Transforming self-rated health and the SF-36 scales to include death and improve interpretability.
BACKGROUND
Most measures of health-related quality of life are undefined for people who die. Longitudinal analyses are often limited to a healthier cohort (survivors) that cannot be identified prospectively, and that may have had little change in health.


OBJECTIVE
To develop and evaluate methods to transform a single self-rated health item (excellent to poor; EVGGFP) and the physical component score of the SF-36 (PCS) to new variables that include a defensible value for death.


METHODS
Using longitudinal data from two large studies of older adults, health variables were transformed to the probability of being healthy in the future, conditional on the current observed value; death then has the value of 0. For EVGGFP, the new transformations were compared with some that were published earlier, based on different data. For the PCS, how well three different transformations, based on different definitions of being healthy, discriminated among groups of patients, and detected change in time were assessed.


RESULTS
The new transformation for EVGGFP was similar to that published previously. Coding the 5 categories as 95, 90, 80, 30, and 15, and coding dead as 0 is recommended. The three transformations of the PCS detected group differences and change at least as well as the standard PCS.


CONCLUSIONS
These easily interpretable transformed variables permit keeping persons who die in the analyses. Using the transformed variables for longitudinal analyses of health when deaths occur, either for secondary or primary analysis, is recommended. This approach can be applied to other measures of health.
Diehr
P
P
Department of Biostatistics, University of Washington, Seattle, Washington 98195-7232, USA.
Patrick
D L
DL
Spertus
J
J
Kiefe
C I
CI
McDonell
M
M
Fihn
S D
SD
D000368:Aged; D003627:Data Interpretation, Statistical; D003643:Death; D003657:Decision Making; D005260:Female; D006304:Health Status; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D011788:Quality of Life; D012372:ROC Curve; D011795:Surveys and Questionnaires