Parametric modelling of prevalent cohort data with uncertainty in the measurement of the initial onset date

2019 ◽  
Vol 26 (2) ◽  
pp. 389-401
Author(s):  
J. H. McVittie ◽  
D. B. Wolfson ◽  
D. A. Stephens
Biometrics ◽  
1993 ◽  
Vol 49 (1) ◽  
pp. 1 ◽  
Author(s):  
Mei-Cheng Wang ◽  
Ron Brookmeyer ◽  
Nicholas P. Jewell

2019 ◽  
Vol 38 (12) ◽  
pp. 2103-2114 ◽  
Author(s):  
Chi Hyun Lee ◽  
Jing Ning ◽  
Richard J. Kryscio ◽  
Yu Shen

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3333-3345 ◽  
Author(s):  
David B Wolfson ◽  
Ana F Best ◽  
Vittorio Addona ◽  
Julian Wolfson ◽  
Shahinaz M Gadalla

It is frequently of interest to estimate the time that individuals survive with a disease, that is, to estimate the time between disease onset and occurrence of a clinical endpoint such as death. Epidemiologic survival data are commonly collected from either an incident cohort, whose members' disease onset occurs after the study baseline date, or from a cohort with prevalent disease that is followed forward in time. Incident cohort survival data are limited by study termination, while prevalent cohort data provide biased (left-truncated) survival data. In this article, we investigate the advantages of a study design featuring simultaneous follow-up of prevalent and incident cohorts to the estimation of the survivor function. Our analyses are supported by simulations and illustrated using data on survival after myotonic dystrophy diagnosis from the United Kingdom Clinical Practice Research Datalink (CPRD). We demonstrate that the NPMLE using combined incident and prevalent cohort data estimates the true survivor function very well, even for moderate sample sizes, and ameliorates the disadvantages of using a purely incident or prevalent cohort.


AIDS ◽  
1998 ◽  
Vol 12 (12) ◽  
pp. 1537-1544 ◽  
Author(s):  
Jan C.M. Hendriks ◽  
Glen A. Satten ◽  
Erik J. C. van Ameijden ◽  
Hans A.M. van Druten ◽  
Roel A. Coutinho ◽  
...  

1996 ◽  
Vol 15 (10) ◽  
pp. 1023-1032 ◽  
Author(s):  
XINHUA LIU ◽  
WEI-YANN TSAI ◽  
YAAKOV STERN

2020 ◽  
Author(s):  
Christopher Greenwood ◽  
George Joseph Youssef ◽  
Primrose Letcher ◽  
Elizabeth Spry ◽  
Lauryn Hagg ◽  
...  

Aims: To explore the process of applying counterfactual thinking in examining causal predictors of substance use trajectories in observational cohort data. Specifically, we examine the extent to which quality of the parent-adolescent relationship and affiliations with deviant peers are causally related to trajectories of alcohol, tobacco, and cannabis use across adolescence and into young adulthood. Methods: Data were drawn from the Australian Temperament Project, a population-based cohort study that has followed a sample of young Australians from infancy to adulthood since 1983. Parent-adolescent relationship quality and deviant peer affiliations were assessed at age 13-14 years. Latent curve models were fitted for past month alcohol, tobacco, and cannabis use (n = 1,590) from age 15-16 to 27-28 years (5 waves). Confounding factors were selected in line with the counterfactual framework. Results: Following confounder adjustment, higher quality parent-adolescent relationships were associated with lower baseline cannabis use, but not alcohol or tobacco use trajectories. In contrast, affiliations with deviant peers were associated with higher baseline binge drinking, tobacco, and cannabis use, and an earlier peak in the cannabis use trajectory. Conclusions: Confounding adjustments weakened several estimated associations and the interpretation of such associations as causal is not without limitations. Nevertheless, findings suggested causal effects of both parent-adolescent relationships and deviant peer affiliations on the trajectory of substance use. Causal effects were however more pervasive (i.e., more substance types) and protracted for deviant peer affiliations. The current study encourages the exploration of causal relationships in observational cohort data, when relevant limitations are transparently acknowledged.


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