Literature DB >> 3427161

Biases in prevalent cohorts.

R Brookmeyer1, M H Gail.   

Abstract

Several natural history studies of the Acquired Immunodeficiency Syndrome (AIDS) have been based on the follow-up of individuals infected with the AIDS virus prior to enrollment. The natural time scale for studying the preclinical course of AIDS is the time since first infection; however, in these studies the time at infection was a random unknown quantity. The biases inherent in using follow-up time instead of time from infection are investigated for estimation of both the cumulative distribution function and the hazard ratio for proportional hazards models with both fixed and time-dependent covariates. Although the magnitudes of the biases depend on the shape of the epidemic curve, a number of bounds on the biases are established. These results are useful for interpreting prevalent cohort studies and then comparing them with studies on newly infected individuals in order to assess consistency of results across studies.

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Year:  1987        PMID: 3427161

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  23 in total

1.  Natural history of diseases: Statistical designs and issues.

Authors:  Nicholas P Jewell
Journal:  Clin Pharmacol Ther       Date:  2016-08-18       Impact factor: 6.875

2.  Estimating incident population distribution from prevalent data.

Authors:  Kwun Chuen Gary Chan; Mei-Cheng Wang
Journal:  Biometrics       Date:  2012-02-07       Impact factor: 2.571

Review 3.  The Healthy Worker Survivor Effect: Target Parameters and Target Populations.

Authors:  Daniel M Brown; Sally Picciotto; Sadie Costello; Andreas M Neophytou; Monika A Izano; Jacqueline M Ferguson; Ellen A Eisen
Journal:  Curr Environ Health Rep       Date:  2017-09

4.  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

5.  Marker processes in survival analysis.

Authors:  N P Jewell; J D Kalbfleisch
Journal:  Lifetime Data Anal       Date:  1996       Impact factor: 1.588

6.  A meta-analysis of estimates of the AIDS incubation distribution.

Authors:  P C Cooley; L E Myers; D N Hamill
Journal:  Eur J Epidemiol       Date:  1996-06       Impact factor: 8.082

7.  Accounting for length-bias and selection effects in estimating the distribution of menstrual cycle length.

Authors:  Kirsten J Lum; Rajeshwari Sundaram; Thomas A Louis
Journal:  Biostatistics       Date:  2014-07-14       Impact factor: 5.899

8.  Premature Death Among Primary Care Patients With a History of Self-Harm.

Authors:  Matthew J Carr; Darren M Ashcroft; Evangelos Kontopantelis; David While; Yvonne Awenat; Jayne Cooper; Carolyn Chew-Graham; Nav Kapur; Roger T Webb
Journal:  Ann Fam Med       Date:  2017-05       Impact factor: 5.166

9.  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

10.  Effect of cervical cytologic status on the association between human papillomavirus type 16 DNA load and the risk of cervical intraepithelial neoplasia grade 3.

Authors:  Long Fu Xi; Nancy B Kiviat; Denise A Galloway; Xiao-Hua Zhou; Jesse Ho; Laura A Koutsky
Journal:  J Infect Dis       Date:  2008-08-01       Impact factor: 5.226

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