Evidence map›Paper›PMID 34046812›Full record

ArticleLifetime data analysis2021

Augmented likelihood for incorporating auxiliary information into left-truncated data.

Yidan Shi, Leilei Zeng, Mary E Thompson, Suzanne L Tyas

Abstract read
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In one paragraph

Article in Lifetime data analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Yidan ShiDepartment of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.ORCID 0000-0002-3190-6724
Leilei ZengDepartment of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada. lzeng@uwaterloo.ca.ORCID 0000-0002-2336-7249
Mary E ThompsonDepartment of Statistics and Actuarial Science, University of Waterloo, 200 University Ave W, Waterloo, ON, N2L 3G1, Canada.ORCID 0000-0003-2354-8880
Suzanne L TyasSchool of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.ORCID 0000-0003-3007-3940

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Time-to-event data are often subject to left-truncation. Lack of consideration of the sampling condition will introduce bias and loss in efficiency of the estimation. While auxiliary information from the same or similar cohorts may be available, challenges arise due to the practical issue of accessibility of individual-level data and taking account of various sampling conditions for different cohorts. In this paper, we introduce a likelihood-based method to incorporate information from auxiliary data to eliminate the left-truncation problem and improve efficiency. A one-step Monte-Carlo Expectation-Maximization algorithm is developed to calculate an augmented likelihood through creating pseudo-data sets which extend the form and conditions of the observed sample. The method is illustrated by both a real dataset and simulation studies.

Indexed as

AlgorithmsBiasComputer SimulationHumansLikelihood FunctionsMonte Carlo MethodAugmented likelihoodAuxiliary informationLeft-truncationTime-to-event data

Identifiers

PMID34046812

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.