Literature DB >> 7377187

Length biased sampling in etiologic studies.

R Simon.   

Abstract

The problem of estimating and comparing the frequency of a characteristic in newly diagnosed patients based upon a length biased sample of living patients is described in this paper. This problem is of considerable importance in immunogenetics for determining whether a characteristic is related to disease etiology, or whether, instead, it is of prognostic importance for individuals who have already developed the disease. Maximum likelihood estimation of the proportion of newly diagnosed patients having a characteristic is outlined. The estimator and its variance depend upon the proportion of living patients having the characteristic and upon the survivals of patients with and without the characteristic in the length biased sample.

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Year:  1980        PMID: 7377187     DOI: 10.1093/oxfordjournals.aje.a112920

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


  17 in total

1.  Bias.

Authors:  Miguel Delgado-Rodríguez; Javier Llorca
Journal:  J Epidemiol Community Health       Date:  2004-08       Impact factor: 3.710

2.  Estimating duration in partnership studies: issues, methods and examples.

Authors:  Bart Burington; James P Hughes; William L H Whittington; Brad Stoner; Geoff Garnett; Sevgi O Aral; King K Holmes
Journal:  Sex Transm Infect       Date:  2010-04       Impact factor: 3.519

3.  Composite Partial Likelihood Estimation Under Length-Biased Sampling, With Application to a Prevalent Cohort Study of Dementia.

Authors:  Chiung-Yu Huang; Jing Qin
Journal:  J Am Stat Assoc       Date:  2012-09-01       Impact factor: 5.033

4.  True and false positive peaks in genomewide scans: applications of length-biased sampling to linkage mapping.

Authors:  J D Terwilliger; W D Shannon; G M Lathrop; J P Nolan; L R Goldin; G A Chase; D E Weeks
Journal:  Am J Hum Genet       Date:  1997-08       Impact factor: 11.025

5.  Estimation in a competing risks proportional hazards model under length-biased sampling with censoring.

Authors:  Jean-Yves Dauxois; Agathe Guilloux; Syed N U A Kirmani
Journal:  Lifetime Data Anal       Date:  2013-03-03       Impact factor: 1.588

6.  Analyzing Length-biased Data with Semiparametric Transformation and Accelerated Failure Time Models.

Authors:  Yu Shen; Jing Ning; Jing Qin
Journal:  J Am Stat Assoc       Date:  2009-09-01       Impact factor: 5.033

Review 7.  Nonparametric and semiparametric regression estimation for length-biased survival data.

Authors:  Yu Shen; Jing Ning; Jing Qin
Journal:  Lifetime Data Anal       Date:  2016-04-16       Impact factor: 1.588

8.  Inference of Tamoxifen's Effects on Prevention of Breast Cancer from a Randomized Controlled Trial.

Authors:  Yu Shen; Jing Qin; Joseph P Costantino
Journal:  J Am Stat Assoc       Date:  2007-12-01       Impact factor: 5.033

9.  Imputation for semiparametric transformation models with biased-sampling data.

Authors:  Hao Liu; Jing Qin; Yu Shen
Journal:  Lifetime Data Anal       Date:  2012-08-18       Impact factor: 1.588

10.  Sample size calculations for prevalent cohort designs.

Authors:  Hao Liu; Yu Shen; Jing Ning; Jing Qin
Journal:  Stat Methods Med Res       Date:  2016-07-11       Impact factor: 3.021

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