Improving communication of cancer survival statistics-feasibility of implementing model-based algorithms in routine publications

BACKGROUND

Routine reporting of cancer patient survival is important, both to monitor the effectiveness of health care and to inform about prognosis following a cancer diagnosis. A range of different survival measures exist, each serving different purposes and targeting different audiences. It is important that routine publications expand on current practice and provide estimates on a wider range of survival measures. We examine the feasibility of automated production of such statistics.

METHODS

We used data on 23 cancer sites obtained from the Cancer Registry of Norway (CRN). We propose an automated way of estimating flexible parametric relative survival models and calculating estimates of net survival, crude probabilities, and loss in life expectancy across many cancer sites and subgroups of patients.

RESULTS

For 21 of 23 cancer sites, we were able to estimate survival models without assuming proportional hazards. Reliable estimates of all desired measures were obtained for all cancer sites.

DISCUSSION

It may be challenging to implement new survival measures in routine publications as it can require the application of modeling techniques. We propose a way of automating the production of such statistics and show that we can obtain reliable estimates across a range of measures and subgroups of patients.

Association of metformin use and survival in patients with cutaneous melanoma and diabetes

BACKGROUND

Metformin use has been associated with improved survival in patients with different types of cancer, but research regarding the effect of metformin on cutaneous melanoma (CM) survival is sparse and inconclusive.

OBJECTIVES

To investigate the association between metformin use and survival among patients with CM and diabetes.

METHODS

All adult patients with a primary invasive CM between 2007 and 2014 were identified in the Swedish Melanoma Registry and followed until death, or end of follow-up on 31 December 2017 in this population-based cohort study. Patients with both CM and type 2 diabetes mellitus were assessed further. Overall survival (OS) and melanoma-specific survival (MSS) were the primary endpoints. Cox proportional hazard models estimating crude and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were used comparing peridiagnostic use vs. nonuse of metformin. Dose response was evaluated based on defined daily doses.

RESULTS

Among a total of 23 507 patients, 1162 patients with CM and type 2 diabetes mellitus were included in the final cohort, with a median follow-up time of 4.1 years (interquartile range 2.4-6.1). Peridiagnostic metformin use was associated with a significantly decreased risk of death by any cause (HR 0.68, 95% CI 0.57-0.81). Cumulative pre- and postdiagnostic metformin use was also associated with improved OS: the HR for prediagnostic use was 0.90 (95% CI 0.86-0.95) for every 6 months of use and the HR for postdiagnostic use ranged from 0.98 (95% CI 0.97-0.98) for 0-6 months to 0.59 (0.49-0.70) for 24-30 months of use. No association was found for metformin use and MSS.

CONCLUSIONS

Metformin use was associated with improved OS in patients with CM and diabetes regardless of timing (pre-, post- or peridiagnostic use) and followed a dose-response pattern. However, further research regarding the underlying mechanisms is warranted.

Real world treatment utilization patterns in patients with castration-resistant prostate cancer

BACKGROUND

Studies describing treatment utilization for castration-resistant prostate cancer (CRPC) are limited. We aimed to describe the treatment utilization of a contemporary population-based CRPC cohort between 2006 and 2016.

METHODS

We identified 1699 men with a PC diagnosis between 2005 and 2015, who developed CRPC between 2006 and 2015 in the Stockholm region of Sweden. Demographic information, stage and grade at PC diagnosis, stage at CRPC, prostate-specific antigen (PSA) nadir, PSA doubling time, treatment utilization rate within 1 year of CRPC diagnosis, reason for stopping therapy, treatment sequence trajectory, overall and PC specific survival was described.

