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.

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.

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.

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.

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.

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.

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.

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.

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.

Impact of delays in radiotherapy of head and neck cancer on outcome.

BACKGROUND: In head and neck cancer (HNC), the relationship between a delay in starting radiotherapy (RT) and the outcome is unclear. The aim of the present study was to determine the impact of the amount of time before treatment intervention (TTI) and the growth kinetics of individual tumors on treatment outcomes and survival. METHODS: Two hundred sixty-two HNC patients with 273 primary tumors, treated with definitive (chemo) RT, were retrospectively analyzed. The TTI was defined as the time interval between the date of histopathologic diagnosis and the first day of the RT course. Volumetric data on 57 tumors were obtained from diagnostic and RT planning computer tomography (CT) scans in order to calculate the tumor growth kinetic parameters. RESULTS: No significant association between locoregional control or cause-specific hazards and TTI was found. The log hazard for locoregional recurrence linearly increased during the first 40 days of waiting for RT, although this was not significant. The median tumor volume relative increase rate and tumor volume doubling time was 3.2%/day and 19 days, respectively, and neither had any impact on locoregional control or cause-specific hazards. CONCLUSION: The association between a delay in starting RT and the outcome is complex and does not harm all patients waiting for RT. Further research into imaging-derived kinetic data on individual tumors is warranted in order to identify patients at an increased risk of adverse outcomes due to a delay in starting RT.

Course and Outcome of Erythema Migrans in Pregnant Women.

Information on Lyme borreliosis (LB) during pregnancy is limited. In the present study, the course and outcome of erythema migrans (EM) in 304 pregnant women, diagnosed in the period 1990-2015, was assessed and compared with that in age-matched non-pregnant women. The frequency of unfavorable outcome of pregnancies was also evaluated. The pregnant women reported constitutional symptoms less frequently than the non-pregnant women (22.4% vs. 37.2%, p < 0.001). Pregnant women diagnosed with EM later during pregnancy had a lower probability of reporting constitutional symptoms (odds ratio = 0.97 for 1-week difference in gestation week at diagnosis of EM, 95% CI: 0.94-0.99, p = 0.02). The outcome of pregnancy was unfavorable in 42/304 (13.8%) patients: preterm birth in 22/42 (52.4%), fetal/perinatal death in 10/42 (23.8%), and/or anomalies in 15/42 (35.7%). Several patients had potential explanation(s) for the unfavorable outcome. In conclusion, the course of early LB during pregnancy is milder than in age-matched non-pregnant women. The outcome of pregnancy with the treatment approach used in the present study (i.v. ceftriaxone 2 g once daily for 14 days) is favorable.

Improvements in the multidisciplinary care are beneficial for survival in amyotrophic lateral sclerosis (ALS): experience from a tertiary ALS center.

Objective: The Ljubljana ALS Centre, established in 2002, is the only tertiary center for amyotrophic lateral sclerosis (ALS) in Slovenia. The aim of our study was to evaluate the impact of therapeutic interventions and improvements in the multidisciplinary care on the survival of our patients.Methods: All patients diagnosed with ALS at our center during years 2003-2005 (early group) and 2011-2012 (late group) were included in this retrospective cohort study (n = 124). Kaplan-Meier survival analysis and multiple regression analysis with Cox proportional hazards model were performed to compare survival and to evaluate the differences between the two cohorts.Results: Median survival from the time of diagnosis was 13.0 (95% CI 10.2-15.8) months in the early group and 21.8 (95% CI 17.2-26.4) months in the late group (p = 0.005). In the Cox proportional hazards analysis, the late group of patients was associated with better survival independently of all other prognostic factors (hazard ratio (HR)=0.51, 95% CI = 0.32-0.81, p = 0.004). Survival was also associated with patients' age, use of noninvasive ventilation (NIV) and gastrostomy. The model fit significantly improved when the interaction between the NIV use and the observed time period was added to the model (HR = 0.34, 95% CI = 0.12-0.96, p = 0.041).Conclusions: Our findings suggest that improvements in the multidisciplinary care were beneficial for survival of our patients with ALS. The survival benefit in the late group of our patients could be partially explained by the improvements in the NIV use at our center.

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.

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.

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.

DNA Methylation and Hydroxymethylation Profile of CD34+-Enriched Cell Products Intended for Autologous CD34+ Cell Transplantation.

Epigenetic dysregulation has been shown to limit functional capacity of aging hematopoietic stem cells, which may contribute to impaired outcome of hematopoietic stem cell-based therapies. The aim of our study was to gain better insight into the epigenetic profile of CD34+-enriched cell products intended for autologous CD34+ cell transplantation in patients with cardiomyopathy. We found global DNA methylation content significantly higher in immunoselected CD34+ cells compared to leukocytes in leukapheresis products (2.33 ± 1.03% vs. 1.84 ± 0.86%, p = 0.04). Global DNA hydroxymethylation content did not differ between CD34+ cells and leukocytes (p = 0.30). By measuring methylation levels of 94 stem cell transcription factors on a ready-to-use array, we identified 15 factors in which average promoter methylation was significantly different between leukocytes and CD34+ cells. The difference was highest for HOXC12 (58.18 ± 6.47% vs. 13.34 ± 24.18%, p = 0.0009) and NR2F2 (51.65 ± 25.89% vs. 7.66 ± 21.43%, p = 0.0045) genes. Our findings suggest that global DNA methylation and hydroxymethylation patterns as well as target methylation profile of selected genes in CD34+-enriched cell products do not differ significantly compared to leukapheresis products and, thus, can tell us little about the functional capacity and regenerative properties of CD34+ cells. Future studies should examine other CD34+ cell graft characteristics, which may serve as prognostic tools for autologous CD34+ cell transplantation.