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Table 2 Estimated associations between baseline predictor and change in health from appropriate use of change score analyses (i.e. in situations where RTM was assumed not to occur), as shown in Figure 3

From: Bias in the study of prediction of change: a Monte Carlo simulation study of the effects of selective attrition and inappropriate modeling of regression toward the mean

 

50% attrition rate

 

70% attrition rate

 

Dependency (bdrop)

bpred(SE)

Coverage of 95% C.I.

Diff

bpred(SE)

Coverage of 95% C.I.

Diff

Pred.

V1

V2

      

0

0

0

.10 (.04)

96%

0.00

.10 (.05)

96%

0.00

.3

.3

0

.13 (.04)

86%

0.08

.13 (.05)

91%

0.11

.3

.3

.1

.12 (.04)

90%

0.04

.13 (.05)

92%

0.06

.3

.3

.3

.10 (.04)

94%

-0.02

.10 (.06)

93%

-0.03

  1. Dependency is the magnitude (bdrop) of the regression of liability of dropping out on each of the three study variables. Pred = baseline predictor. V1 = the main variable at baseline (baseline health), V2 = the main variable at follow-up (follow-up health). SE = standard error. bpred = regression coefficient from predictor to change in health. Coverage of 95% C.I. = the percentage of the 500 samples with an estimated bpred with a 95% confidence interval containing the true population value. Diff = estimated change score (follow-up health minus baseline health). bpred, SE, and Diff are average results over the 500 generated samples. N in the original samples was 1000. The first line shows results when attrition was completely random.
  2. The true population value was bpred = .10.