Assessing the effectiveness of adding gliclazide or pioglitazone in patients with type 2 diabetes using post-market observational data
Bibliographic record
Abstract
Background: Both observational and experimental studies have shown substantial differences between pre-market evidence of efficacy and post-market evaluation of effectiveness, which highlights the need to evaluate both efficacy and effectiveness of new therapies. However, in diabetes, the direct comparison between effectiveness and efficacy on glycemic control is challenging given the non-systematic timing of the measurement of glycated hemoglobin (HbA1c) in real-life practice Objectives: To estimate the effectiveness and efficacy of adding pioglitazone or gliclazide to metformin in an adult population with type 2 diabetes using novel methods to estimate glycemic control and compare it to results obtained in an efficacy randomized controlled trial (RCT). The secondary aim is to examine the effectiveness of these medications in a subgroup of population who are usually excluded from efficacy trials, but in whom the medication is still prescribed: patients older than 75 years old. Methods: A retrospective cohort study was conducted using a large UK anonymised primary care research database, the Clinical Practice Research Datalink, to examine the effectiveness of pioglitazone and metformin compared with gliclazide and metformin. The population was selected to match the inclusion and exclusion criteria from a published RCT. HbA1c change between week 0 and 52, estimated using each patient's values during the follow-up by functional principal component analysis, was compared to the RCT results. Sensitivity analyses were conducted to assess the impact of limiting the analysis to patients whose medication dosage and adherence were similar to that achieved in the RCT. The same method was used to evaluate the comparative effectiveness in those over 75 years old who were excluded from the RCT.Results: The pioglitazone or gliclazide groups had a similar HbA1c change (pioglitazone -0.53%, 95%CI -0.69, -0.37 compared to gliclazide -0.46%, 95%CI -0.55, -0.36; difference between groups -0.08, 95% CI - 0.27, 0.10), which was less than the change observed in the RCT (-0.99% and -1.01% respectively). However, when limited to the subgroup of patients with equivalent medication dosage and adherence to that achieved in the RCT, our results approached those from the RCT: -1.11% (95%CI -1.52, -0.69,) and -0.69% (95%CI -0.97, -0.41) respectively. For patients over the age of 75, the addition of pioglitazone led to a change in HbA1c of -0.62% (95%CI -1.30, 0.07) compared to gliclazide -0.19% (95%CI -0.39, 0.00); difference between groups -0.24% (95%CI -0.73, 0.25).Conclusion: The addition of either pioglitazone or gliclazide to metformin resulted in similar reduction in the HbA1c by 0.5%, and approached results obtained in the RCT when restricted to patients with comparable adherence and medication dosage. Similar results were obtained for those over the age of 75, but were non-conclusive given the small sample size.
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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 | 0.052 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".