Illustration of different modelling assumptions for estimation of loss in expectation of life due to cancer

Therese M.-L. Andersson,Paul C. Lambert,Mark J. Rutherford

doi:10.1186/s12874-019-0785-x

Therese M.-L. Andersson, Paul C. Lambert + Show 1 more

Open Access

https://doi.org/10.1186/s12874-019-0785-x

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Abstract

BackgroundThe life expectancy of cancer patients, and the loss in expectation of life as compared to the life expectancy without cancer, is a useful measure of cancer patient survival and complement the more commonly reported 5-year survival. The estimation of life expectancy and loss in expectation of life generally requires extrapolation of the survival function, since the follow-up is not long enough for the survival function to reach 0. We have previously shown that the survival of the cancer patients can be extrapolated by breaking down the all-cause survival into two component parts, the expected survival and the relative survival, and make assumptions for extrapolation of these functions independently. When extrapolating survival from a model including covariates such as calendar year, age at diagnosis and deprivation status, care has to be taken regarding the assumptions underlying the extrapolation. There are often different alternative ways for modelling covariate effects or for assumptions regarding the extrapolation.MethodsIn this paper we describe and discuss different alternative approaches for extrapolation of survival when estimating life expectancy and loss in expectation of life for cancer patients. Flexible parametric models within a relative survival setting are used, and examples are presented using data on colon cancer in England.ResultsGenerally, the different modelling assumptions and approaches give small differences in the estimates of loss in expectation of life, however, the results can differ for younger ages and for conditional estimates.ConclusionSensitivity analyses should be performed to evaluate the effect of the assumptions made when modelling and extrapolating survival to estimate the loss in expectation of life.

Highlights

Introduction and BackgroundA useful summary measure for survival data is the mean survival time, or life expectancy, as an alternative to survival proportions at selected time points
For most types of cancer the excess mortality is low after some years from diagnosis, so the expected mortality dominates for longterm follow-up, and the extrapolation mostly depends on the extrapolation of the expected survival
We have previously shown that extrapolations of all-cause survival among cancer patients is possible by extrapolating the cause-specific survival using a relative survival framework

Summary

Introduction

Introduction and BackgroundA useful summary measure for survival data is the mean survival time, or life expectancy, as an alternative to survival proportions at selected time points. For most types of cancer the excess mortality is low after some years from diagnosis, so the expected mortality dominates for longterm follow-up, and the extrapolation mostly depends on the extrapolation of the expected survival. This approach has previously been proposed by Hakama and Hakulinen, by using grouped data (a life tables of relative survival), by assuming that the cancer patients have a constant excess hazard after the available follow-up [3]. There are often different alternative ways for modelling covariate effects or for assumptions regarding the extrapolation

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Conclusion