Cure fraction, modelling and estimating in a population-based cancer survival analysis

In population-based cancer studies, cure is said to occur when the mortality (hazard)rate in the diseased group of individuals returns to the same level as that expected in the general population. The optimal method for monitoring the progress of patient care across the full spectrum of provider settings is through the population-based study of cancer patient survival, which is only possible using data collected by population-based cancer registries. The probability of cure, statistical cure, is defined for a cohort of cancer patients as the percent of patients whose annual death rate equals the death rate of general cancer-free population. Recently models have been introduced, so called cure fraction models, that estimates the cure fraction as well as the survival time distribution for those uncured. The colorectal cancer survival data from the Surveillance, Epidemiology and End Results (SEER) program, USA, is used. The aim is to evaluate the cure fraction models and compare these methods to other methods used to monitor time trends in cancer patient survival, and to highlight some problems using these models

Development and application of statistical methods for population-based cancer patient survival

The overarching aim of this work has been to develop and apply statistical methods for estimating cancer patient survival from population-based register data. Particular focus has been on statistical methods that can be used for presenting cancer survival statistics from administrative health data registers in a manner that is relevant for physicians and patients. Study 1: In this study we clarify and discuss the relative merits of estimates of crude and net cancer patient survival, respectively. In addition, we demonstrate how period analysis, applied in a competing risks setting, can be utilised to predict crude survival probabilities applicable to newly diagnosed cancer patients. As a motivating clinical example, we use data from the National Prostate Cancer Register to assess the impact of prognostic factors on the risk of prostate cancer death in relation to death from other causes than prostate cancer, and event-free survival, among recently diagnosed patients. We conclude that the period estimates of crude survival o er a useful basis for risk communication between physicians and clinicians and advocate their use as means to answer prognostic questions. Study 2: Late adverse health e ects in cancer patients are a growing problem given the longer survival seen for most cancers. Deaths that occur as a consequence of treatment toxicity can be regarded as indirect deaths due to cancer. In this methodological study we extend exible parametric survival models for relative survival by partitioning the overall excess mortality from cancer into two component parts; excess mortality from diseases of the circulatory system, DCS, (assumed caused by the treatment), and remaining excess cancer mortality. We present summary measures for quantifying the risk for death from late e ects of treatment relative to the overall risk of dying of breast cancer, or causes unrelated to the cancer. The method is illustrated using data obtained from the Swedish Cancer Register on women diagnosed with breast cancer in Sweden between 1973 and 1992. Study 3: Survival after Hodgkin lymphoma has increased substantially in the past four decades, following the development of e ective multi-agent chemotherapy, introduction of combinedmodality therapy with reductions in radiation eld size and dose, and more apt evaluation of treatment response. The aim of this study was to present clinically interpretable estimates of temporal trends in the burden of fatal excess DCS mortality among Hodgkin lymphoma survivors who were treated in the 1970's through 1990's, and to predict the future clinical burden among patients diagnosed more recently. Using data from the Swedish Cancer Registry we showed how the excess DCS mortality, within 20 years after diagnosis, has decreased continually since the mid-1980s and is expected to further decrease among patients diagnosed in the modern era. However, when accounting for competing causes of death, we found that excess DCS mortality constitutes a relatively small proportion of the overall mortality among Hodgkin lymphoma patients in Sweden. Study 4: In this study we show how recently developed exible parametric cure models, combined with competing risks theory, can be used to estimate crude probabilities that cancer patients who are alive will eventually die from their cancer, or from other causes, respectively. Moreover, we show how to 'update' the prognosis for patients who have survived some time after their diagnosis via the use of conditional probabilities. The method is discussed and demonstrated using data from the Swedish Cancer Register on patients diagnosed with melanoma, colon cancer and acute myeloid leukemia between 1973 and 2007

Advanced survival models for risk-factor analysis in scrapie

Because of the confounding effects of long incubation duration and flock management, accurate epidemiological studies of scrapie outbreaks are difficult to carry out. In this study, 641 Manech red-faced sheep from six scrapie-affected field flocks in Pyrénées Atlantiques, France, were monitored for clinical scrapie over a 6–9 year period. Over this period, 170 scrapie clinical cases were recorded and half of the culled animals were submitted for post-mortem transmissible spongiform encephalopathy diagnosis to assess their infectious status. Collected data were analysed using a ‘mixture cure model’ approach, which allowed for the discriminating effect of PrP genotype and flock origin on incidence and incubation period. Simulations were performed to evaluate the applicability of such a statistical model to the collected data. As expected, ARR heterozygote sheep were less at risk of becoming infected than ARQ/ARQ individuals and had a greater age at clinical onset. Conversely, when compared with ARQ/ARQ, the VRQ haplotype was associated with an increased infection risk, but not a shorter incubation period. Considering the flock effect, we observed that a high incidence rate was not associated with shorter incubation periods and that the incubation period could be significantly different in flocks harbouring similar infection risks. These results strongly support the conclusion that other parameters, such as the nature of the agent or flock management, could interfere with epidemiological dynamics of the infection in scrapie-affected flocks

