Scores Interpretation.

Population level data:

The change of 1-2 units or greater in PCS or MCS scores has shown to be clinically and socially relevant (Kazis et al 2004, Kazis 2006).

At the population level small changes in PCS or MCS may have relevant policy implications. CMS explored the relationship between changes in health status and health expenditures, utilization of services, and experiences of care using data from the Medicare Health Outcomes Survey, the Managed Care CHAPS Enrollee Survey and the MCBS Cost and Use Data. Controlling for age, gender, race, education, marital status, region of residence, smoking status, Medicaid dual eligibility and the presence or absence of hypertension, myocardial infarction, angina pectoris/CAD, non-skin cancer, diabetes, and emphysema/asthma/COPD their results suggest:

Note: The SF-36 rather than the VR-36^© was used.

Selim et al (2006) explored the probability of being alive with the same or better (than would be expected by chance) PCS or MCS at 2 years and mortality, while adjusting for case-mix at the VHA and Medicare Advantage Program. They did not find significant differences in the probability of being alive with the same or better PCS except for the South region (VHA 65.8% vs. MAP 62.5%, P = .0014).

Clinical and Individual level data:

Depending upon the design and purpose of the study, the definition of clinically relevant may vary. Several individual repeated measures (at least three) are required to understand the clinical significance of an observed change. As figure 8 illustrates, few repeated measures may result in a misleading trend (more noise, less signal) of PCS (dotted line) due to regression towards the mean. This threat to internal validity arises when a variable is extreme on its first measurement but it will tend to be closer to the true average on its second measurement. Paradoxically, if it is extreme on its second measurement, it will tend to have been closer to the average on its first. On the other hand, more than 3 measurements are more likely to capture true trends towards improvement or worsening (less noise, more signal). The increasing use of mHealth may facilitate obtaining several measurements across time. Go to section When to use VR to read more about mHealth.

At the individual level, 6.5 units in PCS and 7.9 units in MCS are required to consider a change clinically relevant (Ware et al. 1996).

There are several techniques to quantify the responsiveness of an instrument towards a clinically meaningful change (Lydick et al 1993, Guyatt 2002). They may be estimated using effect sizes, supplemented by more traditional anchor-based methods of benchmarking (i.e. cross-sectional or longitudinal approaches) (Samsa, 1999). Anchor-based methodology measures a patient change score against a clinically relevant or outside change (the anchor or independent standard) (Lydick et al 1993). Effect size is the mean change of the individual divided by the variability of the whole group or the subset of stable subjects. It is used to translate “the before and after changes” in a “one group” situation into a standard unit of measurement that will provide a clearer understanding of health status results (Kazis et al. 1989). An effect size of 0.2 is considered small, 0.5 is moderate, while 0.8 or greater is considered large (Cohen et al 1988).

The VR-36^© (and VR-12^©) has been employed in relation to both anchor based and distribution based techniques to gauge the clinical significance of a change following an intervention. The significant associations of VR-36^©/VR-12^© PCS and MCS summary measures with clinically recognizable and meaningful changes in randomized clinical trials and observational naturalistic prospective studies strongly suggests that these measures can be applied to other empirical inquiries designed to quantify a clinical benefit.