Meeting the Assumptions of Inverse-Intensity Weighting for Longitudinal Data Subject to Irregular Follow-Up: Suggestions for the Design and Analysis of Clinic-Based Cohort Studies
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Eleanor M. Pullenayegum
Abstract
Clinic-based cohort studies enroll patients on first being admitted to the clinic, and follow them as part of usual care, with interest being in the marginal mean of the outcome process. As the required frequency of follow-up varies among patients, these studies often feature irregular visit times, with no two patients sharing a visit time. Inverse-intensity weighting has been developed to handle this, however it requires that the visit process be conditionally independent of the outcome given the observed history. When patients schedule visits in response to changes in their health (for example a disease flare), the conditional independence assumption is no longer plausible, leading to biased results. We suggest additional information that can be collected to ensure that conditional independence holds, and examine how this might be used in the analysis. This allows clinic-based cohort studies to be used to determine longitudinal outcomes without incurring bias due to irregular follow-up.
Funding statement: EMP received operating funds from the Natural Sciences and Engineering Research Council, and is supported by a New Investigator Award from the Canadian Institutes of Health Research. The funding agreements ensured the author’s independence in designing the study, interpreting the data, writing, and publishing the report.
Declarations of interest: none.
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Articles in the same Issue
- Editorial
- The mean prevalence
- Research Articles
- Heterogeneous indirect effects for multiple mediators using interventional effect models
- Sleep habits and their association with daytime sleepiness among medical students of Tanta University, Egypt
- Population attributable fractions for continuously distributed exposures
- A real-time search strategy for finding urban disease vector infestations
- Disease mapping models for data with weak spatial dependence or spatial discontinuities
- A comparison of cause-specific and competing risk models to assess risk factors for dementia
- A simple index of prediction accuracy in multiple regression analysis
- A comparison of approaches for estimating combined population attributable risks (PARs) for multiple risk factors
- Posterior predictive treatment assignment methods for causal inference in the context of time-varying treatments
- Random effects tumour growth models for identifying image markers of mammography screening sensitivity
- Extrapolating sparse gold standard cause of death designations to characterize broader catchment areas
- Extending balance assessment for the generalized propensity score under multiple imputation
- Regression analysis of unmeasured confounding
- The Use of Logic Regression in Epidemiologic Studies to Investigate Multiple Binary Exposures: An Example of Occupation History and Amyotrophic Lateral Sclerosis
- Meeting the Assumptions of Inverse-Intensity Weighting for Longitudinal Data Subject to Irregular Follow-Up: Suggestions for the Design and Analysis of Clinic-Based Cohort Studies
