Accounting for Deaths in Longitudinal Studies Using the SF-36

BACKGROUND: Commonly used measures such as the Physical Component Scale of the Short Form 36-item health survey (PCS) are undefined at death, limiting longitudinal analyses to survivors, a healthier cohort that cannot be identified prospectively, and that might have had little change in health. One proposed approach is to transform the PCS into the Physical Component Transformed, with Deaths included (PCTD), which is the probability of being healthy 1 year later and for which deaths logically have a value of zero. Data missing for other reasons than death have not been considered. OBJECTIVE: To examine the performance of the PCTD, to determine the influence of including deaths, the additional effects of imputing missing values and adjusting for covariates, and the calibration of the PCTD in different populations. METHODS: We imputed missing values of the PCTD, calling the new variable the PCTDI. We compared the distributions of the PCS, PCTD, and PCTDI cross-sectionally and over time. In 3 different populations, we determined whether the PCTD accurately predicted the probability of being healthy 1 year later. RESULTS: The patients who died did not have extreme values on the PCTD. The experience of the cohort was best described by the PCTDI. The calibration of the PCTD was surprisingly good in all the populations examined. Results were similar for the physical function index. CONCLUSION: The PCTDI is an improvement over the PCS, in which patients who had died have no influence, and over the PCTD, where they might have too much influence. We recommend the PCTDI for longitudinal analyses of physical health when deaths occur, for primary or secondary analysis

Cohort · Cohort study · Covariate · Limiting · Longitudinal study · Missing data · Statistics · Chronic Disease Management Strategies · Demography · Health disparities and outcomes · Insurance, Mortality, Demography, Risk Management · Mathematics · Medicine · Gerontology

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