A Successive Differences Method for Growth Curves with Missing Data and Random Observation Times
作者:
NeilC. Schwertman,
LanceK. Heilbrun,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1986)
卷期:
Volume 81,
issue 396
页码: 912-916
ISSN:0162-1459
年代: 1986
DOI:10.1080/01621459.1986.10478349
出版商: Taylor & Francis Group
关键词: Geisser—Greenhouse correction;Regression;Repeated measures;Serum cholesterol
数据来源: Taylor
摘要:
Incomplete growth curve data can be analyzed by the successive differences (SD) method, which uses the difference in consecutive pairs of observations for all subjects having two or more repeated measures. We have generalized it to handle varying observation times as well by partitioning the time interval spanned by all repeated measures into subintervals. The model is developed, including test statistics for the hypotheses of parallelism and of no change in response level over time, assuming parallelism. This generalized SD method is then applied to repeated serum cholesterol measurements on a subsample of 1,072 men from a prospective cohort study of 8,006 Hawaiian Japanese men living on Oahu.
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