Literature DB >> 3605062

A simple estimator of minimum detectable relative risk, sample size, or power in cohort studies.

B Armstrong.   

Abstract

In planning simple cohort mortality studies, researchers need to know what size of relative risk may be confidently detected with the projected size of the cohort and length of follow-up. Although methods for the calculation of such minimum detectable risks have been devised for case-control studies and for cohort studies where internal comparisons are the focus, this has not been explicitly done for cohort studies using external comparisons. This paper describes a simple procedure designed explicitly for investigating the adequacy of cohort size at the planning stage of a study. An example is presented of a retrospective cohort study of men in a Canadian factory. A method is shown for estimating the minimum detectable underlying relative risk for lung cancer in this cohort.

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Year:  1987        PMID: 3605062     DOI: 10.1093/aje/126.2.356

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  18 in total

1.  How Confident Are We about Observational Findings in Healthcare: A Benchmark Study.

Authors:  Martijn J Schuemie; M Soledad Cepeda; Marc A Suchard; Jianxiao Yang; Yuxi Tian; Alejandro Schuler; Patrick B Ryan; David Madigan; George Hripcsak
Journal:  Harv Data Sci Rev       Date:  2020-01-31

2.  Experiences from longitudinal studies of aging: An international perspective.

Authors:  D J Deeg; G H Van Der Zanden
Journal:  J Cross Cult Gerontol       Date:  1991-01

3.  Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system.

Authors:  Patrick B Ryan; Martijn J Schuemie; Susan Gruber; Ivan Zorych; David Madigan
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

4.  Replication of the OMOP experiment in Europe: evaluating methods for risk identification in electronic health record databases.

Authors:  Martijn J Schuemie; Rosa Gini; Preciosa M Coloma; Huub Straatman; Ron M C Herings; Lars Pedersen; Francesco Innocenti; Giampiero Mazzaglia; Gino Picelli; Johan van der Lei; Miriam C J M Sturkenboom
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

5.  Alternative outcome definitions and their effect on the performance of methods for observational outcome studies.

Authors:  Christian G Reich; Patrick B Ryan; Martijn J Schuemie
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

6.  Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system.

Authors:  Patrick B Ryan; Martijn J Schuemie; David Madigan
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

7.  Empirical performance of LGPS and LEOPARD: lessons for developing a risk identification and analysis system.

Authors:  Martijn J Schuemie; David Madigan; Patrick B Ryan
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

8.  The impact of drug and outcome prevalence on the feasibility and performance of analytical methods for a risk identification and analysis system.

Authors:  Christian G Reich; Patrick B Ryan; Marc A Suchard
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

9.  Evaluating performance of risk identification methods through a large-scale simulation of observational data.

Authors:  Patrick B Ryan; Martijn J Schuemie
Journal:  Drug Saf       Date:  2013-10       Impact factor: 5.606

10.  Longitudinal relationships among visual acuity, daily functional status, and mortality: the Salisbury Eye Evaluation Study.

Authors:  Sharon L Christ; D Diane Zheng; Bonnielin K Swenor; Byron L Lam; Sheila K West; Stacey L Tannenbaum; Beatriz E Muñoz; David J Lee
Journal:  JAMA Ophthalmol       Date:  2014-12       Impact factor: 7.389

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