Propensity score analysis with missing data using a multi-task neural network.

ABSTRACT:

Background

Propensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable missing values make estimating propensity scores extremely challenging. We propose a new method for estimating propensity scores in data with missing values.

Materials and methods

Both simulated and real-world datasets are used in our experiments. The simulated datasets were constructed under 2 scenarios, the presence (T = 1) and the absence (T = 0) of the true effect. The real-world dataset comes from LaLonde's employment training program. We construct missing data with varying degrees of missing rates under three missing mechanisms: MAR, MCAR, and MNAR. Then we compare MTNN with 2 other traditional methods in different scenarios.

SUBMITTER: Yang S 

PROVIDER: S-EPMC9930709 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature