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BMC Medical Research Methodology
Published in: BMC Medical Research Methodology 1/2017

Open Access 01-12-2017 | Research article

On the impact of nonresponse in logistic regression: application to the 45 and Up study

Authors: Joanna J. J. Wang, Mark Bartlett, Louise Ryan

Published in: BMC Medical Research Methodology | Issue 1/2017

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Abstract

Background

In longitudinal studies, nonresponse to follow-up surveys poses a major threat to validity, interpretability and generalisation of results. The problem of nonresponse is further complicated by the possibility that nonresponse may depend on the outcome of interest. We identified sociodemographic, general health and wellbeing characteristics associated with nonresponse to the follow-up questionnaire and assessed the extent and effect of nonresponse on statistical inference in a large-scale population cohort study.

Methods

We obtained the data from the baseline and first wave of the follow-up survey of the 45 and Up Study. Of those who were invited to participate in the follow-up survey, 65.2% responded. Logistic regression model was used to identify baseline characteristics associated with follow-up response. A Bayesian selection model approach with sensitivity analysis was implemented to model nonignorable nonresponse.

Results

Characteristics associated with a higher likelihood of responding to the follow-up survey include female gender, age categories 55–74, high educational qualification, married/de facto, worked part or partially or fully retired and higher household income. Parameter estimates and conclusions are generally consistent across different assumptions on the missing data mechanism. However, we observed some sensitivity for variables that are strong predictors for both the outcome and nonresponse.

Conclusions

Results indicated in the context of the binary outcome under study, nonresponse did not result in substantial bias and did not alter the interpretation of results in general. Conclusions were still largely robust under nonignorable missing data mechanism. Use of a Bayesian selection model is recommended as a useful strategy for assessing potential sensitivity of results to missing data.
Appendix
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Metadata
Title
On the impact of nonresponse in logistic regression: application to the 45 and Up study
Authors
Joanna J. J. Wang
Mark Bartlett
Louise Ryan
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Research Methodology / Issue 1/2017
Electronic ISSN: 1471-2288
DOI
https://doi.org/10.1186/s12874-017-0355-z

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