We propose a logistic regression analysis of unmatched or frequency matched case-control studies with conditional maximum likelihood estimation through post hoc stratification. In this model fewer parameters have to be estimated. ⋯ A more refined post hoc stratification reduces computing time, but to the cost of a larger bias and a loss in efficiency. The model was also applied to data of unmatched case-control studies on laryngeal cancer, oesophageal cancer and lung cancer.
AbstractWe propose a logistic regression analysis of unmatched or frequency matched case-control studies with conditional maximum likelihood estimation through post hoc stratification. In this model fewer parameters have to be estimated. With a simulation study we show that parameter estimates have smaller variance and are less biased. Also, the residual confounding effect was quantified. A more refined post hoc stratification reduces computing time, but to the cost of a larger bias and a loss in efficiency. The model was also applied to data of unmatched case-control studies on laryngeal cancer, oesophageal cancer and lung cancer.