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

Wang, JJJ; Bartlett, M; Ryan, L

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

Wang, JJJ

Bartlett, M Ryan, L

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Publication Type:: Journal Article
Citation:: BMC Medical Research Methodology, 2017, 17 (1)
Issue Date:: 2017-05-08

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Field	Value	Language
dc.contributor.author	Wang, JJJ https://orcid.org/0000-0003-0088-7003	en_US
dc.contributor.author	Bartlett, M	en_US
dc.contributor.author	Ryan, L https://orcid.org/0000-0001-5957-2490	en_US
dc.date.available	2017-04-27	en_US
dc.date.issued	2017-05-08	en_US
dc.identifier.citation	BMC Medical Research Methodology, 2017, 17 (1)	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125194
dc.description.abstract	© 2017 The Author(s). 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.	en_US
dc.relation	http://purl.org/au-research/grants/arc/CE140100049
dc.relation.ispartof	BMC Medical Research Methodology	en_US
dc.relation.isbasedon	10.1186/s12874-017-0355-z	en_US
dc.subject.classification	General & Internal Medicine	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Logistic Models	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Longitudinal Studies	en_US
dc.subject.mesh	Follow-Up Studies	en_US
dc.subject.mesh	Aged	en_US
dc.subject.mesh	Middle Aged	en_US
dc.subject.mesh	Female	en_US
dc.subject.mesh	Male	en_US
dc.subject.mesh	Surveys and Questionnaires	en_US
dc.title	On the impact of nonresponse in logistic regression: Application to the 45 and Up study	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	17	en_US
utslib.for	1117 Public Health and Health Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

Abstract:

© 2017 The Author(s). 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.

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http://hdl.handle.net/10453/125194