Current estimates of the cure fraction: a feasibility study of statistical cure for breast and colorectal cancer

A semi-Markov multistate cure model for estimating intervention effects in stepped wedge design trials

Multistate models are useful for studying exposures that affect transitions among a set of health states. However, they can be challenging to apply when exposures are time-varying. We develop a multistate model and a method of likelihood construction that allows application of the model to data in which interventions or other exposures can be time-varying and an individual may to be exposed to multiple intervention conditions while progressing through states. The model includes cure proportions, reflecting the possibility that some individuals will never leave certain states. We apply the approach to analyze patient vaccination data from a stepped wedge design trial evaluating two interventions to increase uptake of human papillomavirus vaccination. The states are defined as the number of vaccine doses the patient has received. We model state transitions as a semi-Markov process and include cure proportions to account for individuals who will never leave a given state (e.g. never receive their next dose). Multistate models typically quantify intervention effects as hazard ratios contrasting the intensities of transitions between states in intervention versus control conditions. For multistate processes, another clinically meaningful outcome is the change in the percentage of the study population that has achieved a specific state (e.g. completion of all required doses) by a specific point in time due to an intervention. We present a method for quantifying intervention effects in this manner. We apply the model to both simulated and real-world data and also explore some conditions under which such models may give biased results.

Complete prevalence and indicators of cancer cure: enhanced methods and validation in Italian population-based cancer registries

Objectives

To describe the procedures to derive complete prevalence and several indicators of cancer cure from population-based cancer registries.

Materials and methods

Cancer registry data (47% of the Italian population) were used to calculate limited duration prevalence for 62 cancer types by sex and registry. The incidence and survival models, needed to calculate the completeness index (R) and complete prevalence, were evaluated by likelihood ratio tests and by visual comparison. A sensitivity analysis was conducted to explore the effect on the complete prevalence of using different R indexes. Mixture cure models were used to estimate net survival (NS); life expectancy of fatal (LEF) cases; cure fraction (CF); time to cure (TTC); cure prevalence, prevalent patients who were not at risk of dying as a result of cancer; and already cured patients, those living longer than TTC at a specific point in time. CF was also compared with long-term NS since, for patients diagnosed after a certain age, CF (representing asymptotical values of NS) is reached far beyond the patient's life expectancy.

Results

For the most frequent cancer types, the Weibull survival model stratified by sex and age showed a very good fit with observed survival. For men diagnosed with any cancer type at age 65-74 years, CF was 41%, while the NS was 49% until age 100 and 50% until age 90. In women, similar differences emerged for patients with any cancer type or with breast cancer. Among patients alive in 2018 with colorectal cancer at age 55-64 years, 48% were already cured (had reached their specific TTC), while the cure prevalence (lifelong probability to be cured from cancer) was 89%. Cure prevalence became 97.5% (2.5% will die because of their neoplasm) for patients alive >5 years after diagnosis.

Conclusions

This study represents an addition to the current knowledge on the topic providing a detailed description of available indicators of prevalence and cancer cure, highlighting the links among them, and illustrating their interpretation. Indicators may be relevant for patients and clinical practice; they are unambiguously defined, measurable, and reproducible in different countries where population-based cancer registries are active.

Measuring population-level cure patterns for cancer patients in the United States

While the life expectancy of cancer survivors has substantially improved over time in the United States, the extent to which cancer patients are cured is not known. Population-level cure patterns are important indicators to quantify cancer survivorships. This population-based cohort study included 8978,721 cancer patients registered in the Surveillance, Epidemiology and End Results (SEER) databases between 1975 and 2018. The primary outcome was cure fractions. Five-year cure probability, time to cure and median survival time of uncured cases were also assessed. All four measures were calculated using flexible parametric models, according to 46 cancer sites, three summary stages, individual age and calendar year at diagnosis. In 2018, cure fractions ranged from 2.7% for distant liver cancer to 100.0% for localized/regional prostate cancer. Localized cancer had the highest cure fraction, followed by regional cancer and distant cancer. Except for localized breast cancer, older patients generally had lower cure fractions. There were 38 cancer site and stage combinations (31.2%) that achieved 95% of cure within 5 years. Median survival time of the uncured cases ranged from 0.3 years for distant liver cancer to 10.9 years for localized urinary bladder cancer. A total of 117 cancer site and stage combinations (93.6%) had increased cure fraction over time. A considerable proportion of cancer patients were cured at the population-level, and the cure patterns varied substantially across cancer site, stage and age at diagnosis. Increases in cure fractions over time likely reflected advances in cancer treatment and early detection.

