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Factors Associated With Attrition in a Multicenter Longitudinal Observational Study of Patients With Advanced Cancer
Pedro E Perez-Cruz1, Omar Shamieh2, Carlos Eduardo Paiva3
1Department of Internal Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile.
Context:
Attrition is common in longitudinal observational studies in palliative care. Few studies have examined predictors of attrition.
Objectives:
To identify patient characteristics at enrollment associated with attrition in palliative oncology outpatient setting.
Methods:
In this longitudinal observational study, advanced cancer patients enrolled in an outpatient multicenter study were assessed at baseline and two to five weeks later. We compared baseline characteristics between patients who returned for follow-up and those who dropped out.
Results:
Seven hundred forty-four patients were enrolled from Jordan, Brazil, Chile, Korea, and India. Attrition rate was 33%, with variation among countries (22%-39%; P = 0.023). In univariate analysis, baseline predictors for attrition were cognitive failure (odds ratio [OR] 1.23 per point in Memorial Delirium Assessment Scale; P < 0.01), functional status (OR 1.55 per 10-point decrease in Karnofsky Performance Status; P < 0.01), Edmonton Symptom Assessment Scale [ESAS] physical score (OR 1.03 per point; P < 0.01), ESAS emotional score (OR 1.05 per point; P < 0.01), and shorter duration between cancer diagnosis and palliative care referral in months (OR 0.89 per log; P = 0.028). In multivariate analysis, cognitive failure (OR 1.12 per point; P = 0.007), ESAS physical score (OR 1.18 per point; P = 0.027), functional status (OR 1.35 per 10-point decrease; P < 0.001), and shorter duration from cancer diagnosis (OR 0.86 per log; P = 0.01) remained independent predictors of attrition.
Conclusion:
Advanced cancer patients with cognitive failure, increased physical symptoms, poorer performance status, and shorter duration from cancer diagnosis were more likely to dropout. These results have implications for research design, patient selection, and data interpretation in longitudinal observational studies.
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