Understanding changes in scores and how to interpret the changes are critical in the field of HRQOL measurement. Because there is no single gold-standard method for estimating MCID, multiple methods from both anchor- and distribution-based approaches and triangulation of all the methods to establish a possible range of MCID are recommended [17
]. Using data from a doubly randomized preference trial in post-traumatic stress disorder patients (the OPT trial), our analysis suggests that the plausible range of MCID values for the HRQOL health-utility EQ-5D and QWB-SA in the population of PTSD patients were between 0.04 and 0.10, and 0.02 to 0.05, respectively. Empirical works on MCIDs for the EQ-5D or QWB-SA has been done on several disease states and were ranged from -0.01 to 0.14 [15
]. However, those MCID estimates for the EQ-5D were based on the U.K. scoring algorithm or EQ-5D VAS instead of the U.S. scoring method used in the current study. Two studies using the U.S. population-based scoring model reported similar range of MCID values between 0.07 and 0.09 for the EQ-5D utility [18
]. For the QWB-SA, our range of MCID values was consistent with previous studies [22
The clinical anchors (CGI-I, CGI-S, and PSS-I for classifying treatment response status) used in our analysis were most appropriate as they were highly clinically relevant and strongly correlated with the HRQOL health-utility EQ-5D and QWB-SA. In addition, the anchor-based approach utilized well-established methods (OLS regression and ROC curve analysis) to estimate the MCIDs and produced rather similar results even if with different anchors used. The AUCs resulted from ROC analysis were rather large for both EQ-5D and QWB-SA indicating that the HRQOL health-utility measures had great ability to discriminate correctly treatment responders from non-responders. Although multiple methods are necessary to estimate a range of MCID values, Revicki and colleagues (2008) [17
] further recommended that results from the anchor-based approach have the most weight due to their clinical advantages over the distribution-based approach. That is, it is more likely that the ranges of MCID values in the population of PTSD patients would be between 0.05 to 0.08 and 0.03 to 0.05 for the EQ-5D and QWB-SA, respectively.
Both EQ-5D and QWB-SA are assumed to measure the same underlying construct of overall HRQOL in terms of health utility. The primary use of HRQOL health-utility measures is to calculate the quality-adjusted life year (QALY), a function of both quantity and quality of life, which is used in health economic evaluations and decision models to help health policy makers to allocate resources effectively. Therefore, it is important to establish their MCIDs and then compare them between the EQ-5D and QWB-SA. Our results showed that the plausible range of MCID values for the EQ-5D was almost twice that of the range for the QWB-SA. It was more likely because the two HRQOL health-utility instruments: (1) measure different health state descriptive systems thus different number of possible health states (243 possible health states for the EQ-5D versus 945 for the QWB-SA), (2) assess preferences for the multiple health states using different methods, i.e. time-trade off method used for the EQ-5D and rating-scale for the QWB-SA, and (3) use different scoring functions.
There were, however, some limitations in the current analysis. First, we did not apply multiple imputation methods for the missing data. Instead, we assumed that any missing assessments of the clinical anchors and HRQOL health-utility measures were missing completely at random (MCAR), meaning that our results would be similar whether or not there were missing data. Secondly, as there were very few worsening cases, the anchor-based methods focused mainly on the responses of those who were clinically improved rather than those worsened. Future work to assess the MCIDs for those who are clinically worsened is in need. Nevertheless, more than often the MCID is used in the context of a treatment effect, thus the MCID results in our study can still be applied to detect minimal clinically improvement in score changes. Finally, in our study, CGI-I questions were given to patients at 10-week post-treatment. The main limitation of using anchor-based approach that relies on global ratings is that these retrospective ratings are potentially susceptible to recall bias. As discussed above, it is important to estimate a range of MCID values from several different methods rather than a point estimate.
Our analysis to determine the plausible ranges of MCID values for the EQ-5D and QWB-SA followed the recommendations by Revicki and colleagues (2008) [17
]: longitudinal data were obtained from the clinical trial, multiple anchors were used and they were highly clinically relevant and strongly correlated with the HRQOL instruments, methodologically sound methods utilizing OLS regression and ROC curve analysis were applied in the anchor-based approach, and triangulation of multiple methods using both anchor- and distribution-based approaches to produce plausible ranges of MCID values.