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All Times EDT

Thursday, October 1
Thu, Oct 1, 1:15 PM - 2:30 PM
Concurrent Session

Odds Ratio Estimation in Error-Prone, Observational HIV Cohort Data (309576)

Gustavo G. C. Amorim, Vanderbilt University Medical Center 
*Sarah C. Lotspeich, Vanderbilt University 
Pamela Shaw, University of Pennsylvania 
Bryan E. Shepherd, Vanderbilt University Medical Center 
Ran Tao, Vanderbilt University Medical Center 

Keywords: data audits, logistic regression, measurement error, observational data, semiparametric

Persons living with HIV engage in clinical care often, so observational HIV research cohorts generate especially large amounts of routine clinical data. Increasingly, these data are being used in biomedical research, but available information can be error prone and biased statistical estimates can mislead results. The Caribbean, Central, and South America network for HIV epidemiology is one such cohort; fortunately, data audits have been conducted. Risk of AIDS defining event after initiating antiretroviral therapy is of clinical interest, expected to be associated with CD4 lab value and AIDS status. Error-prone values for 5109 patients were in the research database, and validated data were available (substantiated by clinical source documents) on only 117 patients. Instead of naive (unaudited) or complete case (audited) analysis, we propose a novel semiparametric likelihood method using all available information (unaudited and audited) to obtain unbiased, efficient odds ratios with error prone outcome and covariates. Point estimates were farther from the null than the naive analysis, directionality agreed with the complete case analysis, but had narrower confidence intervals.