RESULTS

Treatment for men with de novo metastatic disease (n = 463) was 32%, treatment for men with progressive metastatic disease after PC diagnosis (n = 66) was 44%, treatment for men with nonmetastatic CRPC (n = 113) was 34% and treatment for those with an unknown stage at time of CRPC diagnosis (n = 857) was 12%. Docetaxel was used in 39%, abiraterone acetate plus prednisone in 15%, enzalutamide in 13%, cabazitaxel in 11% and radium-223 in 5% of treatments. Treatment increased from 22% in 2006-2009 for metastatic cancer to 50% in 2013-2015 (p < .001). Factors associated with treatment were an unknown stage at diagnosis (OR: 0.3, 95% CI: 0.2-0.4), age ≥75 years (OR: 0.2, 95% CI: 0.1 - 0.3), PSA doubling time >3 months (OR: 0.4, 95% CI: 0.3 - 0.6) and a diagnosis between 2013 and 2015 (OR: 3.4, 95% CI: 2.0 - 5.8).

CONCLUSIONS

Despite treatment availability, in this large real-world cohort we found treatment utilization to remain low.

Temporal improvements noted in life expectancy of patients with colorectal cancer; a Dutch population-based study

OBJECTIVE

Specific survival estimates are needed for the increasing number of colorectal cancer (CRC) survivors. The aim of this population-based study was to determine conditional loss in expectation of life (LEL) due to CRC.

STUDY DESIGN AND SETTING

All surgically treated patients with CRC registered in the Netherlands Cancer Registry with stage I-III between 1990-2016, were included (N = 203,216). Estimates of conditional LEL were predicted using flexible parametric models and the total life years lost due to cancer were estimated.

RESULTS

LEL decreased with older age and patients with rectal cancer or higher disease stage had highest LEL. In 2010, LEL for sixty-year old male and female patients was 2 vs. 2, 4 vs. 4, and 7 vs. 8 years for colon cancer, and 2 vs. 2, 4 vs. 5 and 7 vs. 8 years for rectal cancer, respectively. Conditional LEL in patients with CRC decreased during follow-up. Patients with combined stage I-III colon and rectal cancer in 2010 lost an estimated 18,628 and 11,336 life years.

CONCLUSION

This study quantified the impact of CRC on patient's life expectancy, both on individual and population level and demonstrated temporal improvements in CRC survival. These results provide meaningful information that can be used during follow-up.

A multistate model incorporating estimation of excess hazards and multiple time scales

As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population-level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user-friendly Stata software with accompanying example code.

Relapse Risk and Loss of Lifetime After Modern Combined Modality Treatment of Young Patients With Hodgkin Lymphoma: A Nordic Lymphoma Epidemiology Group Study

PURPOSE

Estimates of short- and long-term survival for young patients with classic Hodgkin lymphoma (cHL) are of considerable interest. We investigated cHL prognosis in the era of contemporary treatment at different milestones during the follow-up.

PATIENTS AND METHODS

On the basis of a Nordic cohort of 2,582 patients diagnosed at ages 18 to 49 years between 2000 and 2013, 5-year relapse risks and 5-year restricted losses in expectation of lifetime were estimated for all patients and for patients who achieved event-free survival (EFS) for 12 (EFS12), 24 (EFS24), 36 (EFS36) or 60 (EFS60) months. The median follow-up time was 9 years (range, 2.9 to 16.8 years).

RESULTS

The 5-year overall survival was 95% (95% CI, 94% to 96%). The 5-year risk of relapse was 13.4% (95% CI, 12.1% to 14.8%) overall but decreased to 4.2% (95% CI, 3.8% to 4.6%) given that patients reached EFS24. Relapse risk for patients treated with six to eight courses of bleomycin, etoposide, doxorubicin, cyclophosphamide, vincristine, procarbazine, and prednisone (BEACOPP) was comparable to that of patients treated with six to eight courses of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) despite more adverse risk criteria among patients treated with BEACOPP. Both from diagnosis and if EFS24 was reached, the losses in expectation of lifetime during the following 5 years were small (from diagnosis, 45 days [95% CI, 35 to 54 days] and for patients who reached EFS24, 13 days [95% CI, 7 to 20 days]). In stage-stratified analyses of 5-year restricted loss in expectation of lifetime, patients with stages I to IIA disease had no noteworthy excess risk of death after they reached EFS24, whereas risk remained measurable for patients with stages IIB to IV cHL.

