Two Types of Survival Curves of Different Lines of Progeric Mice.

For most of their lifespan, the probability of death for many animal species increases with age. Gompertz law states that this increase is exponential. In this work, we have compared previously published data on the survival kinetics of different lines of progeric mice. Visual analysis showed that in six lines of these rapidly aging mutants, the probability of death did not strictly depend on age. In contrast, ten lines of progeric mice have survival curves similar to those of the control animals, that is, in agreement with Gompertz law, similar to the shape of an exponential curve upside down. Interestingly, these ten mutations cause completely different cell malfunctions. We speculate that what these mutations have in common is a reduction in the lifespan of cells and/or an acceleration of the transition to the state of cell senescence. Thus, our analysis, similar to the conclusions of many previously published works, indicates that the aging of an organism is a consequence of the aging of individual cells.

Juvenile survival curves in a solitary ground squirrel with a prolonged hibernation: effects of individual characteristics, environment, and maternal investment.

Juvenile survival is a key life-history influence on population dynamics and adaptive evolution. We analyzed the effects of individual characteristics, early environment, and maternal investment on juvenile survival in a large solitary hibernating rodent-yellow ground squirrel Spermophilus fulvus using Cox mixed-effects models. Only 48% of weaned pups survived to dispersal and 17% survived to hibernation. Early life expectancy was primarily determined by individual characteristics and, to a lesser extent, by the early environment. The strongest and positive predictor of juvenile survival was body mass which crucially affected mortality immediately after weaning. Males suffered higher mortality than females after the onset of dispersal; however, the overall difference between sexes was partly masked by high rates of mortality in the first days after emergence in both sexes. Later emerged juveniles had lower life expectancy than the earliest pups. The overall effect of local juvenile density was positive. Prolonged lactation did not enhance juvenile survival: Pups nursed longer survived shorter than the young nursed for a shorter period. Our findings support the hypothesis that females of S. fulvus cannot effectively regulate maternal expenditures to mitigate the effects of unfavorable conditions on their offspring. The strategy to deal with seasonal time constraints on life history in female S. fulvus suggests an early termination of maternal care at the cost of juvenile quality and survival. This female reproductive strategy corresponds to a "fast-solitary" life of folivorous desert-dwelling S. fulvus and other solitary ground squirrels with prolonged hibernation.

Investigation of plasma parameters, distributions, and optical emission for the anti-microbial performance of non-woven fabric under direct current glow discharge.

The distribution of electron temperature Te and density Ne for direct current glow plasma discharge was investigated, using a single Langmuir probe, inserted inside the plasma cell. The radial temperature distribution has the same values, except with a small increment variation at the cathode edge, and an axial decrement for the temperature Te distribution profiles from the cathode fall region, passing the abnormal glow region, up to the faraway axial region. The radial distribution of the electron density Ne has its highest value at the cathode, with very intense plasma at the cathode fall region, and more Ne decrement in the abnormal glow region, passing the abnormal glow region up to the faraway axial region. In the axial Ne distribution, an increase in Ne from the cathode fall region reaches maximum values in the abnormal glow region and decreases in the faraway axial region. The optimal plasma surface treatment of non-woven silk fabric (n-WSF) can be achieved by utilizing a high plasma density and low energy of electrons to inactivate viable cells attached to (n-WSF) at very short application times, leading to complete inactivation, where the bacterial inactivation rate increases in the abnormal glow region. Based on analyses of the experimental data of initial and final densities of viable cells using survival curves in the abnormal glow discharge region, a dramatic inhibitory effect of plasma discharge on the residual survival microbe ratio was observed.

Clinical effectiveness and safety of olaparib in BRCA-mutated, HER2-negative metastatic breast cancer in a real-world setting: final analysis of LUCY.

