Skip to main content
Article thumbnail
Location of Repository

Predicting the Future Burden of Cancer on Society

By Mark John Rutherford

Abstract

Due to third party copyright restrictions the published articles have been removed from appendix 2, 3 and 4 of the electronic version of this thesis. The unabridged version can be consulted, on request, at the University of Leicester’s David Wilson Library.Evaluating the burden of cancer on society is of great interest to health officials and planning authorities. It is of particular importance to be able to correctly estimate the burden of cancer in the coming years in order that appropriate provisions can be put in place. The vast majority of developed, and also developing, countries have a cancer registry set-up and have at least 20 years of complete data. In the leading developed countries, the cancer registry data is complete and reliable for the past 50 years. Using this data it is possible to estimate key quantities that can be used to assess the burden of cancer.\ud Prevalence gives a good proxy for the burden of cancer on society; it gives an estimate of the number of people who are alive having had a previous cancer diagnosis. Prevalence can be estimated by combining models for incidence and patient survival. To accurately model the prevalence, it is important to develop the best methods for modelling the incidence and patient survival from population-based cancer registries. Therefore, as part of this thesis, novel methods have been developed for projecting cancer incidence into the future using an approach that treats the data continuously. Also, methods for projecting cancer patient survival have been assessed and improved as part of the work by effectively estimating the quantities in continuous time. These projected estimates have been combined to give future estimates of cancer prevalence.\ud Making predictions is obviously fraught with danger and, therefore, it should be made clear that these projections are liable to be uncertain and based on strong assumptions. However, if the assumptions of these models are fully understood, they may well provide a useful tool for health and financial planning in terms of assessing the disease burden due to the differing forms of cancer

Publisher: University of Leicester
Year: 2012
OAI identifier: oai:lra.le.ac.uk:2381/10201

Suggested articles

Citations

  1. (1997). A comparison of three methods of analysis for age-period-cohort models with application to incidence data on nonHodgkins lymphoma.
  2. (1999). A critique of the Bayesian Information Criterion for model selection.
  3. (2004). A methodological comparison of age-period-cohort models: The intrinsic estimator and conventional generalized linear models.
  4. (1950). A technique for analyzing some factors affecting the incidence of syphilis.
  5. (1992). Accuracy of death certificates - a population-based, complete-coverage, one-year autopsy study in East Germany.
  6. (2005). Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for timedependent effects.
  7. (2004). Age-period-cohort analysis of breast cancer mortality rates in Andalucia (Spain).
  8. (1971). Age-period-cohort analysis of colorectal cancer in East Anglia,
  9. (2010). Age-period-cohort modeling.
  10. (2001). Age-period-cohort modelling of breast cancer incidence in the Nordic countries. Statistics in Medicine,
  11. (2007). Age-period-cohort models for the Lexis diagram.
  12. (2011). Age-period-cohort models in cancer surveillance research: Ready for prime time?
  13. (1999). Age-period-cohort models: A comparative study of available methodologies.
  14. (2007). Age-period-cohort projections of breast cancer incidence in a rapidly transitioning Chinese population.
  15. (1991). Age-specific incidence and prevalence: A statistical perspective.
  16. Age, period and cohort models applied to cancer mortality rates.
  17. (1987). Age, period and cohort models: review of knowledge and implementation in GLIM.
  18. An alternative approach to age adjustment of cancer survival rates.
  19. (1985). An alternative approach to statistical age-period-cohort analysis.
  20. (2011). and the EUROCARE Working Group.
  21. (2004). and the EUROPREVAL Working Group. Colon cancer prevalence and estimation of differing care needs of colon cancer patients.
  22. (1997). Angelis. Estimating the completeness of prevalence based on cancer registry data.
  23. (2006). Approaches to fitting age-period-cohort models with unequal intervals.
  24. (2004). Are patients diagnosed with breast cancer before age 50 years ever cured?
  25. (2002). Attribution of deaths following cancer treatment.
  26. (1994). Bayesian analysis of survival on multiple time scales.
  27. (1986). Bayesian cohort models for general cohort table analyses.
  28. (1995). Bayesian computation and stochastic systems.
  29. (2005). Bayesian projections: what are the effects of excluding data from younger age groups?
  30. (1991). Black-white differences in Hodgkin’s disease incidence in the United States by age, sex, histology subtype and time.
  31. (2010). Breast cancer survival in England, Norway and Sweden: A population-based comparison.
  32. (2004). Burden of cervical cancer in Europe: estimates for
  33. (2008). Can the incidence and prevalence of coronary heart disease be determined from routinely collected national data? population-based estimates for New Zealand in 2001-03.
  34. (2001). Can we conquer cancer in the twenty-first century?
  35. (2008). Cancer mortality in the United Kingdom: projections to the year 2025.
  36. (1999). Cancer patient survival - patterns, comparisons, trends: A population-based cancer registry study in Finland.
  37. (2004). Cancer patient survival in Sweden at the beginning of the third millennium - predictions using period analysis.
  38. (2000). Cancer prevalence estimates based on tumour registry data in the Surveillance, Epidemiology, and End Results (SEER) Program.
  39. (2008). Cancer prevalence in the United Kingdom: Estimates for
  40. (1999). Cancer surveillance series: Interpreting trends in prostate cancer - Part I: Evidence of the effects of screening in recent prostate cancer incidence, mortality, and survival rates.
  41. (1982). Cancer survival corrected for heterogeneity in patient withdrawal.
  42. (2011). Cancer survival in
  43. (1995). Changes in incidence of and mortality from breast cancer in England and Wales since introduction of screening.
  44. (2011). Changing mortality for motor neuron disease in France (1968-2007): an age-period-cohort analysis.
  45. (2008). Childhood leukaemia: long-term excess mortality and the proportion ‘cured’.
  46. (2011). Choosing the relative survival method for cancer survival estimation.
  47. (1976). Cohort analysts’ futile quest: Statistical attempts to separate age, period and cohort effects.
  48. Comparing smoothing techniques in Cox models for exposure-response relationships. Statistics in Medicine,
  49. (1741). Comparison of different approaches to incidence prediction based on simple interpolation techniques. Statistics in Medicine,
  50. (2011). Comparison of methods for calculating relative survival in population-based studies,
  51. (2007). Costs of cancer in the Nordic countries a comparative study of health care costs and public income loss compensation payments related to cancer in the Nordic countries in
  52. (2002). Cure model analysis in cancer: an application to data from the children’s cancer group.
  53. (2009). Cure’ from breast cancer among two populations of women followed for 23 years after diagnosis.
  54. (2003). Data fitting with a spline using a real-coded genetic algorithm.
  55. Data quality and quality control of a population-based cancer registry. experience in Finland. Acta Oncologica
  56. (1996). Deriving more up-todate estimates of long-term patient survival.
  57. (2006). Disentangling age, cohort and time effects in the additive model*.
  58. (2008). Do cancer predictions work?
  59. (1986). Do the predictions for cancer incidence come true? Experience from Finland.
  60. (1965). Does socioeconomic status influence the prospect of cure from colon cancer - a populationbased study in
  61. (2005). Early life events and later risk of colorectal cancer: ageperiod-cohort modelling in the Nordic countries and Estonia.
  62. (2006). Estimates of long-term survival for newly diagnosed cancer patients.
  63. (2007). Estimates of the cancer incidence and mortality
  64. (2002). Estimates of the incidence and prevalence of renal cell carcinoma in Italy in
  65. (2002). Estimates of the world-wide prevalence of cancer for 25 sites in the adult population.
  66. (2007). Estimating and modeling the cure fraction in population-based cancer survival analysis.
  67. (2002). Estimating cancer prevalence using mixture models for cancer survival.
  68. (2008). Estimating complete prevalence of cancers diagnosed in childhood.
  69. (1999). Estimating relative survival among people registered with cancer in England and Wales.
  70. (2010). Estimating the crude probability of death due to cancer and other causes using relative survival models.
  71. (1994). Estimating the variance of cancer prevalence from population-based registries.
  72. (2000). Estimating the world cancer burden: Globocan
  73. (2002). Estimation and projections of cancer prevalence from cancer registry data.
  74. (1997). Estimation and projections of colorectal cancer trends in Italy.
  75. (2010). Expected long-term survival of patients diagnosed with acute myeloblastic leukemia during 2006-2010.
  76. Expected long-term survival of patients diagnosed with multiple myeloma in 2006-2010. Haematologica,
  77. (2007). Flexible parametric models for relative survival, with application in coronary heart disease.
  78. (2002). Flexible parametric proportional-hazards and proportionalodds models for censored survival data, with application to prognostic modelling and estimation of treatment effects.
  79. (1989). Flexible regression models with cubic splines.
  80. (2009). Further development of flexible parametric models for survival analysis.
  81. (2009). Future of cancer incidence in the United States: burdens upon an aging, changing nation.
  82. (2005). Generating survival times to simulate Cox proportional hazards models.
  83. (2000). Global cancer statistics in the year
  84. (2008). Globocan
  85. (2000). Handheld cellular telephone use and risk of brain cancer.
  86. (2001). How will ageing affect Finland? OECD Economics Department Working Papers 295,
  87. (1979). Identification and estimation of age-period-cohort models in the analysis of discrete archival data. Sociological Methodology,
  88. (2008). Identification of the age-period-cohort model and the extended chain-ladder model.
  89. (1992). Inaccuracies of death certificate information.
  90. (2006). Incidence trends and projections for childhood cancer in Ontario.
  91. (2006). Incidence trends of prostate cancer in East Anglia, before and during the era of PSA diagnostic testing.
  92. (1996). Increase in testicular cancer incidence in six European countries: a birth cohort phenomenon.
  93. (2011). Increased mortality in women with breast cancer detected during pregnancy and different periods postpartum. Cancer Epidemiology Biomarkers & Prevention,
  94. (1975). Indirect standardization and multiplicative models for rates, with reference to the age adjustment of cancer incidence and relative frequency data.
  95. (2006). Influence of alternative mammographic screening scenarios on breast cancer incidence predictions (Finland).
  96. (1993). Influence of death certificate errors on cancer mortality trends.
  97. (1996). Information on death certificates: Cause for concern?
  98. (1973). Information theory and an extension of the maximum likelihood principle,
  99. (1959). Instructions to IBM 650 programmers in processing survival computations.
  100. (2010). Interferon alfa-2a versus combination therapy with interferon alfa-2a, interleukin-2, and fluorouracil in patients with untreated metastatic renal cell carcinoma (mrc re04/eortc gu 30012): an openlabel randomised trial.
  101. (2006). Interpreting trends in cancer patient survival.
  102. (1968). Lead time gained by diagnostic screening for breast cancer.
  103. (1977). Life expectancy and age-period-cohort effects: analysis and projections of mortality in Spain between
  104. (2008). Lung cancer incidence and mortality: current trends and projections based on data from Schleswig-Holstein. Dtsch Med Wochenschr,
  105. (2005). Lung cancer rate predictions using generalized additive models.
  106. (2010). Measuring cancer survival in populations: relative survival vs cancer-specific survival.
  107. (1999). Mixture models for cancer survival analysis: application to population-based data with covariates.
  108. Model-based projections for deriving up-to-date cancer survival estimates: An international evaluation.
  109. (2007). Modeling of the cure fraction in survival studies.
  110. (1997). Modeling of time trends and interactions in vital rates using restricted regression splines.
  111. (2003). Modelling Survival Data
  112. (2009). Modelling the trend of bovine spongiform encephalopathy prevalence in France: Use of restricted cubic spline regression in age-periodcohort models to estimate the efficiency of control measures.
  113. (1987). Models for temporal variations in cancer rates. I: Age-period and age-cohort models.
  114. (1987). Models for temporal variations in cancer rates. II: Age-periodcohort models.
  115. (2010). Mortality trends for primary liver cancer in Puglia, Italy.
  116. (2004). Multimodel inference: Understanding AIC and BIC in model selection.
  117. (2006). Multivariable regression model building by using fractional polynomials:
  118. (2000). National cancer prevalence estimation in France.
  119. (1958). Nonparametric estimation from incomplete observations.
  120. (1978). Nonparametric inference for a family of counting processes.
  121. (2003). On crude and age-adjusted relative survival rates.
  122. (2011). On estimation in relative survival.
  123. (1977). On long-term relative survival rates.
  124. (2000). On spline estimators and prediction intervals in nonparametric regression.
  125. (2002). Overdiagnosis due to prostate-specific antigen screening: Lessons from U.S. prostate cancer incidence trends.
  126. (2008). Partial cancer prevalence in Japan up to 2020: Estimates based on incidence and survival data from population-based cancer registries.
  127. (1982). Partial residuals for the proportional hazards regression model.
  128. Period analysis for ‘up-to-date’ cancer survival data: theory, empirical evaluation, computational realisation and applications.
  129. (2000). Permutation tests for joinpoint regression with applications to cancer rates.
  130. (2006). Predicting the future burden of cancer.
  131. (2003). Predicting the future: projections help researchers allocate resources.
  132. (2006). Predicting the lung cancer burden: Accounting for selection of the patients with respect to general population mortality.
  133. (1993). Prediction of cancer incidence in the Nordic countries up to the years 2000 and 2010. A collaborative study of the five Nordic Cancer Registries.
  134. (2003). Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches.
  135. (2015). Predictions of skin cancer incidence in the Netherlands up to
  136. (2009). Prevalence of patients with colorectal cancer requiring follow-up or active treatment.
  137. (2010). Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial.
  138. (2001). Projecting cancer incidence and mortality using Bayesian ageperiod-cohort models.
  139. Projecting cancer incidence using age-period-cohort models incorporating restricted cubic splines. Statistics in Medicine (submitted),
  140. (2011). Projecting prevalence by stage of care for prostate cancer and estimating future health service needs: protocol for a modelling study.
  141. (2001). Projections of lung cancer mortality in West Germany: a case study in Bayesian prediction.
  142. (2010). Projections of the cost of cancer care in the United States:
  143. (2004). Providing more up-to-date estimates of patient survival: a comparison of standard survival analysis with period analysis using lifetable methods and proportional hazards models.
  144. (2011). Quantifying differences in breast cancer survival between England and Norway,
  145. (2006). Re: “Bayesian projections: what are the effects of excluding data from younger age groups?”.
  146. (2007). Recent major progress in long-term cancer patient survival disclosed by modeled period analysis.
  147. (2001). Recent trends and future projections of lymphoid neoplasms–a Bayesian age-period-cohort analysis.
  148. (1987). Regression analysis of relative survival rates.
  149. (1972). Regression models and life-tables.
  150. (2004). Regression models for relative survival.
  151. (1988). Regression models in clinical studies: Determining relationships between predictors and response.
  152. (1994). Regression using fractional polynomials of continuous covariates: Parsimonious parametric modelling.
  153. (1990). Relative survival and the estimation of net survival: Elements for further discussion.
  154. (1973). Some methodological issues in cohort analysis of archival data.
  155. (2009). Stata Statistical Software: Release 11. College Station, TX: StataCorp LP. Statistics Finland.
  156. (1990). Statistical inference in the Lexis diagram.
  157. (2005). Statistical modeling and projections of lung cancer mortality in 4 industrialized countries.
  158. (1967). Statistiske metoder ved dødelikhetsundersøkelser. statistical memoirs. (in Norwegian),
  159. (2009). Survival expectations of patients diagnosed with Hodgkin’s lymphoma in 2006-2010.
  160. (2010). The age-period-cohort conundrum as two fundamental problems.
  161. (1980). The analysis of rates and of survivorship using log-linear models.
  162. (1983). The analysis of rates using Poisson regression models.
  163. (2007). The cancer burden in the United Kingdom in
  164. (2003). The changing epidemiology of lung cancer in Europe.
  165. (2009). The comparison of alternative smoothing methods for fitting non-linear exposure-response relationships with Cox models in a simulation study.
  166. (1998). The cure for colon cancer: Results from the eurocare study.
  167. (1982). The effects of early treatment, lead time and length bias on the mortality experienced by cases detected by screening.
  168. (2008). The Epi package.
  169. (1983). The estimation of age, period and cohort effects for vital rates.
  170. The future burden of cancer in England: incidence and numbers of new patients in
  171. (2010). The future burden of cancer in London compared with England.
  172. (2009). The present and future burden of urinary bladder cancer in the world.
  173. (2008). The present and the future of breast cancer burden in the Kingdom of Saudi Arabia.
  174. (2011). The prevalence and burden of symptoms amongst cancer patients attending palliative care in two African countries,
  175. (1986). The prevalence of cancer.
  176. (2003). The prevalence of patients with colorectal carcinoma under care in the U.S.
  177. (2004). The relative impact and future burden of prostate cancer in the United States. J Urol, 172(5 Pt 2):S13–6; discussion S17,
  178. (1961). The relative survival rate: A statistical methodology.
  179. (1972). Theory and applications of hazard plotting for censored failure data.
  180. (1992). Time trend and age-period-cohort effects on incidence of esophageal cancer
  181. (2011). Time trends and ageperiod-cohort analyses on incidence rates of nasopharyngeal carcinoma during 1993-2007 in Wuhan, China. Cancer Epidemiology, In Press, Corrected Proof:–,
  182. (1990). Trends in cancer survival in 11 European populations from
  183. (2003). Trends in prostate cancer incidence and mortality: an analysis of mortality change by screening intensity.
  184. (2007). Tutorial in biostatistics: competing risks and multistate models.
  185. (2010). UK cancer survival statistics.
  186. (2006). Up-to-date and precise estimates of cancer patient survival: Model-based period analysis.
  187. (2009). Up-to-date cancer survival: Period analysis and beyond.
  188. (2006). Up-to-date estimates of cancer patient survival even with common latency in cancer registration.
  189. (2002). Up-to-date long-term survival curves of patients with cancer by period analysis.
  190. (2003). Up-to-date survival curves of children with cancer by period analysis.
  191. (2002). Use of period analysis for providing more upto-date estimates of long-term survival rates: empirical evaluation among 370,000 cancer patients in Finland.
  192. (1985). Using age, period and cohort models to estimate future mortality rates.
  193. (1998). Van Den Eeden. Cause of death in men diagnosed with prostate carcinoma.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.