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Does Cox analysis of a randomized survival study yield a causal treatment effect?

Overview of attention for article published in Lifetime Data Analysis, June 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)

Mentioned by

1 blog
3 tweeters


36 Dimensions

Readers on

53 Mendeley
Does Cox analysis of a randomized survival study yield a causal treatment effect?
Published in
Lifetime Data Analysis, June 2015
DOI 10.1007/s10985-015-9335-y
Pubmed ID

Odd O. Aalen, Richard J. Cook, Kjetil Røysland


Statistical methods for survival analysis play a central role in the assessment of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and many other fields. The most common approach to analysis involves fitting a Cox regression model including a treatment indicator, and basing inference on the large sample properties of the regression coefficient estimator. Despite the fact that treatment assignment is randomized, the hazard ratio is not a quantity which admits a causal interpretation in the case of unmodelled heterogeneity. This problem arises because the risk sets beyond the first event time are comprised of the subset of individuals who have not previously failed. The balance in the distribution of potential confounders between treatment arms is lost by this implicit conditioning, whether or not censoring is present. Thus while the Cox model may be used as a basis for valid tests of the null hypotheses of no treatment effect if robust variance estimates are used, modeling frameworks more compatible with causal reasoning may be preferrable in general for estimation.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 17 32%
Researcher 14 26%
Student > Doctoral Student 6 11%
Professor > Associate Professor 5 9%
Student > Bachelor 2 4%
Other 8 15%
Unknown 1 2%
Readers by discipline Count As %
Mathematics 21 40%
Medicine and Dentistry 13 25%
Engineering 3 6%
Computer Science 3 6%
Unspecified 2 4%
Other 6 11%
Unknown 5 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 13. 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 October 2019.
All research outputs
of 13,777,258 outputs
Outputs from Lifetime Data Analysis
of 67 outputs
Outputs of similar age
of 271,936 outputs
Outputs of similar age from Lifetime Data Analysis
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Altmetric has tracked 13,777,258 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 67 research outputs from this source. They receive a mean Attention Score of 1.6. This one has done particularly well, scoring higher than 98% of its peers.
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 271,936 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% 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