PURPOSE: The interim analysis of the phase IIIb LUCY trial demonstrated the clinical effectiveness of olaparib in patients with germline BRCA-mutated (gBRCAm), human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (mBC), with median progression-free survival (PFS) of 8.11 months, which was similar to that in the olaparib arm of the phase III OlympiAD trial (7.03 months). This prespecified analysis provides final overall survival (OS) and safety data. METHODS: The open-label, single-arm LUCY trial of olaparib (300 mg, twice daily) enrolled adults with gBRCAm or somatic BRCA-mutated (sBRCAm), HER2-negative mBC. Patients had previously received a taxane or anthracycline for neoadjuvant/adjuvant or metastatic disease and up to two lines of chemotherapy for mBC. RESULTS: Of 563 patients screened, 256 (gBRCAm, n = 253; sBRCAm, n = 3) were enrolled. In the gBRCAm cohort, median investigator-assessed PFS (primary endpoint) was 8.18 months and median OS was 24.94 months. Olaparib was clinically effective in all prespecified subgroups: hormone receptor status, previous chemotherapy for mBC, previous platinum-based chemotherapy (including by line of therapy), and previous cyclin-dependent kinase 4/6 inhibitor use. The most frequent treatment-emergent adverse events (TEAEs) were nausea (55.3%) and anemia (39.2%). Few patients (6.3%) discontinued olaparib owing to a TEAE. No deaths associated with AEs occurred during the study treatment or 30-day follow-up. CONCLUSION: The LUCY patient population reflects a real-world population in line with the licensed indication of olaparib in mBC. These findings support the clinical effectiveness and safety of olaparib in patients with gBRCAm, HER2-negative mBC. CLINICAL TRIAL REGISTRATION: Clinical trials registration number: NCT03286842.

Elevated Troponin I as a Marker for Unfavorable Outcomes in Acute Ischemic Stroke.

OBJECTIVE: To assess if elevated cardiac troponin I (cTnI) serves as a sign of unfavorable functional outcomes in ischemic stroke. METHODS: In this single-center prospective cohort study, 100 consecutive patients admitted with acute ischemic stroke (normal troponin I group n = 52, raised troponin I group n = 48) were included. Hospital mortality was documented in both groups; the remaining patients were followed up to 90 days. Then two groups were compared in terms of unfavorable short-term outcomes (Modified Rankin Scale > 3) and mortality. Multivariate logistic regression was conducted to determine the predictive value of elevated cTnI. The Kaplan-Meier curve was drawn and compared to determine the difference in survival between the two groups. To find out the most probable cut-off level for an unfavorable outcome, a receiver operating characteristic (ROC) analysis was conducted. RESULT:  A higher frequency of coronary artery disease (p=0.030), higher National Institutes of Health Stroke Scale (NIHSS) (p=0.008) score, and lower Glasgow Coma Scale (GCS) (p=0.002) was observed in raised troponin I group. Even after the exclusion of confounding elevated troponin I was found to be an independent predictor of unfavorable outcomes (adjusted odds ratio, OR 8.25 {95% confidence interval, CI: 2.65-25.75}; p<0.001). The patients with raised troponin I had a significantly lower rate of survival after 90 days (p=0.022). The elevated troponin I was observed to have a significantly high accuracy (p<0.001; area under curve, AUC: 0.768 {moderate accuracy}, 95% CI: 0 .676 to 0.861) in predicting unfavorable outcomes. CONCLUSION: Elevated cTnI is independently associated with unfavorable short-term outcomes. It is also associated with a lower rate of survival.

Ball divergence for the equality test of crossing survival curves.

It is a very common problem to test survival equality using the right-censored time-to-event data in clinical research. Although the log-rank test is popularly used in various studies, it may become insensitive when the proportional hazards assumption is violated. As follows, there have a variety of statistical methods being proposed to identify the discrepancy between crossing survival curves or hazard functions. The omnibus tests against general alternatives are usually preferred due to their wide applicability to complicated scenarios in real applications. In this paper, we propose two novel statistics to estimate the ball divergence using the right-censored survival data, and then implement them in the equality test on survival time in two independent groups. The simulation analysis demonstrates their efficiency in identifying the survival discrepancy. Compared to the existing methods, our proposed methods present higher power in situations with complex distributions, especially when there is a scale shift between groups. Real examples illustrate its advantage in practical applications.

dsSurvival 2.0: privacy enhancing survival curves for survival models in the federated DataSHIELD analysis system.

