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ASSESSING RESPONSIVENESS OF HEALTH MEASUREMENTS

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Link validity & reliability testing to purpose of the measure Some examples: 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.

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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 how able it is to distinguish between groups Normally considered an element of validity for an evaluative measurement.

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(Responsiveness, cont’d) There is little consensus over how responsiveness should be assessed. This may be because responsiveness requires a finer breakdown into different types; this is not normally done; Different facets of responsiveness are relevant to different types of measure.

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Five conceptions of responsiveness 1. “The smallest change that could potentially be detected” 2. “The smallest change that could reliably be detected (beyond error)” 3. “The change typically observed in a population” 4. “The change observed in the subset of the population judged to have changed” 5. “The change seen in those judged to have made an important change”.

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Preliminary decisions (Before we begin) 1. What parameter is to be measured? (Pain, QoL, etc.) 2. Whose perspective is important: the patient’s, the clinician’s or society’s? 3. What if these conflict? (e.g. if the doctor says the patient is better on this measure but the patient feels they are not, is it responsive? Is it valid?) 4. Responsive to what? Differences between groups; within a group over time, or to compare changes over time between two groups? 5. What unit of analysis? (Average scores, or individual classification such as a diagnosis?)

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Approaches to estimating responsiveness 1.Theoretical, modeling (equivalent to content validity) 2. Empirical, Internal evaluation (equivalent to concurrent validity) 3. Empirical, External comparison (equivalent to criterion validity)

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

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2. Internal empirical approach Apply measure before & after; calculate an effect size statistic Because measurement scales vary, results are expressed in standard deviation units: (M t - M c )/SD c Effect size is comparable to a z score: if normal distribution, indicates how many percentiles a patient will move following treatment.

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

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Effect size statistics (2) Refinements include correction for level of reliability. E.g., Wyrich proposed including standard error of mean in denominator: SEM = SD 1 * sqrt(1-alpha) However, a high alpha does not ensure responsiveness if the measure includes inter-correlated traits that do not change.

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

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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

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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” Estimated internally (using scale itself), or externally (using some other criterion)

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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 expert 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’.

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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.

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3. External criteria for responsiveness 1. Establish MID or MCID. Group patients who improve (or deteriorate) more than the MID and compare to rest using the measure 2. Various statistics: Sensitivity, specificity & ROCs Point-biserial correlations Regression to analyze average scale change on the measure for each MCID unit change

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1.0 0.8 0.6 0.4 0.2 0.0 Sensitivity (true positives) 1-specificity (false positives) 0.0 0.2 0.4 0.6 0.8 1.0 AIMS2 ( Arthritis Impact Measurement Scale) HAQ (Health Assessment Questionnaire) SF-36 (Short-Form 36) ROC Curve for 3 Instruments in Detecting an MCID

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Questions for Discussion 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 X 1 and X 2 & use this to simulate the effect size? Are MIDs constant across scale range? (next slide).

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Large None Size of change PoorGoodHealth status Notional Size of an MCID at Various Levels of Overall Health Physical function? Cognition?

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