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Table 4 Logistic regression coefficients for the missing data model.

From: Genetic Analysis Workshop 13: Simulated longitudinal data on families for a system of oligogenic traits

Model αA M i,j-1 M i,j-2 M sp,j M sp,j-1 M sp,j-2 NA mo NA fa CHOL ij WT ij OC i MS ij Visit (j)
1 -3.88 2.390 1.58 1.06          -0.005 0.003 0.093 0.297 0.0256
2 -4.36 2.650 1.85 0.86 3.83 -1.42 -1.13       -0.005 0.003 0.121 0.770 0.0213
3 -3.87 2.380 1.58 1.06     -0.010      -0.005 0.003 0.092 0.297 0.0256
4 -4.35 2.650 1.85 0.86 3.82 -1.14 -1.13 0.054      -0.005 0.003 0.120 0.770 0.0214
5 -1.52 0.919   1.80      0.87 0.88 -0.18 -0.41 -0.005 0.003 0.224   -0.4510
6 -1.71 0.903   1.74 1.710 0.64 0.73 -0.17 -0.31 -0.005 0.003 -0.4490      
  1. Aα, intercept; M ij , indicator for subject i's visit j being missing (with i replaced by sp, mo, fa, sib for spouse, mother, father, and sib respectively); , average missingness proportion for subject i up to and including visit j (if the second subscript is omitted, the average is taken over the entire history); MS, marital status; NA, indicator for parents' being not available in the data set; OC, only child; CHOL, cholesterol; WT, weight.