Disparities in Breast Cancer Outcomes and How to Resolve Them

There has been a 40% decline in breast cancer age-adjusted death rate since 1990. Black American women have not experienced as great a decline; indeed, the Black-White disparity in mortality in the United States is greater today than it has ever been. Certain states (areas of residence), however, do not see such dramatic differences in outcome by race. This latter finding suggests much more can be done to reduce disparities and prevent deaths. Interventions to get high-quality care (screening, diagnostics, and treatment) involve understanding the needs and concerns of the patient and addressing those needs and concerns. Patient navigators are 1 way to improve outcomes.

GD2 and GD3 gangliosides as diagnostic biomarkers for all stages and subtypes of epithelial ovarian cancer

Background

Ovarian cancer (OC) is the deadliest gynecological cancer, often diagnosed at advanced stages. A fast and accurate diagnostic method for early-stage OC is needed. The tumor marker gangliosides, GD2 and GD3, exhibit properties that make them ideal potential diagnostic biomarkers, but they have never before been quantified in OC. We investigated the diagnostic utility of GD2 and GD3 for diagnosis of all subtypes and stages of OC.

Methods

This retrospective study evaluated GD2 and GD3 expression in biobanked tissue and serum samples from patients with invasive epithelial OC, healthy donors, non-malignant gynecological conditions, and other cancers. GD2 and GD3 levels were evaluated in tissue samples by immunohistochemistry (n=299) and in two cohorts of serum samples by quantitative ELISA. A discovery cohort (n=379) showed feasibility of GD2 and GD3 quantitative ELISA for diagnosing OC, and a subsequent model cohort (n=200) was used to train and cross-validate a diagnostic model.

Results

GD2 and GD3 were expressed in tissues of all OC subtypes and FIGO stages but not in surrounding healthy tissue or other controls. In serum, GD2 and GD3 were elevated in patients with OC. A diagnostic model that included serum levels of GD2+GD3+age was superior to the standard of care (CA125, p<0.001) in diagnosing OC and early-stage (I/II) OC.

Conclusion

GD2 and GD3 expression was associated with high rates of selectivity and specificity for OC. A diagnostic model combining GD2 and GD3 quantification in serum had diagnostic power for all subtypes and all stages of OC, including early stage. Further research exploring the utility of GD2 and GD3 for diagnosis of OC is warranted.

Cancer Cure and Consequences on Survivorship Care: Position Paper from the Italian Alliance Against Cancer (ACC) Survivorship Care Working Group

A multidisciplinary panel of experts and cancer patients developed a position paper to highlight recent evidence on "cancer cure" (ie, the possibility of achieving the same life expectancy as the general population) and discuss the consequences of this concept on follow-up and rehabilitation strategies. The aim is to inform clinicians, patients, and health-care policy makers about strategies of survivorship care for cured cancer patients and consequences impacting patient lives, spurring public health authorities and research organizations to implement resources to the purpose. Two identifiable, measurable, and reproducible indicators of cancer cure are presented. Cure fraction (CF) is >60% for breast and prostate cancer patients, >50% for colorectal cancer patients, and >70% for patients with melanoma, Hodgkin lymphoma, and cancers of corpus uteri, testis (>90%), and thyroid. CF was >65% for patients diagnosed at ages 15-44 years and 30% for those aged 65-74 years. Time-to-cure was consistently <1 year for thyroid and testicular cancer patients and <10 years for patients with colorectal and cervical cancers, melanoma, and Hodgkin lymphoma. The working group agrees that the evidence allows risk stratification of cancer patients and implementation of personalized care models for timely diagnosis, as well as treatment of possible cancer relapses or related long-term complications, and preventive measures aimed at maintaining health status of cured patients. These aspects should be integrated to produce an appropriate follow-up program and survivorship care plan(s), avoiding stigma and supporting return to work, to a reproductive life, and full rehabilitation. The "right to be forgotten" law, adopted to date only in a few European countries, may contribute to these efforts for cured patients.

