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Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research

Overview of attention for article published in BMC Medical Research Methodology, October 2016
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  • Above-average Attention Score compared to outputs of the same age (58th percentile)

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4 tweeters

Citations

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6 Dimensions

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13 Mendeley
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Title
Adjusting for overdispersion in piecewise exponential regression models to estimate excess mortality rate in population-based research
Published in
BMC Medical Research Methodology, October 2016
DOI 10.1186/s12874-016-0234-z
Pubmed ID
Authors

Miguel Angel Luque-Fernandez, Aurélien Belot, Manuela Quaresma, Camille Maringe, Michel P. Coleman, Bernard Rachet

Abstract

In population-based cancer research, piecewise exponential regression models are used to derive adjusted estimates of excess mortality due to cancer using the Poisson generalized linear modelling framework. However, the assumption that the conditional mean and variance of the rate parameter given the set of covariates x i are equal is strong and may fail to account for overdispersion given the variability of the rate parameter (the variance exceeds the mean). Using an empirical example, we aimed to describe simple methods to test and correct for overdispersion. We used a regression-based score test for overdispersion under the relative survival framework and proposed different approaches to correct for overdispersion including a quasi-likelihood, robust standard errors estimation, negative binomial regression and flexible piecewise modelling. All piecewise exponential regression models showed the presence of significant inherent overdispersion (p-value <0.001). However, the flexible piecewise exponential model showed the smallest overdispersion parameter (3.2 versus 21.3) for non-flexible piecewise exponential models. We showed that there were no major differences between methods. However, using a flexible piecewise regression modelling, with either a quasi-likelihood or robust standard errors, was the best approach as it deals with both, overdispersion due to model misspecification and true or inherent overdispersion.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 38%
Student > Ph. D. Student 2 15%
Other 2 15%
Student > Master 1 8%
Student > Bachelor 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Medicine and Dentistry 6 46%
Social Sciences 2 15%
Psychology 1 8%
Mathematics 1 8%
Biochemistry, Genetics and Molecular Biology 1 8%
Other 1 8%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 15 January 2020.
All research outputs
#7,495,771
of 14,322,412 outputs
Outputs from BMC Medical Research Methodology
#754
of 1,319 outputs
Outputs of similar age
#107,813
of 267,402 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 14,322,412 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,319 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.5. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,402 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them