OBJECTIVE: Survival models are used extensively in biomedical sciences, where they allow the investigation of the effect of exposures on health outcomes. It is desirable to use diverse data sets in survival analyses, because this offers increased statistical power and generalisability of results. However, there are often challenges with bringing data together in one location or following an analysis plan and sharing results. DataSHIELD is an analysis platform that helps users to overcome these ethical, governance and process difficulties. It allows users to analyse data remotely, using functions that are built to restrict access to the detailed data items (federated analysis). Previous works have provided survival modelling functionality in DataSHIELD (dsSurvival package), but there is a requirement to provide functions that offer privacy enhancing survival curves that retain useful information. RESULTS: We introduce an enhanced version of the dsSurvival package which offers privacy enhancing survival curves for DataSHIELD. Different methods for enhancing privacy were evaluated for their effectiveness in enhancing privacy while maintaining utility. We demonstrated how our selected method could enhance privacy in different scenarios using real survival data. The details of how DataSHIELD can be used to generate survival curves can be found in the associated tutorial.

Drug Survival of Tumor Necrosis Factor-Alpha Inhibitors and Switched Subsequent Biologic Agents in Patients with Psoriasis: A Retrospective Study.

INTRODUCTION: This study aimed to retrospectively examine the drug survival of tumor necrosis factor (TNF)-alpha inhibitors and switched subsequent biologic agents after discontinuation of TNF inhibitors. METHODS: This real-world setting study was conducted at a single academic center. We included patients who were treated with adalimumab (n = 111), certolizumab pegol (n = 12), and infliximab (n = 74) at Jichi Medical University Hospital from 1 January 2010 to 31 July 2021. RESULTS: No significant differences were noted in drug survival between the three TNF inhibitors. The 10-year drug survival rate for adalimumab and infliximab was 14% and 18%, respectively. Of the patients who discontinued TNF inhibitors for any reason (n = 137), 105 chose biologics as their subsequent treatment. The subsequent biologics included 31 cases of TNF inhibitors (adalimumab in 20, certolizumab pegol in 1, and infliximab in 10), 19 of interleukin-12/23 inhibitor (ustekinumab), 42 of interleukin-17 inhibitors (secukinumab in 19, brodalumab in 9, and ixekizumab in 14) and 13 of interleukin-23 inhibitors (guselkumab in 11, risankizumab in 1, and tildrakizumab in 1). Cox proportional hazards analysis for the subsequent drugs in cases of discontinuation due to inadequate efficacy revealed that female sex was a predictor of drug discontinuation (hazard ratio 2.58, 95% confidence interval 1.17-5.70) and that taking interleukin-17 inhibitors rather than TNF inhibitors was a predictor of drug persistence (hazard ratio 0.37, 95% confidence interval 0.15-0.93). CONCLUSIONS: Interleukin-17 inhibitors may be a favorable option for patients who need to switch from TNF inhibitors due to inadequate efficacy. However, this study is limited by the small number of cases and its retrospective design.

With many biologic options available for the treatment of psoriasis, choosing the optimal drug can be challenging, especially when switching drugs. Tumor necrosis factor (TNF) inhibitors are the oldest category of biologics used for psoriasis, with adalimumab and infliximab being available since 2010 and certolizumab pegol since 2019 in Japan. In this study, we examined the drug survival of TNF inhibitors in patients treated with adalimumab (n = 111), certolizumab pegol (n = 12), and infliximab (n = 74) at Jichi Medical University Hospital from 1 January 2010 to 31 July 2021. No significant differences were noted in drug survival between the three TNF inhibitors, and the 10-year drug survival rate for adalimumab and infliximab was 14% and 18%, respectively. We examined the drug survival of subsequent biologics used by patients who discontinued TNF inhibitors for any reason (n = 137) and found that among patients who discontinued TNF inhibitors due to inadequate efficacy, female sex was a predictor of drug discontinuation and that taking interleukin-17 inhibitors rather than TNF inhibitors was a predictor of drug continuation. The study results suggest that interleukin-17 inhibitors is a favorable option for patients who discontinue TNF inhibitors due to inadequate efficacy and need to switch to other agents. However, this study has limitations, including the small number of cases and the single-center and retrospective study design.

