Evaluating cancer screening programs using survival analysis.

The main purpose of cancer screening programs is to provide early treatment to patients that are diagnosed with cancer on a screening test, thus increasing their chances of survival. To test this hypothesis directly, one should compare the survival of screen-detected cases to the survival of their counterparts not included to the program. In this study, we develop a general notation and use it to formally define the comparison of interest. We explain why the naive comparison between screen-detected and interval cases is biased and show that the total bias that arises in this case can be decomposed as a sum of lead time bias, length time bias, and bias due to overdetection. With respect to the estimation, we show what can be estimated using existing methods. To fill in the missing gap, we develop a new nonparametric estimator that allows us to estimate the survival of the control group, that is, the survival of cancer cases that would be screen-detected among those not included to the program. By joining the proposed estimator with existing methods, we show that the contrast of interest can be estimated without neglecting any of the biases. Our approach is illustrated using simulations and empirical data.

Expected life years compared to the general population.

For cohorts with long-term follow-up, the number of years lost due to a certain disease yields a measure with a simple and appealing interpretation. Recently, an overview of the methodology used for this goal has been published, and two measures have been proposed. In this work, we consider a third option that may be useful in settings in which the other two are inappropriate. In all three measures, the survival of the given dataset is compared to the expected survival in the general population which is calculated using external mortality tables. We thoroughly analyze the differences between the three measures, their assumptions, interpretation, and the corresponding estimators. The first measure is defined in a competing risk setting and assumes an excess hazard compared to the population, while the other two measures also allow estimation for groups that live better than the general population. In this case, the observed survival of the patients is compared to that in the population. The starting point of this comparison depends on whether the entry into the study is a hazard changing event (e.g., disease diagnosis or the age at which the inclusion criteria were met). Focusing on the newly defined life years difference measure, we study the estimation of the variance and consider the possible challenges (e.g., extrapolation) that occur in practice. We illustrate its use with a dataset of French Olympic athletes. Finally, an efficient R implementation has been developed for all three measures which make this work easily available to subsequent users.

Nutritional Status and Health-Related Quality of Life in Men with Advanced Castrate-Resistant Prostate Cancer.

Background Despite professional recommendations malnutrition is not adequately addressed in cancer patients. Here, we explored whether nutritional status (NS) is associated with HRQoL in men with metastatic castrate-resistant prostate cancer (mCRPC). Methods: Men with mCRPC enrolled into this prospective observational study were allocated to one of the four NS categories based on clinical, laboratory, and patient self-reported criteria: well-nourished (WN), nutritional risk without criteria for cachexia/sarcopenia (NR), sarcopenia, and cachexia. The HRQoL was evaluated by the Functional Assessment of Cancer Therapy-Prostate (FACT-P) questionnaire. Association between NS and self-reported HRQoL was sought by the linear regression model, which was adjusted for known prognostic variables and body mass index. Results: Over the period of two years, 141 patients were enrolled. Their median age was 74.1 years (IQR 68.6-79.4 years) and majority of them were minimally symptomatic. Fifty-nine patients (41.8%) were WN, followed by 24 (17%), 42 (29.8%), and 16 (11.4%) patients with NR, sarcopenia, and cachexia, respectively. As compared to WN patients, all three other NS categories were significant negative predictors of HRQoL (P < 0.04). Conclusions: Abnormal NS is highly prevalent in men with mCRPC and is negatively associated with their HRQoL, which supports the recommendation for management of malnutrition in these patients.

Inference for transition probabilities in non-Markov multi-state models.

Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling assumption is that the multi-state stochastic process is Markovian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regression analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research.

Analysis of time-to-event for observational studies: Guidance to the use of intensity models.

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

Female and male US Olympic athletes live 5 years longer than their general population counterparts: a study of 8124 former US Olympians.

