Illuminating our understanding of statins and Alzheimers Disease and Dementia using modern causal inference methods

Abstract Antihyperlipidemic agents, including statins, currently rank as the second most frequently prescribed drugs in the U.S. While there is adequate evidence supporting the benefits of statins for the prevention of CVD events, evidence to support the beneficial or harmful effects of statins on cognition and the risk of Alzheimer’s disease and related dementias (ADRD) is inconclusive. Findings from two large randomized controlled trials of statins found no effect on cognitive outcomes over the short-term (4 to 5 years). Results from observational studies have been mixed. Given the widespread use of statins, this knowledge gap represents a huge opportunity for illuminating viable prevention strategies for ADRD. The most plausible explanation for these inconsistent results is that the effectiveness of statin treatment varies across patient characteristics such as age, sex, and the presence of chronic conditions. Importantly, most trials do not have adequate statistical power to examine these heterogeneous treatment effects (HTEs). Further, trials typically include participants who are healthier than the general population of statin-users and less likely to experience side effects, and often exclude participants who do not tolerate statins. Large, administrative observational data sources can provide sufficient sample sizes to address these limitations, but traditional analytical techniques are insufficient in the presence of strong confounding. In this study, we propose to leverage a widespread clinical prescription guideline and established statistical methods in economics, specifically regression discontinuity (RD) designs, to address confounding and approximate a randomized trial. Our data (N=175,234) will come from the Health Improvement Network (THIN) database which includes general practices in the UK covering about 5% of the total population. In 2008, the National Institute for Health and Care Excellence in the UK passed a guideline which recommends statin use when a patient’s 10-year CVD risk score exceeds 20%. Since treatment is given or withheld according to this guideline, we assume that patients who are “near” the 20% cutoff will be similar except for the treatment received, thus creating the ideal setup to estimate the causal effect of statins. We will also apply an honest causal forest, a state-of-the-art causal inference and supervised learning method, to flexibly identify novel patient subgroups who could most benefit from statin use. The proposed research will (Aim 1) estimate the effect of statins on ADRD risk, using an RD design, accounting for adherence. We will (Aim 2) then estimate the effect of statins on ADRD risk across a priori (hypothesis-driven HTEs) and newly identified (data-driven HTEs) patient subgroups, using RD and machine-learning methods. Finally, we will (Aim 3) quantify the reduction in ADRD cases that could be achieved if specific subgroups of the population are treated with statins. Using contemporary methods, this innovative study will provide more valid and public health relevant estimates of the effects of statins on ADRD risk. By also examining HTEs and identifying groups that may particularly benefit or be harmed from statins, this study will allow us to move towards targeted “precision medicine” approach for the prevention of ADRD.