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Recursos bibliográficos en papel y digitales -
- libros, artículos de revistas,
ponencias de eventos, etc. -
» Resultado: 4 registros
| Registro 1 de 4 | |
| Autor: | Hudgens, Michael G. - Maathuis, Marloes H. - Gilbert, Peter B. - |
| Título: | Nonparametric estimation of the joint distribution of a survival time subject to interval censoring and a continuous mark variable |
| Fuente: | Biometrics. v.63, n.2. International Biometric Society |
| Páginas: | pp. 372-380 |
| Año: | jun. 2007 |
| Resumen: | This article considers three nonparametric estimators of the joint distribution function for a survival time and a continuous mark variable when the survival time is interval censored and the mark variable may be missing for interval-censored observations. Finite and large sample properties are described for the nonparametric maximum likelihood estimator (NPMLE) as well as estimators based on midpoint imputation (MIDMLE) and coarsening the mark variable (CMLE). The estimators are compared using data from a simulation study and a recent phase III HIV vaccine efficacy trial where the survival time is the time from enrollment to infection and the mark variable is the genetic distance from the infecting HIV sequence to the HIV sequence in the vaccine. Theoretical and empirical evidence are presented indicating the NPMLE and MIDMLE are inconsistent. Conversely, the CMLE is shown to be consistent in general and thus is preferred. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 2 de 4 | |
| Autor: | Shepherd, Bryan E. - Gilbert, Peter B. - Jemiai, Yannis - Rotnitzky, Andrea |
| Título: | Sensitivity analyses comparing outcomes only existing in a subset selected post-randomization, conditional on covariates, with application to HIV vaccine trials |
| Fuente: | Biometrics. v.62, n.2. International Biometric Society |
| Páginas: | pp. 332-342 |
| Año: | jun. 2006 |
| Resumen: | In many experiments, researchers would like to compare between treatments and outcome that only exists in a subset of participants selected after randomization. For example, in preventive HIV vaccine efficacy trials it is of interest to determine whether randomization to vaccine causes lower HIV viral load, a quantity that only exists in participants who acquire HIV. To make a causal comparison and account for potential selection bias we propose a sensitivity analysis following the principal stratification framework set forth by b2Frangakis and Rubin (2002, Biometrics 58, 21-29). Our goal is to assess the average causal effect of treatment assignment on viral load at a given baseline covariate level in the always infected principal stratum (those who would have been infected whether they had been assigned to vaccine or placebo). We assume stable unit treatment values (SUTVA), randomization, and that subjects randomized to the vaccine arm who became infected would also have become infected if randomized to the placebo arm (monotonicity). It is not known which of those subjects infected in the placebo arm are in the always infected principal stratum, but this can be modeled conditional on covariates, the observed viral load, and a specified sensitivity parameter. Under parametric regression models for viral load, we obtain maximum likelihood estimates of the average causal effect conditional on covariates and the sensitivity parameter. We apply our methods to the world’s first phase III HIV vaccine trial. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 3 de 4 | |
| Autor: | Gilbert, Peter B. - Rossini, A.J. - Shankarappa, Raj |
| Título: | Two-sample tests for comparing intra-individual genetic sequence diversity between populations |
| Fuente: | Biometrics. v.61, n.1. International Biometric Society |
| Páginas: | pp. 106-117 |
| Año: | mar. 2005 |
| Resumen: | Consider a study of two groups of individuals infected with a population of a genetically related heterogeneous mixture of viruses, and multiple viral sequences are sampled from each person. Based on estimates of genetic distances between pairs of aligned viral sequences within individuals, we develop four new tests to compare intra-individual genetic sequence diversity between the two groups. This problem is complicated by two levels of dependency in the data structure: (i) Within an individual, any pairwise distances that share a common sequence are positively correlated: and (ii) for any two pairings of individuals which share a person, the two differences in intra-individual distances between the paired individuals are positively correlated. The first proposed test is based on the difference in mean intra-individual pairwise distances pooled over all individuals in each group, standardized by a variance estimate that corrects for the correlation structure using U-statistic theory. The second procedure is a nonparametric rank-based analog of the first test, and the third test contrasts the set of subject-specific average intra-individual pairwise distances between the groups. These tests are very easy to use and solve correlation problem (i). The fourth procedure is based on a linear combination of all possible U-statistics calculated on independent, identically distributed sequence subdatasets, over the two levels (i) and (ii) of dependencies in the data, and is more complicated than the other tests but can be more powerful. Although the proposed methods are empirical and do not fully utilize knowledge from population genetics, the tests reflect biology through the evolutionary models used to derive the pairwise sequence distances. The new tests are evaluated theoretically and in a simulation study, and are applied to a dataset of 200 HIV sequences sampled from 21 children. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
| Registro 4 de 4 | |
| Autor: | Gilbert, Peter B. - Bosch, Ronald J. - Hudgens, Michael G. - |
| Título: | Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIV vaccine trials |
| Fuente: | Biometrics. v.59, n.3. International Biometric Society |
| Páginas: | pp. 531-541 |
| Año: | sep. 2003 |
| Resumen: | Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics 58, 21-29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias. |
| Solicitar por: | HEMEROTECA B + datos de Fuente |
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