Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1746-1762 |
| Seitenumfang | 17 |
| Fachzeitschrift | Statistical Methods in Medical Research |
| Jahrgang | 29 |
| Ausgabenummer | 6 |
| Frühes Online-Datum | 17 Sept. 2019 |
| Publikationsstatus | Veröffentlicht - 1 Juni 2020 |
Abstract
Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.
ASJC Scopus Sachgebiete
- Medizin (insg.)
- Epidemiologie
- Mathematik (insg.)
- Statistik und Wahrscheinlichkeit
- Gesundheitsberufe (insg.)
- Gesundheits-Informationsmanagement
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in: Statistical Methods in Medical Research, Jahrgang 29, Nr. 6, 01.06.2020, S. 1746-1762.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Simultaneous inference for multiple marginal generalized estimating equation models
AU - Ristl, Robin
AU - Hothorn, Ludwig
AU - Ritz, Christian
AU - Posch, Martin
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.
AB - Motivated by small-sample studies in ophthalmology and dermatology, we study the problem of simultaneous inference for multiple endpoints in the presence of repeated observations. We propose a framework in which a generalized estimating equation model is fit for each endpoint marginally, taking into account dependencies within the same subject. The asymptotic joint normality of the stacked vector of marginal estimating equations is used to derive Wald-type simultaneous confidence intervals and hypothesis tests for multiple linear contrasts of regression coefficients of the multiple marginal models. The small sample performance of this approach is improved by a bias adjustment to the estimate of the joint covariance matrix of the regression coefficients from multiple models. As a further small sample improvement a multivariate t-distribution with appropriate degrees of freedom is specified as reference distribution. In addition, a generalized score test based on the stacked estimating equations is derived. Simulation results show strong control of the family-wise type I error rate for these methods even with small sample sizes and increased power compared to a Bonferroni-Holm multiplicity adjustment. Thus, the proposed methods are suitable to efficiently use the information from repeated observations of multiple endpoints in small-sample studies.
KW - dependent observations
KW - Generalized estimating equations
KW - multiple endpoints
KW - multiple testing
KW - small samples
UR - http://www.scopus.com/inward/record.url?scp=85073982153&partnerID=8YFLogxK
U2 - 10.1177/0962280219873005
DO - 10.1177/0962280219873005
M3 - Article
C2 - 31526178
AN - SCOPUS:85073982153
VL - 29
SP - 1746
EP - 1762
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
SN - 0962-2802
IS - 6
ER -