Choice of knee cartilage thickness change metric for different treatment goals in efficacy studies
| Autor: | Felix Eckstein, Robert J. Buck, Marie-Pierre Hellio Le Graverand, Wolfgang Wirth |
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| Rok vydání: | 2017 |
| Předmět: |
Cartilage
Articular medicine.medical_specialty Knee Joint Osteoarthritis Treatment goals 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Rheumatology Statistics medicine Humans Longitudinal Studies skin and connective tissue diseases Extreme value theory 030203 arthritis & rheumatology business.industry Cartilage Osteoarthritis Knee medicine.disease Treatment efficacy Knee cartilage Surgery Anesthesiology and Pain Medicine medicine.anatomical_structure Sample size determination Case-Control Studies Disease Progression Female sense organs Metric (unit) business Algorithms Follow-Up Studies |
| Zdroj: | Seminars in arthritis and rheumatism. 47(3) |
| ISSN: | 1532-866X |
| Popis: | Introduction In knee osteoarthritis, local increase and decrease in cartilage thickness has been observed over short time intervals. Hence, averaging cartilage change across large regions may not capture the complexity of structural alterations in disease progression. This study aims to examine the relative performance of different metrics of cartilage thickness change for different clinical studies scenarios. Materials and methods Metrics for assessing cartilage thickness change were characterized by conventional measures of change versus absolute values (the magnitude) of change, and by different methods of summarizing change over (sub-) regions. Sample sizes for these metrics were derived for 6–24-month observation periods, and for different treatment efficacies. Treatment effects were derived from an observational trial with 6-, 12-, and 24-month follow-up, ranging from slowing cartilage loss to stimulating cartilage growth. Results Projected sample sizes ranged from 10 to >10,000 patients/arm (median = 164), depending on metric choice, treatment efficacy, and observation period. The smallest sample sizes for metrics using magnitude of change typically were half the size of those using conventional measures of change. Extreme values, e.g., minimum change or average of last four-ordered values of absolute change, required smaller sample sizes than metrics averaging over one or more regions. Conclusions Metrics using extreme magnitudes of change were most efficient in detecting differences between treatment and placebo, i.e., involved the smallest sample sizes across different DMOAD study lengths and treatment efficacies. Ancillary metrics can be used to clarify whether differences between treatment and placebo indicate structural benefit when needed. |
| Databáze: | OpenAIRE |
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