Doctor of Philosophy / Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.

Doctor of Philosophy / Over the past decade, population-based cancer registry data have been used increasingly worldwide to evaluate and improve the quality of cancer care. The utility of the conclusions from such studies relies heavily on the data quality and the methods used to analyse the data. Interpretation of comparative survival from such data, examining either temporal trends or geographical differences, is generally not easy. The observed differences could be due to methodological and statistical approaches or to real effects. For example, geographical differences in cancer survival could be due to a number of real factors, including access to primary health care, the availability of diagnostic and treatment facilities and the treatment actually given, or to artefact, such as lead-time bias, stage migration, sampling error or measurement error. Likewise, a temporal increase in survival could be the result of earlier diagnosis and improved treatment of cancer; it could also be due to artefact after the introduction of screening programs (adding lead time), changes in the definition of cancer, stage migration or several of these factors, producing both real and artefactual trends. In this thesis, I report methods that I modified and applied, some technical issues in the use of such data, and an analysis of data from the State of New South Wales (NSW), Australia, illustrating their use in evaluating and potentially improving the quality of cancer care, showing how data quality might affect the conclusions of such analyses. This thesis describes studies of comparative survival based on population-based cancer registry data, with three published papers and one accepted manuscript (subject to minor revision). In the first paper, I describe a modified method for estimating spatial variation in cancer survival using empirical Bayes methods (which was published in Cancer Causes and Control 2004). I demonstrate in this paper that the empirical Bayes method is preferable to standard approaches and show how it can be used to identify cancer types where a focus on reducing area differentials in survival might lead to important gains in survival. In the second paper (published in the European Journal of Cancer 2005), I apply this method to a more complete analysis of spatial variation in survival from colorectal cancer in NSW and show that estimates of spatial variation in colorectal cancer can help to identify subgroups of patients for whom better application of treatment guidelines could improve outcome. I also show how estimates of the numbers of lives that could be extended might assist in setting priorities for treatment improvement. In the third paper, I examine time trends in survival from 28 cancers in NSW between 1980 and 1996 (published in the International Journal of Cancer 2006) and conclude that for many cancers, falls in excess deaths in NSW from 1980 to 1996 are unlikely to be attributable to earlier diagnosis or stage migration; thus, advances in cancer treatment have probably contributed to them. In the accepted manuscript, I described an extension of the work reported in the second paper, investigating the accuracy of staging information recorded in the registry database and assessing the impact of error in its measurement on estimates of spatial variation in survival from colorectal cancer. The results indicate that misclassified registry stage can have an important impact on estimates of spatial variation in stage-specific survival from colorectal cancer. Thus, if cancer registry data are to be used effectively in evaluating and improving cancer care, the quality of stage data might have to be improved. Taken together, the four papers show that creative, informed use of population-based cancer registry data, with appropriate statistical methods and acknowledgement of the limitations of the data, can be a valuable tool for evaluating and possibly improving cancer care. Use of these findings to stimulate evaluation of the quality of cancer care should enhance the value of the investment in cancer registries. They should also stimulate improvement in the quality of cancer registry data, particularly that on stage at diagnosis. The methods developed in this thesis may also be used to improve estimation of geographical variation in other count-based health measures when the available data are sparse.

Abstract Monitoring regional variation in the survival of cancer patients is an important tool for assessing realisation of regional equity in cancer care. When regions are small or sparsely populated, the random component in the total variation across the regions becomes prominent. The broad aim of this doctoral thesis is to develop methods for assessing regional variation in the cause-specific and relative survival of cancer patients in a country and for quantifying the public health impact of the regional variation in the presence of competing hazards of death using summary measures that are interpretable also for policy-makers and other stakeholders. Methods for summarising the survival of a patient population with incomplete follow-up in terms of the mean and median survival times are proposed. A cure fraction model with two sets of random effects for regional variation is fitted to cause-specific survival data in a Bayesian framework using Markov chain Monte Carlo simulation. This hierarchical model is extended to the estimation of relative survival where the expected survival is estimated by region and considered as a random quantity. The public health impact of regional variation is quantified by the extra survival time and the number of avoidable deaths that would be gained if the patients achieved the most favourable level of relative survival. The methods proposed were applied to real data sets from the Finnish Cancer Registry. Estimates of the mean and the median survival times of colon and thyroid cancer patients, respectively, were corrected for the bias that was caused by the inherent selection of patients during the period of diagnosis with respect to their age at diagnosis. The cure fraction model allowed estimation of regional variation in cause-specific and relative survival of breast and colon cancer patients, respectively, with a parsimonious number of parameters yielding reasonable estimates also for sparsely populated hospital districts. / Tiivistelmä Syöpäpotilaiden elossaolon alueellisen vaihtelun seuraaminen on tärkeää arvioitaessa syövänhoidon oikeudenmukaista jakautumista alueittain. Kun alueet ovat pieniä tai harvaan asuttuja, alueellisen kokonaisvaihtelun satunnainen osa kasvaa merkittäväksi. Tämän väitöstutkimuksen tavoitteena on kehittää menetelmiä, joilla pystytään arvioimaan maan sisäistä alueellista vaihtelua lisäkuolleisuudessa, jonka itse syöpä potilaille aiheuttaa, ja tiivistämään alueellisen vaihtelun kansanterveydellinen merkitys mittalukuihin, jotka ottavat kilpailevan kuolleisuuden huomioon ja ovat myös päättäjien tulkittavissa. Ehdotetuilla menetelmillä voidaan potilaiden ennustetta kuvailla käyttäen elossaolo-ajan keskiarvoa ja mediaania, vaikka potilaiden seuruu olisi keskeneräinen. Potilaiden syykohtaiselle kuolleisuudelle sovitetaan bayesiläisittäin MCMC-simulaatiota hyödyntäen malli, jossa parantuneiden potilaiden osuuden kuvaamisen lisäksi alueellinen vaihtelu esitetään kahden satunnaisefektijoukon avulla. Tämä hierarkkinen malli laajennetaan suhteellisen elossaolon estimointiin, jossa potilaiden odotettu elossaolo estimoidaan alueittain ja siihen liittyvä satunnaisvaihtelu otetaan huomioon. Alueellisen vaihtelun kansanterveydellistä merkitystä mitataan elossaoloajan keskimääräisellä pidentymällä sekä vältettävien kuolemien lukumäärällä, jotka voitaisiin saavuttaa, mikäli suotuisin suhteellisen elossaolon taso saavutettaisiin kaikilla alueilla. Kehitettyjä menetelmiä käytettiin Suomen Syöpärekisterin aineistojen analysointiin. Paksusuoli- ja kilpirauhassyöpäpotilaiden elinaikojen keskiarvojen ja mediaanien estimaatit oikaistiin harhasta, joka aiheutui potilaiden luontaisesta valikoitumisesta diagnosointijakson aikana iän suhteen. Parantuneiden osuuden satunnaisefektimalli mahdollisti rintasyöpäpotilaiden syykohtaisen kuolleisuuden ja paksusuolisyöpäpotilaiden suhteellisen elossaolon kuvaamisen vähäisellä määrällä parametreja ja antoi järkeenkäyvät estimaatit myös harvaan asutuille sairaanhoitopiireille.