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Assessing variability and uncertainty in orthopedic randomized controlled trials.

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Low statistical power in orthopedic randomized controlled trials (RCTs) is common, with poor reporting of variability and uncertainty parameters. This often leads to inconclusive results, impacting clinical decision-making.

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Area of Science:

  • Orthopedic Surgery
  • Clinical Trials
  • Biostatistics

Background:

  • Low statistical power is a persistent issue in clinical medicine, particularly in orthopedics.
  • This lack of power contributes to high uncertainty and wide confidence intervals (CI) in study findings.
  • It necessitates an evaluation of power calculation reporting and its correspondence with observed data in orthopedic trials.

Purpose of the Study:

  • To assess the reporting of power calculation components in orthopedic randomized controlled trials (RCTs).
  • To evaluate the correspondence between estimated and observed data for key variability and uncertainty parameters (standard deviation [SD], mean difference [MD], CI).
  • To identify factors contributing to low statistical power in orthopedic research.

Main Methods:

  • Systematic review of 160 orthopedic RCTs published between 2016-2017 in 8 major journals.
  • RCTs with 1:1 allocation and a continuous primary outcome were included.
  • Assessment of reported estimated SD, MD, and observed SD, MD, and CI for the primary outcome.

Main Results:

  • 58% of studies reported estimated SD, and 86% reported estimated MD in power calculations.
  • The median ratio of estimated to observed SD was 1.0 (IQR -0.76 to 1.32) in 43% of studies.
  • Only 31% of studies reported the CI of MD; 42% of negative studies had estimated MD within the observed CI.

Conclusions:

  • Key parameters of data variability and uncertainty are poorly reported in orthopedic RCTs' power analyses and results.
  • Overly optimistic estimates of mean difference (MD) may contribute to low power.
  • Inconclusive results, indicated by overlapping confidence intervals, were common, highlighting the need for improved statistical practices.