Abstract:
:We examine Goodman and Kruskal's lambda using Efron's approach to regression and analysis of variance (ANOVA) for zero-one outcome data. For a binary response cross-classified by a single nominal predictor, we present a computationally simple ANOVA table in which lambda is analogous to Pearson's R-square. We characterize the relationship between lambda and the commonly used apparent error rate in logistic regression, and show that lambda is based implicitly on a prediction rule for a saturated model with classification level 0.5. This relationship suggests that we can correct the apparent error rate for chance by defining a natural generalization of lambda that we call PRE, the proportional reduction in error. We illustrate the use of lambda and PRE in an analysis of prognostic factors for one-year survival in children with the acquired immunodeficiency syndrome (AIDS).
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Makuch RW,Rosenberg PS,Scott Gdoi
10.1002/sim.4780080511subject
Has Abstractpub_date
1989-05-01 00:00:00pages
619-31issue
5eissn
0277-6715issn
1097-0258journal_volume
8pub_type
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