Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population.

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Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population.

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2017 nî lūn-bûn

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2017年の論文

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年学术文章

@zh-my

2017年学术文章

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2017年學術文章

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2017年學術文章

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2017年學術文章

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Evaluating disease prediction ...... that of the target population.

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Evaluating disease prediction ...... that of the target population.

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prefLabel

Evaluating disease prediction ...... that of the target population.

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P1476

Evaluating disease prediction ...... that of the target population.

@en

P2093

Alice S Whittemore

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Alison J Canchola

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Scott Powers

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Valerie McGuire

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P2860

Adjustment for selection bias in observational studies with application to the analysis of autopsy data

A structural approach to selection bias.

External validity of risk models: Use of benchmark values to disentangle a case-mix effect from incorrect coefficients.

Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies.

Assessing the goodness of fit of personal risk models

Summarising and validating test accuracy results across multiple studies for use in clinical practice

Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks.

A new concordance measure for risk prediction models in external validation settings.

High breast cancer incidence rates among California teachers: results from the California Teachers Study (United States).

Predictive accuracy of novel risk factors and markers: A simulation study of the sensitivity of different performance measures for the Cox proportional hazards regression model.

P304

962280217723945

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P31

scholarly article

P356

10.1177/0962280217723945

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P50

Leslie Bernstein

P577

2017-01-01T00:00:00Z

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P698

28812439

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P921

covariate

P932

5895541

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