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TAS-102, a combination of trifluridine and tipiracil in which tipiracil interferes with the deactivation of trifluridine, improved overall and progression-free survival in patients whose disease had progressed after treatment with fluorouracil-containing drug combinations. Fluoropyrimidines have long represented the cornerstone of treatment for colorectal cancer. 1 Such compounds act primarily as inhibitors of thymidylate synthase, the rate-limiting enzyme in the synthesis of pyrimidine nucleotides. 2 Fluorouracil has been combined with folinic acid (also known as leucovorin) to enhance the capacity of fluorouracil to bind to thymidylate synthase. 2 The addition of irinotecan (FOLFIRI) or oxaliplatin (FOLFOX) to fluorouracil and folinic acid, in combination with either a vascular endothelial growth factor inhibitor (bevacizumab) or an epidermal growth factor inhibitor (e.g., cetuximab or panitumumab) if the tumor contains a wild-type RAS gene, represents contemporary standard therapy and has extended . . .

Background There is increasing interest in the development and use of clinical prediction models, but a lack of evidence-supported guidance on the merits of different modelling approaches. This is especially true for time-to-event outcomes, where limited studies have compared the vast number of modelling approaches available. This study compares prediction accuracy and variable importance measures for four modelling approaches in prediction of time-to-revision surgery following total knee arthroplasty (TKA) and total hip arthroplasty (THA). Methods The study included 321,945 TKA and 151,113 THA procedures performed between 1 January 2003 and 31 December 2017. Accuracy of the Cox model, Weibull parametric model, flexible parametric model, and random survival forest were compared, with patient age, sex, comorbidities, and prosthesis characteristics considered as predictors. Prediction accuracy was assessed using the Index of Prediction Accuracy (IPA), c-index, and smoothed calibration curves. Variable importance rankings from the Cox model and random survival forest were also compared. Results Overall, the Cox and flexible parametric survival models performed best for prediction of both TKA (integrated IPA 0.056 (95% CI [0.054, 0.057]) compared to 0.054 (95% CI [0.053, 0.056]) for the Weibull parametric model), and THA revision. (0.029 95% CI [0.027, 0.030] compared to 0.027 (95% CI [0.025, 0.028]) for the random survival forest). The c-index showed broadly similar discrimination between all modelling approaches. Models were generally well calibrated, but random survival forest underfitted the predicted risk of TKA revision compared to regression approaches. The most important predictors of revision were similar in the Cox model and random survival forest for TKA (age, opioid use, and patella resurfacing) and THA (femoral cement, depression, and opioid use). Conclusion The Cox and flexible parametric models had superior overall performance, although all approaches performed similarly. Notably, this study showed no benefit of a tuned random survival forest over regression models in this setting.

Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this article, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web‐based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.

Dynamic regression models, including the quantile regression model and Aalen's additive hazards model, are widely adopted to investigate evolving covariate effects. Yet lack of monotonicity respecting with standard estimation procedures remains an outstanding issue. Advances have recently been made, but none provides a complete resolution. In this article, we propose a novel adaptive interpolation method to restore monotonicity respecting, by successively identifying and then interpolating nearest monotonicity-respecting points of an original estimator. Under mild regularity conditions, the resulting regression coefficient estimator is shown to be asymptotically equivalent to the original. Our numerical studies have demonstrated that the proposed estimator is much more smooth and may have better finite-sample efficiency than the original as well as, when available as only in special cases, other competing monotonicity-respecting estimators. Illustration with a clinical study is provided.

