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Activity Number: 35
Type: Contributed
Date/Time: Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #309811
Title: Adjusting Odds Ratios for Misdiagnosis of Cases in Case Control Studies Using Optimal Classifiers
Author(s): Dat Huynh*+ and Ron Brookmeyer
Companies: UCLA Biostatistics and UCLA
Keywords: HIV infection ; classification ; bootstrapping
Abstract:

In a case-control study, misdiagnosis of cases can bias the estimated odds ratio. Here we consider the situation when cases are one of two types and we wish to estimate the odds ratio associated with one of the case types. The complication is that often the case type cannot be perfectly determined and instead biomarkers are used to classify the cases by type. The work was motivated by case-control studies for risk factors associated with recent HIV infection but the duration of infection cannot be directly observed. We use a validation data set where biomarkers are available and the case type is known to develop a classification rule. We discuss how to adjust the odds ratio from a case control study using the classification rule from the validation data set and how to choose optimal classification rules. Our criterion for the optimal classifier is one that minimizes the width of the adjusted odds ratio. We use bootstrapping to assess confidence intervals and to identify optimal classifiers. We develop classification rules using a sequential risk score algorithm using biomarkers that discriminates HIV infected individuals who are infected< 1 year versus those infected >1 year.


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