Assessing Responsiveness of Health Measurements

1. Assessing Responsiveness of Health Measurements Ian McDowell, INTA, Santiago, March 20, 2001

2. Link Purpose of Measure to Validation Method For example: • In a diagnostic instrument, inter-rater and test-retest reliability are important; • For an evaluative measure, internal consistency is paramount. • For a prognostic or diagnostic instrument, criterion validity is relevant; • For an evaluative measure, construct validation is central.

3. Responsiveness • For outcome measures, sensitivity to change is a crucial characteristic • ‘Responsiveness’ refers to how sensitive a measure is in indicating change over time or contrast between groups • Normally considered an element of validity for an evaluative measurement.

4. (Responsiveness, cont’d) • There is little consensus over how responsiveness should be assessed. • This may be because responsiveness requires a finer breakdown than is normally given; • Different facets of responsiveness are relevant to different types of measure.

5. Conceptions of Responsiveness • The smallest change that could potentially be detected; • The smallest change that could reliably be detected beyond error; • The change typically observed in a population; • The change observed in the subset of the population judged to have changed; • The change seen in those judged to have made an important change.

6. Preliminary Decisions(Before We Begin!) • What parameter is to be measured? (Pain, QoL, etc.) • Whose perspective is important: the patient’s, the clinician’s or society’s? • What if these conflict? • Responsive to what? Differences between groups; within a group over time, or to compare changes over time between two groups? • Unit of analysis? (Average scores, or individual classification such as a diagnosis?)

7. Approaches to Estimating Responsiveness 1. Theoretical (equivalent to content validity) 2. Empirical; Internal evaluation (equivalent to concurrent validity) 3. Empirical; External comparison (equivalent to criterion validity)

8. 1. Modeling Approach • Content should reflect the types of change expected to occur with the therapy: states, not traits • There should be no floor or ceiling effects • Scoring must ensure that change is not diluted in other factors that do not vary • Scale must have fine enough gradation

9. 2. Internal Empirical Approach • Apply scale before & after; calculate effect size statistic • Because measurement scales vary, results are expressed in standard deviation units: (Mt - Mc)/SDc • Effect size comparable to a z score: if normal distribution, indicates how many percentiles a patient will move following treatment.

10. Effect Size Statistics 1. Use a t-test and report statistically significant differences as indicators of responsiveness 2. Removing the n from the denominator to make independent of sample size 3. Denominator can be SD of the baseline scores, or of scores among stable subjects, or of change in scores.

11. Effect Size Statistics (2) • Refinements include correction for level of reliability. E.g., Wyrich proposed standard error of mean in denominator: SEM = SD1 * %(1-") • However, a high alpha does not ensure responsiveness if the measure includes inter-correlated traits that do not change.

12. Effect size alpha Impact of including Alpha in Effect Size Calculation (at difference of 1.5 and SD of 3)

13. Comment: Effect Sizes • Useful for comparing responsiveness of different health measures • Helpful in calculating the power of a study However: • Formulae seem somewhat arbitrary • Effect sizes offer no indication of the clinical change represented by a given shift in scores

14. The MID as a Criterion • Introduces theme of Minimally Important Difference (MID) and its cousin, the MCID. • MCID: “The smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient’s management” • Estimate internally (using scale itself), or externally (using some other criterion)

15. Setting Internal MIDs 1. Apply the measurement; select change threshold seen as important by clinical experts. How much would the outcome have to change before they would alter treatment? 2. Present clinicians with written scenarios and compare each with the previous one. MCID = average difference in scores between pairs rated as ‘a little less’ or ‘a little more’.

16. Externally-Based MIDs • Clinicians view patient scenarios and rate whether they changed significantly or not. • Patients can judge the change in their own condition: ‘no change’, ‘a little better’, etc. • Alternatively, clinically assess patients, then randomly assign pairs of them to hold conversations about their illness, leading to ratings of whether they were ‘better’ than the other, ‘much better’, etc.

17. 3. External Criteria for Responsiveness 1. Establish MID or MCID. Group patients who improve (or deteriorate) > MID and compare to rest using the measure 2. Various statistics: • Sensitivity, specificity & ROCs • Point-biserial correlations • Regression to analyse average scale change on the measure for each MCID unit change

18. Sensitivity (true positives) 1.0 0.8 0.6 0.4 0.2 0.0 SF-36 AIMS2 HAQ 0.0 0.2 0.4 0.6 0.8 1.0 1-specificity (false positives) ROC Curve for 3 Instruments in Detecting an MCID

19. Questions for Discussion • Are MIDs constant across range? (next slide) • How can we encourage people to routinely report before and after changes in scores, SD, and alpha? • Should we apply a measure to standard scenarios to get X1 and X2 &use this to simulate the effect size? • How does this all apply to nutritional assessments?

20. Large Size of change Cognition? Physical function? None Poor Health status Good Notional Size of an MCID at Various Levels of Overall Health