probability of endorsing that item (). The exact shape and location of the S-shaped curve for each item depends on the estimated difficulty and discrimination parameters; a reverse S-shaped curve indicates negative discrimination, which suggests a poorly performing item that is best excluded. Curves that form a straight horizontal line indicate that the item provides no discrimination or is undefined, while a straight vertical line indicates perfect discrimination just above and below that point, but no ability to distinguish between those with levels of the latent trait further above or below it. See Ainsworth (2011) for a detailed discussion. Items were evaluated at the level of the subscale with the intent of maintaining the broad framework of the SIP. Inadequate items were removed individually based on manual inspection of ICCs. Because ICCs on a subscale are a function of how those items work together (and not just the sum of individual ICCs), subscale ICCs were re-evaluated after each step in the reduction process. Fit was initially evaluated using chi-square analyses, with non-significant values indicating appropriate model fit. However, since the significance of χ2 model fit tends to increase with larger sample sizes, the priority was to achieve consensus among root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker-Lewis index (TLI) fit indices. For RMSEA, good fit was defined as a test statistic ≤ .05, with values ≤ .08 considered adequate fit (). Good fit for CFI and TLI was defined by a test statistic ≥ .95, with adequate fit attained by values of ≥ .90 or larger (). Following the removal of all poorly functioning items as demonstrated by model fit indices and visual inspection of ICCs, chi-square difference tests were conducted to determine whether the removal of additional items significantly improved model fit across further iterations of the subscale. For instances in which two items shared the same ICC characteristics (suggesting potential redundancy), items were evaluated for uniqueness relative to other items and clinical relevance for chronic pain and retained or removed accordingly. Since this process was exploratory in nature, removal of items occasionally produced changes in ICCs which suggested that inclusion of that item may yield better model fit. In such cases, effects of re-adding
remarkably similar to the SIP suggests that the loss of these items did not contribute to substantial losses in convergent validity. are several objectives to pursue in the future with regard to the utility of the SIP-CP. Although measurements in IRT are based on items rather than the sample, there is still a need to ensure the heterogeneity of this sample by testing whether the factor structure of the SIP-CP can be replicated in another large, independent sample of individuals seeking treatment for chronic pain. Formal testing of the local independence assumption for dimension scores should examine whether there are highly similar or highly correlated items across subscales, while testing for parameter invariance by examining differential item functioning across groups (by age, sex, socioeconomic status, pain duration, etc.) would further establish whether the SIP-CP can be applied to a broader population of individuals with chronic pain. Sensitivity to treatment-related change, a strength of the original SIP, should also be examined in the SIP-CP. In addition, the SIP-CP does not include item weights, as those used in the SIP were based on expert judgment, rather than empirical inquiry. Further efforts to determine and test empirically supported item weights, like those generated through IRT methods by Lindeboom and colleagues (2004), may be of use. Finally, tests examining the relative utility of the SIP-CP in relation to other measures of disability may be informative. For example the SF-36 (Ruta, Garratt, Abdalla, Buckingham, & Russell, 1993) and US National Institutes of Health-sponsored PROMIS Pain Interference (Amtmann et al., 2010) measures, along with many other options, are available and it is not clear that the item burden of the SIP-CP, which is greater than most other available measures, offers more useful or broad information with regard to assessing changes in individual patient functioning. Our clinical experience suggests that the SIP (and now SIP-CP) provides a richer source of information and stands to offer greater utility clinical and research endeavors, but future work will have to evaluate the accuracy of those observations empirically.
3 Results of the Linear Regression Analyses for the SIP and SIP-CP Predicting Functioning SIP SIP-CP Step Predictor Δ r 2 Β (final) Δ r 2 Β Depression – BDI-II 1 Age Sex Pain Duration .03* -.16*** .04 .02 .03* -.18*** .04 .03 2 Pain Intensity .05*** .06 .05*** .05 3 Physical Disability Psychosocial Disability .40*** .03 .63*** .32*** .09+ .54*** Total R2 .47 .40 Depression – BCMDI 1 Age Sex Pain Duration .02* -.02 -.05 -.02 .02* -.03 -.05 -.02 2 Pain Intensity .08*** .12** .08*** .13*** 3 Physical Disability Psychosocial Disability .45*** .11* .63*** .41*** .15*** .58*** Total R2 .56 .52 Pain-Related Anxiety 1 Age Sex Pain Duration .01 < -.01 -.07+ < .01 .01 -.01 -.06 .01 2 Pain Intensity .08*** .14*** .08*** .14*** 3 Physical Disability Psychosocial Disability .26*** .14*** .43*** .25*** .16*** .42*** Total R2 .34 .33 (Tables continues)
Table 3 (con’t) SIP SIP-CP Step Predictor Δ r 2 Β Δ r 2 Β (final) Pain Acceptance 1 Age Sex Pain Duration .04*** .03 .18*** < .01 .04*** .04 .18*** < .01 2 Pain Intensity .06*** -.14*** .06*** -.14*** 3 Physical Disability Psychosocial Disability .18*** -.08+ -.39*** .15*** -.15*** -.32*** Total R2 .29 .26 Classes of Pain Medication 1 Age Sex Pain Duration < .01 .02 -.03 -.02 < .01 .02 -.02 -.01 2 Pain Intensity .04*** .10* .04*** .10** 3 Physical Disability Psychosocial Disability .06*** .24*** .03 .06*** .23*** .04 Total R2 .10 .10 Pain-Related Medical Visits 1 Age Sex Pain Duration .01* -.11*** <.01 .03 .01* -.12*** .01 .03 2 Pain Intensity .04*** .11** .04*** .11** 3 Physical Disability Psychosocial Disability .07*** .16*** .15*** .06*** .15*** .16*** Total R2 .12 .12 + p < .10, * p < .05, ** p < .005, ***p < .001
Supplementary Table 5 Item Selection Process for Alertness Behavior Subscale Iteration Original 1 2 Final Item number & adjustment (+/-) N/A -2 -7 -6 χ2 80.075 55.384 46.167 25.038 df 35 27 20 14 <.0001 .001 .008 .0342 RMSEA .065 .054 .060 .047 CFI .975 .985 .985 .992 TLI .968 .980 .978 .989 Note. RMSEA <.05, CFI >.95, TLI >.95 constitute good fit. Supplementary 6 Item Selection Process for Emotional Behavior Subscale Iteration Original 1 2 3 4 Final Item number & adjustment (+/-) N/A -6 -3 -8 +3, +8 -2 +2, -8 χ2 113.591 48.069 39.869 25.811 40.366 33.259
Supplementary Table 10 Selection Process for Eating Subscale Iteration Original 1 2 3 Final Item number & adjustment (+/-) N/A -3 -4 -7 +7, -2 χ2 21.495 13.773 7.133 0.872 2.588 df 14 9 5 2 2 p-value .0896 .1306 .2109 .6468 .2742 RMSEA .027 .027 .024 <.0001 .020 CFI .948 .967 .984 1 .996 TLI .922 .945 .969 1.025 .988 Note. RMSEA <.05, CFI >.95, TLI >.95 constitute good fit.