Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis.
Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any tr...
| Publicado en: | Statistical Methods in Medical Research Vol. 28; no. 1; pp. 151 - 170 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133860293&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133860293 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Jan2019 vid: 28 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 133860293 133860293 NLM28670972 133860293 10.1177/0962280217717761 NLM28670972 133860293 ppf: 151 ppct: 19 formats: tig: atl: Testing for qualitative heterogeneity: An application to composite endpoints in survival analysis. aug: au: Oulhaj, Abderrahim El Ghouch, Anouar Holman, Rury R. affil: Institute of public health, College of Medicine & Health Sciences, United Arab Emirates University (UAEU), United Arab Emirates sug: subj: Biological Assay Statistics and Numerical Data Survival Analysis Clinical Trials Methods Alzheimer's Disease Drug Therapy Sample Size Human Treatment Outcomes Cox Proportional Hazards Model Data Analysis, Statistical Double-Blind Studies Validation Studies Comparative Studies Evaluation Research Multicenter Studies Questionnaires Scales ab: Composite endpoints are frequently used in clinical outcome trials to provide more endpoints, thereby increasing statistical power. A key requirement for a composite endpoint to be meaningful is the absence of the so-called qualitative heterogeneity to ensure a valid overall interpretation of any treatment effect identified. Qualitative heterogeneity occurs when individual components of a composite endpoint exhibit differences in the direction of a treatment effect. In this paper, we develop a general statistical method to test for qualitative heterogeneity, that is to test whether a given set of parameters share the same sign. This method is based on the intersection-union principle and, provided that the sample size is large, is valid whatever the model used for parameters estimation. We propose two versions of our testing procedure, one based on a random sampling from a Gaussian distribution and another version based on bootstrapping. Our work covers both the case of completely observed data and the case where some observations are censored which is an important issue in many clinical trials. We evaluated the size and power of our proposed tests by carrying out some extensive Monte Carlo simulations in the case of multivariate time to event data. The simulations were designed under a variety of conditions on dimensionality, censoring rate, sample size and correlation structure. Our testing procedure showed very good performances in terms of statistical power and type I error. The proposed test was applied to a data set from a single-center, randomized, double-blind controlled trial in the area of Alzheimer's disease. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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