Checking assumptions: advancing the analysis of sex and gender in health sciences

BACKGROUND: Sex and gender are dissociable constructs, each including multiple components. Based on the analytic problems associated with dichotomising continuous variables, we aimed to synthesize a new approach to collecting and analysing sex and gender data in health research, in contrast to the conventional use of dichotomous tickboxes to code sex/gender. METHODS: Using a literature review and data simulations, we examined the magnitude of the statistical and methodological problems associated with the use of a single dichotomised sex/gender variable, including construct validity, predictive validity, measurement error, residual confounding, misclassification and bias due to cut points, power, and representative sampling. RESULTS: Using the dichotomous sex/gender predictor rather than a continuous sex/gender predictor increased residual confounding up to 80% and misclassification of individual participants up to 50%. Further, there was substantial bias in model parameters when continuous sex/gender variables were dichotomised. Finally, we demonstrate that using the dichotomous sex/gender predictor decreased statistical power, in some cases by more than 50%. CONCLUSIONS: Using a dichotomous sex/gender predictor in place of continuous sex/gender predictors, when applicable, has profound impacts on the modelling and the validity of statistical inferences. Accordingly, we proposed measurement and analytic strategies for new multi-variable data collection and analyses of existing binarized data in relation to sex and gender, to reduce these statistical problems and improve model quality.