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05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Estimating risk from underpowered, but statistically significant, studies: was APPROVe on TARGET?
1School of Pharmacy, The University of Queensland, Brisbane, Australia. a.lacaze@uq.edu.au
Journal of Clinical Pharmacy and Therapeutics
|October 26, 2011
Summary
Poor statistical power can inflate effect size estimates in significant trial results, a bias termed "significant-result bias." Trial simulations can quantify this bias, offering more accurate interpretations of underpowered studies.
Area of Science:
- Clinical Trials
- Biostatistics
- Medical Research Methodology
Background:
- Statistical power is crucial for interpreting non-significant results.
- The impact of low statistical power on inflated effect size estimates in significant findings is less recognized.
Purpose of the Study:
- To introduce and define "significant-result bias."
- To quantify the inflation of effect size estimates due to poor statistical power in significant results using trial simulations.
Main Methods:
- Utilized trial simulations to model and estimate "significant-result bias."
- Applied simulation methods to assess bias in the APPROVe trial's thrombotic event rate.
Main Results:
- Demonstrated that poor statistical power can lead to inflated effect size estimates when results are statistically significant.
- Quantified potential significant-result bias in the APPROVe trial's thrombotic event data.
Conclusions:
- Significant-result bias can lead to overestimation of treatment effects in underpowered studies with significant findings.
- Trial simulations offer a quantitative method to assess significant-result bias when independent evidence on effect size is available.
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