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Correcting for the dependent competing risk of treatment using inverse probability of censoring weighting and copulas in the estimation of natural conception chances

Overview of attention for article published in Statistics in Medicine, August 2014
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Title
Correcting for the dependent competing risk of treatment using inverse probability of censoring weighting and copulas in the estimation of natural conception chances
Published in
Statistics in Medicine, August 2014
DOI 10.1002/sim.6280
Pubmed ID
Authors

N. van Geloven, R. B. Geskus, B. W. Mol, A. H. Zwinderman

Abstract

When estimating the probability of natural conception from observational data on couples with an unfulfilled child wish, the start of assisted reproductive therapy (ART) is a competing event that cannot be assumed to be independent of natural conception. In clinical practice, interest lies in the probability of natural conception in the absence of ART, as this probability determines the need for therapy. We thus want to estimate the marginal cumulative pregnancy distribution. Without assumptions on the dependence structure between the two competing events, this marginal distribution is not identifiable. We first use inverse probability of censoring weighting assuming that the factors influencing the choice to start ART are known. Then, we parameterize the event distributions for conception and for start of ART and use copulas to account for the dependency between both events. By using these two ways of correcting for the dependent risk of treatment, we obtain a plausible estimation region for the cumulative pregnancy curve and for the prognostic effect of tubal tests.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 44%
Researcher 4 25%
Librarian 1 6%
Student > Bachelor 1 6%
Student > Doctoral Student 1 6%
Other 2 13%
Readers by discipline Count As %
Mathematics 7 44%
Medicine and Dentistry 4 25%
Nursing and Health Professions 1 6%
Psychology 1 6%
Economics, Econometrics and Finance 1 6%
Other 0 0%
Unknown 2 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 August 2014.
All research outputs
#16,703,088
of 24,565,648 outputs
Outputs from Statistics in Medicine
#2,410
of 4,026 outputs
Outputs of similar age
#138,073
of 234,900 outputs
Outputs of similar age from Statistics in Medicine
#24
of 54 outputs
Altmetric has tracked 24,565,648 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,026 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 29th percentile – i.e., 29% of its peers scored the same or lower than it.
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We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.