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Stroke aetiological classification reliability and effect on trial sample size: systematic review, meta-analysis and

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Inter-observer variability in stroke classification increases sample size requirements for clinical trials. Misclassification rates of 5% and 20% significantly inflated sample sizes needed to detect treatment effects in cardioembolic stroke studies.

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Area of Science:

  • Neurology
  • Clinical Trials
  • Biostatistics

Background:

  • Inter-observer variability in stroke aetiological classification impacts trial power and treatment effect estimation.
  • Misclassification of stroke aetiology can lead to underpowered studies and biased results.
  • Accurate stroke classification is crucial for designing effective clinical trials.

Purpose of the Study:

  • To model the effect of stroke aetiological misclassification on the required sample size in a hypothetical cardioembolic (CE) stroke trial.
  • To quantify the impact of varying misclassification rates on trial power and sample size calculations.
  • To assess the reliability of different stroke classification systems.

Main Methods:

  • Systematic review to assess inter-observer reliability of stroke classification systems.
  • Modeling the effect of misclassification in a hypothetical anticoagulant trial for CE stroke.
  • Using bootstrapping on data from the Virtual International Stroke Trials Archive to simulate misclassification effects.
  • Calculating required sample sizes for survival and stroke recurrence outcomes under different misclassification rates.

Main Results:

  • Inter-observer reliability varied from 'fair' to 'very good', suggesting 5% and 20% misclassification rates.
  • A 5% misclassification rate inflated sample size by 19.7% for survival outcomes and 20.5% for recurrence.
  • A 20% misclassification rate inflated sample size by 77.8% for survival outcomes and 93.8% for recurrence.

Conclusions:

  • Stroke aetiological classification systems exhibit significant inter-observer variability.
  • Misclassification due to this variability substantially increases the sample size needed for clinical trials.
  • Addressing classification reliability is essential to improve the efficiency and power of stroke research.