ObjectiveTo explore the method of constructing time-dependent variables of clinical prognostic model, and to combine marginal structure model with clinical prognostic model to provide a more accurate tool for individualized prognostic assessment of patients. MethodsThrough data simulation, a training dataset with sample size of 7 000 and a validation dataset with sample size of 3 000 were constructed, and the predictive efficacy of ignoring treatment model, baseline no-treatment model, baseline treatment prediction model and marginal structure prediction model were respectively compared under different follow-up times and different situations. ResultsAt 2 follow-up time points, there was no significant difference between the marginal structure prediction model and the baseline treatment prediction model, but they were higher than the neglected treatment model and the baseline no treatment model. At 5 follow-up time points, the prediction ability of the marginal structure prediction model was significantly higher than that of the other three prediction models. ConclusionIn the case of time-dependent treatment in the observational cohort, the change of treatment after baseline should be considered when constructing the clinical prognosis model, otherwise the prediction accuracy of the prognosis model will be reduced.
This study introduced the inverse probability weight and overlap weight by propensity score and how to test the balance and estimate the effect after weighting. Four R packages that can be used for propensity score weight analysis were introduced and compared.