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Statistical recommendations for count, binary, and ordinal data in rare disease cross-over trials.

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Summary

Statistical methods for rare disease crossover trials are limited. This study compared various methods for count, binary, and ordinal outcomes, finding no single best approach but highlighting specific strengths for different scenarios.

Keywords:
Cross-overEpidermolysis bullosa simplexGEE-like semiparametric modelGeneralized pairwise comparison (GPC)GuidanceModel averagingNparLDRare DiseasesRecommendationRepeated measuresSmall sample size

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Rare Diseases

Background:

  • Statistical method recommendations for rare disease trials, particularly crossover designs, are limited.
  • An international consortium of statisticians compared state-of-the-art methodologies using an illustrative dataset from epidermolysis bullosa research.
  • The comparison focused on count, binary, and ordinal outcome variables to inform recommendations.

Purpose of the Study:

  • To compare various statistical methodologies for rare disease crossover trials.
  • To provide evidence-based recommendations for selecting appropriate statistical methods based on outcome variable type and study goals.
  • To identify methods that optimize statistical power and account for specific design features like period and carry-over effects.

Main Methods:

  • Parametric methods (model averaging).
  • Semiparametric methods (generalized estimating equations type [GEE-like]).
  • Nonparametric methods (generalized pairwise comparisons [GPC] and a marginal model via nparLD).

Main Results:

  • No single method uniformly outperformed others across all outcome types.
  • The prioritized unmatched GPC method demonstrated high power, particularly for prioritizing clinically relevant time points.
  • Model averaging and GEE-like methods showed favorable results for binary outcomes and properly accounted for period/carry-over effects.
  • The nonparametric marginal model achieved high power for ordinal outcomes, even with small sample sizes, and accommodated longitudinal/interaction effects.

Conclusions:

  • The optimal statistical method depends on balancing statistical power, accounting for crossover/period/carry-over effects, and prioritizing clinically relevant time points.
  • Specific methods like GPC, model averaging, GEE-like, and nonparametric marginal models offer distinct advantages in different rare disease trial scenarios.
  • Further research and clear guidelines are needed to support robust statistical analysis in rare disease crossover trials.