title: Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models.
abstract: OBJECTIVE
  Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration.
  
  
  METHODS
  We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings.
  
  
  RESULTS
  AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome-underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG.
  
  
  CONCLUSIONS
  When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.
authors.affiliation: Department of Psychology, University of Pennsylvania Philadelphia, PA, USA.
authors.forename: Lois A
authors.initials: LA
authors.lastname: Gelfand
authors.affiliation: Department of Psychology, Arizona State University Tempe, AZ, USA.
authors.forename: David P
authors.initials: DP
authors.lastname: MacKinnon
authors.affiliation: Department of Psychology, University of Pennsylvania Philadelphia, PA, USA.
authors.forename: Robert J
authors.initials: RJ
authors.lastname: DeRubeis
authors.affiliation: Department of Psychology, Oklahoma State University Stillwater, OK, USA.
authors.forename: Amanda N
authors.initials: AN
authors.lastname: Baraldi
keywords: causal modeling; monte carlo simulation; potential outcomes; statistical mediation; survival analysis