Patients with cytogenetically normal acute myeloid leukemia (CN-AML) may harbor prognostically relevant gene mutations and thus be categorized into one of the three 2022 European LeukemiaNet (ELN) genetic-risk groups. Nevertheless, there remains heterogeneity with respect to relapse-free survival (RFS) within these genetic-risk groups. Our training set included 306 adults on Alliance for Clinical Trials in Oncology studies with de novo CN-AML aged < 60 years who achieved a complete remission and for whom centrally reviewed cytogenetics, RNA-sequencing, and gene mutation data from diagnostic samples were available (Alliance trial A152010). To overcome deficiencies of the Cox proportional hazards model when long-term survivors are present, we developed a penalized semi-parametric mixture cure model (MCM) to predict RFS where RNA-sequencing data comprised the predictor space. To validate model performance, we employed an independent test set from the German Acute Myeloid Leukemia Cooperative Group (AMLCG) consisting of 40 de novo CN-AML patients aged < 60 years who achieved a complete remission and had RNA-sequencing of their pre-treatment sample. For the training set, there was a significant non-zero cure fraction ( p  = 0.019) with 28.5% of patients estimated to be cured. Our MCM included 112 genes associated with cure, or long-term RFS, and 87 genes associated with latency, or shorter-term time-to-relapse. The area under the curve and C-statistic were respectively, 0.947 and 0.783 for our training set and 0.837 and 0.718 for our test set. We identified a novel, prognostically relevant molecular signature in CN-AML, which allows identification of patient subgroups independent of 2022 ELN genetic-risk groups. Trial registration Data from companion studies CALGB 8461, 9665 and 20202 (trials registered at www.clinicaltrials.gov as, respectively, NCT00048958, NCT00899223, and NCT00900224) were obtained from Alliance for Clinical Trials in Oncology under data sharing study A152010. Data from the AMLCG 2008 trial was registered at www.clinicaltrials.gov as NCT01382147.

In the consumer credit industry, assessment of default risk is critically important for the financial health of both the lender and the borrower. Methods for predicting risk for an applicant using credit bureau and application data, typically based on logistic regression or survival analysis, are universally employed by credit card companies. Because of the manner in which the predictive models are fit using large historical sets of existing customer data that extend over many years, default trends, anomalies, and other temporal phenomena that result from dynamic economic conditions are not brought to light. We introduce a modification of the proportional hazards survival model that includes a time-dependency mechanism for capturing temporal phenomena, and we develop a maximum likelihood algorithm for fitting the model. Using a very large, real data set, we demonstrate that incorporating the time dependency can provide more accurate risk scoring, as well as important insight into dynamic market effects that can inform and enhance related decision making.

Tout au long de l’histoire de la République populaire de Chine, l’âge au mariage n’a cessé d’augmenter sous l’effet des politiques étatiques et des évolutions socioéconomiques sans que le mariage ne perde de son attrait. Simultanément, le célibat définitif subi par certains groupes de population – dû à un déséquilibre du rapport des sexes sur le marché matrimonial et aux préférences de genre en termes de choix du conjoint – fait l’objet d’une inquiétude croissante. Cette étude utilise une analyse de survie de type cure afin de modéliser conjointement les déterminants de la probabilité et du calendrier du premier mariage. Nous évaluons les évolutions du mariage parmi plusieurs générations successives à l’aide de données provenant de multiples vagues de l’Enquête sociale générale sur la Chine. Les résultats suggèrent que, pour la plupart des cohortes d’hommes et de femmes, un faible niveau d’éducation correspond à des mariages précoces, mais avec de moindres chances de se marier tout au long de la vie. Pour les plus jeunes cohortes d’hommes, le fait de résider dans des provinces moins développées est associé à une entrée dans le mariage plus précoce, mais à une probabilité réduite de se marier. Parmi les plus jeunes cohortes de femmes, habiter une grande ville apparaît comme le principal facteur d’affaiblissement de l’intensité du mariage. Abstract Throughout the history of the People’s Republic of China, age at marriage has increased as a result of state policy intervention and socioeconomic changes, although the popularity of marriage remains undiminished. At the same time, concern is growing over forced lifelong singlehood among segments of the population, which is due to a sex ratio imbalance in the marriage market and gender differentiation in mate preferences. To address that research gap, this study adopts cure survival analysis to jointly model the determinants of first-marriage likelihood and timing. Data from multiple rounds of the Chinese General Social Survey are used to assess changes in marriage over successive birth cohorts. The results suggest that, among most male and female cohorts, a lower level of education is linked with younger ages at marriage, although with lower chances of ever marrying. For younger male cohorts, residence in less developed provinces is found to be associated with earlier marriage entry but reduced marriage likelihood. Among younger female cohorts, living in metropolitan cities stands out as the most important factor in reducing marriage propensity.

