Health-Related Quality of Life in Outcome Studies Ron D. Hays, Ph.D UCLA Division of General Internal Medicine & Health Services Research GCRC Summer Session.

Slides:



Advertisements
Similar presentations
Patient-Reported Outcomes Measurement Information System.
Advertisements

How does PROMIS compare to other HRQOL measures? Ron D. Hays, Ph.D. UCLA, Los Angeles, CA Symposium 1077: Measurement Tools to Enhance Health- Related.
PROMIS DEVELOPMENT METHODS, ANALYSES AND APPLICATIONS Presented at the Patient-Reported Outcomes Measurement Information System (PROMIS): A Resource for.
PROMIS: The Right Place at the Right Time? David Cella, Ph.D. Department of Medical Social Sciences Northwestern University Chair, PROMIS Steering Committee.
15-minute Introduction to PROMIS Ron D. Hays, Ph.D UCLA Division of General Internal Medicine & Health Services Research Roundtable Meeting on Measuring.
Why Patient-Reported Outcomes Are Important: Growing Implications and Applications for Rheumatologists Ron D. Hays, Ph.D. UCLA Department of Medicine RAND.
Cross-Cultural Use of Measurements: Development of the Chinese SF-36 Health Survey Xinhua S. Ren, Ph.D. Boston University School of Public Health, Boston,
1 8/14/2015 Evaluating the Significance of Health-Related Quality of Life Change in Individual Patients Ron Hays October 8, 2004 UCLA GIM/HSR.
1 Health-Related Quality of Life Ron D. Hays, Ph.D. - UCLA Department of Medicine: Division of General Internal Medicine.
Performance of PROMIS® CATs Versus Short forms in Detecting Change Ron D. Hays October 3, 2011 International Association for Computer.
Primer on Evaluating Reliability and Validity of Multi-Item Scales Questionnaire Design and Testing Workshop October 25, 2013, 3:30-5:00pm Wilshire.
Rare Diseases and PROMIS : Opportunities Natcher Conference Center March 1, 2013 James Witter MD, PhD FACR CSO PROMIS Medical Officer: Rheumatic Diseases.
Patient-Reported Outcomes Measurement Information System (PROMIS): Opportunities in Health Services Research Steven Clauser, Ph.D. National Cancer Institute.
FDA Approach to Review of Outcome Measures for Drug Approval and Labeling: Content Validity Initiative on Methods, Measurement, and Pain Assessment in.
Is the Minimally Important Difference Really 0.50 of a Standard Deviation? Ron D. Hays, Ph.D. June 18, 2004.
1 9/23/2015 Patient Reported Outcomes Measurement Information System (PROMIS) Patient Reported Outcomes Measurement Information System (PROMIS) National.
Use of Health-Related Quality of Life Measures to Assess Individual Patients July 24, 2014 (1:00 – 2:00 PDT) Kaiser Permanente Methods Webinar Series Ron.
Patient-Centered Outcomes of Health Care Comparative Effectiveness Research February 3, :00am – 12:00pm CHS 1 Ron D.Hays, Ph.D.
Health-Related Quality of Life as an Indicator of Quality of Care May 4, 2014 (8:30 – 11:30 PDT) HPM216: Quality Assessment/ Making the Business Case for.
SAS PROC IRT July 20, 2015 RCMAR/EXPORT Methods Seminar 3-4pm Acknowledgements: - Karen L. Spritzer - NCI (1U2-CCA )
1 10/12/2015 Health-Related Quality of Life Assessment Health-Related Quality of Life Assessment Ron D. Hays, Ph.D. November 27, 2002 (8:30-9:30.
1 Assessing the Minimally Important Difference in Health-Related Quality of Life Scores Ron D. Hays, Ph.D. UCLA Department of Medicine October 25, 2006,
PROMIS ® : Advancing the Science of PRO measurement Common Data Elements NIH CDE Webinar September 8, 2015 Ashley Wilder Smith, PhD, MPH Chief, Outcomes.
Health-Related Quality of Life Measures (HLT POL 239B)
1 10/19/2015  Course materials copyrighted 2003 by Ron D. Hays A Comprehensive Approach to the Measurement of Health Outcomes Ron D. Hays, Ph.D. UCLA.
Measuring Health-Related Quality of Life Ron D. Hays, Ph.D. UCLA Department of Medicine RAND Health Program UCLA Fielding School of Public Health
Development of Physical and Mental Health Summary Scores from PROMIS Global Items Ron D. Hays ( ) UCLA Department of Medicine
Introduction to the Patient-Reported Outcomes Measurement Information System (PROMIS) UCLA Center for East-West Medicine 2428 Santa Monica Blvd., Suite.
