Crude Probability Of Death Research Articles

Time trends in the use of curative treatment in men 70 years and older with nonmetastatic prostate cancer.

Undertreatment of otherwise healthy men in their seventies with prostate cancer has been reported previously. Using information in a Swedish prostate cancer research database, patterns of management and cancer-specific mortality were compared across age groups in over 70,000 men diagnosed with intermediate- or high-risk nonmetastatic prostate cancer between 2008 and 2020. Crude probabilities of death were estimated non-parametrically. Staging procedures, primary treatment, and cancer death were compared using regression models, adjusting for patient and tumor characteristics. During the study period, the proportion of men treated with curative intent increased in ages 70-74 (intermediate-risk from 45% to 72% and high-risk from 49% to 84%), 75-79 (intermediate-risk from 11% to 52% and high-risk from 12% to 70%), and 80-84 years (intermediate-risk from < 1% to 14% and high-risk from < 1% to 30%). Older age was associated with lower likelihoods of staging investigations and curative treatment, also after adjustment for tumor characteristics and comorbidity. Men treated with curative intent and those initially managed conservatively had lower crude risks of prostate cancer death than men receiving androgen deprivation treatment (ADT). In adjusted analyses, ADT was associated with higher prostate cancer mortality than curative treatment across ages and risk groups. Among men managed conservatively, prostate cancer mortality was higher in ages 70 and above. Use of curative treatment increased substantially in older men with prostate cancer between 2008 and 2020. Our findings suggest reduced age-bias and under-treatment, likely reflecting improved individualized decision-making and adherence to guidelines recommending more active management of older men.

Using population-based data to evaluate the impact of adherence to endocrine therapy on survival in breast cancer through the web-application BreCanSurvPred

We show how the use and interpretation of population-based cancer survival indicators can help oncologists talk with breast cancer (BC) patients about the relationship between their prognosis and their adherence to endocrine therapy (ET). The study population comprised a population-based cohort of estrogen receptor positive BC patients (N = 1268) diagnosed in Girona and Tarragona (Northeastern Spain) and classified according to HER2 status (+ / −), stage at diagnosis (I/II/III) and five-year cumulative adherence rate (adherent > 80%; non-adherent ≤ 80%). Cox regression analysis was performed to identify significant prognostic factors for overall survival, whereas relative survival (RS) was used to estimate the crude probability of death due to BC (PBC). Stage and adherence to ET were the significant factors for predicting all-cause mortality. Compared to stage I, risk of death increased in stage II (hazard ratio [HR] 2.24, 95% confidence interval [CI]: 1.51–3.30) and stage III (HR 5.11, 95% CI 3.46–7.51), and it decreased with adherence to ET (HR 0.57, 95% CI 0.41–0.59). PBC differences were higher in non-adherent patients compared to adherent ones and increased across stages: stage I: 6.61% (95% CI 0.05–13.20); stage II: 9.77% (95% CI 0.59–19.01), and stage III: 22.31% (95% CI 6.34–38.45). The age-adjusted survival curves derived from this modeling were implemented in the web application BreCanSurvPred (https://pdocomputation.snpstats.net/BreCanSurvPred). Web applications like BreCanSurvPred can help oncologists discuss the consequences of non-adherence to prescribed ET with patients.

Non-parametric estimation of reference adjusted, standardised probabilities of all-cause death and death due to cancer for population group comparisons

BackgroundEnsuring fair comparisons of cancer survival statistics across population groups requires careful consideration of differential competing mortality due to other causes, and adjusting for imbalances over groups in other prognostic covariates (e.g. age). This has typically been achieved using comparisons of age-standardised net survival, with age standardisation addressing covariate imbalance, and the net estimates removing differences in competing mortality from other causes. However, these estimates lack ease of interpretability. In this paper, we motivate an alternative non-parametric approach that uses a common rate of other cause mortality across groups to give reference-adjusted estimates of the all-cause and cause-specific crude probability of death in contrast to solely reporting net survival estimates.MethodsWe develop the methodology for a non-parametric equivalent of standardised and reference adjusted crude probabilities of death, building on the estimation of non-parametric crude probabilities of death. We illustrate the approach using regional comparisons of survival following a diagnosis of rectal cancer for men in England. We standardise to the covariate distribution and other cause mortality of England as a whole to offer comparability, but with close approximation to the observed all-cause region-specific mortality.ResultsThe approach gives comparable estimates to observed crude probabilities of death, but allows direct comparison across population groups with different covariate profiles and competing mortality patterns. In our illustrative example, we show that regional variations in survival following a diagnosis of rectal cancer persist even after accounting for the variation in deprivation, age at diagnosis and other cause mortality.ConclusionsThe methodological approach of using standardised and reference adjusted metrics offers an appealing approach for future cancer survival comparison studies and routinely published cancer statistics. Our non-parametric estimation approach through the use of weighting offers the ability to estimate comparable survival estimates without the need for statistical modelling.

Reference-adjusted and standardized all-cause and crude probabilities as an alternative to net survival in population-based cancer studies.

