Application of Marginal Structural Models with Inverse Probability of Treatment Weighting in Electronic Health Records to Investigate the Benefits and Risks of First Line Type II Diabetes Treatments.

Background: Electronic healthcare records (EHRs) provide opportunities to estimate the effects of type two diabetes (T2DM) treatments on outcomes such as cancer and cardiovascular disease. Marginal structural models (MSMs) with inverse probability of treatment weights (IPTW) can correctly estimate the causal effect of time-varying treatment in the presence of time-dependent confounders such as HbA1c. Dynamic MSMs can be used to compare dynamic treatment strategies. This thesis applies weighted MSMs and dynamic MSMs to explore risks and benefits of early-stage T2DM treatments, and considers the practicalities/impact of using these models in a complex clinical setting with a challenging data source. Methods and Findings: A cohort of patients with newly diagnosed T2DM was identified from the Clinical Practice Research Datalink. MSMs with IPTW were used to estimate the causal effect of metformin monotherapy on cancer risk, and the effects of metformin and sulfonylurea monotherapies on risks of MI, stroke, all-cause mortality, and HbA1c trajectory. Dynamic MSMs were implemented to compare HbA1c thresholds for treatment initiation on risks of MI, stroke, all-cause mortality (ACM) and glucose control. No association was found between metformin use and cancer risk. Metformin and sulfonylureas led to better HbA1c control than diet only, as expected, and there was some evidence of reduced MI risk with long-term metformin use. Changes in estimates between standard models and weighted models were generally in the expected direction given hypothesised time-dependent confounding. For stroke and ACM, results were less conclusive, with some suggestions of residual confounding. Higher HbA1c thresholds for treatment initiation reduced the likelihood of reaching target HbA1c, and there was a suggestion that higher initiation thresholds increased MI risk. Conclusions: Fitting weighted MSMs and dynamic MSMs was feasible using routine primary care data. The models appeared to work well in controlling for strong time-dependent confounding with short-term outcomes; results for longer-term outcomes were less conclusive.