Beyond Intention-to-Treat Analyses in Randomized Trials: Utilizing Per-Protocol Estimands and Observational Data to Investigate Strategies for Cardiovascular Disease Prevention

Abstract

Randomized trials are considered the gold standard study design for conducting comparative effectiveness research. Yet, evidence from randomized trials to inform cardiovascular disease (CVD) prevention efforts remains scarce for some patient populations, partly due to restrictive eligibility criteria and shorter follow-up of surrogate outcomes. Emulating target trials in observational data can provide insights into the effects of existing medications for CVD prevention in new target populations and on longer-term clinical endpoints. Regardless of study design, that is, whether we use data from randomized trials or emulate target trials in observational data, complementing intention-to-treat analyses, which estimate the effect of the assigned treatment strategies, with appropriate per-protocol analyses, which estimate effects that would have been observed had there been full adherence to the assigned treatment strategies, can be important to facilitate informed clinical decision-making. In this dissertation, I estimate both intention-to-treat and per-protocol effects using data from a large-scale, pragmatic randomized trial and from electronic health records and health insurance claims to elucidate the impact of medications important for CVD prevention in populations for whom evidence has been limited based on existing intention-to-treat analyses of randomized trials. In Chapter 1, I estimated the effect of adhering to assigned treatment strategies of pravastatin or usual care on death and CVD in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack – Lipid-Lowering Trial. The high initiation of lipid-lowering therapy in the usual care comparator group has been previously suggested to explain the null intention-to-treat findings. I demonstrate that deviations from the pravastatin treatment strategy may better explain the intention-to-treat results, showing that pravastatin reduced the risk of death and CVD even when allowing individuals in the usual care group to initiate lipid-lowering therapy. In Chapter 2, I estimated the effect of statin therapy versus usual care on the 5-year risk of CVD among women with breast cancer. I first specified the target trial and then emulated the trial using electronic health record data from a large cohort of patients at several Kaiser Permanente networks. While intention-to-treat findings showed no difference in risk of CVD over five years, the risk of CVD was lower for the statin therapy group compared to usual care in per-protocol analyses, though estimates were imprecise. In Chapter 3, I estimated the effects of adding GLP1-RA to SGLT2i and adding SGLT2i to GLP1-RA on glycemic control and intermediate cardiovascular risk factors at one-year, and risk of CVD over three-years among persons with inadequately controlled type 2 diabetes. I outlined the protocol of two target trials, then emulated them using electronic health records and claims data from 12 insurance/healthcare systems in the US. Regardless of initial background treatment, adding SGLT2i to GLP1-RA reduced HbA1c, systolic blood pressure, and body mass index after one-year. Estimates for the effect of combination therapy on cardiovascular events were imprecise, and additional data are needed to confirm whether adding SGLT2i or GLP1-RA leads to long-term protection against cardiovascular disease. In conclusion, when evidence from intention-to-treat analyses of existing randomized trials is insufficient to guide clinical decision-making alone, using per-protocol effects and pragmatic trials as a framework for conducting comparative effectiveness research in observational data can help inform future CVD prevention efforts for populations at high risk, such as breast cancer survivors and patients with type 2 diabetes.Population Health Science