Association between red blood cell distribution width and all-cause mortality in unselected critically ill patients: Analysis of the MIMIC-III database.

Background: Although red cell distribution width (RDW) is widely observed in clinical practice, only a few studies have looked at all-cause mortality in unselected critically ill patients, and there are even fewer studies on long-term mortality. The goal of our study was to explore the prediction and inference of mortality in unselected critically ill patients by assessing RDW levels. Methods: We obtained demographic information, laboratory results, prevalence data, and vital signs from the unselected critically ill patients using the publicly available MIMIC-III database. We then used this information to analyze the association between baseline RDW levels and unselected critically ill patients using Cox proportional risk analysis, smoothed curve fitting, subgroup analysis, and Kaplan-Meier survival curves for short, intermediate, and long-term all-cause mortality in unselected critically ill patients. Results: A total of 26,818 patients were included in our study for the final data analysis after screening in accordance with acceptable conditions. Our study investigated the relationship between RDW levels and all-cause mortality in a non-selected population by a smoothed curve fit plots and COX proportional risk regression models integrating cubic spline functions reported results about a non-linear relationship. In the fully adjusted model, we found that RDW values were positively associated with 30-day, 90-day, 365-day, and 4-year all-cause mortality in 26,818 non-selected adult patients with HRs of 1.10 95%CIs (1.08, 1.12); 1.11 95%CIs (1.10, 1.13); 1.13 95%CIs (1.12, 1.14); 1.13 95%CIs (1.12, 1.14). Conclusion: In unselected critically ill patients, RDW levels were positively associated with all-cause mortality, with elevated RDW levels increasing all-cause mortality.

Four-year drug survival of apremilast in patients with psoriasis.

This real-world study at a single academic center retrospectively examined the drug survival of apremilast for patients with psoriasis. Retrospective information was extracted from the medical records of patients with psoriasis treated with apremilast at the Department of Dermatology, Jichi Medical University Hospital, between March 1, 2017, and June 31, 2021. In total, 281 patients were included in this study. Of these patients, 22% had psoriatic arthritis and 57% had a history of prior systemic treatment, including biologics, before the initiation of apremilast. The 1-, 2-, 3-, and 4-year drug survival rates were 54%, 41%, 32%, and 30%, respectively. Cox regression analysis revealed that sex, duration of plaque psoriasis (<10 years vs ≥10 years), presence of psoriatic arthritis, involvement of scalp lesions, involvement of palmoplantar lesion, involvement of nail lesions, having cardiometabolic comorbidities, and a history of prior systemic treatment did not have any significant impact on drug survival. The most common reason for apremilast discontinuation was inadequate efficacy (27%), followed by adverse events (12%). Approximately 49% of the patients experienced one or more adverse events. Diarrhea was the most common adverse event (24%), followed by nausea (19%) and headache (11%).

Treatment trends of muscle invasive bladder cancer: Evidence from the Surveillance, Epidemiology, and End Results database, 1988 to 2013.

