Weighted least squares analysis of repeated categorical measurements with outcomes subject to nonresponse.
In this paper, we describe a two-step weighted least squares method for analyzing repeated categorical outcomes when some individuals are not observed at all times of follow-up. Other weighted least squares methods for analyzing repeated measures data with missing responses have previously been proposed by Koch, Imrey, and Reinfurt (1972, Biometrics 28, 663-692) and Woolson and Clarke (1984, Journal of the Royal Statistical Society, Series A 147, 87-99). These methods give consistent estimators if the responses are missing completely at random, as discussed in Rubin (1976, Biometrika 63, 581-592). We propose a two-step method that will give consistent results under the weaker condition of missing at random, and compare it with the other two methods.
Lipsitz
S R
SR
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.
Laird
N M
NM
Harrington
D P
DP
D000293:Adolescent; D015982:Bias; D001699:Biometry / Q000379:methods; D002648:Child; D003627:Data Interpretation, Statistical; D005260:Female; D006331:Heart Diseases / Q000453:epidemiology; D006801:Humans; D016018:Least-Squares Analysis; D008137:Longitudinal Studies; D015233:Models, Statistical; D012307:Risk Factors