Show simple item record

dc.contributor.authorHaase, Trutz
dc.contributor.authorComber, Harry
dc.contributor.authorSharp, Linda
dc.contributor.authorde Camargo Cancela, Marianna
dc.contributor.authorJohnson, Howard
dc.contributor.authorPratschke, Jonathan
dc.date.accessioned2017-03-03T09:07:47Z
dc.date.available2017-03-03T09:07:47Z
dc.date.issued2016-02-29
dc.identifier.citationMechanisms and mediation in survival analysis: towards an integrated analytical framework. 2016, 16:27 BMC Med Res Methodolen
dc.identifier.issn1471-2288
dc.identifier.pmid26927506
dc.identifier.doi10.1186/s12874-016-0130-6
dc.identifier.urihttp://hdl.handle.net/10147/621092
dc.descriptionBACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.en
dc.description.abstractA 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.
dc.description.abstractThe 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.
dc.description.abstractThe 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.
dc.description.abstractThe 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.
dc.language.isoenen
dc.rightsArchived with thanks to BMC medical research methodologyen
dc.subjectRESEARCHen
dc.subjectHEALTH OUTCOMESen
dc.subject.meshAge Factors
dc.subject.meshAged
dc.subject.meshCause of Death
dc.subject.meshColonic Neoplasms
dc.subject.meshFemale
dc.subject.meshHumans
dc.subject.meshIntegrated Advanced Information Management Systems
dc.subject.meshMale
dc.subject.meshMiddle Aged
dc.subject.meshModels, Statistical
dc.subject.meshNegotiating
dc.subject.meshProportional Hazards Models
dc.subject.meshSensitivity and Specificity
dc.subject.meshSex Factors
dc.subject.meshSurvival Analysis
dc.titleMechanisms and mediation in survival analysis: towards an integrated analytical framework.en
dc.typeArticleen
dc.identifier.journalBMC medical research methodologyen
refterms.dateFOA2018-08-27T19:13:54Z
html.description.abstractA 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.
html.description.abstractThe 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.
html.description.abstractThe 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.
html.description.abstractThe 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.


Files in this item

Thumbnail
Name:
MechanismsMediationSurvivalAna ...
Size:
821.7Kb
Format:
PDF
Description:
OA Article

This item appears in the following Collection(s)

Show simple item record