In population-based cancer survival studies, the most common measure to compare population groups is age-standardized marginal relative survival, which under assumptions can be interpreted as marginal net survival; the probability of surviving if it was not possible to die of causes other than the cancer under study (if the age distribution was that of a common reference population). The hypothetical nature of this definition has led to confusion and incorrect interpretation. For any measure to be fair in terms of comparing cancer survival, then differences between population groups should depend only on differences in excess mortality rates due to the cancer and not differences in other-cause mortality rates or differences in the age distribution. We propose using crude probabilities of death and all-cause survival which incorporate reference expected mortality rates. This makes it possible to obtain marginal crude probabilities and all-cause probability of death that only differ between population groups due to excess mortality rate differences. Choices have to be made regarding what reference mortality rates to use and what age distribution to standardize to. We illustrate the method and some potential choices using data from England for men diagnosed with melanoma. Various marginal measures are presented and compared. The new measures help enhance understanding of cancer survival and are a complement to the more commonly used measures.

Direct modeling of the crude probability of cancer death and the number of life years lost due to cancer without the need of cause of death: a pseudo-observation approach in the relative survival setting.

SummaryIn population-based cancer studies, net survival is a crucial measure for population comparison purposes. However, alternative measures, namely the crude probability of death (CPr) and the number of life years lost (LYL) due to death according to different causes, are useful as complementary measures for reflecting different dimensions in terms of prognosis, treatment choice, or development of a control strategy. When the cause of death (COD) information is available, both measures can be estimated in competing risks setting using either cause-specific or subdistribution hazard regression models or with the pseudo-observation approach through direct modeling. We extended the pseudo-observation approach in order to model the CPr and the LYL due to different causes when information on COD is unavailable or unreliable (i.e., in relative survival setting). In a simulation study, we assessed the performance of the proposed approach in estimating regression parameters and examined models with different link functions that can provide an easier interpretation of the parameters. We showed that the pseudo-observation approach performs well for both measures and we illustrated their use on cervical cancer data from the England population-based cancer registry. A tutorial showing how to implement the method in R software is also provided.

Temporal trends in net and crude probability of death from cancer and other causes in the Australian population, 1984–2013

BackgroundWhile net probabilities of death in the relative survival framework ignore competing causes of death, crude probabilities allow estimation of the real risk of cancer deaths. This study quantifies temporal trends in net and crude probabilities of death. MethodsAustralian population-based cohort of 2,015,903 people aged 15-89 years, diagnosed with a single primary invasive cancer from 1984 to 2013 with mortality follow-up to 31 December 2014. Survival was analyzed with the cohort method. Flexible parametric relative survival models were used to estimate both probability measures by diagnosis year for all cancers and selected leading sites. ResultsFor each site, excess mortality rates reduced over time, especially for prostate cancer. While both the 10-year net and crude probability of cancer deaths decreased over time, specific patterns varied. For example, the crude probability of lung cancer deaths for males aged 50 years decreased from 0.90 (1984) to 0.79 (2013); whereas the corresponding probabilities for kidney cancer were 0.64 and 0.18 respectively. Patterns for crude probabilities of competing deaths were relatively constant. Although for younger patients, both net and crude measures were similar, crude probability of competing deaths increased with age, hence for older ages net and crude measures were different except for lung and pancreas cancers. ConclusionsThe observed reductions in probabilities of death over three decades for Australian cancer patients are encouraging. However, this study also highlights the ongoing mortality burden following a cancer diagnosis, and the need for continuing efforts to improve cancer prevention, diagnosis and treatment.

Improving survival in end-stage renal disease: A case study.

Background: With the increase of life expectancy, *On behalf of the REIN registry. end-stage renal disease (ESRD) is affecting a growing number of people. Simultaneously, renal replacement therapies (RRTs) have considerably improved patient survival. We investigated the way current RRT practices would affect patients' survival. Methods: We used a multi-state model to represent the transitions between RRTs and the transition to death. The concept of "crude probability of death" combined with this model allowed estimating the proportions of ESRD-related and ESRD-unrelated deaths. Estimating the ESRD-related death rate requires comparing the mortality rate between ESRD patients and the general population. Predicting patients' courses through RRTs and Death states could be obtained by solving a system of Kolmogorov differential equations. The impact of practice on patient survival was quantified using the restricted mean survival time (RMST) which was compared with that of healthy subjects with same characteristics. Results: The crude probability of ESRD-unrelated death was nearly zero in the youngest patients (18-45 years) but was a sizeable part of deaths in the oldest (≥70 years). Moreover, in the oldest patients, the proportion of expected death was higher in patient without vs. with diabetes because the former live older. In men aged 75 years at first RRT, the predicted RMSTs in patients with and without diabetes were, respectively, 61% and 69% those of comparable healthy men. Conclusion: Using the concept of "crude probability of death" with multi-state models is feasible and useful to assess the relative benefits of various treatments in ESRD and help patient long-term management.

