Evaluating the Utility of Linked Administrative Data for Nonresponse Bias Adjustment in a Piggyback Longitudinal Survey

Nearly all panel surveys suffer from unit nonresponse and the risk of nonresponse bias. Just as the analytic value of panel surveys increase with their length, so does cumulative attrition, which can adversely affect the representativeness of the resulting survey estimates. Auxiliary data can be useful for monitoring and adjusting for attrition bias, but traditional auxiliary sources have known limitations. We investigate the utility of linked-administrative data to adjust for attrition bias in a standard piggyback longitudinal design, where respondents from a preceding general population cross-sectional survey, which included a data linkage request, were recruited for a subsequent longitudinal survey. Using the linked-administrative data from the preceding survey, we estimate attrition biases for the first eight study waves of the longitudinal survey and investigate whether an augmented weighting scheme that incorporates the linked-administrative data reduces attrition biases. We find that adding the administrative information to the weighting scheme generally leads to a modest reduction in attrition bias compared to a standard weighting procedure and, in some cases, reduces variation in the point estimates. We conclude with a discussion of these results and remark on the practical implications of incorporating linked-administrative data in piggyback longitudinal designs.

Strategies to maximise study retention and limit attrition bias in a prospective cohort study of men reporting a history of injecting drug use released from prison: the prison and transition health study

Background There are significant challenges associated with studies of people released from custodial settings, including loss to follow-up in the community. Interpretation of findings with consideration of differences between those followed up and those not followed up is critical in the development of evidence-informed policies and practices. We describe attrition bias in the Prison and Transition Health (PATH) prospective cohort study, and strategies employed to minimise attrition. Methods PATH involves 400 men with a history of injecting drug use recruited from three prisons in Victoria, Australia. Four interviews were conducted: one pre-release (‘baseline’) and three interviews at approximately 3, 12, and 24 months post-release (‘follow-up’). We assessed differences in baseline characteristics between those retained and not retained in the study, reporting mean differences and 95% confidence intervals (95% CIs). Results Most participants (85%) completed at least one follow-up interview and 162 (42%) completed all three follow-up interviews. Retained participants were younger than those lost to follow-up (mean diff − 3.1 years, 95% CI -5.3, − 0.9). There were no other statistically significant differences observed in baseline characteristics. Conclusion The high proportion of participants retained in the PATH cohort study via comprehensive follow-up procedures, coupled with extensive record linkage to a range of administrative datasets, is a considerable strength of the study. Our findings highlight how strategic and comprehensive follow-up procedures, frequent contact with participants and secondary contacts, and established working relationships with the relevant government departments can improve study retention and potentially minimise attrition bias.

From Heartbreak to Heart Disease: A Narrative Review on Depression as an Adjunct to Cardiovascular Disease

Patients with cardiovascular disease (CVD) commonly have subclinical depression and are often delayed in their diagnosis. Literature suggests an increased association of depression and adverse cardiovascular events like myocardial infarction and heart failure. Prevalence of depression in developed countries is approximately 16.6%, and it confers higher cardiovascular mortality even after attrition bias and confounding factors are eliminated. Pharmacological and cognitive-behavioral therapy have been extensively studied, and are generally safe and effective in alleviating depressive symptoms in patients with CVD. However, their impact on cardiovascular outcomes is still unclear. Results of randomized controlled trials have shown antidepressants, especially selective serotonin reuptake inhibitors, to be safe and effective for healing a “broken heart.” This review outlines the prevalence of depression in patients with CVD, the pathophysiological mechanism causing cardiovascular events with depression, and a link between depression and CVD. There is a wealth of literature explaining the precursor of CVD in depression, and like all chronic diseases, inflammation seems to be the culprit in this case as well.

Who Returns? Understanding Varieties of Longitudinal Participation in MIDUS

Objectives: This study describes a major effort to reinstate dropouts from the MIDUS longitudinal study and compare baseline characteristics among subgroups of participants to better understand predictors of retention, attrition, and reinstatement. Methods: All living dropouts were contacted, and 651 reinstated participants were interviewed in person (31.4% response rate). Age, gender, education, marital status, parental status, and physical and mental health were compared among the following groups: longitudinal sample, reinstated sample, those fielded for reinstatement who did not return, and those who dropped out at the 2nd or 3rd wave. Results: Multivariate analyses revealed that reinstated participants were younger, male, unmarried, and less educated and had children at baseline compared to longitudinal participants. Reinstatement was unsuccessful among those with poorer mental health at baseline compared to longitudinal participants. Discussion: This study informs reinstatement efforts, adjustment for attrition bias, and use of post-baseline data to examine aging consequents of early life vulnerability.

Strategies to maximise study retention and limit attrition bias in a prospective cohort study of men reporting a history of injecting drug use released from prison: The Prison and Transition Health study

Background There are significant challenges associated with studies of people released from custodial settings, including loss to follow-up in the community. Interpretation of findings with consideration of differences between those followed up and those not followed up is critical in the development of evidence-informed policies and practices. We describe attrition bias in the Prison and Transition Health (PATH) prospective cohort study, and strategies employed to minimise attrition.Methods PATH involves 400 men with a history of injecting drug use recruited from three prisons in Victoria, Australia. Four interviews were conducted: one pre-release (‘baseline’) and three interviews at approximately 3, 12, and 24 months post-release (‘follow-up’). We assessed differences in baseline characteristics between those retained and not retained in the study, using two-sample tests of proportions and t-tests.Results Most participants (85%) completed at least one follow-up interview and 162 (42%) completed all three follow-up interviews. Retained participants were younger than those lost to follow-up (mean diff − 3.1 years, 95% CI -5.3, -0.9). There were no other statistically significant differences observed in baseline characteristics.Conclusion The high proportion of participants retained in the PATH cohort study via comprehensive follow-up procedures, coupled with extensive record linkage to a range of administrative datasets, is a considerable strength of the study. Our findings highlight how strategic and comprehensive follow-up procedures, frequent contact with participants and secondary contacts, and established working relationships with the relevant government departments can improve study retention and potentially minimise attrition bias.

Apparent sunk cost effect in rational agents

Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats have been reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments in detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.

Prediction of Attrition in Large Longitudinal Studies: Tree-based methods versus Multinomial Logistic Models

Identifying predictors of attrition is essential for designing longitudinal studies such that attrition bias can be minimised, and for identifying the variables that can be used as auxiliary in statistical techniques to help correct for non-random drop-out. This paper provides a comparative overview of predictive techniques that can be used to model attrition and identify important risk factors that help in its prediction. Logistic regression and several tree-based machine learning methods were applied to Wave 2 dropout in an illustrative sample of 5000 individuals from a large UK longitudinal study, Understanding Society. Each method was evaluated based on accuracy, AUC-ROC, plausibility of key assumptions and interpretability. Our results suggest a 10% improvement in accuracy for random forest compared to logistic regression methods. However, given the differences in estimation procedures we suggest that both models could be used in conjunction to provide the most comprehensive understanding of attrition predictors.