Objective: Guidelines for muscle-invasive bladder cancer (MIBC) recommend that patients receive neoadjuvant chemotherapy with radical cystectomy as treatment over radical cystectomy alone. Though trends and practice patterns of MIBC have been defined using the National Cancer Database, data using the Surveillance, Epidemiology, and End Results (SEER) program have been poorly described. Methods: Using the SEER database, we collected data of MIBC according to the American Joint Commission on Cancer. We considered differences in patient demographics and tumor characteristics based on three treatment groups: chemotherapy (both adjuvant and neoadjuvant) with radical cystectomy, radical cystectomy, and chemoradiotherapy. Multinomial logistic regression was performed to compare likelihood ratios. Temporal trends were included for each treatment group. Kaplan-Meier curves were performed to compare cause-specific survival. A Cox proportional-hazards model was utilized to describe predictors of survival. Results: Of 16 728 patients, 10 468 patients received radical cystectomy alone, 3236 received chemotherapy with radical cystectomy, and 3024 received chemoradiotherapy. Patients who received chemoradiotherapy over radical cystectomy were older and more likely to be African American; stage III patients tended to be divorced. Patients who received chemotherapy with radical cystectomy tended to be males; stage II patients were less likely to be Asian than Caucasian. Stage III patients were less likely to receive chemoradiotherapy as a treatment option than stage II. Chemotherapy with radical cystectomy and chemoradiotherapy are both underutilized treatment options, though increasingly utilized. Kaplan-Meier survival curves showed significant differences between stage II and III tumors at each interval. A Cox proportional-hazards model showed differences in gender, tumor stage, treatment modality, age, and marital status. Conclusion: Radical cystectomy alone is still the most commonly used treatment for muscle-invasive bladder cancer based on temporal trends. Significant disparities exist in those who receive radical cystectomy over chemoradiotherapy for treatment.

Impact of KRAS Mutation on Survival Outcome of Patients With Metastatic Colorectal Cancer in Jordan.

Background Colorectal cancer (CRC) is the most prevalent cancer in males, with an incidence rate (IR) of 13.1%, and the second most prevalent cancer in females, with an IR of 8.4%, coming after breast cancer in Jordan. The present study was motivated by conflicting clinical data regarding the prognostic impact of Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation in patients with metastatic colorectal cancer (mCRC). Our study aimed to investigate if KRAS mutation conferred a negative prognostic value in Jordanian patients with mCRC. Materials and methods The current study is a retrospective study that collected data from a cohort of 135 mCRC patients diagnosed between 1 January 2017 and 1 January 2022 at our Oncology Department at the Jordanian Military Cancer Center (MCAC) using our patients' electronic medical records. The last follow-up date was 1 September 2022. From the cohort, we obtained data regarding age, sex, date of diagnosis, metastatic spread, KRAS status, either mutated KRAS or wild-type KRAS, and location of the primary tumor. All patients underwent tumor tissue biopsies to determine KRAS mutational status based on quantitative polymerase chain reaction and reverse hybridization from an accredited diagnostic laboratory at Jordan University Hospital. Statistical analysis was carried out to address the associations between KRAS mutation and the patients-tumor characteristics and their prognosis on survival. Results KRAS mutation was found in 40.3% of the participants in the study, and 56.7% had the wild type. There was a predilection of KRAS mutation, with 67% on the right side versus 33% on the left side (p = 0.018). Kaplan-Meier survival analysis showed worse survival outcomes in KRAS mutant patients (p = 0.002). The median overall survival in the KRAS mutant patients was 17 months (95% confidence interval (CI): 13.762-19.273) compared to 21 months (95% CI: 20.507-27.648) in patients with wild-type KRAS. Additionally, the Cox regression model identified that KRAS mutation carries a poorer prognosis on survival outcome hazard ratio (HR: 2.045, 95% CI: 1.291-3.237, p = 0.002). The test also showed statistical significance in the metastatic site (lung only). But this time, it was associated with a better survival outcome (HR: 0.383, 95% CI: 0.186-0.788, p = 0.009). Conclusion The present study shows that the presence of KRAS mutation has been found to negatively impact the prognosis and survival outcome of Jordanian patients with mCRC.

A comparison of different methods to adjust survival curves for confounders.

