Literature DB >> 14649845

Design of panel studies for disease progression with multiple stages.

Wei-Ting Hwang1, Ron Brookmeyer.   

Abstract

A panel study consists of individuals who have data collected at periodic follow-up visits or pre-specified time points following entry into the study. The objective of this paper is to consider design issues in a panel study when the response variable is the stage of disease, and with focus on the transition intensities. Important design issues include the choice of the time interval between follow-up visits and sample size considerations. We study the effects of time intervals between follow-up visits on the precision of the transition intensities estimators. We also consider the power of statistical tests on the ratio of transition intensities. Discussion is extended to incorporate heterogeneity in the population in which frailty is introduced to describe subject-specific transition intensities.

Mesh:

Year:  2003        PMID: 14649845     DOI: 10.1023/a:1025884719728

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  13 in total

1.  Estimating the extent of tracking in interval-censored chain-of-events data.

Authors:  G A Satten
Journal:  Biometrics       Date:  1999-12       Impact factor: 2.571

2.  Fitting semi-Markov models to interval-censored data with unknown initiation times.

Authors:  G A Satten; M R Sternberg
Journal:  Biometrics       Date:  1999-06       Impact factor: 2.571

3.  The design of a panel study under an alternating Poisson process assumption.

Authors:  P S Albert; C H Brown
Journal:  Biometrics       Date:  1991-09       Impact factor: 2.571

4.  Sampling design of multiwave studies with an application to the Massachusetts Health Care Panel Study.

Authors:  R Chappell
Journal:  Stat Med       Date:  1991-12       Impact factor: 2.373

5.  Using auxiliary variables for improved estimates of survival time.

Authors:  S W Lagakos
Journal:  Biometrics       Date:  1977-06       Impact factor: 2.571

6.  Heterogeneity in survival analysis.

Authors:  O O Aalen
Journal:  Stat Med       Date:  1988-11       Impact factor: 2.373

7.  The Clinical Dementia Rating (CDR): current version and scoring rules.

Authors:  J C Morris
Journal:  Neurology       Date:  1993-11       Impact factor: 9.910

8.  A stochastic model for censored-survival data in the presence of an auxiliary variable.

Authors:  S W Lagakos
Journal:  Biometrics       Date:  1976-09       Impact factor: 2.571

9.  Evaluating serial cancer marker studies in patients at risk of recurrent disease.

Authors:  M H Gail
Journal:  Biometrics       Date:  1981-03       Impact factor: 2.571

10.  Statistical analysis of the stages of HIV infection using a Markov model.

Authors:  I M Longini; W S Clark; R H Byers; J W Ward; W W Darrow; G F Lemp; H W Hethcote
Journal:  Stat Med       Date:  1989-07       Impact factor: 2.373

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  1 in total

Review 1.  Estimation and assessment of markov multistate models with intermittent observations on individuals.

Authors:  J F Lawless; N Nazeri Rad
Journal:  Lifetime Data Anal       Date:  2014-10-21       Impact factor: 1.588

  1 in total

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