Daphne Z Kounali1, Katherine S Button2, Glyn Lewis3, Anthony E Ades4. 1. School of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, 39 Whatley Road, Bristol BS8 2PS, UK. Electronic address: daphne.kounali@bristol.ac.uk. 2. School of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, 39 Whatley Road, Bristol BS8 2PS, UK; Department of Psychology, University of Bath, 6.19 Wessex House, Bath BA2 7AY, UK. 3. Division of Psychiatry, Faculty of Brain Sciences, School of Life and Medical Sciences, University College London, 67-73 Riding House St., London W1W 7EJ, UK. 4. School of Social and Community Medicine, Faculty of Health Sciences, University of Bristol, 39 Whatley Road, Bristol BS8 2PS, UK.
Abstract
OBJECTIVES: We present a meta-analytic method that combines information on treatment effects from different instruments from a network of randomized trials to estimate instrument relative responsiveness. STUDY DESIGN AND SETTING: Five depression-test instruments [Beck Depression Inventory (BDI I/II), Patient Health Questionnaire (PHQ9), Hamilton Rating for Depression 17 and 24 items, Montgomery-Asberg Depression Rating] and three generic quality of life measures [EuroQoL (EQ-5D), SF36 mental component summary (SF36 MCS), and physical component summary (SF36 PCS)] were compared. Randomized trials of treatments for depression reporting outcomes on any two or more of these instruments were identified. Information on the within-trial ratios of standardized treatment effects was pooled across the studies to estimate relative responsiveness. RESULTS: The between-instrument ratios of standardized treatment effects vary across trials, with a coefficient of variation of 13% (95% credible interval: 6%, 25%). There were important differences between the depression measures, with PHQ9 being the most responsive instrument and BDI the least. Responsiveness of the EQ-5D and SF36 PCS was poor. SF36 MCS performed similarly to depression instruments. CONCLUSION: Information on relative responsiveness of several test instruments can be pooled across networks of trials reporting at least two outcomes, allowing comparison and ranking of test instruments that may never have been compared directly.
OBJECTIVES: We present a meta-analytic method that combines information on treatment effects from different instruments from a network of randomized trials to estimate instrument relative responsiveness. STUDY DESIGN AND SETTING: Five depression-test instruments [Beck Depression Inventory (BDI I/II), Patient Health Questionnaire (PHQ9), Hamilton Rating for Depression 17 and 24 items, Montgomery-Asberg Depression Rating] and three generic quality of life measures [EuroQoL (EQ-5D), SF36 mental component summary (SF36 MCS), and physical component summary (SF36 PCS)] were compared. Randomized trials of treatments for depression reporting outcomes on any two or more of these instruments were identified. Information on the within-trial ratios of standardized treatment effects was pooled across the studies to estimate relative responsiveness. RESULTS: The between-instrument ratios of standardized treatment effects vary across trials, with a coefficient of variation of 13% (95% credible interval: 6%, 25%). There were important differences between the depression measures, with PHQ9 being the most responsive instrument and BDI the least. Responsiveness of the EQ-5D and SF36 PCS was poor. SF36 MCS performed similarly to depression instruments. CONCLUSION: Information on relative responsiveness of several test instruments can be pooled across networks of trials reporting at least two outcomes, allowing comparison and ranking of test instruments that may never have been compared directly.
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