CONCLUSION

Real-world data on young patients with cHL from the Nordic countries show excellent outcomes. The outlook is particularly favorable for patients who reach EFS24, which supports limited relapse-oriented clinical follow-up.

Risk for Arterial and Venous Thrombosis in Patients With Myeloproliferative Neoplasms: A Population-Based Cohort Study

BACKGROUND

Patients with myeloproliferative neoplasms (MPNs) are reported to be at increased risk for thrombotic events. However, no population-based study has estimated this excess risk compared with matched control participants.

OBJECTIVE

To assess risk for arterial and venous thrombosis in patients with MPNs compared with matched control participants.

DESIGN

Matched cohort study.

SETTING

Population-based setting in Sweden from 1987 to 2009, with follow-up to 2010.

PATIENTS

9429 patients with MPNs and 35 820 matched control participants.

MEASUREMENTS

The primary outcomes were rates of arterial and venous thrombosis. Flexible parametric models were used to calculate hazard ratios (HRs) and cumulative incidence with 95% CIs.

RESULTS

The HRs for arterial thrombosis among patients with MPNs compared with control participants at 3 months, 1 year, and 5 years were 3.0 (95% CI, 2.7 to 3.4), 2.0 (CI, 1.8 to 2.2), and 1.5 (CI, 1.4 to 1.6), respectively. The corresponding HRs for venous thrombosis were 9.7 (CI, 7.8 to 12.0), 4.7 (CI, 4.0 to 5.4), and 3.2 (CI, 2.9 to 3.6). The rate was significantly elevated across all age groups and was similar among MPN subtypes. The 5-year cumulative incidence of thrombosis in patients with MPNs showed an initial rapid increase followed by gentler increases during follow-up. The HR for venous thrombosis decreased during more recent calendar periods.

LIMITATION

No information on individual laboratory results or treatment.

CONCLUSION

Patients with MPNs across all age groups have a significantly increased rate of arterial and venous thrombosis compared with matched control participants, with the highest rates at and shortly after diagnosis. Decreases in the rate of venous thrombosis over time likely reflect advances in clinical management.

PRIMARY FUNDING SOURCE

The Cancer Research Foundations of Radiumhemmet, Blodcancerfonden, the Swedish Research Council, the regional agreement on medical training and clinical research between Stockholm County Council and Karolinska Institutet, the Adolf H. Lundin Charitable Foundation, and Memorial Sloan Kettering Cancer Center.

Clinical Diagnosis of Mental Disorders Immediately Before and After Cancer Diagnosis: A Nationwide Matched Cohort Study in Sweden

IMPORTANCE

Psychiatric comorbidities are common among patients with cancer. However, whether or not there is increased risk of mental disorders during the diagnostic workup leading to a cancer diagnosis was unknown.

OBJECTIVE

To examine the relative risks of depression, anxiety, substance abuse, somatoform/conversion disorder, and stress reaction/adjustment disorder during the periods before and after cancer diagnosis compared with individuals without cancer.

DESIGN, SETTING, AND PARTICIPANTS

Nationwide matched cohort study from January 1, 2001, to December 31, 2010, in a Swedish population and health registers.

MAIN OUTCOMES AND MEASURES

We estimated the time-varying hazard ratios (HRs) of the first clinical diagnosis of the studied mental disorders from 2 years before cancer diagnosis, through the time of diagnosis, and until 10 years after diagnosis, adjusting for age, sex, calendar period, and educational level. To assess milder mental conditions and symptoms, we further assessed the use of related psychiatric medications for patients with cancer diagnosed during 2008-2009.

