Daring to draw causal claims from non-randomized studies of primary care interventions

Abstract

In conclusion, the causal inference framework states that, when causal conditions hold (consistency, positivity, exchangeability), causal effects can still be estimated for non-randomized primary care interventions (Supplementary Table S1). If one or more conditions are violated, the impact of these violations must be further investigated (for instance, through applying sensitivity analyses for unmeasured confounders). Causal inference methods provide analytical tools to deal many sources of bias that cannot be dealt with using conventional regression methods: MSMs may be applied to overcome adjustment problems arising from time-dependent confounding, IV analyses can be used to address unmeasured confounding and mediation analyses can elucidate causal pathways of an intervention effect (Supplementary Table S2). New advances in causal inference offer promising ways to conduct our primary care studies, improve the quality of evidence that we produce and ensure that changes to our practices and health systems are based on sound, robust evidence of the causal effects of the interventions studied. Causal methods are the future and should be at the forefront of the quantitative armamentarium for primary care researchers.