Medication adherence with fixed-dose versus free-equivalent combination therapies: Systematic review and meta-analysis
Bibliographic record
Abstract
Objective: We conducted a large-scale meta-analysis and subgroup analysis to compare the effect of fixed-dose combination (FDC) therapy with that of free-equivalent combination (FEC) therapy on medication adherence. Methods: Studies published in Web of Science, PubMed, Cochrane Library, ScienceDirect, and Embase up to May 2022 were identified according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The primary assessed outcomes were the medication possession ratio (MPR) and proportion of days covered (PDC). We investigated the probability of being adherent to the prescribed treatment (MPR or PDC ≥80%) or the average estimate of these two parameters. Studies reporting such results were included in this meta-analysis. The summary measures were reported as the risk ratio (RR) and the weighted mean difference (MD) with 95% of confidence interval (CI) using the random-effects model of DerSimonian and Laird. The quality of the cohort studies was assessed using the Newcastle-Ottawa scale. Results: Of the 1,814 screened studies, 61 met the predefined inclusion criteria. The meta-analysis of the results showed that compared to FEC, FDC significantly improved the medication compliance of patients by 1.29 times (95% CI:1.23–1.35, p < 0.00001). I 2 of 99% represent high heterogeneity across studies. The mean difference in medication adherence between FDC and FEC was 0.10 (95% CI: 0.06–0.14, p < 0.00001) with an I 2 estimate of 100%. Subgroup analyses were performed for studies that reported adherence outcomes according to disease type, period of evaluation and compliance indicators. A sensitivity analysis was conducted to exclude the results of low-quality studies, as well as studies in which there was ambiguity in the method of calculating the estimator. Conclusion: Analysis of the assessed parameters for the intention-to-treat and subgroup populations suggests that FDC can improve adherence to treatment and its advantages over FEC may increase over time. Further research is needed to better understand how medical conditions affect the impact of reduced pill burden on adherence, particularly in diseases other than cardiovascular disease and type 2 diabetes mellitus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".