Measuring Health-Related Quality of Life
1 Health-Related Quality of Life Assessment as an Indicator of Quality of Care (HPM 216) Ron D. Hays April 11, 2013(8:30-11:30 am) Wilshire Blvd.
Impact of using a next button in a web-based health survey on time to complete and reliability of measurement Ron D. Hays (Rita Bode, Nan Rothrock, William.
1 Session 6 Minimally Important Differences Dave Cella Dennis Revicki Jeff Sloan David Feeny Ron Hays.
Presented By Ron D. Hays, Ph.D. April 8, 2010 (MNRS Pre-Conference Workshop) Domains of PROMIS and how they were developed Dynamic Tools to Measure Health.
1 12/3/2015 Measuring Self-Reported Health Ron D. Hays, Ph.D. UCLA GIM & HSR November 27, 2007 (9:00-10:00 am) Gonda Building (Room 1357)
Hermann P. G. Schneider, Alastair H. MacLennan and David Feeny
Item Response Theory (IRT) Models for Questionnaire Evaluation: Response to Reeve Ron D. Hays October 22, 2009, ~3:45-4:05pm
Evaluating Self-Report Data Using Psychometric Methods Ron D. Hays, PhD February 6, 2008 (3:30-6:30pm) HS 249F.
1 12/18/2015 Comprehensive Approach to Measuring Health Outcomes Ron D. Hays, Ph.D. UCLA GIM & HSR October 23, 2006 (3:15-4:45 pm) MacDonald.
Evaluating Self-Report Data Using Psychometric Methods Ron D. Hays, PhD February 8, 2006 (3:00-6:00pm) HS 249F.
Measurement of Outcomes Ron D. Hays Accelerating eXcellence In translational Science (AXIS) January 17, 2013 (2:00-3:00 pm) 1720 E. 120 th Street, L.A.,
Considerations in Comparing Groups of People with PROs Ron D. Hays, Ph.D. UCLA Department of Medicine May 6, 2008, 3:45-5:00pm ISPOR, Toronto, Canada.
Patient-Reported Physical Functioning Ron D. Hays November 27, 2012 (11:15-11:30) UCLA Department of Medicine MCID for Orthopaedic Devices Silver Springs,
Impact of using a next button in a web-based health survey on time to complete and reliability of measurement Ron D. Hays (Rita Bode, Nan Rothrock, William.
Estimating Minimally Important Differences on PROMIS Domain Scores Ron D. Hays UCLA Department of Medicine/Division of General Internal Medicine & Health.
Health-Related Quality of Life (HRQOL) Assessment in Outcome Studies Ron D. Hays, Ph.D. UCLA/RAND GCRC Summer Course “The.
Introduction to Neuro-QoL
Introduction to ASCQ-MeSM
Psychometric Evaluation of Items Ron D. Hays
NIH: Patient-Reported Outcomes Measurement Information System (PROMIS®) Ron D. Hays Functional Vision and Visual Function November 10, 2016, 8:55-9:15am.
Evaluating IRT Assumptions
Introduction to ASCQ-Me®
Health-Related Quality of Life Assessment in Outcome Studies
Introduction to Neuro-QoL
Proportion of patients reporting scores ≥age-matched and gender-matched normative PRO values at baseline and 24 weeks in the (A) AMBITION and (B) ADACTA.
Introduction to Neuro-QoL
Introduction to ASCQ-Me®
The Impact of Pain Management on Quality of Life
A Multi-Dimensional PSER Stopping Rule
Correlations between observed patient-reported outcomes and disease activity scores at week 24. Correlations between observed patient-reported outcomes.
Evaluating the Psychometric Properties of Multi-Item Scales
Health-Related Quality of Life Measures (HS249T: Decision Analysis and Cost-Effectiveness Analysis) Ron D. Hays, Ph.D. UCLA Division.
ID Dr. Natālija Nikrus, Dr. Maija Vikmane, Prof. Oskars Kalējs
Spydergram of mean SF-36 domain scores at baseline and weeks 12 (A) and 24 (B) for sarilumab 150 mg and 200 mg+csDMARDs compared with placebo+csDMARDs.
Estimating Minimally Important Differences (MIDs)
Health-Related Quality of Life as an indicator of Quality of Care
Evaluating the Significance of Individual Change
GIM & HSR Research Seminar: October 5, 2018
How to Measure Quality of Life
Patient-reported Outcome Measures
Post hoc analysis of differences from placebo in the percentage of patients reporting improvements ≥MCID at week 24. Post hoc analysis of differences from.
Presentation transcript:

Health-Related Quality of Life in Outcome Studies Ron D. Hays, Ph.D UCLA Division of General Internal Medicine & Health Services Research GCRC Summer Session July 18, 2011 (8:00-9:00 am) 1 st floor Conference Room 1357, UCLA

