A Cox model for gradually disappearing events

Innovations in medicine provide us longer and healthier life, leading lower mortality. Sooner rather than later, much greater longevity would be possible for us due to artificial intelligence advances in health care. Similarly, Advanced Driver Assistance Systems (ADAS) in highly automated vehicles may reduce or even eventually eliminate accidents by perceiving dangerous situations, which would minimize the number of accidents and lead to fewer loss claims for insurance companies. To model the survivor function capturing greater longevity as well as the number of claims reflecting less accidents in the long run, in this paper, we study a Cox process whose intensity process is piecewise-constant and decreasing. We derive its ultimate distributional properties, such as the Laplace transform of intensity integral process, the probability generating function of point process, their associated moments and cumulants, and the probability of no more claims for a given time point. In general, this simple model may be applicable in many other areas for modeling the evolution of gradually disappearing events, such as corporate defaults, dividend payments, trade arrivals, employment of a certain job type (e.g., typists) in the labor market, and release of particles. In particular, we discuss some potential applications to insurance.

Association of Gender, Diagnosis, and Obesity With Retention Rate of Secukinumab in Spondyloarthropathies: Results Form a Multicenter Real-World Study

Background: Secukinumab has been shown effective for psoriatic arthritis (PsA) and axial spondylarthritis (AxSpA) in randomized trials. The aim of this study was to analyze baseline patient and disease characteristics associated with a better retention rate of secukinumab under real-world conditions.Patients and Methods: Real-life, prospective multicenter observational study involving 138 patients, 61 PsA and 77 AxSpA, who were analyzed at baseline, 6, 12 months and subsequently every year after starting secukinumab regardless of the line of treatment. Demographics and disease characteristics, measures of activity, secukinumab use, and adverse events were collected. Drug survival was analyzed using Kaplan-Meier curves and factors associated with discontinuation were evaluated using Cox regression. The machine-learning J48 decision tree classifier was also applied.Results: During the 1st year of treatment, 75% of patients persisted with secukinumab, but accrued 71% (n = 32) in total losses (n = 45). The backward stepwise (Wald) method selected diagnosis, obesity, and gender as relevant variables, the latter when analyzing the interactions. At 1 year of follow-up, the Cox model showed the best retention rate in the groups of AxSpa women (95%, 95% CI 93–97%) and PsA men (89%, 95% CI 84–93%), with the worst retention in PsA women (66%, 95% CI 54–79%). The J48 predicted secukinumab retention with an accuracy of 77.2%. No unexpected safety issues were observed.Conclusions: Secukinumab shows the best retention rate at 1 year of treatment in AxSpA women and in PsA men, independently of factors such as the time of disease evolution, the line of treatment or the initial dose of the drug.

MRI-Based Random Survival Forest Model Improves the Prediction of Progression-Free Survival to Induction Chemotherapy plus Concurrent Chemoradiotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma

BackgroundThe present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC+CCRT).MethodsEligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients.ResultsA total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.73 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P<0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P<0.001), while comparable in the Cox model (P>0.05).ConclusionThe RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.

Does Machine Learning Offer Added Value Vis-à-Vis Traditional Statistics? An Exploratory Study on Retirement Decisions Using Data from the Survey of Health, Ageing, and Retirement in Europe (SHARE)

Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement.

Serum apolipoprotein B to apolipoprotein A-I ratio is an independent predictor of liver metastasis from locally advanced rectal cancer in patients receiving neoadjuvant chemoradiotherapy plus surgery

Background The ratio of serum apolipoprotein B (apoB) to apolipoprotein A-I (apoAI) had been reported as a prognostic factor in colorectal cancer. This retrospective study aimed to assess the implication of apoB-to-apoAI ratio in predicting liver metastasis from rectal cancer (RC). Methods The clinical data of 599 locally advanced RC patients treated with chemoradiotherapy followed by surgery were reviewed. Serum apoAI, apoB and apoB-to-apoAI ratio were analyzed for their correlation with the liver-metastasis-free, other-metastasis-free and overall survivals, together with the pretreatment and postsurgical pathoclinical features of the patients. Univariate and multivariate survival analyses were realized through the Kaplan-Meier approach and Cox model, respectively. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for independent predictors. Results Carbohydrate antigen 19 − 9 ≥ 26.3 U/ml, apoB-to-apoAI ratio ≥ 0.63, tumor regression grade 5 − 3, pT4 and pN + stage emerged as independent predictors of poorer liver-metastasis-free survival. The hazard ratios were 1.656 (95% CI, 1.094–2.506), 1.919 (95% CI, 1.174–3.145), 1.686 (95% CI, 1.053–2.703), 1.890 (95% CI, 1.110–3.226) and 2.012 (95% CI, 1.314–2.077), respectively. Except apoB-to-apoAI ratio, the other 4 factors were also independent predictors of poorer other-metastasis-free and overall survivals. And the independent predictors of poorer overall survival also included age ≥ 67 years old, distance to anal verge < 5 cm. Conclusions Serum apoB-to-apoAI ratio could be used as a biomarker for prediction of liver metastasis risk in locally advanced RC.

