Literature DB >> 12185892

Prediction of survival and opportunistic infections in HIV-infected patients: a comparison of imputation methods of incomplete CD4 counts.

Geert Molenberghs1, Paige L Williams, Stuart R Lipsitz.   

Abstract

In evaluating the risk of mortality or development of opportunistic infections in HIV-infected patients, the number of CD4 lymphocyte cells per cubic millimetre of blood is widely recognized as one of the best available predictors of such future events. However, its usefulness is limited by the incompleteness and variability of such CD4 measurements during follow-up. Because of these limitations, analysis of such data requires the missing measurements to be 'filled in' or the patients without them to be excluded. We consider multiple imputation of CD4 values based partly on information from other health status measures such as haemoglobin, as well as on the event status of interest. These alternative health status measures are also considered as possible independent predictors of survival endpoints. Our work is motivated by a cohort of 1530 patients enrolled in two AIDS clinical trials. We compare our approach to other strategies such as basing evaluation of risk on baseline CD4, the last measured CD4 before an event, or a time-dependent covariate based on carrying the last CD4 value forward; we conclude with a strong recommendation for multiple imputation.

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Year:  2002        PMID: 12185892     DOI: 10.1002/sim.1118

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

1.  Analysis of the benefits of a Mediterranean diet in the GISSI-Prevenzione study: a case study in imputation of missing values from repeated measurements.

Authors:  Federica Barzi; Mark Woodward; Rosa Maria Marfisi; Gianni Tognoni; Roberto Marchioli
Journal:  Eur J Epidemiol       Date:  2006       Impact factor: 8.082

2.  Determinants of survival in adult HIV patients on antiretroviral therapy in Oromiyaa, Ethiopia.

Authors:  Andinet Worku Alemu; Miguel San Sebastián
Journal:  Glob Health Action       Date:  2010-10-29       Impact factor: 2.640

3.  The effect of missing data on linkage disequilibrium mapping and haplotype association analysis in the GAW14 simulated datasets.

Authors:  Pamela A McCaskie; Kim W Carter; Simon R McCaskie; Lyle J Palmer
Journal:  BMC Genet       Date:  2005-12-30       Impact factor: 2.797

4.  Survival analysis of gastric cancer patients with incomplete data.

Authors:  Abbas Moghimbeigi; Lily Tapak; Ghodaratolla Roshanaei; Hossein Mahjub
Journal:  J Gastric Cancer       Date:  2014-12-26       Impact factor: 3.720

  4 in total

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