Analysis of recurrent events: a systematic review of randomised controlled trials of interventions to prevent falls
Bibliographic record
Abstract
RATIONALE: there are several well-developed statistical methods for analysing recurrent events. Although there are guidelines for reporting the design and methodology of randomised controlled trials (RCTs), analysis guidelines do not exist to guide the analysis for RCTs with recurrent events. Application of statistical methods that do not account for recurrent events may provide erroneous results when used to test the efficacy of an intervention. It is unknown what proportion of RCTs of falls prevention studies have utilised statistical methods that incorporate recurrent events. METHODS: we conducted a systematic review of RCTs of interventions to prevent falls in community-dwelling older persons. We searched Medline from 1994 to November 2006. We determined the proportion of studies that reported using three statistical methods appropriate for the analysis of recurrent events (negative binomial regression, Andersen-Gill extension of the Cox model and the WLW marginal model). RESULTS: fewer than one-third of 83 papers that reported falls as an outcome utilised any appropriate statistical method (negative binomial regression, Andersen-Gill extension of the Cox model and Cox marginal model) to analyse recurrent events and fewer than 15% utilised graphical methods to represent falls data. CONCLUSION: RCTs that have a recurrent event end-point should include an analysis appropriate for recurrent event data such as negative binomial regression, Andersen-Gill extension of the Cox model and/or the WLW marginal model. We recommend that researchers and clinicians seek consultation with a statistician with expertise in recurrent event methodology.
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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 | 0.117 | 0.375 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.015 |
| Bibliometrics | 0.040 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".