Measuring individual benefits of psychiatric treatment using longitudinal binary outcomes: Application to antipsychotic benefits in non-cannabis and cannabis users.

ABSTRACT: We present and evaluate a method for predicting individual treatment benefits based on random effects logistic regression models of binary outcomes that change over time. The method uses empirical Bayes predictors based on patients' characteristics and responses to treatment. It is applicable to both 1-dimentional and 2-dimentional personalized medicine models. Comparisons between predicted and true benefits for simulated new patients using correlations, relative biases and mean-squared errors were used to evaluate prediction performance. The predicted benefits had relatively small biases and relatively high correlations with the true benefits in the simulated new patients. The predictors also captured estimated overall population trends in the evolution of individual benefits. The propose

SUBMITTER: Zhang X 

PROVIDER: S-EPMC9030227 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature