On Estimation in Relative Survival

Estimation of relative survival has become the first and the most basic step when reporting cancer survival statistics. Standard estimators are in routine use by all cancer registries. However, it has been recently noted that these estimators do not provide information on cancer mortality that is independent of the national general population mortality. Thus they are not suitable for comparison between countries. Furthermore, the commonly used interpretation of the relative survival curve is vague and misleading. The present article attempts to remedy these basic problems. The population quantities of the traditional estimators are carefully described and their interpretation discussed. We then propose a new estimator of net survival probability that enables the desired comparability between countries. The new estimator requires no modeling and is accompanied with a straightforward variance estimate. The methods are described on real as well as simulated data.

[1]  Paul W Dickman,et al.  Estimating and modeling the cure fraction in population-based cancer survival analysis. , 2007, Biostatistics.

[2]  Buckley Jd,et al.  Additive and multiplicative models for relative survival rates. , 1984 .

[3]  Sally R. Hinchliffe,et al.  How much of the deprivation gap in cancer survival can be explained by variation in stage at diagnosis: An example from breast cancer in the East of England , 2013, International journal of cancer.

[4]  P C Lambert,et al.  Estimating the crude probability of death due to cancer and other causes using relative survival models , 2010, Statistics in medicine.

[5]  E. Feuer,et al.  Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival. , 2000, Statistics in medicine.

[6]  M. Hakama,et al.  Estimating the expectation of life in cancer survival studies with incomplete follow-up information. , 1977, Journal of chronic diseases.

[7]  P. Stattin,et al.  How can we make cancer survival statistics more useful for patients and clinicians: An illustration using localized prostate cancer in Sweden , 2013, Cancer Causes & Control.

[8]  J. Estève,et al.  Relative survival and the estimation of net survival: elements for further discussion. , 1990, Statistics in medicine.

[9]  Coraline Danieli,et al.  Cancer net survival on registry data: Use of the new unbiased Pohar‐Perme estimator and magnitude of the bias with the classical methods , 2013, International journal of cancer.

[10]  O. Aalen,et al.  Survival and Event History Analysis: A Process Point of View , 2008 .

[11]  F. Ederer,et al.  The relative survival rate: a statistical methodology. , 1961, National Cancer Institute monograph.

[12]  Paul C. Lambert,et al.  Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model , 2011 .

[13]  T. Hakulinen,et al.  Cancer survival corrected for heterogeneity in patient withdrawal. , 1982, Biometrics.

[14]  Paul W Dickman,et al.  Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models , 2011, BMC medical research methodology.

[15]  Timo Hakulinen,et al.  Regression Analysis of Relative Survival Rates , 1987 .

[16]  Bernard Rachet,et al.  Cancer survival in five continents: a worldwide population-based study (CONCORD). , 2008, The Lancet. Oncology.

[17]  R. Henderson,et al.  An approach to estimation in relative survival regression. , 2008, Biostatistics.

[18]  Arnold Knijn,et al.  EUROCARE-4. Survival of cancer patients diagnosed in 1995-1999. Results and commentary. , 2009, European journal of cancer.

[19]  T. Hakulinen On long-term relative survival rates. , 1977, Journal of chronic diseases.

[20]  P. Lambert,et al.  Estimating net survival in population‐based cancer studies , 2013, International journal of cancer.

[21]  James F. Watkins,et al.  Analysing Survival Data from Clinical Trials and Observational Studies. , 1995 .

[22]  James M. Robins,et al.  Estimating the marginal survival function in the presence of time dependent covariates , 2001 .

[23]  P. Sasieni,et al.  Proportional excess hazards , 1996 .

[24]  H. Brenner,et al.  An alternative approach to age adjustment of cancer survival rates. , 2004, European journal of cancer.

[25]  P. Lambert,et al.  Temporal trends in mortality from diseases of the circulatory system after treatment for Hodgkin lymphoma: a population-based cohort study in Sweden (1973 to 2006). , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[26]  Paul W Dickman,et al.  Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models , 2012, BMC Medical Research Methodology.

[27]  P. Lambert,et al.  Temporal trends in the proportion cured for cancer of the colon and rectum: A population‐based study using data from the Finnish Cancer Registry , 2007, International journal of cancer.

[28]  Susanne Rosthøj,et al.  Competing risks as a multi-state model , 2002, Statistical methods in medical research.

[29]  P. Royston,et al.  Flexible parametric proportional‐hazards and proportional‐odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects , 2002, Statistics in medicine.

[30]  T. Hakulinen,et al.  Mixture models for cancer survival analysis: application to population-based data with covariates. , 1999, Statistics in medicine.

[31]  Paul W Dickman,et al.  Regression models for relative survival , 2004, Statistics in medicine.

[32]  T. Hakulinen,et al.  Age-standardisation of relative survival ratios of cancer patients in a comparison between countries, genders and time periods. , 2009, European journal of cancer.

[33]  Paul C Lambert,et al.  Flexible parametric models for relative survival, with application in coronary heart disease , 2007, Statistics in medicine.

[34]  O. Aalen,et al.  Adjusting and Comparing Survival Curves by Means of an Additive Risk Model , 1998, Lifetime data analysis.

[35]  T. Scheike,et al.  Dynamic regression hazards models for relative survival , 2008, Statistics in medicine.

[36]  David R. Cox,et al.  Regression models and life tables (with discussion , 1972 .

[37]  F. Berrino,et al.  The cure for colon cancer: Results from the EUROCARE study , 1998, International journal of cancer.

[38]  Nancy Reid,et al.  On “A conversation with Sir David Cox” , 1994, Issue 5.2, Spring 2023.

[39]  M Vaeth,et al.  Simple parametric and nonparametric models for excess and relative mortality. , 1989, Biometrics.

[40]  Janez Stare,et al.  Relative survival analysis in R , 2006, Comput. Methods Programs Biomed..

[41]  Paul C. Lambert,et al.  Further Development of Flexible Parametric Models for Survival Analysis , 2009 .

[42]  R. Henderson,et al.  Goodness of fit of relative survival models , 2005, Statistics in medicine.

[43]  P. Lambert,et al.  Temporal trends in the proportion cured among adults diagnosed with acute myeloid leukaemia in Sweden 1973–2001, a population‐based study , 2010, British journal of haematology.