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models

<p>Abstract</p> <p>Background</p> <p>When the mortality among a cancer patient group returns to the same level as in the general population, that is, the patients no longer experience excess mortality, the patients still alive are considered "statistically cured". Cure models can be used to estimate the cure proportion as well as the survival function of the "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be specified. It can sometimes be hard to find a survival function flexible enough to fit the observed data, for example, when there is high excess hazard within a few months from diagnosis, which is common among older age groups. This has led to the exclusion of older age groups in population-based cancer studies using cure models.</p> <p>Methods</p> <p>Here we have extended the flexible parametric survival model to incorporate cure as a special case to estimate the cure proportion and the survival of the "uncured". Flexible parametric survival models use splines to model the underlying hazard function, and therefore no parametric distribution has to be specified.</p> <p>Results</p> <p>We have compared the fit from standard cure models to our flexible cure model, using data on colon cancer patients in Finland. This new method gives similar results to a standard cure model, when it is reliable, and better fit when the standard cure model gives biased estimates.</p> <p>Conclusions</p> <p>Cure models within the framework of flexible parametric models enables cure modelling when standard models give biased estimates. These flexible cure models enable inclusion of older age groups and can give stage-specific estimates, which is not always possible from parametric cure models.</p

Modelling survival of patients with multiple cancers

Only abstract. Paper copies of master’s theses are listed in the Helka database (http://www.helsinki.fi/helka). Electronic copies of master’s theses are either available as open access or only on thesis terminals in the Helsinki University Library.Vain tiivistelmä. Sidottujen gradujen saatavuuden voit tarkistaa Helka-tietokannasta (http://www.helsinki.fi/helka). Digitaaliset gradut voivat olla luettavissa avoimesti verkossa tai rajoitetusti kirjaston opinnäytekioskeilla.Endast sammandrag. Inbundna avhandlingar kan sökas i Helka-databasen (http://www.helsinki.fi/helka). Elektroniska kopior av avhandlingar finns antingen öppet på nätet eller endast tillgängliga i bibliotekets avhandlingsterminaler.With increasing number of subsequent primary cancers there is a growing concern to know how cancer patients survive with their subsequent cancer compared to those with their respective first cancer. Results of earlier studies have been conflicting and have not lead to firm conclusions. One reason for conflicting results might be a lack appropriate methodology as survival from subsequent cancer has usually not been adjusted for an extra hazard due to an underlying first cancer. This study presents four alternative models for estimating survival of patients with multiple cancers. Models are extensions and modifications to those proposed earlier for estimating relative and cause-specific survival of patients with a single cancer. The assessment of survival from subsequent cancer raised a need for introducing new concepts, especially when survival of patients with their multiple cancers of the same site is concerned. Survival estimates from cancer are compared between the models, and between a first and subsequent tumour of the same site. The importance of adjusting survival from subsequent cancer to that from a underlying first cancer is also highlighted. The results show that survival from cancer as a first and subsequent tumour can be reliably assessed with the newly introduced models based either on the relative and cause-specific survival. The results also show that survival from cancer as a first and subsequent tumour may be dependent on the site of cancer and whether patients' cancers are of the same site or not. Nevertheless, survival from a subsequent cancer is not usually different from that from a respective first cancer. However, even with large population-based data, a lack of power often prevents the detection of modest differences in survival.MONISYÖPÄPOTILAIDEN ELINAIKAENNUSTEET TAUSTA Yhä useammalla syöpäpotilaalla on diagnosoitu kaksi tai useampia primaarisyöpiä. Näiden monisyöpäpotilaiden joukko kasvaa, koska useimpien syöpäpotilaiden elinaikaennusteet ovat parantuneet ja odotettavissa oleva elinikä on pidentynyt. Tämän potilasjoukon kasvaessa on yhä kiinnostavampaa tietää, kuinka monisyöpäpotilaat selviytyvät uudesta primaarisyövästään verrattuna niihin potilaihin, jotka sairastavat samaa syöpää ensimmäisenä syöpänään. Aiheesta tehtyjen aikaisempien tutkimusten tulokset ovat olleet ristiriitaisia. Ristiriitaisten tulosten taustalla saattaa olla käytettyjen analyysimenetelmien heikkous: Seuraajasyöpään liittyvää elinaikaennustetta arvioitaessa ei ole huomioitu taustalla olevan ensimmäisen syövän vaikutusta kuolleisuuteen. TAVOITE: MONISYÖPÄPOTILAIDEN ELINAIKAENNUSTEET MAHDOLLIKSI Väitöskirjassa esitellään neljä vaihtoehtoista tilastollista mallia, joilla monisyöpäpotilaiden eloonjäämistä voidaan arvioida ensimmäisen ja seuraajasyövän osalta. Uudet vaihtoehdot ovat laajennuksia ja mukaelmia malleista, joita käytetään arvioitaessa syöpäpotilaiden suhteellista (relative) ja syykohtaista (cause-specific) eloonjäämistä. Eloonjäämistä vertaillaan mallien välillä sekä saman syövän suhteen ensimmäisenä ja seuraajasyöpänä. Toiseen syöpään liittyvän eloonjäämisen arvioiminen loi tarpeen luoda uusia käsitteitä, erityisesti kun kyse oli kahdesti samaan primaarisyöpään sairastuneista monisyöpäpotilaista: On pystyttävä arvioimaan eloon-jäämistä esim. rintasyövän suhteen sekä ensimmäisenä että seuraajasyöpänä. TULOKSET JA JOHTOPÄÄTÖKSET Eloonjäämistä ensimmäisen ja seuraajasyövän suhteen voidaan arvioida uusilla suhteelliseen ja syykohtaiseen eloonjäämiseen perustuvilla malleilla. Seuraajasyöpään liittyvää eloonjäämistä ei tulisi arvioida, jollei taustalla olevaan ensimmäiseen syöpään liittyvää kuolleisuutta ole otettu huomioon. Eloonjäämisennusteisiin ensimmäisen ja seuraajasyövän suhteen vaikuttavat mm. syövän sijainti ja se, onko monisyöpäpotilaalla kaksi samaa primaarisyöpää vai ei. Useimmiten eloonjäämisennusteet eivät eroa ensimmäisen ja seuraajasyövän välillä. Voimakkaiden päätelmien teko on edelleen vaikeaa, sillä käytettävissä olevat väestöpohjaisetkin aineistot ovat toistaiseksi varsin suppeita