OBJECTIVE: To quantify US female and male Olympic athletes' longevity and the years of life lost or saved due to multiple causes of death as compared with the US general population. METHODS: Former US athletes who had participated in the summer or winter Olympic Games at least once between 1912 and 2012 were included. Olympians' date of birth, death and the underlying causes of death were certified by the National Death Index. The Olympians' overall and cause-specific mortality were compared with the US general population based on the US life tables, adjusted by sex, period and age. Mortality differences between the populations were quantified using the years lost/years saved (YS) method. RESULTS: 8124 US Olympians (2301 women and 5823 men) lived 5.1 years longer (YS 95% CI 4.3 to 6.0) than the general population, based on 2309 deaths observed (225 women, 2084 men). Different causes of death contributed to longevity for Olympians as follows: 2.2 years were saved (1.9 to 2.5) from cardiovascular diseases (CVDs); cancer, 1.5 years (1.3 to 1.8); respiratory diseases (eg, influenza, pneumonia), 0.8 years (0.7 to 0.9); external causes (eg, accidents, homicides), 0.5 years (0.4 to 0.6); endocrine and metabolic diseases (eg, diabetes, hyperlipidaemia), 0.4 years (0.2 to 0.5) and digestive system diseases (eg, cirrhosis, hepatic failure), 0.3 years (0.2 to 0.4). Mortality rates due to nervous system disorders (eg, Alzheimer's and Parkinsons's diseases) and mental illness (eg, dementia, schizophrenia) were not different from the general population. CONCLUSION: US Olympians lived longer than the general population, an advantage mainly conferred by lower risks of CVD and cancer. Nervous system disorders and mental illness did not differ between US Olympians and the general population.

Using pseudo-observations for estimation in relative survival.

A common goal in the analysis of the long-term survival related to a specific disease is to estimate a measure that is comparable between populations with different general population mortality. When cause of death is unavailable or unreliable, as for example in cancer registry studies, relative survival methodology is used-in addition to the mortality data of the patients, we use the data on the mortality of the general population. In this article, we focus on the marginal relative survival measure that summarizes the information about the disease-specific hazard. Under additional assumptions about latent times to death of each cause, this measure equals net survival. We propose a new approach to estimation based on pseudo-observations and derive two estimators of its variance. The properties of the new approach are assessed both theoretically and with simulations, showing practically no bias and a close to nominal coverage of the confidence intervals with the precise formula for the variance. The approximate formula for the variance has sufficiently good performance in large samples where the precise formula calculation becomes computationally intensive. Using bladder cancer data and simulations, we show that the behavior of the new approach is very close to that of the Pohar Perme estimator but has the important advantage of a simpler formula that does not require numerical integration and therefore lends itself more naturally to further extensions.

Diabetes and smoking are more important for prognosis of patients with peripheral arterial disease than some genetic polymorphisms.

Background: The role of genetic polymorphisms in peripheral arterial disease (PAD) is incompletely understood. We tested whether selected single nucleotide polymorphisms (SNPs) were associated with PAD and with adverse events in an observational study cohort. Also, the role of diabetes and smoking was studied. Patients and methods: 742 patients with PAD and 713 age- and gender-matched control subjects were subjected to yearly physical and laboratory investigations and were managed for 5 years according to the European guidelines on cardiovascular disease prevention. The occurrence of all-cause death, cardiovascular death, non-fatal myocardial infarction, ischemic stroke or critical limb ischemia (major events) and revascularization procedures (minor events) was recorded. In 655 patients with PAD and 612 control subjects the following SNPs were determined: rs1466408, rs13428968 and rs12803 of NR4A2 gene, rs10499563 of IL6 gene, rs668 and rs12953 of PECAM1 gene, and rs10861032 of Chr12 locus. Results: The distribution of selected SNPs did not differ between patients with PAD and control subjects, and neither between subjects with or without major adverse events. In contrast, diabetes and smoking affected survival and event-free survival. Among patients with PAD, diabetes doubled the hazard ratio (HR) for cardiovascular death and smoking doubled the HR for death or major event. The 5-year survival of diabetics with PAD was 0.80 (CI 0.75-0.85) and of non-diabetics with PAD 0.87 (CI 0.84-0.90), p = 0.045. The 5-year survival of active smokers with PA D was 0.80 (CI 0.75-0.62), of former smokers 0.83 (CI 0.79-0.88), and of never-smokers 0.89 (CI 0.86-0.93), p = 0.024. Conclusions: SNPs of NR4A2, IL6, PECAM1 and Chr12 were not associated with PAD or with major adverse events. However, diabetes and smoking were associated with worse survival and event-free survival in patients with PAD.

Explained variation of excess hazard models.

The availability of longstanding collection of detailed cancer patient information makes multivariable modelling of cancer-specific hazard of death appealing. We propose to report variation in survival explained by each variable that constitutes these models. We adapted the ranks explained (RE) measure to the relative survival data setting, ie, when competing risks of death are accounted for through life tables from the general population. RE is calculated at each event time. We introduce weights for each death reflecting its probability to be a cancer death. RE varies between -1 and +1 and can be reported at given times in the follow-up and as a time-varying measure from diagnosis onward. We present an application for patients diagnosed with colon or lung cancer in England. The RE measure shows reasonable properties and is comparable in both relative and cause-specific settings. One year after diagnosis, RE for the most complex excess hazard models reaches 0.56, 95% CI: 0.54 to 0.58 (0.58 95% CI: 0.56-0.60) and 0.69, 95% CI: 0.68 to 0.70 (0.67, 95% CI: 0.66-0.69) for lung and colon cancer men (women), respectively. Stage at diagnosis accounts for 12.4% (10.8%) of the overall variation in survival among lung cancer patients whereas it carries 61.8% (53.5%) of the survival variation in colon cancer patients. Variables other than performance status for lung cancer (10%) contribute very little to the overall explained variation. The proportion of the variation in survival explained by key prognostic factors is a crucial information toward understanding the mechanisms underpinning cancer survival. The time-varying RE provides insights into patterns of influence for strong predictors.

