ReviewBMC medical research methodology2020
A Bayesian natural cubic B-spline varying coefficient method for non-ignorable dropout.
Review in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Statistical Methods for Binary Outcomes Adjusting for Outcome Dependent Sampling in Longitudinal Studies with Nonignorable Dropout.bioRxiv : the preprint server for biology · 2025Article
- The Constrained Disorder Principle Overcomes the Challenges of Methods for Assessing Uncertainty in Biological Systems.Journal of personalized medicine · 2024Review
- Statistical methods for clinical trials interrupted by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic: A review.Statistical methods in medical research · 2024Review
- An adaptive enrichment design using Bayesian model averaging for selection and threshold-identification of predictive variables.Biometrics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundDropout is a common problem in longitudinal clinical trials and cohort studies, and is of particular concern when dropout occurs for reasons that may be related to the outcome of interest. This paper reviews common parametric models to account for dropout and introduces a Bayesian semi-parametric varying coefficient model for exponential family longitudinal data with non-ignorable dropout.
methodsTo demonstrate these methods, we present results from a simulation study and estimate the impact of drug use on longitudinal CD4
resultsIn simulation studies, we show that semi-parametric methods reduce bias and mean squared error when parametric model assumptions are violated. In analyses of the Women's Interagency HIV Study data, we find important differences in estimates of changes in CD4
conclusionsNon-ignorable dropout is an important consideration when analyzing data from longitudinal clinical trials and cohort studies. While methods that account for non-ignorable dropout must make some unavoidable assumptions that cannot be verified from the observed data, many methods make additional parametric assumptions. If these assumptions are not met, inferences can be biased, making more flexible methods with minimal assumptions important.
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Registered trials
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