BACKGROUND: To date, clinical trials have relied almost exclusively on the statistical significance of changes in scores from outcome measures in interpreting the effectiveness of treatment interventions. It is becoming increasingly important, however, to determine the clinical rather than statistical significance of these change scores. OBJECTIVE: To determine cutoff values for change scores that distinguish patients who have clinically improved from those who have not. METHOD: Data were obtained from 165 back and 100 neck patients undergoing chiropractic treatment. Patients completed the Bournemouth Questionnaire (BQ) before treatment and the BQ and Patient's Global Impression of Change (PGIC) scale after treatment. Three statistical methods were applied to individual change scores on the BQ. These were (1) the Reliable Change Index (RCI); (2) the effect size (ES); and (3) the raw and percentage change scores. The PGIC scale was used as the "gold standard" of clinically significant change. RESULTS: The RCI, using the cutoff value of >1.96, appropriately identified clinical improvement in back patients but not in neck patients. An individual ES of approximately 0.5 had the highest sensitivity and specificity in distinguishing back and neck patients who had undergone clinically significant improvement from those who had not. In terms of raw score changes, percentage BQ change scores [(raw change score/baseline score) x 100] of 47% and 34% were identified as having the highest sensitivity and specificity in distinguishing clinically significant improvement from nonimprovement in back and neck patients, respectively. CONCLUSION: This study provides a methodological framework for identifying clinically significant change in patients. This approach has important implications in providing clinically relevant information about the effect of a treatment intervention in an individual patient.
BACKGROUND: To date, clinical trials have relied almost exclusively on the statistical significance of changes in scores from outcome measures in interpreting the effectiveness of treatment interventions. It is becoming increasingly important, however, to determine the clinical rather than statistical significance of these change scores. OBJECTIVE: To determine cutoff values for change scores that distinguish patients who have clinically improved from those who have not. METHOD: Data were obtained from 165 back and 100 neck patients undergoing chiropractic treatment. Patients completed the Bournemouth Questionnaire (BQ) before treatment and the BQ and Patient's Global Impression of Change (PGIC) scale after treatment. Three statistical methods were applied to individual change scores on the BQ. These were (1) the Reliable Change Index (RCI); (2) the effect size (ES); and (3) the raw and percentage change scores. The PGIC scale was used as the "gold standard" of clinically significant change. RESULTS: The RCI, using the cutoff value of >1.96, appropriately identified clinical improvement in backpatients but not in neck patients. An individual ES of approximately 0.5 had the highest sensitivity and specificity in distinguishing back and neck patients who had undergone clinically significant improvement from those who had not. In terms of raw score changes, percentage BQ change scores [(raw change score/baseline score) x 100] of 47% and 34% were identified as having the highest sensitivity and specificity in distinguishing clinically significant improvement from nonimprovement in back and neck patients, respectively. CONCLUSION: This study provides a methodological framework for identifying clinically significant change in patients. This approach has important implications in providing clinically relevant information about the effect of a treatment intervention in an individual patient.
Authors: F Dámaso Fernández-Ginés; Manuel Cortiñas-Sáenz; Desirée Agudo-Ponce; Ana Navajas-Gómez de Aranda; José A Morales-Molina; Carmen Fernández-Sánchez; Francisco Sierra-García; Héctor Mateo-Carrasco Journal: Int Wound J Date: 2019-11-25 Impact factor: 3.315
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Authors: Matthew J Page; Joanne E McKenzie; Sally E Green; Dorcas E Beaton; Nitin B Jain; Mario Lenza; Arianne P Verhagen; Stephen Surace; Jessica Deitch; Rachelle Buchbinder Journal: J Clin Epidemiol Date: 2015-06-16 Impact factor: 6.437
Authors: Barbara S Koppel; John C M Brust; Terry Fife; Jeff Bronstein; Sarah Youssof; Gary Gronseth; David Gloss Journal: Neurology Date: 2014-04-29 Impact factor: 9.910
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