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Do alternative methods for analysing count data produce similar estimates? Implications for meta-analyses.

Peter Herbison1, M Clare Robertson2, Joanne E McKenzie3

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Different statistical methods for analyzing count outcomes in randomized trials yield similar results, except when event rates are high. Dichotomizing data or analyzing time to first event can obscure treatment differences, impacting meta-analysis precision.

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Epidemiology

Background:

  • Randomized trials frequently involve count outcomes (e.g., falls, exacerbations).
  • These outcomes are analyzed using diverse methods: counts, continuous, or dichotomized data.
  • The comparability of intervention effect estimates across these methods for meta-analysis is unclear.

Purpose of the Study:

  • To assess if various analytical methods for count outcomes produce sufficiently similar intervention effect estimates for meta-analysis.
  • To compare the performance of different statistical approaches under varying conditions of overdispersion and event rates.

Main Methods:

  • Simulated data for 10,000 trials with varying overdispersion, event rates, and effect sizes.
  • Analyzed simulated data using nine methods: rate ratio, Poisson, negative binomial regression, dichotomized risk ratio, time to first event survival analyses, ratio of means, and ratio of medians.
  • Applied similar analyses to individual patient data from eight fall prevention trials.

Main Results:

  • All methods yielded similar effect sizes when no treatment difference existed.
  • Moderate differences were also similar, except when events became more common.
  • High event rates led to discrepancies: dichotomized risk ratios and time-to-first-event hazard ratios differed from negative binomial rate ratios.
  • Method choice impacted estimate precision, potentially affecting pooled effects and heterogeneity.

Conclusions:

  • Dichotomizing outcomes or analyzing time to first event at high event rates reduces information on treatment differences.
  • Similar results are obtained otherwise, but precision differences can affect meta-analysis.
  • Further research is needed on how varying variances influence confidence intervals in pooled estimates.