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Mechanisms and mediation in survival analysis: towards an integrated analytical framework

Overview of attention for article published in BMC Medical Research Methodology, February 2016
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2 tweeters

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20 Dimensions

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95 Mendeley
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Title
Mechanisms and mediation in survival analysis: towards an integrated analytical framework
Published in
BMC Medical Research Methodology, February 2016
DOI 10.1186/s12874-016-0130-6
Pubmed ID
Authors

Jonathan Pratschke, Trutz Haase, Harry Comber, Linda Sharp, Marianna de Camargo Cancela, Howard Johnson

Abstract

A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy of this approach in identifying complex causal pathways that mediate the effects of a socio-economic baseline covariate on the hazard of death from colon cancer. The results show that this approach has the potential to shed light on a class of research questions which is of particular relevance in health research.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 95 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 1 1%
Unknown 94 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 24%
Student > Master 19 20%
Researcher 11 12%
Student > Doctoral Student 9 9%
Student > Bachelor 9 9%
Other 17 18%
Unknown 7 7%
Readers by discipline Count As %
Medicine and Dentistry 21 22%
Social Sciences 15 16%
Psychology 12 13%
Nursing and Health Professions 9 9%
Mathematics 6 6%
Other 16 17%
Unknown 16 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 June 2021.
All research outputs
#13,335,184
of 21,346,377 outputs
Outputs from BMC Medical Research Methodology
#1,314
of 1,901 outputs
Outputs of similar age
#143,696
of 280,117 outputs
Outputs of similar age from BMC Medical Research Methodology
#1
of 1 outputs
Altmetric has tracked 21,346,377 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,901 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.4. This one is in the 27th percentile – i.e., 27% of its peers scored the same or lower than it.
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