Treatment specific survival curves are an important tool to illustrate the treatment effect in studies with time-to-event outcomes. In non-randomized studies, unadjusted estimates can lead to biased depictions due to confounding. Multiple methods to adjust survival curves for confounders exist. However, it is currently unclear which method is the most appropriate in which situation. Our goal is to compare forms of inverse probability of treatment weighting, the G-Formula, propensity score matching, empirical likelihood estimation and augmented estimators as well as their pseudo-values based counterparts in different scenarios with a focus on their bias and goodness-of-fit. We provide a short review of all methods and illustrate their usage by contrasting the survival of smokers and non-smokers, using data from the German Epidemiological Trial on Ankle-Brachial-Index. Subsequently, we compare the methods using a Monte-Carlo simulation. We consider scenarios in which correctly or incorrectly specified models for describing the treatment assignment and the time-to-event outcome are used with varying sample sizes. The bias and goodness-of-fit is determined by taking the entire survival curve into account. When used properly, all methods showed no systematic bias in medium to large samples. Cox regression based methods, however, showed systematic bias in small samples. The goodness-of-fit varied greatly between different methods and scenarios. Methods utilizing an outcome model were more efficient than other techniques, while augmented estimators using an additional treatment assignment model were unbiased when either model was correct with a goodness-of-fit comparable to other methods. These "doubly-robust" methods have important advantages in every considered scenario.

A win ratio approach for comparing crossing survival curves in clinical trials.

Many clinical trials include time-to-event or survival data as an outcome. To compare two survival distributions, the log-rank test is often used to produce a P-value for a statistical test of the null hypothesis that the two survival curves are identical. However, such a P-value does not provide the magnitude of the difference between the curves regarding the treatment effect. As a result, the P-value is often accompanied by an estimate of the hazard ratio from the proportional hazards model or Cox model as a measurement of treatment difference. However, one of the most important assumptions for Cox model is that the hazard functions for the two treatment groups are proportional. When the hazard curves cross, the Cox model could lead to misleading results and the log-rank test could also perform poorly. To address the problem of crossing curves in survival analysis, we propose the use of the win ratio method put forward by Pocock et al. as an estimand for analysing such data. The subjects in the test and control treatment groups are formed into all possible pairs. For each pair, the test treatment subject is labelled a winner or a loser if it is known who had the event of interest such as death. The win ratio is the total number of winners divided by the total number of losers and its standard error can be estimated using Bebu and Lachin method. Using real trial datasets and Monte Carlo simulations, this study investigates the power and type I error and compares the win ratio method with the log-rank test and Cox model under various scenarios of crossing survival curves with different censoring rates and distribution parameters. The results show that the win ratio method has similar power as the log-rank test and Cox model to detect the treatment difference when the assumption of proportional hazards holds true, and that the win ratio method outperforms log-rank test and Cox model in terms of power to detect the treatment difference when the survival curves cross.

Deconstructing the Kaplan-Meier curve: Quantification of treatment effect using the treatment effect process.

In studies of survival and its association with treatment and other prognostic variables, elapsed time alone will often show itself to be among the strongest, if not the strongest, of the predictor variables. Kaplan-Meier curves will show the overall survival of each group and the general differences between groups due to treatment. However, the time-dependent nature of treatment effects is not always immediately transparent from these curves. More sophisticated tools are needed to spotlight the treatment effects. An important tool in this context is the treatment effect process. This tool can be potent in revealing the complex myriad of ways in which treatment can affect survival time. We look at a recently published study in which the outcome was relapse-free survival, and we illustrate how the use of the treatment effect process can provide a much deeper understanding of the relationship between time and treatment in this trial.

The probability of bacterial spores surviving a thermal process: The 12D myth and other issues with its quantitative assessment.

The concepts of "D-value," "thermal death time" and "commercial sterility," innovative and useful at their inception, are based on untenable assumptions, notably that the log-linear isothermal inactivation model has universal applicability, that extrapolation over several orders of magnitude below the detection level is permissible, and that total microbial inactivation is theoretically impossible. Almost all commonly observed inactivation patterns, the log-linear is just a special case, can be described by both deterministic and fully stochastic models, examples of which are given. Unlike the deterministic, the stochastic models predict either complete elimination of the targeted cells or spores in realistic finite time, or residual survival. In most cases, the published survival data do not contain enough information to establish which actually happens. The microbial safety of thermally processed foods can be compromised not only by under-processing but also by a variety of mishaps whose occurrence probabilities are unrelated to the inactivation kinetics. Moreover, the available sampling plans to detect microbial contamination in sterilized containers through incubation alone are insensitive to levels of potential safety concerns. In principle, many of these issues could be resolved by developing new dramatically improved detection methods and/or verifiable methods to predict very low levels of microbial survival.

