Research

Estimating Minimally Important Differences for the Worst Pain Rating of the Brief Pain Inventory–Short Form


 

Table 4. Regression of Changes in BPI Worst Pain Item on Changes in Anchors and Baseline Anchor Ratings
VARIABLEPREDICTORbβSIG.
Change in BPI current painMain effect0.8170.724<0.001
Interaction with baseline anchor−0.024−0.1070.001
Change in EQ-5D Health State IndexMain effect−3.548−0.349<0.001
Interaction with baseline anchor0.2200.0210.465
Change in EQ-5D Pain itemMain effect1.8050.352<0.001
Interaction with baseline anchor0.2070.0800.261
Change in FACT-B TOIMain effect−0.098−0.406<0.001
Interaction with baseline anchor0.0000.0280.756
Change in FACT-G Physical Well-BeingMain effect−0.163−0.321<0.001
Interaction with baseline anchor−0.004−0.1330.024
Change in FACT-G total scoreMain effect−0.048−0.2310.025
Interaction with baseline anchor0.000−0.1300.209

b, regression coefficient; β, standardized regression coefficient; Sig., significance level.

Possible ranges: BPI Pain Right Now 0 (least) to 10 (most), EQ-5D Health State Index scores −0.594 (worst) to 1.00 (best), EQ-5D Pain item scores 1 (none) to 3 (severe), FACT-B TOI scores 4 (worst) to 92 (best), FACT-G Physical Well-Being scores 0 (worst) to 28 (best), FACT-G total score 8 (worst) to 108 (best), BPI Worst Pain item 0 (least) to 10 (most).

Changes in all anchors are significantly (P < 0.05) associated with changes in BPI-SF worst pain ratings. A one-point increase in BPI-SF current pain rating and EQ-5D pain item is associated with increases (positive b score) in the BPI-SF worst pain rating, and a one-point increase in EQ-5D Index, FACT-B TOI, FACT-G Physical Well-Being, and FACT-G total scores is associated with decreases (negative b score) in the BPI-SF worst pain ratings. The change in anchor-by-baseline anchor interaction was statistically significant only for the BPI current pain and FACT-G PWB items.

A post hoc confirmatory analysis was done replicating these analyses using data from the baseline to week 49 interval (n = 1,250). Results indicate a slightly stronger correlation between the anchors and the change scores. (Spearman's correlations range from 0.372 for FACT-TOI to 0.644 for BPI-SF current pain rating.) Mean change scores of BPI-SF worst pain ratings by each of the six anchors and regression coefficients were similar to those for the baseline to week 25 interval. For instance, mean change scores for the EQ-5D Pain item for stable patients ranged from 0.25–0.56, 1.58–295 for an improvement of one category, and 1.75–2.80 for a worsening of one category compared with 0.50–0.51, 1.71–1.98, and 2.56–3.16, respectively, for the baseline to week 25 interval.

Distribution-Based Analysis

The distribution-based estimates for the BPI-SF worst pain rating are presented in Table 5. There appears to be consistency with the 1 SEM estimates, the 0.50 effect size, and the 0.50 Guyatt's statistic.

Table 5. Distribution-Based Measures of the BPI-SF Worst Pain Rating
STANDARD ERROR OF MEASUREMENTAEFFECT SIZEBGUYATT'S STATISTICC
r(Day 1–8)r(Week 105–109)0.200.500.800.200.50
1.5991.2740.5701.4252.2790.5671.417

The results from the three distribution-based approaches presented in this table will be combined with those of the anchor-based results to estimate the MID.

a The standard error of measurement is a measure of the precision of a test instrument. It is calculated on the basis of sample data using the sample standard deviation and the sample reliability coefficient. Intraclass correlation coefficients (ICCs) for BPI-SF worst pain rating from day 1 to day 8 and week 105 to week 109 in patients whose FACT-B overall QOL ratings change by <10% are 0.685 (n = 926) and 0.800 (n = 109), respectively.b Alternatively referred to as Cohen's d, the effect size is calculated by dividing the difference between the pretest and posttest scores by the standard deviation at pretest. The standard deviation of BPI-SF worst pain rating at baseline (n = 1,877) is 2.849.c Alternatively referred to as the responsiveness statistic, Guyatt's statistic is calculated by dividing the difference between pretest and posttest changes by the standard deviation of change observed for a group of stable patients. The standard deviation of change in BPI-SF worst pain rating from baseline to week 25 in patients whose ECOG performance rating does not change (n = 1,120) is 2.833.