Methods for Extrapolating Survival Analyses for the Economic Evaluation of Advanced Therapy Medicinal Products

There are two significant challenges for analysts conducting economic evaluations of advanced therapy medicinal products (ATMPs): (i) estimating long-term treatment effects in the absence of mature clinical data, and (ii) capturing potentially complex hazard functions. This review identifies and critiques a variety of methods that can be used to overcome these challenges. The narrative review is informed by a rapid literature review of methods used for the extrapolation of survival analyses in the economic evaluation of ATMPs. There are several methods that are more suitable than traditional parametric survival modelling approaches for capturing complex hazard functions, including, cure-mixture models and restricted cubic spline models. In the absence of mature clinical data, analysts may augment clinical trial data with data from other sources to aid extrapolation, however, the relative merits of employing methods for including data from different sources is not well understood. Given the high and potentially irrecoverable costs of making incorrect decisions concerning the reimbursement or commissioning of ATMPs, it is important that economic evaluations are correctly specified, and that both parameter and structural uncertainty associated with survival extrapolations are considered. Value of information analyses allow for this uncertainty to be expressed explicitly, and in monetary terms.

The Extrapolation Performance of Survival Models for Data With a Cure Fraction: A Simulation Study

OBJECTIVES

Curative treatments can result in complex hazard functions. The use of standard survival models may result in poor extrapolations. Several models for data which may have a cure fraction are available, but comparisons of their extrapolation performance are lacking. A simulation study was performed to assess the performance of models with and without a cure fraction when fit to data with a cure fraction.

METHODS

Data were simulated from a Weibull cure model, with 9 scenarios corresponding to different lengths of follow-up and sample sizes. Cure and noncure versions of standard parametric, Royston-Parmar, and dynamic survival models were considered along with noncure fractional polynomial and generalized additive models. The mean-squared error and bias in estimates of the hazard function were estimated.

RESULTS

With the shortest follow-up, none of the cure models provided good extrapolations. Performance improved with increasing follow-up, except for the misspecified standard parametric cure model (lognormal). The performance of the flexible cure models was similar to that of the correctly specified cure model. Accurate estimates of the cured fraction were not necessary for accurate hazard estimates. Models without a cure fraction provided markedly worse extrapolations.

CONCLUSIONS

For curative treatments, failure to model the cured fraction can lead to very poor extrapolations. Cure models provide improved extrapolations, but with immature data there may be insufficient evidence to choose between cure and noncure models, emphasizing the importance of clinical knowledge for model choice. Dynamic cure fraction models were robust to model misspecification, but standard parametric cure models were not.

Cancer cure for 32 cancer types: results from the EUROCARE-5 study

BACKGROUND

Few studies have estimated the probability of being cured for cancer patients. This study aims to estimate population-based indicators of cancer cure in Europe by type, sex, age and period.

METHODS

7.2 million cancer patients (42 population-based cancer registries in 17 European countries) diagnosed at ages 15-74 years in 1990-2007 with follow-up to 2008 were selected from the EUROCARE-5 dataset. Mixture-cure models were used to estimate: (i) life expectancy of fatal cases (LEF); (ii) cure fraction (CF) as proportion of patients with same death rates as the general population; (iii) time to cure (TTC) as time to reach 5-year conditional relative survival (CRS) >95%.

RESULTS

LEF ranged from 10 years for chronic lymphocytic leukaemia patients to <6 months for those with liver, pancreas, brain, gallbladder and lung cancers. It was 7.7 years for patients with prostate cancer at age 65-74 years and >5 years for women with breast cancer. The CF was 94% for testis, 87% for thyroid cancer in women and 70% in men, 86% for skin melanoma in women and 76% in men, 66% for breast, 63% for prostate and <10% for liver, lung and pancreatic cancers. TTC was <5 years for testis and thyroid cancer patients diagnosed below age 55 years, and <10 years for stomach, colorectal, corpus uteri and melanoma patients of all ages. For breast and prostate cancers, a small excess (CRS < 95%) remained for at least 15 years.

CONCLUSIONS

Estimates from this analysis should help to reduce unneeded medicalization and costs. They represent an opportunity to improve patients' quality of life.

Prognosis and cure of long-term cancer survivors: A population-based estimation

BACKGROUND

Increasing evidence of cure for some neoplasms has emerged in recent years. The study aimed to estimate population-based indicators of cancer cure.

