title: Mechanisms and mediation in survival analysis: towards an integrated analytical framework.
abstract: BACKGROUND
  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.
authors.affiliation: Department of Economics and Statistics, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Italy. jpratschke@unisa.it.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Pratschke
authors.affiliation: Social & Economic Consultant, Templeogue Road, Terenure, Dublin, 6W, Ireland.
authors.forename: Trutz
authors.initials: T
authors.lastname: Haase
authors.affiliation: National Cancer Registry Ireland, Building 6800, Cork Airport Business Park, Kinsale Road, Cork, Ireland.
authors.forename: Harry
authors.initials: H
authors.lastname: Comber
authors.affiliation: Institute of Health & Society, Newcastle University, The Baddiley-Clark Building, Richardson Road, Newcastle upon Tyne, NE2 4AX, UK.
authors.forename: Linda
authors.initials: L
authors.lastname: Sharp
authors.affiliation: National Cancer Registry Ireland, Building 6800, Cork Airport Business Park, Kinsale Road, Cork, Ireland.
authors.forename: Marianna
authors.initials: M
authors.lastname: de Camargo Cancela
authors.affiliation: Health Intelligence Unit, Health Service Executive, Red Brick House, Stewarts Hospital Campus, Palmerstown, Dublin, 20, Ireland.
authors.forename: Howard
authors.initials: H
authors.lastname: Johnson
mesh_terms: D000367:Age Factors; D000368:Aged; D002423:Cause of Death; D003110:Colonic Neoplasms / Q000175:diagnosis / Q000401:mortality* / Q000628:therapy; D005260:Female; D006801:Humans; D016490:Integrated Advanced Information Management Systems; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017008:Negotiating; D016016:Proportional Hazards Models; D012680:Sensitivity and Specificity; D012737:Sex Factors; D016019:Survival Analysis