Contacting authors to retrieve individual patient data: study protocol for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Individual patient data (IPD) meta-analysis is considered the "gold standard" for exploring the effectiveness of interventions in different subgroups of patients. However, obtaining IPD is time-consuming and contact with the researchers responsible for the original trials is usually required. To date, there are no studies evaluating different strategies to optimize the process for retrieval of IPD from such researchers. Our aim is to examine the impact of providing incentives to the researchers responsible for the trials eligible for a meta-analysis to submit their IPD. METHODS/DESIGN: We updated our previously published systematic reviews for type 1 diabetes mellitus comparing long- and intermediate-acting insulin regimens (from January 2013 to June 2015) and for Alzheimer's dementia comparing cognitive enhancers (from January 2015 to May 2015). Eligible were randomized controlled trials (RCTs) fulfilling the eligibility criteria of the systematic reviews. We will randomly allocate authors of the reports of these RCTs into an intervention or control group. Those allocated to the intervention group will be contacted by email, mail, and phone, and will be asked to provide the IPD from their RCT and will be given a financial incentive. Those allocated to the control group will be contacted by email, mail, and phone, but will not receive a financial incentive. Our primary outcome will be the proportion of authors who provide the IPD. The secondary outcomes will be the time to return the dataset (defined as the period between the information request and the authors' response with the dataset), and completeness of data. We will compare the response rates in the two groups using the odds ratio and the corresponding 95 % confidence interval. We will also use binary logistic regression and cox regression analyses to examine whether different RCT characteristics, such as study size and sponsor information, influence the probability of providing IPD and the time needed to share the data. DISCUSSION: This study will determine whether a financial incentive affects response rates when seeking IPD from the original researchers. We will disseminate our findings in an open access scientific journal and present results at national and international conferences. TRIAL REGISTRATION: This trial is registered in Clinical Trials.gov, ID number NCT02569411 . Date of registration 5 October 2015.
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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 | 0.118 | 0.199 |
| Meta-epidemiology (narrow) | 0.009 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.149 | 0.035 |
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".