Handling of missing data with multiple imputation in observational studies that address causal questions: protocol for a scoping review.

ABSTRACT:

Introduction

Observational studies in health-related research often aim to answer causal questions. Missing data are common in these studies and often occur in multiple variables, such as the exposure, outcome and/or variables used to control for confounding. The standard classification of missing data as missing completely at random, missing at random (MAR) or missing not at random does not allow for a clear assessment of missingness assumptions when missingness arises in more than one variable. This presents challenges for selecting an analytic approach and determining when a sensitivity analysis under plausible alternative missing data assumptions is required. This is particularly pertinent with multiple imputation (MI), which is often justified by assuming data are MAR. The obj

SUBMITTER: Mainzer R 

PROVIDER: S-EPMC9896184 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature