46 Tue, 22 Aug 2006 12:13:00 PDT Common goals in epidemiologic studies of infectious diseases include identification of the infectious agent, description of the modes of transmission and characterization of factors that influence the probability of transmission from infected to uninfected individuals. In the case of AIDS, the agent has been identified as the Human Immunodeficiency Virus (HIV), and transmission is known to occur through a variety of contact mechanisms including unprotected sexual intercourse, transfusion of infected blood products and sharing of needles in intravenous drug use. Relatively little is known about the probability of IV transmission associated with the various modes of contact, or the role that other cofactors play in promoting or suppressing transmission. Here, transmission probability refers to the probability that the virus is transmitted to a susceptible individual following exposure consisting of a series of potentially infectious contacts. The infectivity of HIV for a given route of transmission is defined to be the per contact probability of infection. Knowledge of infectivity and its relationship to other factors is important in understanding the dynamics of the AIDS epidemic and in suggesting appropriate measures to control its spread.The primary source of empirical data about infectivity comes from sexual partners of infected individuals. Partner studies consist of a series of such partnerships, usually heterosexual and monogamous, each composed of an initially infected "index case" and a partner who may or may not be infected by the time of data collection. However, because the infection times of both partners may be unknown and the history of contacts uncertain, any quantitative characterization of infectivity is extremely difficult. Thus, most statistical analyses of partner study data involve the simplifying assumption that infectivity is a constant common to all partnerships.The major objectives of this work are to describe and discuss the design and analysis of partner studies, providing a general statistical framework for investigations of infectivity and risk factors for HIV transmission. The development is largely based on three papers: Jewell and Shiboski (1990), Kim and Lagakos (1990), and Shiboski and Jewell (1992). Nicholas P. Jewell Epidemiology http://works.bepress.com/nicholas_jewell/45 http://works.bepress.com/nicholas_jewell/45 Tue, 22 Aug 2006 12:12:55 PDT We analyze temporal stability and geographic trends in cumulative case fatality rates and average doubling times of severe acute respiratory syndrome (SARS). In part, we account for correlations between case fatality rates and doubling times through differences in control measures. We discuss factors that may alter future estimates of case fatality rates. We also discuss reasons for heterogeneity in doubling times among countries and the implications for the control of SARS in different countries and parameterization of epidemic models. Alison P. Galvani Disease Modeling Epidemiology http://works.bepress.com/nicholas_jewell/44 http://works.bepress.com/nicholas_jewell/44 Tue, 22 Aug 2006 12:12:51 PDT Statistical analyses of data form studies of Human Immunodeficiency Virus (HIV) transmission in partners of infected individuals often focus on estimation of the per contact probability of virus transmission, or infectivity. Of particular interest is evaluating whether the infectivity changes during the course of a partnership and in identifying factors that influence the infectiousness of the initially infected partner (called the index case) and the susceptibility of the uninfected partner. Estimation and inference are complicated by limitations in partner study data, which may include unknown time of infection for either or both partners, inaccurate or incomplete information on the number and frequency of contacts and uncertain disease status of the index case. Jewell and Shiboski (1990a) developed statistical methods for partner studies in which data is retrospectively ascertained using techniques that rely on knowledge of the numbers of contacts for each partnership. The infectivity was treated as a function of the number of contacts but was assumed not to depend on the length of time of exposure. Here we consider various generalizations of these ideas. In particular, discussion is focused on analysis of data where the (chronological) time of exposure is observed in addition to or rather than the number of contacts and using models that allow variation in the infectivity according to time since infection of the index case. Where possible, methods are illustrated on data sets on heterosexual transmission. Stephen C. Shiboski Disease Modeling Epidemiology Statistical Theory and Methods