The heart of the matter: years-saved from cardiovascular and cancer deaths in an elite athlete cohort with over a century of follow-up.

To quantify the years of life saved from cardiovascular (CVD), cancer and overall deaths among elite athletes according to their main type of physiological effort performed in the Olympic Games. All French athletes participating in the Games from 1912 to 2012, with vital status validated and cause of death (if concerned) identified by the national registries were included (n = 2814, 455 died) and classified according to 6 groups of effort: POWER (continuous effort < 45 s); INTERMEDIATE (45 s ≤ continuous effort < 600 s); ENDURANCE (continuous effort ≥ 600 s); POLYVALENT (participating in different events entering different classifications), INTERMITTENT (intermittent effort, i.e. team sports); PRECISION (targeting events). The theoretical years-lost method was adapted to calculate gains in longevity (years-saved) according to specific-risks under the competing risks model and was implemented in R software. Considering overall-deaths, all groups significantly saved, on average, 6.5 years of life (95% CI 5.8-7.2) compared to the general population. This longevity advantage is mainly driven by a lower risk of cancer which, isolated, contributed to significantly save 2.3 years of life (95% CI 1.2-1.9) on average in each group. The risk of CVD-related mortality in the ENDURANCE and PRECISION groups is not significantly different from the general population. The other groups significantly saved, on average, 1.6 years of life (95% CI 1.2-1.9) from CVD death. The longevity benefits in elite athletes are associated with the type of effort performed during their career, mainly due to differences on the CVD-risk of death.

Long-term outcomes of high dose treatment and autologous stem cell transplantation in follicular and mantle cell lymphomas - a single centre experience.

BACKGROUND: Advanced follicular lymphoma (FL) and mantle cell lymphoma (MCL) are incurable diseases with conventional treatment. The high dose treatment (HDT) with autologous stem cell transplantation (ASCT), however, offers a certain proportion of these patients the prospect of a prolonged disease-free and overall survival. The aim of this study was to investigate the event free survival (EFS) and overall survival (OS) in patients with FL and MCL treated with ASCT. PATIENTS AND METHODS: Seventeen patients with FL and 29 patients with MCL were included, 15 of them were transplanted to consolidate the response to second line treatment and 24 to consolidate their first remission, respectively. All were conditioned with total body irradiation (TBI) and high dose cyclophosphamide between 2006 and 2014 and all were transplanted with peripheral blood stem cells. RESULTS: The estimated 5-year OS for FL was 87.8% (95% confidence interval [CI] 59.5%-96.8%) and for MCL 79.3% (95% CI 56.1%-91.1%), respectively. The estimated 5-year EFS for FL was 76.0% (95% CI 48.0%-90.3%) and for MCL 69.8% (95% CI 45.5%-84.8%), respectively. There were no secondary hematological malignancies observed in either group. CONCLUSIONS: Based on above results, the ASCT with TBI is a good treatment option in terms of long-term survival for patients with follicular and mantle cell lymphoma demonstrating a relatively low rate of late toxicities and secondary malignancies.

Analysing population-based cancer survival - settling the controversies.

BACKGROUND: The relative survival field has seen a lot of development in the last decade, resulting in many different and even opposing suggestions on how to approach the analysis. METHODS: We carefully define and explain the differences between the various measures of survival (overall survival, crude mortality, net survival and relative survival ratio) and study their differences using colon and prostate cancer data extracted from the national population-based cancer registry of Slovenia as well as simulated data. RESULTS: The colon and prostate cancer data demonstrate clearly that when analysing population-based data, it is useful to split the overall mortality in crude probabilities of dying from cancer and from other causes. Complemented by net survival, it provides a complete picture of cancer survival in a given population. But when comparisons of different populations as defined for example by place or time are of interest, our simulated data demonstrate that net survival is the only measure to be used. CONCLUSIONS: The choice of the method should be done in two steps: first, one should determine the measure of interest and second, one should choose among the methods that estimate that measure consistently.

Analyzing disease recurrence with missing at risk information.

When analyzing time to disease recurrence, we sometimes need to work with data where all the recurrences are recorded, but no information is available on the possible deaths. This may occur when studying diseases of benign nature where patients are only seen at disease recurrences or in poorly-designed registries of benign diseases or medical device implantations without sufficient patient identifiers to obtain their dead/alive status at a later date. When the average time to disease recurrence is long enough in comparison with the expected survival of the patients, statistical analysis of such data can be significantly biased. Under the assumption that the expected survival of an individual is not influenced by the disease itself, general population mortality tables may be used to remove this bias. We show why the intuitive solution of simply imputing the patient's expected survival time does not give unbiased estimates of the usual quantities of interest in survival analysis and further explain that cumulative incidence function analysis does not require additional assumptions on general population mortality. We provide an alternative framework that allows unbiased estimation and introduce two new approaches: an iterative imputation method and a mortality adjusted at risk function. Their properties are carefully studied, with the results supported by simulations and illustrated on a real-world example.

Informative censoring in relative survival.

With changing the age distribution at the time of cancer diagnosis, the administrative censoring due to study end may be informative. This problem has been mentioned frequently in the relative survival field, and an estimator aiming to correct this problem has been developed. In this paper, we review the existing methods for estimation in relative survival, demonstrate their deficiencies, and propose weighting to correct both the recently introduced net survival estimator and the Ederer I estimator. Using simulations and real cancer registry data, we evaluate the magnitude of the informative censoring problem. We clarify the assumptions behind the reviewed methods and provide guidance to their usage in practice.

What is relative survival and what is its role in haematology?

In many haematological diseases, the survival probability is the key outcome. However, when the population of patients is rather old and the follow-up long, a significant proportion of deaths cannot be attributed to the studied disease. This lessens the importance of common survival analysis measures like overall survival and shows the need for other outcome measures requiring more complex methodology. When disease-specific information is of interest but the cause of death is not available in the data, relative survival methodology becomes crucial. The idea of relative survival is to merge the observed data set with the mortality data in the general population and thus allow for an indirect estimation of the burden of the disease. In this work, an overview of different measures that can be of interest in the field of haematology is given. We introduce the crude mortality that reports the probability of dying due to the disease of interest; the net survival that focuses on excess hazard alone and presents the key measure in comparing the disease burden of patients from populations with different general population mortality; and the relative survival ratio which gives a simple comparison of the patients' and the general population survival. We explain the properties of each measure, and some brief notes are given on estimation. Furthermore, we describe how association with covariates can be studied. All the methods and their estimators are illustrated on a sub-cohort of older patients who received a first allogeneic hematopoietic stem cell transplantation for myelodysplastic syndromes or secondary acute myeloid leukemia, to show how different methods can provide different insights into the data.

Prognostic Impact of Nutritional Status on Overall Survival and Health-Related Quality of Life in Men with Advanced Prostate Cancer.

PURPOSE: Prognostic role of nutritional status (NS) in patients with metastatic castrate-resistant prostate cancer (mCRPC) is unknown. We hypothesized that patients' NS at the presentation of mCRPC is prognostic for health-related quality of life (HRQoL) and overall survival (OS). METHODS: We conducted a prospective observational study in mCRPC patients. At enrollment, we allocated each patient into one of four NS categories: (i) well-nourished (WN), (ii) nutritional risk without sarcopenia/cachexia (NR), (iii) sarcopenia, or (iv) cachexia. We sought the prognostic role of the NS for OS and HRQoL by regression models. RESULTS: 141 patients were included into our study. When compared to WN patients, those with NR and cachexia had a higher chance of worse HRQoL (OR 3.45; 95% CI [1.28 to 9.09], and OR 4.17; 95% CI [1.28 to 12.5], respectively), as well as shorter OS (HR 2.04; 95% CI [1.19 to 3.39] and HR 2.9; 95% CI [1.56 to 5.41], respectively). However, when accounting for possible confounding factors, we could not prove the significant importance of NS for chosen outcomes. CONCLUSIONS: Suboptimal NS might be an unfavorable prognostic factor for HRQoL and OS. Further interventional studies focusing on therapy or prevention are warranted.

Integrating relative survival in multi-state models-a non-parametric approach.

Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a further extension is proposed using relative survival, where mortality due to population causes (i.e. non-disease-related mortality) is evaluated. The objective is to split all mortality in disease and non-disease-related mortality, with and without intermediate events, in datasets where cause of death is not recorded or is uncertain. To this end, population mortality tables are integrated into the estimation process, while using the basic relative survival idea that the overall mortality hazard can be written as a sum of a population and an excess part. Hence, we propose an upgraded non-parametric approach to estimation, where population mortality is taken into account. Precise definitions and suitable estimators are given for both the transition hazards and probabilities. Variance estimating techniques and confidence intervals are introduced and the behaviour of the new method is investigated through simulations. The newly developed methodology is illustrated by the analysis of a cohort of patients followed after an allogeneic hematopoietic stem cell transplantation. The work has been implemented in the R package mstate.

Length of Hospital Stay and Survival of Hospitalized COVID-19 Patients During the Second Wave of the Pandemic: A Single Centre Retrospective Study from Slovenia.

Background: As of writing, there are no publications pertaining to the prediction of COVID-19-related outcomes and length of stay in patients from Slovene hospitals. Objectives: To evaluate the length of regular ward and ICU stays and assess the survival of COVID-19 patients to develop better prediction models to forecast hospital capacity and staffing demands in possible further pandemic peaks. Methods: In this retrospective, single-site study we analysed the length of stay and survival of all patients, hospitalized due to the novel coronavirus (COVID-19) at the peak of the second wave, between November 18th 2020 and January 27th 2021 at the University Clinic Golnik, Slovenia. Results: Out of 407 included patients, 59% were male. The median length of stay on regular wards was 7.5 (IQR 5-13) days, and the median ICU length of stay was 6 (IQR 4-11) days. Age, male sex, and ICU stay were significantly associated with a higher risk of death. The probability of dying in 21 days at the regular ward was 14.4% (95% CI [10.9-18%]) and at the ICU it was 43.6% (95% CI [19.3-51.8%]). Conclusion: The survival of COVID-19 is strongly affected by age, sex, and the fact that a patient had to be admitted to ICU, while the length of hospital bed occupancy is very similar across different demographic groups. Knowing the length of stay and admission rate to ICU is important for proper planning of resources during an epidemic.

Age at menarche and menopause is not associated with two common genetic variants in the methylenetetrahydrofolate reductase (MTHFR) gene.

OBJECTIVES: The study aimed at investigating the independent and the combined effects of the two common genetic variants in the methylenetetrahydrofolate reductase (MTHFR) gene, 677C > T and 1298A > C, and their interaction with lifestyle factors on timing of menarche and natural menopause. METHODS: Postmenopausal women (N = 792) were assessed for the association of the two genetic variants with age at menarche (AM). A subsample of 578 of them who had a natural menopause were further investigated for the association of the two genetic variants with age at natural menopause (ANM). Genotyping was done by means of the TaqMan(®) allelic discrimination method. The effect of genetic variants and of lifestyle factors on AM and ANM were calculated by linear regression models. RESULTS: The study revealed no association between the individual or combined effects of the two genetic variants and AM or ANM. The genetic variant 677C > T showed a significant interaction effect with duration of breastfeeding on ANM (p = 0.047). CONCLUSION: We were unable to replicate previous findings suggesting that the MTHFR gene influences the onset of menarche and natural menopause. The interaction effect between the 677C >T genetic variant and duration of breastfeeding on the timing of natural menopause requires further investigation.

COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?

During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among the most affected countries. This was true even though strict non-pharmaceutical interventions (NPIs) to control the progression of the epidemic were being enforced. Using a semi-parametric Bayesian model developed for the purpose of this study, we explore if and how the changes in mobility, their timing and the activation of contact tracing can explain the differences in the epidemic progression of the two waves. To fit the model, we use data on daily numbers of deaths, patients in hospitals, intensive care units, etc., and allow transmission intensity to be affected by contact tracing and mobility (data obtained from Google Mobility Reports). Our results imply that though there is some heterogeneity not explained by mobility levels and contact tracing, implementing interventions at a similar stage as in the first wave would keep the death toll and the health system burden low in the second wave as well. On the other hand, sticking to the same timeline of interventions as observed in the second wave and focusing on enforcing a higher decrease in mobility would not be as beneficial. According to our model, the 'dance' strategy, i.e., first allowing the numbers to rise and then implementing strict interventions to make them drop again, has been played at too-late stages of the epidemic. In contrast, a 15-20% reduction of mobility compared to pre-COVID level, if started at the beginning and maintained for the entire duration of the second wave and coupled with contact tracing, could suffice to control the epidemic. A very important factor in this result is the presence of contact tracing; without it, the reduction in mobility needs to be substantially larger. The flexibility of our proposed model allows similar analyses to be conducted for other regions even with slightly different data sources for the progression of the epidemic; the extension to more than two waves is straightforward. The model could help policymakers worldwide to make better decisions in terms of the timing and severity of the adopted NPIs.