RESULTS

The study included 304 118 patients with cancer and 3 041 174 cancer-free individuals who were randomly selected from the Swedish population and individually matched to the patients with cancer on year of birth and sex. The median age at diagnosis for the patients with cancer was 69 years, and 46.9% of the patients were female. The relative rate for all studied mental disorders started to increase from 10 months before cancer diagnosis (HR, 1.1; 95% CI, 1.1-1.2), peaked during the first week after diagnosis (HR, 6.7; 95% CI, 6.1-7.4), and decreased rapidly thereafter but remained elevated 10 years after diagnosis (HR, 1.1; 95% CI, 1.1-1.2). The rate elevation was clear for all main cancers except nonmelanoma skin cancer and was stronger for cancers of poorer prognosis. Compared with cancer-free individuals, increased use of psychiatric medications was noted from 1 month before cancer diagnosis and peaked around 3 months after diagnosis among patients with cancer.

CONCLUSIONS AND RELEVANCE

Patients diagnosed as having cancer had increased risks of several common mental disorders from the year before diagnosis. These findings support the existing guidelines of integrating psychological management into cancer care and further call for extended vigilance for multiple mental disorders starting from the time of the cancer diagnostic workup.

Longitudinal fluctuation in mammographic percent density differentiates between interval and screen-detected breast cancer

Interval breast cancer (IC) has a more aggressive phenotype and higher mortality than screen-detected cancer (SDC). In this case-case study, we investigated whether the size of longitudinal fluctuations in mammographic percent density (PD fluctuation) was associated with the ratio of IC versus SDC among screened women with breast cancer. The primary study population consisted of 1,414 postmenopausal breast cancer cases, and the validation population of 1,241 cases. We calculated PD fluctuation as the quadratic mean of deviations between actual PD and the long-term trend estimated by a mixed effects model. In a logistic regression model we examined the association between PD fluctuation and IC versus SDC including adjustments for PD at last screening, age at diagnosis, BMI and hormone replacement therapy. All statistical tests were two-sided. There were 385 IC and 1,029 SDC in the primary study population, with PD fluctuations of 0.44 and 0.41 respectively (p = 0.0309). After adjustments, PD fluctuation was associated with an increased ratio of IC versus SDC, with an estimated per-standard deviation odds ratio of 1.17 (95% CI = 1.03-1.33), compared to 1.19 (95% CI = 1.04-1.38) in the validation population. In screened women with breast cancer, high fluctuation in mammographic percent density was associated with an increased ratio of IC versus SDC. Whether this is entirely related to a reduced mammographic detectability or to a biological phenotype promoting faster tumor growth remains to be elucidated.

Partitioning of excess mortality in population-based cancer patient survival studies using flexible parametric survival models

Background

Relative survival is commonly used for studying survival of cancer patients as it captures both the direct and indirect contribution of a cancer diagnosis on mortality by comparing the observed survival of the patients to the expected survival in a comparable cancer-free population. However, existing methods do not allow estimation of the impact of isolated conditions (e.g., excess cardiovascular mortality) on the total excess mortality. For this purpose we extend flexible parametric survival models for relative survival, which use restricted cubic splines for the baseline cumulative excess hazard and for any time-dependent effects.

Methods

In the extended model we partition the excess mortality associated with a diagnosis of cancer through estimating a separate baseline excess hazard function for the outcomes under investigation. This is done by incorporating mutually exclusive background mortality rates, stratified by the underlying causes of death reported in the Swedish population, and by introducing cause of death as a time-dependent effect in the extended model. This approach thereby enables modeling of temporal trends in e.g., excess cardiovascular mortality and remaining cancer excess mortality simultaneously. Furthermore, we illustrate how the results from the proposed model can be used to derive crude probabilities of death due to the component parts, i.e., probabilities estimated in the presence of competing causes of death.

Results

The method is illustrated with examples where the total excess mortality experienced by patients diagnosed with breast cancer is partitioned into excess cardiovascular mortality and remaining cancer excess mortality.

Conclusions

The proposed method can be used to simultaneously study disease patterns and temporal trends for various causes of cancer-consequent deaths. Such information should be of interest for patients and clinicians as one way of improving prognosis after cancer is through adapting treatment strategies and follow-up of patients towards reducing the excess mortality caused by side effects of the treatment.

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

Background

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.

Methods

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.

Results

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.

Conclusions

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.