51: Transportability in the Era of Big Data: Challenges and Solutions with Large Target Populations

Music City Center 

Transportability studies are conducted to obtain real-world evidence by extending an estimated effect from a trial sample to a target population of interest, where the trial sample is partially or fully disjointed from the target population. Inverse probability selection weighting (IPSW) and G-computation are widely used statistical methods in these studies. However, previous research highlights challenges when the trial sample is much smaller than the target population, leading to poor model estimation and biased results. This limitation can restrict the applicability of these statistical approaches when seeking evidence for broader populations. In this study, we implement a simulation study to evaluate the performance of statistical methods under varying trial-to-target population size ratios and varying relationships between time-to-event outcomes and potential effect modifiers. We hypothesize that artificially increasing the trial-to-target ratio by taking a random sample from the target population improves model performance when the initial trial-to-target ratio is low.

Transportability

Inverse Probability Selection Weighting (IPSW)

G-computation

Trial-to-Target Population Ratio

Simulation Study 

Biopharmaceutical Section