Yue-Hui Yu, Quan Lu
Prevalence, risk factors and multiple outcomes of treatment delay in Chinese patients with schizophrenia
BMC psychiatry
causal epidemiology regulatory risk analysis seeks evaluate removing reducing exposures would change disease occurrence rates define interventional probability causation ipoc change probability disease harm occurring lifetime specified time interval would caused specified change exposure predicted fully specified causal model define closely related concept causal assigned share cas predicted fraction disease risk would removed prevented specified reduction exposure holding variables fixed traditional approaches used evaluate preventable risk implications epidemiological associations including population attributable fraction paf bradford hill considerations cannot reveal whether removing risk factor would reduce disease incidence argue modern formal causal models coupled causal artificial intelligence cai realistically partial imperfect knowledge underlying disease mechanisms show great promise determining quantifying ipoc cas exposures diseases practical interest briefly review key cai concepts terms apply define ipoc cas present steps quantify ipoc using fully specified causal bayesian network bn model useful bounds quantitative ipoc cas calculations derived twostage clonal expansion tsce model carcinogenesis illustrated applying benzene formaldehyde based available epidemiological partial mechanistic evidence causal bn models benzene risk acute myeloid leukemia aml incorporating mechanistic toxicological epidemiological findings show prolonged highintensity exposure benzene increase risk aml ipoc 7e5 cas 54 contrast causal pathway leading formaldehyde exposure increased risk aml identified consistent much previous mechanistic toxicological epidemiological evidence therefore ipoc cas formaldehydeinduced aml likely zero conclude ipoc approach differentiate likely unlikely causal factors provide useful upper bounds ipoc cas exposures diseases practical importance causal factors ipoc help estimate quantitative impacts health risks reducing exposures even situations mechanistic evidence realistically incomplete individuallevel exposureresponse parameters uncertain illustrates strength gained causal inference using causal models generate testable hypotheses obtaining toxicological data test hypotheses implied modelsand necessary refine models virtuous cycle provides additional insight causal determinations may available weightofevidence considerations alone