Bruce Barrett1, Roger Brown, Marlon Mundt. 1. Department of Family Medicine, University of Wisconsin Medical School, 777 South Mills, Madison, WI 53715, USA. bruce.barrett@fammed.wisc.edu
Abstract
CONTEXT: Evaluative health-related quality-of-life instruments used in clinical trials should be able to detect small but important changes in health status. Several approaches to minimal important difference (MID) and responsiveness have been developed. OBJECTIVES: To compare anchor-based and distributional approaches to important difference and responsiveness for the Wisconsin Upper Respiratory Symptom Survey (WURSS), an illness-specific quality of life outcomes instrument. DESIGN: Participants with community-acquired colds self-reported daily using the WURSS-44. Distribution-based methods calculated standardized effect size (ES) and standard error of measurement (SEM). Anchor-based methods compared daily interval changes to global ratings of change, using: (1) standard MID methods based on correspondence to ratings of "a little better" or "somewhat better," and (2) two-level multivariate regression models. PARTICIPANTS: About 150 adults were monitored throughout their colds (1,681 sick days.): 88% were white, 69% were women, and 50% had completed college. The mean age was 35.5 years (SD = 14.7). RESULTS: WURSS scores increased 2.2 points from the first to second day, and then dropped by an average of 8.2 points per day from days 2 to 7. The SEM averaged 9.1 during these 7 days. Standard methods yielded a between day MID of 22 points. Regression models of MID projected 11.3-point daily changes. Dividing these estimates of small-but-important-difference by pooled SDs yielded coefficients of .425 for standard MID, .218 for regression model, .177 for SEM, and .157 for ES. These imply per-group sample sizes of 870 using ES, 616 for SEM, 302 for regression model, and 89 for standard MID, assuming alpha = .05, beta = .20 (80% power), and two-tailed testing. CONCLUSIONS: Distribution and anchor-based approaches provide somewhat different estimates of small but important difference, which in turn can have substantial impact on trial design.
CONTEXT: Evaluative health-related quality-of-life instruments used in clinical trials should be able to detect small but important changes in health status. Several approaches to minimal important difference (MID) and responsiveness have been developed. OBJECTIVES: To compare anchor-based and distributional approaches to important difference and responsiveness for the Wisconsin Upper Respiratory Symptom Survey (WURSS), an illness-specific quality of life outcomes instrument. DESIGN:Participants with community-acquired colds self-reported daily using the WURSS-44. Distribution-based methods calculated standardized effect size (ES) and standard error of measurement (SEM). Anchor-based methods compared daily interval changes to global ratings of change, using: (1) standard MID methods based on correspondence to ratings of "a little better" or "somewhat better," and (2) two-level multivariate regression models. PARTICIPANTS: About 150 adults were monitored throughout their colds (1,681 sick days.): 88% were white, 69% were women, and 50% had completed college. The mean age was 35.5 years (SD = 14.7). RESULTS: WURSS scores increased 2.2 points from the first to second day, and then dropped by an average of 8.2 points per day from days 2 to 7. The SEM averaged 9.1 during these 7 days. Standard methods yielded a between day MID of 22 points. Regression models of MID projected 11.3-point daily changes. Dividing these estimates of small-but-important-difference by pooled SDs yielded coefficients of .425 for standard MID, .218 for regression model, .177 for SEM, and .157 for ES. These imply per-group sample sizes of 870 using ES, 616 for SEM, 302 for regression model, and 89 for standard MID, assuming alpha = .05, beta = .20 (80% power), and two-tailed testing. CONCLUSIONS: Distribution and anchor-based approaches provide somewhat different estimates of small but important difference, which in turn can have substantial impact on trial design.
Authors: Holger J Schünemann; Lauren Griffith; Roman Jaeschke; Roger Goldstein; David Stubbing; Gordon H Guyatt Journal: J Clin Epidemiol Date: 2003-12 Impact factor: 6.437
Authors: Bruce Barrett; Mary S Hayney; Daniel Muller; David Rakel; Ann Ward; Chidi N Obasi; Roger Brown; Zhengjun Zhang; Aleksandra Zgierska; James Gern; Rebecca West; Tola Ewers; Shari Barlow; Michele Gassman; Christopher L Coe Journal: Ann Fam Med Date: 2012 Jul-Aug Impact factor: 5.166
Authors: Keisha Y Dyer; Yan Xu; Linda Brubaker; Ingrid Nygaard; Alayne Markland; David Rahn; Toby C Chai; Ann Stoddard; Emily Lukacz Journal: Neurourol Urodyn Date: 2011-05-11 Impact factor: 2.696
Authors: Bruce Barrett; Roger Brown; Dave Rakel; David Rabago; Lucille Marchand; Jo Scheder; Marlon Mundt; Gay Thomas; Shari Barlow Journal: Ann Fam Med Date: 2011 Jul-Aug Impact factor: 5.166
Authors: Bruce Barrett; Roger Brown; Dave Rakel; Marlon Mundt; Kerry Bone; Shari Barlow; Tola Ewers Journal: Ann Intern Med Date: 2010-12-21 Impact factor: 25.391
Authors: Ravishankar Jayadevappa; Stanley Bruce Malkowicz; Marsha Wittink; Alan J Wein; Sumedha Chhatre Journal: Health Serv Res Date: 2012-03-14 Impact factor: 3.402
Authors: Bradley C Johnston; Kristian Thorlund; Holger J Schünemann; Feng Xie; Mohammad Hassan Murad; Victor M Montori; Gordon H Guyatt Journal: Health Qual Life Outcomes Date: 2010-10-11 Impact factor: 3.186
Authors: Bruce Barrett; Roger L Brown; Marlon P Mundt; Gay R Thomas; Shari K Barlow; Alex D Highstrom; Mozhdeh Bahrainian Journal: Health Qual Life Outcomes Date: 2009-08-12 Impact factor: 3.186