Integrating Anchor-Based and Distribution-Based Mid Estimates

The distribution-based analyses suggest that the MDC for the worst pain rating, defined as the smallest change that can be reliably differentiated from random fluctuation, is between 1.3 and 1.6 points (see Table 5). This represents the lower bound for establishing the MID.

The results from regression analyses can be used to translate changes between anchors and corresponding changes in BPI-SF worst pain. This strategy can be particularly informative when the MID for an anchor is known. This is the case for the EQ-5D Health State Index, where the MID has been estimated at 0.06 for U.S. Index scores and 0.07 for U.K. Index scores.23 A one-point change in EQ-5D Index translates to a change of −3.548 in BPI-SF worst pain, so a 0.07-point change in EQ-5D Index (the MID for the measure) corresponds to a change of −0.248 in BPI-SF worst pain. In contrast, a one-point change in BPI-SF worst pain (which is smaller than the MID based upon the distribution-based analyses) translates to a change of 0.036 for the EQ-5D Index score (considerably smaller than the MID of 0.07). However, a two-point change in BPI-SF worst pain rating corresponds to a 0.072 change in EQ-5D Index score, which is almost identical to the MID for that measure. This suggests that a two-point change may be a reasonable estimate for the MID of the BPI-SF worst pain rating.

Discussion

Data from both distribution-based and anchor-based approaches were used to develop estimates of the MID for the BPI-SF worst pain rating. Results from these approaches are similar, providing reasonably strong support for establishing a two-point MID for the BPI-SF worst pain rating. Further, the results suggest that this estimate of MID is, for the most part, independent of baseline BPI-SF worst pain ratings. However, there is some evidence to suggest that the direction of change (improvement or worsening) may be important to consider. A number of reports have suggested that a smaller change may be required to be considered clinically important when a patient is improving compared with worsening.13 Also, when considered as a percentage, a one-point change in any scale has a different value for an increase versus a decrease; eg, a change from 2 to 3 is an increase of 50%, while a change from 3 to 2 is a decrease of 33%. Nonetheless, these findings provide important information to researchers for interpreting changes in the BPI-SF worst pain ratings.

In addition, although not specific to the BPI worst pain rating, the findings of this study are consistent with other published MID analyses for a similar item. A recent review of three studies concluded that, for a numerical rating scale of pain intensity ranging 0–10 similar in content to the BPI-SF worst pain rating, changes of around two points represent “meaningful,” “much better,” or “much improved” reductions in chronic pain.24

Several factors contribute to the overall strength of the current results. First, as frequently recommended in the literature,11 both anchor-based and distribution-based methods were used to estimate the MID for the worst pain rating. Second, analyses were based on a large sample, totaling over 1,500 patients for the baseline to week 25 assessment interval. A larger sample size will generally provide a broader distribution of responses, which will likely increase the generalizability of the results. Third, multiple anchors were used to evaluate changes in BPI-SF worst pain ratings. Fourth, analyses were performed across several assessment intervals to determine the strongest relationship between BPI-SF ratings and other anchors. Finally, the regression analyses provide important information about whether baseline differences influence the relationship between BPI-SF and other PRO measures.

Nevertheless, these analyses are not without certain limitations. The sample for the current analyses consisted entirely of breast cancer patients. It is unclear to what extent these results will be relevant for other patient populations. Further research is needed to determine whether the MID for the BPI-SF worst pain rating established in this sample has broader applicability. Also, it must be noted that the recall period varied across assessments. The BPI-SF focuses on the past 24 hours, the FACT uses the past week, and the EQ-5D uses the present moment. It is unclear to what extent these differences in recall periods may have influenced the current results. Finally, the baseline to week 25 interval was used to determine the MID for the BPI-SF worst pain rating based on the higher correlations for this interval. Data from baseline to week 49 are consistent with these results, providing some confirmatory evidence to suggest that these MID estimates are stable.

In conclusion, the findings of the present analyses suggest that the MID estimate for the BPI-SF worst pain rating is two points. This value provides guidance to researchers using the BPI-SF worst pain rating on how to interpret baseline differences as well as change scores in the BPI-SF worst pain rating. Additional analyses could be done in other populations to confirm these findings.

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