Sensitivity analysis for causal inference using inverse probability weighting

Abstract

Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non-parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non-parametric method. We illustrate our method with two medical data sets.

The Inflammatory Potential of the Diet at Midlife Is Associated with Later Healthy Aging in French Adults

“…Finally, a third model was further adjusted for baseline BMI (kg/m²). All models were corrected for possible selection bias via the inverse probability weighting method (31). The probability of inclusion into the present study was calculated for each individual who was "eligible" for inclusion into our analysis, using multivariable logistic regression.…”

Statistical sensitivity analysis for the estimation of fetal alcohol spectrum disorders prevalence

“…Sensitivity analysis-In order to carry out the sensitivity analysis, we followed the setup of Shen et al (12) for causal inference of a treatment effect. While we do not have a treatment under concern, it is well known that under the potential outcomes framework for causal inference, within each treatment group and the corresponding population, the observed treatment assignment is equivalent to our response indicator here, and the average treatment effect is the difference between the two population means that one needs to estimate.…”

“…Following Shen et al (12) sensitivity of the MAR estimate obtained using IPW is affected by the unmeasured 'confounder' denoted as U. It is assumed that Y and R are conditionally independent given X and U.…”

A comparison of confounding adjustment methods with an application to early life determinants of childhood obesity

“…The focus of this paper is on how to appropriately adjust for measured covariates. If residual confounding bias is a concern, there exist multiple sensitivity analyses methods [38][39][40][41][42] that extend these confounding adjustment methods to assess how the results may vary as the amount of residual confounding bias exists. This is beyond the scope of this paper.…”