Paul Madley-Dowd
@pmadleydowd.bsky.social
Research Fellow in Medical Statistics and Health Data Science at the University of Bristol
Using a simple modification to the algorithm we show how to determine whether MI will be valid in subsamples restricted to participants with observed values for some of the incomplete variables
November 26, 2025 at 11:37 AM
Using a simple modification to the algorithm we show how to determine whether MI will be valid in subsamples restricted to participants with observed values for some of the incomplete variables
Our algorithm provides easy to use guidance on how to implement the m-backdoor criterion in your work
November 26, 2025 at 11:37 AM
Our algorithm provides easy to use guidance on how to implement the m-backdoor criterion in your work
The most striking finding to me was that when there was no bias in CRA (and we are using MI to reduce SEs only), including an auxiliary variable with an open path to its own missing data can introduce substantial quantities of bias.
February 19, 2025 at 10:25 AM
The most striking finding to me was that when there was no bias in CRA (and we are using MI to reduce SEs only), including an auxiliary variable with an open path to its own missing data can introduce substantial quantities of bias.
We looked at different missing data mechanisms for both an outcome and an auxiliary variable.
Where the outcome missingness mechanism led to a biased complete records analysis, increasing proportions of missing data reduced the ability of auxiliary variables to remove bias.
Where the outcome missingness mechanism led to a biased complete records analysis, increasing proportions of missing data reduced the ability of auxiliary variables to remove bias.
February 19, 2025 at 10:25 AM
We looked at different missing data mechanisms for both an outcome and an auxiliary variable.
Where the outcome missingness mechanism led to a biased complete records analysis, increasing proportions of missing data reduced the ability of auxiliary variables to remove bias.
Where the outcome missingness mechanism led to a biased complete records analysis, increasing proportions of missing data reduced the ability of auxiliary variables to remove bias.