Health-Related Quality of Life is … What you can do. Functioning Self-care Role Social How you feel about your life. Well-being Emotional well-being Pain Energy

The Tower of Babel (Brueghel, 1563) 4

5  Physical functioning (10 items)  Role limitations/physical (4 items)  Role limitations/emotional (3 items)  Social functioning (2 items)  Emotional well-being (5 items)  Energy/fatigue (4 items)  Pain (2 items)  General health perceptions (5 items)

Patient-Reported Outcomes Measurement Information System (PROMIS), 2004-? An answer to the “Tower of Babel” A commitment of NIH to improve and standardize measurement of patient- reported outcomes (i.e., health- related quality of life)

PROMIS-1 Network: UNC –Chapel Hill ● ● Duke University* ● Stanford ● ● ● University of Pittsburgh ● University of Washington Northwestern ♥ NIH ● NIH ♥Coordinating Center Stony Brook

Psycho- metric Testing Item Bank (IRT-calibrated items) Short Form Instruments CAT Literature Review Item Pool Patient Focus Groups Expert Input and Consensus Existing Items  Questionnaire administered to large representative sample             Secondary Data Analysis Cognitive Testing Translation Expert Review Newly Written Items

Physical Functioning Item Bank Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Item 8 Item 9 Item n        50 Are you able to get in and out of bed? Are you able to stand without losing your balance for 1 minute? Are you able to walk from one room to another? Are you able to walk a block on flat ground? Are you able to run or jog for two miles? Are you able to run five miles?

Computerized Adaptive Testing (CAT) Select questions based on a person’s response to previously administered questions. Iteratively estimate a person’s location on a domain (e.g., fatigue, depressive symptoms) Administer most informative items Desired level of precision can be obtained using the minimal possible number of questions.

Reliability and SEM z-score (mean = 0 and SD = 1) T-score = (z-score * 10) + 50 –Reliability = 1 – SEM 2 (for z-scores) = 0.91 (when SEM = 0.30) = 0.90 (when SEM = 0.32) With 0.90 reliability –95% Confidence Interval for score at mean z-score:  0.62 T-score: 43.8 

CAT assessments can achieve higher precision than fixed forms Rose et al, J Clin Epidemiol 2007 (accepted) SE = 3.2 rel = 0.90 SE = 2.2 rel = 0.95 SF-36 items CAT 10 items Full Item Bank measurement precision (standard error) normed theta values HAQ items SF-12 items representative sample rheumatoid arthritis patients US-Representative Sample

Anxiety Depression Fatigue Pain Interference Sleep Disturbance Physical Function Social Role Mental Physical Social

LowLow HighHigh                                                       Person Fatigue Score Interpretation                                                     Q Q Q Q Q Q Q Q Q Likely Likely “I get tired when I run a marathon” Likely Likely “I get tired when I run a marathon” Unlikely “I get tired when I get out of a chair”Unlikely “I get tired when I get out of a chair” Item Location Q Q Q Q Q Q Q Q Q Q Q Q Q Q Q    

LowLow HighHigh                                                       Interpretation Aids                                                     M = 50, SD = 10 T = (z * 10) + 50    

LowLow HighHigh                                                       Example of high fatigue           Fatigue Score=60                                               This patient’s fatigue score is 60, significantly worse than average (50). People who score 60 on fatigue tend to answer questions as follows: …”I have been too tired to climb one flight of stairs: VERY MUCH …”I have had enough energy to go out with my family: A LITTLE BIT

LowLow HighHigh                                                       Example of low fatigue           Fatigue Score=40                                               This patient’s fatigue score is 40, significantly better than average (50). People who score 40 on fatigue tend to answer questions as follows: …”I have been too tired to climb one flight of stairs: NOT AT ALL …”I have had enough energy to go out with my family: VERY MUCH

Significant Improvement in all but 1 of SF-36 Scales (Change is in T-score metric) 26 Changet-testprob. PF RP BP GH EN SF RE EWB PCS MCS

Defining a Responder: Reliable Change Index (RCI) 27 Note: SD bl = standard deviation at baseline r xx = reliability

Amount of Change in Observed Score Needed for Significant Individual Change 28 ScaleChange Effect sizeReliability PF RP BP GH EN SF RE EWB PCS MCS

7-31% of People in Sample Improve Significantly 29 % Improving% DecliningDifference PF-1013% 2%+ 11% RP-431% 2%+ 29% BP-222% 7%+ 15% GH-5 7% 0%+ 7% EN-4 9% 2%+ 7% SF-217% 4%+ 13% RE-315% 0% EWB-519% 4%+ 15% PCS24% 7%+ 17% MCS22%11%+ 11%

Questions?