Case–Control Study, Two‐Phase

Breslow, Norman E.

doi:10.1002/0470011815.b2a03029

Cited by 10 publications

(4 citation statements)

References 34 publications

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“…7,[17][18][19][20][21][22][23][24]. For approaches that permit estimation of both main and interaction effects, the principle of these designs is similar to the approach that uses sibling controls, that is, increasing the frequency of the rare factors through over-sampling, to increase the power of the study.…”

Section: Discussionmentioning

confidence: 99%

Increased Power to Detect Gene–Environment Interaction Using Siblings Controls

Andrieu¹,

Dondon²,

Goldstein³

2005

Annals of Epidemiology

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Section: Discussionmentioning

confidence: 99%

Increased Power to Detect Gene–Environment Interaction Using Siblings Controls

Andrieu¹,

Dondon²,

Goldstein³

2005

Annals of Epidemiology

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“…Although the ideas behind two-phase studies have a long history (see Reference [1] for a historical account of the ideas), their implementation in epidemiology is more recent [2] and speciÿc issues have been examined in a series of papers since References [3][4][5][6]. We restrict ourselves in this paper to two-phase case-control studies with a validation sample.…”

Section: Introductionmentioning

confidence: 99%

Minmax designs for planning the second phase in a two‐phase case–control study

Schill

Wild

2005

Statistics in Medicine

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Two-phase designs, in which a subsample with validation or complete information data is sampled stratified both on outcome and covariate from a first-phase study with incomplete data, have been proposed over 10 years ago and have been proven to result in efficient estimates with respect to standard designs. The efficiency depends, however, on the sampling fractions within each stratum. Our aim is to present a strategy for obtaining an optimized design, i.e. sampling fractions, that makes use of available phase-one data of an existing case-control study considered as the first-phase sample when the focus is on estimating a parameter vector. No global optimal design exists and local optimal designs depend on scenarios comprising the true disease model and the association between the phase-one and phase-two information. We develop an admissibility test that rejects scenarios inconsistent with the phase-one data and, for the selected scenarios, determine a minmax D- or A-optimal design that protects against worst-case scenarios. This work is applied on two examples.

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“…However, expert time is rare and costly, thus Wild et al expert-assessment is often obtained only for subsets of subjects. So-called 2-phase designs (5) allow an efficient choice of such subsets as was repeatedly shown from a theoretical as well as applied point of view (6)(7)(8). Briefly, in 2-phase studies, the full dataset (the phase-1 dataset) is stratified on the case-control status and exposure estimates available at this stage.…”

mentioning

confidence: 99%