Дизайны экспериментальных исследований биоэквивалентности: с повторными измерениями, перекрёстные, с эффектом переноса и латинского квадрата

A repeated measures design is a randomized block design where each subject receives every treatment, enabling temporal comparisons. This approach reduces variability by using the same subject as a block, facilitating precise within-subject treatment comparisons while minimizing inter-subject differences and improving statistical sensitivity.

A cross-over design is an economical approach where the same patient group sequentially receives various treatments. It provides precision for comparing treatments by using subjects as their own controls, reducing the sample size needed while maintaining statistical power for detecting treatment differences.

A carry-over effect occurs when residual effects from prior treatments distort results in subsequent treatment periods. A wash-out period ensures no residual effects from earlier treatments influence subsequent measurements, maintaining the integrity of treatment comparisons and preventing confounding from previous drug exposure.

A Latin square design is particularly effective for comparing three or more treatments. Each subject receives all treatments while minimizing inter-subject and temporal variations. Its advantages include precision, usefulness in preliminary studies, emphasis on formulation variables, and robust comparative data across multiple treatment conditions.

Latin square designs face two primary challenges: limited degrees of freedom for experimental error when studying fewer treatments, and complex randomization procedures that require careful planning. These constraints can complicate study execution and reduce statistical power for detecting experimental error, particularly in smaller studies.

Randomized block designs, including repeated measures and Latin square approaches, use subjects as blocks to control for inter-subject variability. By having the same subject receive multiple treatments, these designs account for individual differences, improving precision and enabling more sensitive detection of treatment differences.

The choice of design depends on specific study requirements and available resources. Repeated measures designs suit temporal comparisons, cross-over designs offer economy with multiple treatments, and Latin square designs excel for three or more treatments. Researchers must balance precision needs, sample size constraints, and practical feasibility when selecting designs.