Quantifying cancer patient survival : extensions and applications of cure models and life expectancy estimation

Cancer patient survival is the single most important measure of cancer patient care. By quantifying cancer patient survival in different ways further insights can be gained in terms of temporal trends and differences in cancer patient survival between groups. The objective of this thesis is to develop and apply methods for estimating the cure proportion and loss in expectation of life for cancer patients. In paper I, a cure model was used to study temporal trends in survival of patients with acute myeloid leukaemia in Sweden. Cancer patient survival was estimated in a relative survival setting and quantified as the proportion cured and the median survival time of uncured for different age groups and by calendar time of diagnosis. We found a dramatic increase in the cure proportion for the age group 19-40, although almost no improvement was seen for patients aged 70-79 at diagnosis. In paper II, a flexible parametric cure model was developed to overcome some limitations with standard parametric cure models. This model is a special case of a non-mixture cure model, using splines instead of a parametric distribution for the modeling. The fit of the flexible parametric cure model was compared to the fit of a Weibull non-mixture cure model, and shown to be superior in cases when the standard non-mixture cure model did not give a good fit or did not converge. Software was developed to enable use of the method. In paper III, the possibility of using a flexible parametric relative survival model for estimating life expectancy and loss in expectation of life was evaluated. Extrapolation of the survival function is generally needed, and the flexible parametric relative survival model was shown to extrapolate the survival very well. The method was evaluated by comparing survival functions extrapolated from 10 years past diagnosis to observed survival by the use of data with 40 years of follow-up. Software was developed to enable use of the method. In paper IV, the life expectancy and loss in expectation of life was estimated for colon cancer patients in Sweden. Even though relative survival was similar across age for colon cancer patients, the loss in expectation of life varied greatly by age, since young patients have more years to lose. We also found that the life expectancy of colon cancer patients improved over time. However, the improvement has to a large extent mimicked the improvement seen in the general population, and therefore there were no large changes in the loss in expectation of life. In conclusion, the methods presented in this thesis are additional tools for estimating and quantifying population-based cancer patient survival, that can lead to an improved understanding of different aspects of the prognosis of cancer patients

Estimation in a Cox Proportional Hazards Cure Model

Some failure time data come from a population that consists of some subjects who are susceptible to and others who are nonsusceptible to the event of interest. The data typically have heavy censoring at the end of the follow-up period, and a standard survival analysis would not always be appropriate. In such situations where there is good scientific or empirical evidence of a nonsusceptible population, the mixture or cure model can be used (Farewell, 1982, Biometrics 38 , 1041–1046). It assumes a binary distribution to model the incidence probability and a parametric failure time distribution to model the latency. Kuk and Chen (1992, Biometrika 79 , 531–541) extended the model by using Cox's proportional hazards regression for the latency. We develop maximum likelihood techniques for the joint estimation of the incidence and latency regression parameters in this model using the nonparametric form of the likelihood and an EM algorithm. A zero-tail constraint is used to reduce the near nonidentifiability of the problem. The inverse of the observed information matrix is used to compute the standard errors. A simulation study shows that the methods are competitive to the parametric methods under ideal conditions and are generally better when censoring from loss to follow-up is heavy. The methods are applied to a data set of tonsil cancer patients treated with radiation therapy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65901/1/j.0006-341X.2000.00227.x.pd

Turnbull versus Kaplan-Meier estimators of cure rate estimation using interval censored data

This study deals with the analysis of the cure rate estimation based on the Bounded Cumulative Hazard (BCH) model using interval censored data, given that the exact distribution of the data set is unknown. Thus, the non-parametric estimation methods are employed by means of the EM algorithm. The Turnbull and Kaplan Meier estimators were proposed to estimate the survival function, even though the Kaplan Meier estimator faces some restrictions in term of interval survival data. A comparison of the cure rate estimation based on the two estimators was done through a simulation study