Combining data from 2 nested case-control studies of overlapping cohorts to improve efficiency

Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data...

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Published inBiostatistics (Oxford, England) Vol. 10; no. 1; pp. 70 - 79
Main Authors Salim, Agus, Hultman, Christina, Sparén, Pär, Reilly, Marie
Format Journal Article
LanguageEnglish
Published England Oxford University Press 01.01.2009
Oxford Publishing Limited (England)
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Online AccessGet full text
ISSN1465-4644
1468-4357
1468-4357
DOI10.1093/biostatistics/kxn016

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Abstract Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data from a larger nested case-control study of a different outcome in the same or overlapping cohort. Our approach is based on the inverse probability weighting concept, in which the log-likelihood contribution of each individual observation is weighted by the inverse of its probability of inclusion in either study. We illustrate our approach using simulated data and an application where we combine data sets from 2 nested case-control studies to investigate risk factors for anorexia nervosa in a cohort of young women in Sweden.
AbstractList Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data from a larger nested case-control study of a different outcome in the same or overlapping cohort. Our approach is based on the inverse probability weighting concept, in which the log-likelihood contribution of each individual observation is weighted by the inverse of its probability of inclusion in either study. We illustrate our approach using simulated data and an application where we combine data sets from 2 nested case-control studies to investigate risk factors for anorexia nervosa in a cohort of young women in Sweden.
Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data from a larger nested case-control study of a different outcome in the same or overlapping cohort. Our approach is based on the inverse probability weighting concept, in which the log-likelihood contribution of each individual observation is weighted by the inverse of its probability of inclusion in either study. We illustrate our approach using simulated data and an application where we combine data sets from 2 nested case-control studies to investigate risk factors for anorexia nervosa in a cohort of young women in Sweden.Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data from a larger nested case-control study of a different outcome in the same or overlapping cohort. Our approach is based on the inverse probability weighting concept, in which the log-likelihood contribution of each individual observation is weighted by the inverse of its probability of inclusion in either study. We illustrate our approach using simulated data and an application where we combine data sets from 2 nested case-control studies to investigate risk factors for anorexia nervosa in a cohort of young women in Sweden.
Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data from a larger nested case-control study of a different outcome in the same or overlapping cohort. Our approach is based on the inverse probability weighting concept, in which the log-likelihood contribution of each individual observation is weighted by the inverse of its probability of inclusion in either study. We illustrate our approach using simulated data and an application where we combine data sets from 2 nested case-control studies to investigate risk factors for anorexia nervosa in a cohort of young women in Sweden. [PUBLICATION ABSTRACT]
Author Salim, Agus
Hultman, Christina
Sparén, Pär
Reilly, Marie
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Cites_doi 10.1136/bmj.318.7181.421
10.1002/sim.2398
10.1093/biomet/65.1.153
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10.1093/biomet/84.2.379
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ContentType Journal Article
Copyright The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org. 2009
The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.
Copyright_xml – notice: The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org. 2009
– notice: The Author 2008. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.
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Cnattingius (3_10883328) 1999; 56
CLAYTON (2_21359728) 1978; 65
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Jiang (5_21872322) 2006; 25
(12_29714036) 1980; 29
Reilly (9_19716241) 2005; 24
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SubjectTerms Anorexia
Anorexia Nervosa - epidemiology
Anorexia Nervosa - etiology
Biomedical research
Biometry - methods
Case-Control Studies
Cohort Studies
Data Collection - methods
Female
Humans
Likelihood Functions
Meta-Analysis as Topic
Optimization
Probability
Proportional Hazards Models
Psychotic Disorders - epidemiology
Psychotic Disorders - etiology
Risk Factors
Sample Size
Schizophrenia - epidemiology
Schizophrenia - etiology
Statistical analysis
Sweden
Title Combining data from 2 nested case-control studies of overlapping cohorts to improve efficiency
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