Jeffrey Critch

The minimal detectable change cannot reliably replace the minimal important difference

OBJECTIVE We compared the minimal important difference (MID) with the minimal detectable change (MDC) generated by distribution-based methods. STUDY DESIGN Studies of two quality-of-life instruments (Chronic Respiratory Questionnaire [CRQ] and Rhinoconjunctivitis Quality of Life Questionnaire [RQLQ]) and two physician-rated disease-activity indices (Pediatric Ulcerative Colitis Activity Index [PUCAI] and Pediatric Crohns Disease Activity Index [PCDAI]) provided longitudinal data. The MID values were calculated from global ratings of change (small change for CRQ and RQLQ; moderate for PUCAI and PCDAI) using receiver-operating characteristic (ROC) curve and mean change. Results were compared with five distribution-based strategies. RESULTS Of the methods used to calculate the MDC, the 95% limits of agreement and the reliable change index yielded the largest estimates. In the patient-rated psychometric instruments, 0.5 SD was always greater than 1 standard error of measurements (SEM), and both fell between the mean change and the ROC estimates, on two of four occasions. The reliable change index came closest to MID of moderate change. CONCLUSION For patient-rated psychometric instruments, 0.5 SD and 1 SEM provide values closest to the anchor-based estimates of MID derived from small change, and the reliable change index for physician-rated clinimetric indices based on moderate change. Lack of consistency across measures suggests that distribution-based approaches should act only as temporary substitutes, pending availability of empirically established anchor-based MID values.

Using the entire cohort in the receiver operating characteristic analysis maximizes precision of the minimal important difference

OBJECTIVE We compared the minimal important difference (MID) values obtained by the receiver operating characteristics (ROC) curve approach using different strategies on four outcome measures to guide the optimal use of ROC curve. STUDY DESIGN AND SETTING Studies of two psychometric scales (Rhinoconjunctivitis Quality-of-Life Questionnaire [RQLQ] and Chronic Respiratory Questionnaire [CRQ]) and two clinimetric indices (Pediatric Ulcerative Colitis Activity Index [PUCAI] and Pediatric Crohns Disease Activity Index [PCDAI]) instruments provided prospective longitudinal data. The MID was calculated from 7- and 15-point global ratings of change dichotomized in multiple ways, using the ROC curve method. Analysis was performed twice: first, using only the two groups adjacent to the dichotomization point (e.g., including only patients who had a small vs. moderate change); and second, using the entire cohort split at the same cutoff (e.g., including both unchanged subjects with those with small change vs. those who experienced moderate or large change combined). RESULTS Using the entire cohort, rather than just those with ratings adjacent to the dichotomization point, yielded more precise and sensible MID estimates. With one exception, high precision was obtained when using the ROC curve method for any cutoff on the rating scale. CONCLUSION When calculating the MID using the ROC curve method, the use of the entire cohort maximizes precision.

Energy and sports drinks in children and adolescents

Sports drinks and caffeinated energy drinks (CEDs) are commonly consumed by youth. Both sports drinks and CEDs pose potential risks for the health of children and adolescents and may contribute to obesity. Sports drinks are generally unnecessary for children engaged in routine or play-based physical activity. CEDs may affect children and adolescents more than adults because they weigh less and thus experience greater exposure to stimulant ingredients per kilogram of body weight. Paediatricians need to recognize and educate patients and families on the differences between sport drinks and CEDs. Screening for the consumption of CEDs, especially when mixed with alcohol, should be done routinely. The combination of CEDs and alcohol may be a marker for higher risk of substance use or abuse and for other health-compromising behaviours.

Getting the NOD to Move Ahead

A review of: Hugot J-P, Chamaillard M, Zouali H, et al. 2001 Association of NOD2 leucine-rich repeat variants with susceptibility to Crohns disease. Nature 411:599–603; and Ogura Y, Bonen D, Inohara N, et al. 2001 A frameshift mutation in NOD2 associated with susceptibility to Crohns disease. Nature 411:603–606