Long-term crude probabilities of death among breast cancer patients by age and stage: a population-based survival study in Northeastern Spain (Girona\u2013Tarragona 1985\u20132004)

BackgroundWe provide population-based long-term survival indicators of breast cancer patients by quantifying the observed survival, and the probabilities of death due to breast cancer and to other causes by age and tumor stage at diagnosis.MethodsWe included a total of 10,195 female patients diagnosed before 85 years with invasive primary breast cancer in Girona and Tarragona during the periods 1985–1994 and 1995–2004 and followed-up until December 31st 2014. The survival indicators were estimated at 5, 10, 15 and 20 years of follow-up comparing diagnostic periods.ResultsComparing diagnostic periods: I) the probability of death due to other causes did not change; II) the 20-year survival for women diagnosed ≤ 49 years increased 13% (1995–2004 = 68%; 1985–1994:55%), whereas their probability of death due to breast cancer decreased at the same pace (1995–2004 = 29%; 1985–1994 = 42%); III) at 10 years of follow-up, decreases in the probabilities of death due to breast cancer across age groups switched from 11 to 17% resulting in a risk of death reduction of 19% after adjusting by stage. During 1995–2004, the stage-specific 10-year probabilities of death due to breast cancer switched from: 3–6% in stage I, 18–20% in stage II, 34–46% in stage III and surpassed 70% in stage IV beyond 5 years after diagnosis.ConclusionsIn our study, women diagnosed with breast cancer had higher long-term probability to die from breast cancer than from other causes. The improvements in treatment and the lead-time bias in detecting cancer in an early stage resulted in a reduction of 19% in the risk of death between diagnostic periods.

Nonparametric Relative Survival Analysis with the R Package relsurv

Relative survival methods are crucial with data in which the cause of death information is either not given or inaccurate, but cause-specific information is nevertheless required. This methodology is standard in cancer registry data analysis and can also be found in other areas. The idea of relative survival is to join the observed data with the general mortality population data and thus extract the information on the disease-specific hazard. While this idea is clear and easy to understand, the practical implementation of the estimators is rather complex since the population hazard for each individual depends on demographic variables and changes in time. A considerable advance in the methodology of this field has been observed in the past decade and while some methods represent only a modification of existing estimators, others require newly programmed functions. The package relsurv covers all the steps of the analysis, from importing the general population tables to estimating and plotting the results. The syntax mimics closely that of the classical survival packages like survival and cmprsk, thus enabling the users to directly use its functions without any further familiarization. In this paper we focus on the nonparametric relative survival analysis, and in particular, on the two key estimators for net survival and crude probability of death. Both estimators were first presented in our package and are still missing in many other software packages, a fact which greatly hampers their frequency of use. The paper offers guidelines for the actual use of the software by means of a detailed nonparametric analysis of the data describing the survival of patients with colon cancer. The data have been provided by the Cancer Registry of Slovenia.

Estimating the Crude Probability of Death due to Cancer using French Registry Data.

Estimating the Crude Probability of Death due to Cancer using French Registry Data.

The impact of life tables adjusted for smoking on the socio-economic difference in net survival for laryngeal and lung cancer.

Background:Net survival is a key measure in cancer control, but estimates for cancers that are strongly associated with smoking may be biased. General population life tables represent background mortality in net survival, but may not adequately reflect the higher mortality experienced by smokers.Methods:Life tables adjusted for smoking were developed, and their impact on net survival and inequalities in net survival for laryngeal and lung cancers was examined.Results:The 5-year net survival estimated with smoking-adjusted life tables was consistently higher than the survival estimated with unadjusted life tables: 7% higher for laryngeal cancer and 1.5% higher for lung cancer. The impact of using smoking-adjusted life tables was more pronounced in affluent patients; the deprivation gap in 5-year net survival for laryngeal cancer widened by 3%, from 11% to 14%.Conclusions:Using smoking-adjusted life tables to estimate net survival has only a small impact on the deprivation gap in survival, even when inequalities are substantial. Adjusting for the higher, smoking-related background mortality did increase the estimates of net survival for all deprivation groups, and may be more important when measuring the public health impact of differences or changes in survival, such as avoidable deaths or crude probabilities of death.

Product-limit estimators in the analysis of competing risks

Abstract In the case of independent competing risks of death, product-limit (PL) estimators, as explained in the introduction, are derived for the net, crude and partial crude probabilities of death by using the method of maximum likelihood. Such use is made considering all of the causes of death simultaneously, without regarding deaths due to “other causes of death” as withdrawals. The estimators of the probabilities based upon different non-parametric models for competing risks and different actuarial methods of estimation are the PL estimators when the subintervals of the period of observation are so shortened that each interval contains room for either one death or for one or several withdrawals. The estimators, derived from the non-parametric models, converge towards the estimators which result from the model for independent risks. This convergence is in accordance with the convergence property of the corresponding probabilities (Hakulinen, 1977).

Competing risks analysis in mountain pine beetle dynamics

This paper presents an application and evaluation of competing risks analysis (Chiang, 1968) of mountain pine beetle life tables. Three known and one group of unknown risks are used. Interpretation of the results imply that only the crude probability of death from a specific cause is applicable to this situation; net and partial crude probabilities are yet incomplete. None of the risks (factors of mortality) exerted sufficient influence upon the population to be considered factors of regulation or reduction. Evidence remains that the mountain pine beetle is food-regulated at optimum temperature conditions and temperature-regulated at optimum food conditions.