Projects per year
Abstract
Objectives
To investigate whether a complete case logistic regression gives a biased estimate of the exposure odds ratio (OR) if missingness depends on a continuous outcome, but a binary version is used for analysis; to examine whether any bias could be reduced by including a misclassified form of the incomplete outcome as an auxiliary variable in multiple imputation (MI).
Study Design and Setting
Analytical investigation, simulation study, and data from a UK cohort.
Results
There was bias in the exposure OR when the probability of being a complete case was independently associated with the exposure and (continuous) outcome but this was generally small unless the association with the outcome was strong. Where exposure and (continuous) outcome interacted in their effect on this probability, the bias was large, particularly at high levels of missing data. Inclusion of the auxiliary variable resulted in important bias reductions when this had high sensitivity and specificity.
Conclusion
The robustness of logistic regression to missing data is not maintained when the outcome is a binary version of an underlying continuous measure, but the bias will be small unless the association between the continuous outcome and missingness is strong.
To investigate whether a complete case logistic regression gives a biased estimate of the exposure odds ratio (OR) if missingness depends on a continuous outcome, but a binary version is used for analysis; to examine whether any bias could be reduced by including a misclassified form of the incomplete outcome as an auxiliary variable in multiple imputation (MI).
Study Design and Setting
Analytical investigation, simulation study, and data from a UK cohort.
Results
There was bias in the exposure OR when the probability of being a complete case was independently associated with the exposure and (continuous) outcome but this was generally small unless the association with the outcome was strong. Where exposure and (continuous) outcome interacted in their effect on this probability, the bias was large, particularly at high levels of missing data. Inclusion of the auxiliary variable resulted in important bias reductions when this had high sensitivity and specificity.
Conclusion
The robustness of logistic regression to missing data is not maintained when the outcome is a binary version of an underlying continuous measure, but the bias will be small unless the association between the continuous outcome and missingness is strong.
| Original language | English |
|---|---|
| Pages (from-to) | 33-41 |
| Number of pages | 9 |
| Journal | Journal of Clinical Epidemiology |
| Volume | 154 |
| Early online date | 1 Dec 2022 |
| DOIs | |
| Publication status | Published - 1 Feb 2023 |
Bibliographical note
Funding Information:Funding: This work was supported by the Medical Research Council (MR/L012081). The UK Medical Research Council and the Wellcome Trust (Grant ref: 217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. Data collection is funded from a range of sources. KT and RC work in the MRC Integrative Epidemiology Unit which receives funding from the UK Medical Research Council and the University of Bristol (MC_UU_00011/3). JB was supported by a UK Medical Research Council grant (MR/T023953/1). JM is partly funded by the National Institute for Health Research Collaboration West (NIHR ACR West) at University Hospitals Bristol and Weston NHS Foundation Trust, UK.
Publisher Copyright:
© 2022 The Author(s)
Fingerprint
Dive into the research topics of 'Complete case logistic regression with a dichotomised continuous outcome led to biased estimates'. Together they form a unique fingerprint.Projects
- 1 Finished
-
Using linked health and administrative data to reduce bias due to missing data and measurement error in observational research
Cornish, R. P. (Principal Investigator)
1/04/14 → 31/03/19
Project: Research