Bias in a binary risk behaviour model subject to inconsistent reports and dropout in a South African high school cohort study

Abstract

We describe a methodology for analysing self-reported risk behaviour transitional patterns in a binary outcome variable, subject to misclassification and a large loss to follow-up. The motivation stems from the analysis of self-reported transitional patterns in responses to the question 'have you ever smoked a whole cigarette?' in a cohort of South African school children. The partially complete records analysis (PCRA) introduced, estimates the transitional probability as: the ratio of the joint probability of the response at two time points based on the complete records for this time sequence over the marginal probabilities of the response based on the complete records at the first time point, and assumes a non-informative missing pattern. A comparison was made using un-weighted complete records and inverse probability weighted logistic regression. The estimates of the probabilities of reporting ever having smoked a cigarette obtained from the three methods were similar for a particular transition. The PCRA method lacked precision compared with the inverse probability weighted logistic regression. A simulation study indicated an association between bias and reporting error in all three methods. The PCRA method can be considered as a method for the estimation of transition probabilities in a cohort study where there is consistency in the self-reported risk behaviour pattern and the sample size is large at baseline. The inverse probability weighting approach is more precise and is suitable for this setting in order to determine risk factors for the incidence of self-reported substance used in a cohort with a high dropout rate.