Predictive diagnostics for logistic models.

Abstract:

:Novel methodology is implemented to assess the predictive power of covariate information associated with sequential binary events. Logistic models are first fitted on the basis of a subset of the observations and then evaluated sequentially on the rest. The probabilistic forecasts are compared to the outcomes via a scoring function, but as most validation samples are small, the usual reference distribution for the test statistics is inadequate. However, bootstrap-based distributions can easily be constructed. The first example pertains to the evaluation of screening tests for major depression. It illustrates that goodness-of-fit and predictive assessments lead to the selection of very different models. The second example deals with the prediction of a major event in the natural history of HIV-induced disease. It shows that this type of analysis can reveal features missed by other approaches.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Seillier-Moiseiwitsch F

doi

10.1002/(SICI)1097-0258(19961030)15:20<2149::AID-S

subject

Has Abstract

pub_date

1996-10-30 00:00:00

pages

2149-60

issue

20

eissn

0277-6715

issn

1097-0258

pii

10.1002/(SICI)1097-0258(19961030)15:20<2149::AID-S

journal_volume

15

pub_type

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