A pairwise likelihood augmented Cox estimator for left‐truncated data

A pairwise likelihood augmented Cox estimator for left‐truncated data IntroductionSurvival data collected from a prevalent cohort that includes patients who already have the disease at the study enrollment are subject to left truncation. This is because those who died with the disease before the enrollment would have no chances to be selected, whereas the selected patients, having survived until the enrollment, are healthier on average. To avoid overestimating the survival, conventional approaches make inferences conditional on truncation times (Kalbfleisch and Lawless, ; Wang et al., ). These approaches disregard the information about the regression coefficients in the marginal likelihood of the truncation times, and hence loss of efficiency is expected when additional knowledge on the underlying truncation distribution is available (Huang and Qin, ).If the underlying truncation time is uniformly distributed, left truncation reduces to length‐biased sampling (Vardi, ), that is, the probability of selecting a subject is proportional to the length of his or her underlying failure time; see a comprehensive review by Shen et al. (). Among the newly developed regression methods for length‐biased data, many show considerable improvement of efficiency in estimation compared with the conditional approach by incorporating information from the observed truncation times (Qin and Shen, ; Qin et al., ; Huang et http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Wiley

A pairwise likelihood augmented Cox estimator for left‐truncated data

Loading next page...
 
/lp/wiley/a-pairwise-likelihood-augmented-cox-estimator-for-left-truncated-data-3R58sYKt9H
Publisher
Wiley
Copyright
© 2018, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
D.O.I.
10.1111/biom.12746
Publisher site
See Article on Publisher Site

Abstract

IntroductionSurvival data collected from a prevalent cohort that includes patients who already have the disease at the study enrollment are subject to left truncation. This is because those who died with the disease before the enrollment would have no chances to be selected, whereas the selected patients, having survived until the enrollment, are healthier on average. To avoid overestimating the survival, conventional approaches make inferences conditional on truncation times (Kalbfleisch and Lawless, ; Wang et al., ). These approaches disregard the information about the regression coefficients in the marginal likelihood of the truncation times, and hence loss of efficiency is expected when additional knowledge on the underlying truncation distribution is available (Huang and Qin, ).If the underlying truncation time is uniformly distributed, left truncation reduces to length‐biased sampling (Vardi, ), that is, the probability of selecting a subject is proportional to the length of his or her underlying failure time; see a comprehensive review by Shen et al. (). Among the newly developed regression methods for length‐biased data, many show considerable improvement of efficiency in estimation compared with the conditional approach by incorporating information from the observed truncation times (Qin and Shen, ; Qin et al., ; Huang et

Journal

BiometricsWiley

Published: Jan 1, 2018

Keywords: ; ; ; ;

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off