METHODS

Information on more than half a million cancer patients aged 15-74 years collected by population-based Italian cancer registries and mixture cure models were used to estimate the life expectancy of fatal tumors (LEFT), proportions of patients with similar death rates of the general population (cure fraction), and time to reach 5-year conditional relative survival (CRS) >90% or 95% (time to cure).

RESULTS

Between 1990 and 2000, the median LEFT increased >1 year for breast (from 8.1 to 9.4 years) and prostate cancers (from 5.2 to 7.4 years). Median LEFT in 1990 was >5 years for testicular cancers (5.8) and Hodgkin lymphoma (6.3) below 45 years of age. In both sexes, it was ≤0.5 years for pancreatic cancers and NHL in 1990 and in 2000. The cure fraction showed a 10% increase between 1990 and 2000. It was 95% for thyroid cancer in women, 94% for testis, 75% for prostate, 67% for breast cancers, and <20% for liver, lung, and pancreatic cancers. Time to 5-year CRS >95% was <10 years for testis, thyroid, colon cancers, and melanoma. For breast and prostate cancers, the 5-year CRS >90% was reached in <10 years but a small excess remained for >15 years.

CONCLUSIONS

The study findings confirmed that several cancer types are curable. Became aware of the possibility of cancer cure has relevant clinical and social impacts.

Defining and Managing High-Priced Cures: Healthcare Payers' Opinions

OBJECTIVES

Recent regulatory approvals of potentially curative but high-cost treatments have made these therapies a focus of health policy discussions. Cures present new challenges to healthcare payers because they have high upfront costs but have life-long health benefits. The objectives of this study are to understand how healthcare payers define and manage cures. We investigated payers' views on key features of curative treatments and the affordability and value challenges they present.

METHODS

We conducted semistructured interviews in 2016 with key informants in US payer organizations. Interviewees were directly involved in coverage determination for highly effective and curative therapies.

RESULTS

We contacted 24 individuals and 18 participated. When asked what aspects of cures were important for coverage determination, an equal percentage of respondents (61% each) mentioned clinical and economic factors. In defining a cure, half of respondents included an economic element such as no downstream costs associated with the disease. When asked about challenges, 72% of respondents mentioned uncertainty regarding long-term outcomes and 56% mentioned membership churn and competition.

CONCLUSIONS

Payers expressed a novel definition of a cure-which we call a "healthcare cost cure"-that captures both the clinical and economic consequences of treatment. This definition may be more pertinent in fragmentary financing systems that unevenly distribute cure costs and benefits across payers. Overall findings indicate that decision makers desire evidence to ensure that the long-term real-world consequences of covering cures match the expected benefits. Future policies need to balance upfront acquisition costs with downstream financial benefits.

Excess risk of dying of other causes of cured cancer patients

BACKGROUND

The proportion of patients cured of cancer is usually estimated with cure models assuming they have the same death risk as the general population. These patients, even when cured, often maintain an extra death risk compared to the overall population. Our aims were to estimate this extra risk, and to take it into account in estimating cure proportions and relative survival (RS).

METHODS

We used RS mixture model with an additional parameter expressing the extra noncancer death risk of patients, assumed constant with age. We applied the model to the SEER registries survival data (1990-1994 diagnosed patients) with colorectal, breast, and lung cancers, and followed up to 2013.

RESULTS

The estimated relative risk of death for cured patients versus the general population was 1.11 for colorectal, 1.16 for breast, and 2.17 and 2.12, respectively, for female and male lung cancers. Taking this extra risk into account leads, for all cancers, to a higher estimated proportion of cured and a lower RS of uncured patients. In addition, it leads to a higher estimated RS for all patients aged >70 years, and for lung cancer patients aged >50 years, at diagnosis.

CONCLUSIONS

Mortality of survivors not directly due to the diagnosed cancer was significantly higher than in the general population. It affected the estimates of cure proportions for all age classes and RS in the elderly.

Cancer survival: an overview of measures, uses, and interpretation

Survival statistics are of great interest to patients, clinicians, researchers, and policy makers. Although seemingly simple, survival can be confusing: there are many different survival measures with a plethora of names and statistical methods developed to answer different questions. This paper aims to describe and disseminate different survival measures and their interpretation in less technical language. In addition, we introduce templates to summarize cancer survival statistic organized by their specific purpose: research and policy versus prognosis and clinical decision making.