Survival Extrapolation In The Presence Of Cause Specific Hazards

Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow-up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short-term data, assuming a constant additive or multiplicative effect on the hazards for all-cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause-specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all-cause mortality as a sum of latent cause-specific hazards. Assuming proportional cause-specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause-specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause-specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators.345796811U015232027; MRC; Medical Research CouncilLatimer, N., Technical Support Document 14: Undertaking survival analysis for economic evaluations alongside clinical trials - extrapolation with patient-level data (2011), http://www.nicedsu.org.uk, NICE Decision Support Unit, [accessed on 20/12/2012]Jackson, C.H., Sharples, L.D., Thompson, S.G., Survival models in health economic evaluations: balancing fit and parsimony to improve prediction (2010) International Journal of Biostatistics, 6 (1). , Article 34Nelson, C.L., Sun, J.L., Tsiatis, A.A., Mark, D.B., Empirical estimation of life expectancy from large clinical trials: use of left-truncated, right-censored survival analysis methodology (2008) Statistics in Medicine, 27, pp. 5525-5555Viscomi, S., Pastore, G., Dama, E., Zuccolo, L., Pearce, N., Merletti, F., Magnani, C., Life expectancy as an indicator of outcome in follow-up of population-based cancer registries: the example of childhood leukemia (2006) Annals of Oncology, 17, pp. 167-171Connolly, S.J., Hallstrom, A.P., Cappato, R., Schron, E.B., Kuck, K.-H., Zipes, D.P., Greene, H.L., Roberts, R.S., Meta-analysis of the implantable cardioverter defibrillator secondary prevention trials (2000) European Heart Journal, 21 (24), pp. 2071-2078Demiris, N., Sharples, L.D., Bayesian evidence synthesis to extrapolate survival estimates in cost-effectiveness studies (2006) Statistics in Medicine, 25 (11), pp. 1960-1975Louzada-Neto, F., Polyhazard models for lifetime data (1999) Biometrics, 55, pp. 1281-1285Demiris, N., Lunn, D., Sharples, L.D., Survival extrapolation using the poly-Weibull model (2011) Statistical Methods in Medical Research, pp. 1-15. , http://smm.sagepub.com/content/early/2011/11/21/0962280211419645.abstract, (early view) [accessed on 21 November 2011]Lunn, D.J., Thomas, A., Best, N.G., Spiegelhalter, D.J., WinBUGS - a Bayesian modelling framework: concepts, structure, and extensibility (2000) Statistics and Computing, 10 (4), pp. 325-337Lunn, D., WinBUGS Development Interface (WBDev) (2003) ISBA Bulletin, 10 (3), pp. 10-1