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Statistical analysis of efficacy in falls prevention trials
M Clare Robertson1, A John Campbell, Peter Herbison
1Department of Medical and Surgical Sciences, University of Otago Medical School, P.O. Box 913, Dunedin, New Zealand. clare.robertson@stonebow.otago.ac.nz
Negative binomial regression models are recommended for analyzing falls prevention trials. These models effectively handle recurrent fall events and are easier to use than Cox regression, improving intervention efficacy comparisons.
Area of Science:
- Gerontology
- Biostatistics
- Public Health
Background:
- Inconsistent statistical methods hinder the evaluation of randomized controlled trials (RCTs) for elderly falls prevention programs.
- This variability complicates direct comparisons of intervention effectiveness.
Purpose of the Study:
- To compare the efficacy of statistical models for analyzing falls data from RCTs.
- To identify the most suitable statistical approach for evaluating falls prevention interventions.
Main Methods:
- Utilized raw data from two RCTs of a home exercise program.
- Compared Andersen-Gill and marginal Cox regression models with negative binomial regression.
- Models accounted for recurrent, non-normally distributed falls, adjusted for follow-up time, and allowed covariate inclusion.
Main Results:
- All three models yielded similar efficacy results in one trial.
- Cox regression models violated assumptions in the second trial.
- Negative binomial regression models demonstrated greater ease of use.
Conclusions:
- Negative binomial regression models are recommended for evaluating falls prevention programs.
- This approach offers a robust and user-friendly method for analyzing recurrent event data in falls research.
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