Optimal allocations for two treatment comparisons within the proportional odds cumulative logits model

• Published in: PloS one Vol. 16; no. 4; p. e0250119
• Main Author: Moerbeek, Mirjam
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.04.2021; Public Library of Science (PLoS)
• Subjects: Allocations; Categories; Comparative analysis; Cost allocation; Costs; Design optimization; Drafting software; Logits; Medicine and Health Sciences; Physical Sciences; Research and Analysis Methods; Statistical analysis; Survival analysis; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0250119

This paper studies optimal treatment allocations for two treatment comparisons when the outcome is ordinal and analyzed by a proportional odds cumulative logits model. The variance of the treatment effect estimator is used as optimality criterion. The optimal design is sought so that this variance is minimal for a given total sample size or a given budget, meaning that the power for the test on treatment effect is maximal, or it is sought so that a required power level is achieved at a minimal total sample size or budget. Results are presented for three, five and seven ordered response categories, three treatment effect sizes and a skewed, bell-shaped or polarized distribution of the response probabilities. The optimal proportion subjects in the intervention condition decreases with the number of response categories and the costs for the intervention relative to those for the control. The relation between the optimal proportion and effect size depends on the distribution of the response probabilities. The widely used balanced design is not always the most efficient; its efficiency as compared to the optimal design decreases with increasing cost ratio. The optimal design is highly robust to misspecification of the response probabilities and treatment effect size. The optimal design methodology is illustrated using two pharmaceutical examples. A Shiny app is available to find the optimal treatment allocation, to evaluate the efficiency of the balanced design and to study the relation between budget or sample size and power.