Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research: A Large-scale, Real-world Data Study
This study aimed to investigate whether autoencoders, which are unsupervised deep learning architectures, might be leveraged to compute PS.
Methods:
We selected patient-level data of 128,368 first-line treated cancer patients from the Flatiron Health EHR-derived de-identified database. We trained an autoencoder architecture to learn a lower-dimensional patient representation, which we used to compute PS. To compare the performance of an autoencoder-based PS with established methods, we performed a simulation study. We assessed the balancing and adjustment performance using standardized mean differences, root mean square errors (RMSE), percent bias, and confidence interval coverage. To illustrate the application of the autoencoder-based PS, we emulated the PRONOUNCE trial by applying the trial’s protocol elements within an observational database setting, comparing two chemotherapy regimens.
Results:
All methods but the manual variable selection approach led to well-balanced cohorts with average standardized mean differences
Source: Epidemiology - Category: Epidemiology Tags: Methods Source Type: research
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