Deterministic and stochastic survival models of injured ozonated Giardia cysts.

Giardia cysts exposed to short sublethal ozonation in lake waters continue to die-off well after the ozone complete dissipation. This delayed inactivation can be the manifestation of injured cysts' mortality, which the traditional Chick-Watson-Hom type models of disinfection do not account for. But it can be described by a slightly modified version of a general microbial survival model adapted for injured cysts or other targeted microorganisms surviving disinfection. The downward concavity of the cysts' semi-logarithmic survival ratio vs. time relationships suggests that the cysts' deaths had unimodal temporal distribution. Indeed, the cumulative (CDF) forms of the Weibull and lognormal distribution functions both had excellent fit to the experimental survival data. Such a survival pattern can also be described by a fully probabilistic model devised from the injured cysts' Markov chain, where the mortality's probability rate rises linearly with time. The stochastic model explains the ubiquitous observation that microbial survival curves become increasingly irregular and irreproducible as the number of survivors dwindles, regardless of their concavity degree and direction. Although based on ozonated Giardia cyst data, the concept should be applicable to the delayed mortality of other microorganisms surviving sublethal treatments of other kinds but unable to recover and/or multiply. KEY POINTS: • Deterministic and stochastic survival models can describe delayed inactivation. • The Weibull and lognormal distributions can describe cysts' times to mortality. • Stochastic model explains the progressively growing scatter in survival curves.

Glycosyltransferases in Cancer: Prognostic Biomarkers of Survival in Patient Cohorts and Impact on Malignancy in Experimental Models.

Background: Glycosylation changes are a main feature of cancer. Some carbohydrate epitopes and expression levels of glycosyltransferases have been used or proposed as prognostic markers, while many experimental works have investigated the role of glycosyltransferases in malignancy. Using the transcriptomic data of the 21 TCGA cohorts, we correlated the expression level of 114 glycosyltransferases with the overall survival of patients. Methods: Using the Oncolnc website, we determined the Kaplan−Meier survival curves for the patients falling in the 15% upper or lower percentile of mRNA expression of each glycosyltransferase. Results: Seventeen glycosyltransferases involved in initial steps of N- or O-glycosylation and of glycolipid biosynthesis, in chain extension and sialylation were unequivocally associated with bad prognosis in a majority of cohorts. Four glycosyltransferases were associated with good prognosis. Other glycosyltransferases displayed an extremely high predictive value in only one or a few cohorts. The top were GALNT3, ALG6 and B3GNT7, which displayed a p < 1 × 10−9 in the low-grade glioma (LGG) cohort. Comparison with published experimental data points to ALG3, GALNT2, B4GALNT1, POFUT1, B4GALT5, B3GNT5 and ST3GAL2 as the most consistently malignancy-associated enzymes. Conclusions: We identified several cancer-associated glycosyltransferases as potential prognostic markers and therapeutic targets.

Analyzing differences between restricted mean survival time curves using pseudo-values.

Hazard ratios are ubiquitously used in time to event analysis to quantify treatment effects. Although hazard ratios are invaluable for hypothesis testing, other measures of association, both relative and absolute, may be used to fully elucidate study results. Restricted mean survival time (RMST) differences between groups have been advocated as useful measures of association. Recent work focused on model-free estimates of the difference in restricted mean survival through follow-up times, instead of focusing on a single time horizon. The resulting curve can be used to quantify the association in time units with a simultaneous confidence band. In this work a model-based estimate of the curve is proposed using pseudo-values allowing for possible covariate adjustment. The method is easily implementable with available software and makes possible to compute a simultaneous confidence region for the curve. The pseudo-values regression using multiple restriction times is in good agreement with the estimates obtained by standard direct regression models fixing a single restriction time. Moreover, the proposed method is flexible enough to reproduce the results of the non-parametric approach when no covariates are considered. Examples where it is important to adjust for baseline covariates will be used to illustrate the different methods together with some simulations.