Group-Based Trajectory Modelling (GBTM) To Assess the Effect of Medication Adherence on Health-Related Outcomes: A Protocol for A Systematic Review
Bibliographic record
Abstract
Abstract Background: The Group-based trajectory modelling (GBTM) method is increasingly used in pharmacoepidemiologic studies to describe medication adherence trajectories over time. However, assessing the effects of these medication adherence trajectories on health-related outcomes remains challenging. The purpose of this review is to describe studies assessing the effects of medication adherence trajectories estimated by the GBTM method on health-related outcomes. Methods: We will conduct a systematic review according to the recommendations of the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. We will search in the following databases: PubMed, Embase, PsycINFO, Web of Science, CINAHL, and Cochrane database up to April 1st, 2021. Two reviewers will independently select articles and extract data. Discrepancies at every step will be resolved through discussion, and consensus will be reached for all disagreed articles. A third reviewer will act as a referee if needed. We will use tables to synthesize the modalities used to estimate medication adherence trajectories and the effect of adherence trajectories on health-related outcomes. We will identify the types of health-related outcomes studied and how they are defined, the statistical models used, the effect measure yield, and how medication adherence trajectories have been incorporated in the model. We will also review the limitations and biases reported by the authors and their attempts to mitigate them. We will provide a narrative synthesis.Discussion: This review will provide a clear view of the strategies and methods used in medication adherence research to estimate the effects of adherence trajectories on different health-related outcomes. A thorough exploration of how GBTM is used for this specific purpose could represent the first crucial steps towards optimizing the utilization of this method in adherence studies. Systematic review registration: Prospero CRD42021213503.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.152 | 0.198 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".