High-Dimensional Disease Risk Score for Dealing With Residual Confounding Bias in Estimating Treatment Effects With a Survival Outcome

Purpose Health administrative databases often contain no information on some important confounders, leading to residual confounding in the effect estimate. We aimed to explore the performance of high-dimensional disease risk score (hdDRS) to deal with residual confounding bias for estimating causal effects with survival outcomes. Methods We used health administrative data of 49 197 individuals in British Columbia to examine the relationship between tuberculosis infection and time-to-development of cardiovascular disease (CVD). We designed a plasmode simulation exploring the performance of eight hdDRS methods that varied by different approaches to fit the risk score model and also examined results from high-dimensional propensity score (hdPS) and traditional regression adjustment. The log-hazard ratio (log-HR) was the target parameter with a true value of log(3). Results In the presence of strong unmeasured confounding, the bias observed was -0.11 for the traditional method and -0.047 for the hdPS method. The bias ranged from -0.051 to -0.058 for hdDRS methods when risk score models were fitted to the full cohort and -0.045 to -0.049 when risk score models were fitted only to unexposed individuals. All methods showed comparable standard errors and nominal bias-eliminated coverage probabilities. With weak unmeasured confounding, hdDRS and hdPS produced approximately unbiased estimates. Our data analysis, after addressing residual confounding, revealed an 8%-11% higher CVD risk associated with tuberculosis infection. Conclusions Our findings support the use of selected hdDRS methods to address residual confounding bias when estimating treatment effects with survival outcomes. In particular, the hdDRS method using rate-based risk score modeling on unexposed individuals consistently exhibited the least bias. However, the hdPS method showed comparable performance across most evaluated scenarios. We share reproducible R codes to facilitate researchers' adoption and further evaluation of these methods.