Systemic inflammation markers have been linked to increased cancer risk and mortality in a number of studies. However, few studies have estimated pre-diagnostic associations of systemic inflammation markers and cancer risk. Such markers could serve as biomarkers of cancer risk and aid in earlier identification of the disease. This study estimated associations between pre-diagnostic systemic inflammation markers and cancer risk in the prospective UK Biobank cohort of approximately 440,000 participants recruited between 2006 and 2010. We assessed associations between four immune-related markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and risk for 17 cancer sites by estimating hazard ratios (HR) using flexible parametric survival models. We observed positive associations with risk for seven out of 17 cancers with SII, NLR, PLR, and negative associations with LMR. The strongest associations were observed for SII for colorectal and lung cancer risk, with associations increasing in magnitude for cases diagnosed within one year of recruitment. For instance, the HR for colorectal cancer per standard deviation increment in SII was estimated at 1.09 (95% CI 1.02–1.16) in blood drawn five years prior to diagnosis and 1.50 (95% CI 1.24–1.80) in blood drawn one month prior to diagnosis. We observed associations between systemic inflammation markers and risk for several cancers. The increase in risk the last year prior to diagnosis may reflect a systemic immune response to an already present, yet clinically undetected cancer. Blood cell ratios could serve as biomarkers of cancer incidence risk with potential for early identification of disease in the last year prior to clinical diagnosis.

Background Conditional survival (CS) provides dynamic prognostic estimates by considering the patients existing survival time. Since CS for endemic nasopharyngeal carcinoma (NPC) is lacking, we aimed to assess the CS of endemic NPC and establish a web‐based calculator to predict individualized, conditional site‐specific recurrence risk. Methods Using an NPC‐specific database with a big‐data intelligence platform, 10,058 endemic patients with non‐metastatic stage I–IVA NPC receiving intensity‐modulated radiotherapy with or without chemotherapy between April 2009 and December 2015 were investigated. Crude CS estimates of conditional overall survival (COS), conditional disease‐free survival (CDFS), conditional locoregional relapse‐free survival (CLRRFS), conditional distant metastasis‐free survival (CDMFS), and conditional NPC‐specific survival (CNPC‐SS) were calculated. Covariate‐adjusted CS estimates were generated using inverse probability weighting. A prediction model was established using competing risk models and was externally validated with an independent, non‐metastatic stage I–IVA NPC cohort undergoing intensity‐modulated radiotherapy with or without chemotherapy (n = 601) at another institution. Results The median follow‐up of the primary cohort was 67.2 months. The 5‐year COS, CDFS, CLRRFS, CDMFS, and CNPC‐SS increased from 86.2%, 78.1%, 89.8%, 87.3%, and 87.6% at diagnosis to 87.3%, 87.7%, 94.4%, 96.0%, and 90.1%, respectively, for an existing survival time of 3 years since diagnosis. Differences in CS estimates between prognostic factor subgroups of each endpoint were noticeable at diagnosis but diminished with time, whereas an ever‐increasing disparity in CS between different age subgroups was observed over time. Notably, the prognoses of patients that were poor at diagnosis improved greatly as patients survived longer. For individualized CS predictions, we developed a web‐based model to estimate the conditional risk of local (C‐index, 0.656), regional (0.667), bone (0.742), lung (0.681), and liver (0.711) recurrence, which significantly outperformed the current staging system (P < 0.001). The performance of this web‐based model was further validated using an external validation cohort (median follow‐up, 61.3 months), with C‐indices of 0.672, 0.736, 0.754, 0.663, and 0.721, respectively. Conclusions We characterized the CS of endemic NPC in the largest cohort to date. Moreover, we established a web‐based calculator to predict the CS of site‐specific recurrence, which may help to tailor individualized, risk‐based, time‐adapted follow‐up strategies.

A dynamic (e.g., non-isothermal) kinetic model of microbial survival during a lethal process or growth under favorable conditions is either in the form of a differential rate equation from the start or obtained from an algebraic static model by derivation. Examples of the first kind are the original and modified versions of the logistic (Verhulst) equation and of the second the dynamic Weibull survival or Gompertz growth models. In the first-order inactivation kinetics, the isothermal logarithmic survival rate is a function of temperature only. Therefore, converting its static algebraic form into a dynamic differential rate equation, or vice versa, is straightforward. There is also no issue where both the static and dynamic versions of the survival or growth model are already in the form of a differential rate equation as in the logistic equation of growth. In contrast, converting the nonlinear static algebraic Weibull survival model or the Gompertz growth model into a dynamic differential rate equation, requires replacement of the nominal time t by t *, defined as the time which corresponds to the momentary static survival or growth ratio at the momentary temperature. This replacement of the nominal time in the rate equation with a term that contains the momentary survival or growth ratio eliminates inevitable inconsistencies and renders the resulting dynamic model truly predictive. The concept is demonstrated with simulated dynamic microbial survival patterns during a hypothetical thermal sterilization where the temperature fluctuates and with simulated dynamic microbial growth in storage where the temperature oscillates.