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Estimating the variance of estimated trends in proportions when there is no unique subject identifier
Author(s) -
Mountford William K.,
Lipsitz Stuart R.,
Fitzmaurice Garrett M.,
Carter Rickey E.,
Soule Jeremy B.,
Colwell John A.,
Lackland Daniel T.
Publication year - 2007
Publication title -
journal of the royal statistical society: series a (statistics in society)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.103
H-Index - 84
eISSN - 1467-985X
pISSN - 0964-1998
DOI - 10.1111/j.1467-985x.2006.00453.x
Subject(s) - variance (accounting) , identifier , statistics , repeated measures design , econometrics , population , independence (probability theory) , computer science , mathematics , demography , sociology , accounting , business , programming language
Summary. Longitudinal population‐based surveys are widely used in the health sciences to study patterns of change over time. In many of these data sets unique patient identifiers are not publicly available, making it impossible to link the repeated measures from the same individual directly. This poses a statistical challenge for making inferences about time trends because repeated measures from the same individual are likely to be positively correlated, i.e., although the time trend that is estimated under the naïve assumption of independence is unbiased, an unbiased estimate of the variance cannot be obtained without knowledge of the subject identifiers linking repeated measures over time. We propose a simple method for obtaining a conservative estimate of variability for making inferences about trends in proportions over time, ensuring that the type I error is no greater than the specified level. The method proposed is illustrated by using longitudinal data on diabetes hospitalization proportions in South Carolina.