scholarly journals Weighted Approach for Estimating Effects in Principal Strata with Missing Data for a Categorical Post-Baseline Variable in Randomized Controlled Trials

2021 ◽  
pp. 1-29
Author(s):  
Shengchun Kong ◽  
Dominik Heinzmann ◽  
Sabine Lauer ◽  
Tian Lu
Keyword(s):  
Missing Data ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled ◽  
Baseline Variable

scholarly journals Best (but oft-forgotten) practices: missing data methods in randomized controlled nutrition trials

2019 ◽  
Vol 109 (3) ◽  
pp. 504-508 ◽  
Author(s):  
Peng Li ◽  
Elizabeth A Stuart
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Causal Inference ◽  
Randomized Controlled Trials ◽  
Multiple Imputation ◽  
Controlled Trials ◽  
Full Information ◽  
Complete Case ◽  
Full Information Maximum Likelihood ◽  
Randomized Controlled

ABSTRACT Missing data ubiquitously occur in randomized controlled trials and may compromise the causal inference if inappropriately handled. Some problematic missing data methods such as complete case (CC) analysis and last-observation-carried-forward (LOCF) are unfortunately still common in nutrition trials. This situation is partially caused by investigator confusion on missing data assumptions for different methods. In this statistical guidance, we provide a brief introduction of missing data mechanisms and the unreasonable assumptions that underlie CC and LOCF and recommend 2 appropriate missing data methods: multiple imputation and full information maximum likelihood.

Potentially missing data are considerably more frequent than definitely missing data: a methodological survey of 638 randomized controlled trials

2019 ◽  
Vol 106 ◽  
pp. 18-31 ◽  
Author(s):  
Lara A. Kahale ◽  
Batoul Diab ◽  
Assem M. Khamis ◽  
Yaping Chang ◽  
Luciane Cruz Lopes ◽  
...  
Keyword(s):  
Missing Data ◽  
Randomized Controlled Trials ◽  
Controlled Trials ◽  
Randomized Controlled
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