Assessing sensitivity to change: choosing the appropriate change coefficient

Health Qual Life Outcomes. 2005 Apr 5;3:23. doi: 10.1186/1477-7525-3-23.


The past 20-years have seen the development and evaluation of many health status measures. Unlike the high standards demanded of those who conduct and report clinical intervention trials, the methodological rigor for studies examining the sensitivity to change of health status measures are less demanding. It is likely that the absence of a criterion standard for change in health status contributes to this shortcoming. To increase confidence in the results of these types of studies investigators have often calculated multiple change coefficients for the same patient sample. The purpose of this report is to identify the conflict that arises when multiple change coefficients are applied to the same patient sample. Three families of change coefficients based on different assumptions concerning the sample composition are identified: (1) the sample is homogeneous with respect to change; (2) subgroups of patients who truly change by different amounts exist; (3) individual patients, many of whom truly change by different amounts exist. We present several analyses which illustrate a major conceptual conflict: the signal (a measure's true ability to detect change) for some of these coefficients appears in the noise term (measurement error) of the others. We speculate that this dilemma occurs as a result of insufficient preparatory work such as pilot studies to establish the likely change characteristic of the patient population of interest. Uncertainty in the choice of change coefficient could be overcome by conducting pilot studies to ascertain the likely change characteristic of the population of interest. Once the population's change characteristic is identified, the choice of change coefficient should be clear.

MeSH terms

  • Analysis of Variance
  • Clinical Trials as Topic / methods*
  • Clinical Trials as Topic / statistics & numerical data
  • Confidence Intervals
  • Disease Progression
  • Humans
  • Prognosis
  • Research Design
  • Sampling Studies*
  • Sensitivity and Specificity*
  • Sickness Impact Profile*
  • Time Factors
  • Treatment Outcome