Tompsett, Daniel;
Zylbersztejn, Ania;
Hardelid, Pia;
De Stavola, Bianca;
(2022)
Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization due to Infant Respiratory Illness.
American Journal of Epidemiology
10.1093/aje/kwac202.
(In press).
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Abstract
Target trial emulation (TTE) applies the principles of randomised controlled trials to the causal analysis of observational datasets. On challenge that is rarely considered in TTE is the sources of bias that may arise if the variables involved in the definition of eligibility into the trial are missing. We highlight patterns of bias that might arise when estimating the causal effect of a point exposure when restricting the target trial (TT) to individuals with complete eligibility data. Simulations consider realistic scenarios where the variables affecting eligibility modify the causal effect of the exposure and are Missing at Random (MAR) or Missing Not at Random (MNAR). We discuss multiple means to address these patterns of bias, namely, (i) controlling for the collider bias induced by the missing dataon eligibility, and (ii) imputing the missing values of the eligibility variables prior to selection into the TT. Results are compared to when TTE is performed ignoring the impact of missing eligibility. A study of Palivizumab, a monoclonal antibody recommended for the prevention of respiratory hospital admissions due to Respiratory Synctial Virus in high risk infants, is used for illustrations.
| Type: | Article |
|---|---|
| Title: | Target Trial Emulation and Bias Through Missing Eligibility Data: An Application to a Study of Palivizumab for the Prevention of Hospitalization due to Infant Respiratory Illness |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/aje/kwac202 |
| Publisher version: | https://doi.org/10.1093/aje/kwac202 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | Target Trial Emulation, Eligibility, Missing Data, Multiple Imputation, Average Causal Effect |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10162622 |
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