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Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models.

Sabz Ali1, Amjad Ali1, Sajjad Ahmad Khan2

  • 1Department of Statistics, Islamia College University, Peshawar, Pakistan.

Computational and Mathematical Methods in Medicine
|October 18, 2016
PubMed
Summary
This summary is machine-generated.

Maximum Likelihood (ML) and Penalized Quasilikelihood (PQL) methods perform similarly for multilevel ordinal logistic models with five outcome categories. Both estimation methods require at least 50 groups for adequate statistical power.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Multilevel models are frequently used in biomedical research with nested data, such as patients within doctor groups.
  • Ordinal outcome variables, representing categories like disease severity (mild, severe, extremely severe), are common in these models.

Purpose of the Study:

  • To compare the performance of Maximum Likelihood (ML) and Penalized Quasilikelihood (PQL) estimation methods for multilevel cumulative logit models with ordinal outcomes.
  • To determine the sample size requirements for achieving adequate statistical power with these methods.

Main Methods:

  • Simulation study comparing ML and PQL estimation in multilevel cumulative logit models.
  • Analysis focused on three-category and five-category ordinal outcome variables.
  • Power analysis to determine group sample size requirements.

Main Results:

  • For three-category ordinal outcomes, ML estimation outperformed PQL.
  • For five-category ordinal outcomes, PQL performed comparably to ML, with slightly higher power observed for PQL.
  • A minimum of 50 groups is necessary for both ML and PQL methods to achieve a power exceeding 0.80.

Conclusions:

  • The choice between ML and PQL methods for multilevel ordinal logistic models depends on the number of outcome categories.
  • Sample size is a critical factor for achieving sufficient power in these models, with at least 50 groups recommended.
  • Both methods are viable, but PQL may offer a slight advantage in power for models with more outcome categories.