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Observational studies can play a useful role in assessing the comparativeeffectiveness of competing treatments. In a clinical trial the randomization ofparticipants to treatment and control groups generally results in well-balancedgroups with respect to possible confounders, which makes the analysisstraightforward. However, when analysing observational data, the potential forunmeasured confounding makes comparing treatment effects much more challenging.Causal inference methods such as Instrumental Variable and Prior Even RateRatio approaches make it possible to circumvent the need to adjust forconfounding factors that have not been measured in the data or measured witherror. Direct confounder adjustment via multivariable regression and Propensityscore matching also have considerable utility. Each method relies on adifferent set of assumptions and leverages different aspects of the data. Inthis paper, we describe the assumptions of each method and assess the impact ofviolating these assumptions in a simulation study. We propose the prior outcomeaugmented Instrumental Variable method that leverages data from before andafter treatment initiation, and is robust to the violation of key assumptions.Finally, we propose the use of a heterogeneity statistic to decide if two ormore estimates are statistically similar, taking into account theircorrelation. We illustrate our causal framework to assess the risk of genitalinfection in patients prescribed Sodium-glucose co-transporter-2 inhibitorsversus Dipeptidyl peptidase-4 inhibitors as second-line treatment for Type 2Diabets using observational data from the Clinical Practice Research Datalink.

Triangulating Instrumental Variable, confounder adjustment and  Difference-in-Difference methods for comparative effectiveness research in  observational data