HIV and Hodgkin Lymphoma Survival: A Prospective Study in Botswana

PURPOSE People living with HIV (PLWH) experience increased risk of Hodgkin lymphoma (HL) despite effective initiation of antiretroviral therapy (ART). In high-income countries, outcomes following HIV HL have been reported to be non-differential, or inferior for PLWH. We sought to assess the effect of HIV on HL survival in Botswana, an African country with a generalized HIV epidemic and high ART coverage, to describe a context more reflective of global HIV populations. PATIENTS AND METHODS In the Thabatse Cancer Cohort, consenting participants initiating treatment for HL at one of four cancer centers in Botswana were enrolled from 2010 to 2020. Patients were followed quarterly for up to 5 years. The impact of HIV on survival following treatment initiation was assessed using an inverse probability–weighted Cox marginal structural model adjusted for age, performance status, and disease stage. RESULTS Seventy-eight new HL cases were enrolled, 47 (60%) were PLWH and 31 (40%) were HIV-uninfected. Baseline characteristics were similar between groups. The majority (61%) of patients presented with regional disease (stage I or II) with no statistically significant difference by HIV status ( P = .38). Nearly all (87%) PLWH participants were on ART before their HL diagnosis (median ART duration 42 months), and median CD4 count was 413 cells/μL (interquartile range 253-691). Survival, in unadjusted analyses, was lower among patients without HIV compared with PLWH (log rank P = .021). In adjusted analysis, HIV infection was not significantly associated with survival in inverse probability–weighted Cox model (hazard ratio 0.43; 95% CI, 0.16 to 1.16; P = .094). CONCLUSION In this cohort of patients treated for HL in Botswana, survival in PLWH (87% on long-standing ART) was at least as good as in individuals without HIV.

Identification of Candidate Target Genes and Immune Cells in Oral Squamous Cell Carcinoma

Background. The advance of new treatment strategies for more effective management of oral cancer requires identification of novel biological targets. Therefore, the purpose of this study is to identify novel biomarkers associated with oral tumorigenesis and prognostic signature by comparing gene expression profile of oral squamous cell carcinomas (OSCCs). Methods. Four datasets including GSE25099, GSE30784, GSE37991, and GSE41613 were collected from Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Cox model analysis, identification of key genes, and Kaplan-Meier analysis were also performed. The xCell was utilized to analyze the infiltration levels of immune cells. Results. A total of 235 differentially expressed genes (DEGs) were found to be dysregulated in OSCC. These genes were mainly enriched in ECM receptor interaction and focal adhesion. Cox regression analysis identified 10 genes considered as key genes. Kaplan-Meier analysis showed that low expression of SERPINE1 (also known as PAI-1), high expression of CD1C, and C-X3-C motif chemokine receptor 1 (CX3CR1) were associated with well prognostic status in OSCC patients. In addition, we constructed a 3-immune-cell signature (myeloid dendritic cell, T cell CD4+ central memory, and common myeloid progenitor) that may be used to predict the survival status of OSCC patients. Conclusion. Three key genes and 3-immune-cell signature were potential biomarkers for the prognosis of OSCC, and they may serve as potential targets for the treatment of OSCC patients.

Preoperative Inflammatory Response to Esophageal Cancer Is More Reliable Prognostic Factor in Patients Received Esophagectomy

Background: Inflammation and nutrition are closely related to the progression of gastrointestinal malignancies. We aimed to explore the potential of preoperative inflammation-based or nutrition-based biomarkers as predictors of survival in patients with resectable esophageal squamous cell carcinoma (ESCC) using multivariate Cox analysis.Methods: We included 122 patients with resectable ESCC (stages I–IV) in the study. We assessed the inflammation-based modified Glasgow prognostic score (mGPS), nutrition-based modified controlling nutritional status (mCONUT) score, CRP(C-reactive protein),serum albumin, lymphocyte counts, and total cholesterol. The relationships of these biomarkers with overall survival (OS) and recurrence-free survival (RFS) were evaluated. Three Cox model were performed for single parameters(CRP, albumin, lymphocyte, total cholesterol), for mCONUT and mGPS,and for clinicopathological factors.Results: The cut-off values for CRP, albumin, and mCONUT measured using receiver operating characteristic (ROC) curves were 0.3, 3.5, and 4, respectively. Patients with high mGPS and high mCONUT scores were significantly associated with shorter OS and RFS (p < 0.05).Multivariate Cox analysis showed that mGPS,pStage,tumor location were independent prognostic factors both FRS and OS. Also, Cox analysis for single parameters showed that preoperative CRP, lymphocyte counts were independent prognostic factors for RFS and albumin was prognostic factor for OS.Conclusions: Preoperative inflammation-based mGPS is most reliable independent prognostic factor in patients with resectable ESCC. Suppression of preoperative inflammation can be improved nutritional status and may improve the prognosis in these patients.

Effects of Red Blood Cell Transfusions on Distant Metastases of Oral Squamous Cell Carcinomas

There is no consensus on the effect of red blood cell (RBC) transfusions on patients with oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the association between RBC administration and the occurrence of distant metastases (M+) after surgical treatment of OSCC. All medical records of patients who underwent primary surgery for OSCC in our department (2003–2019) were analyzed retrospectively (n = 609). Chi and Cox regression models were used to analyze the influence of transfusion on the development of M+, and survival rates. Kaplan–Meier curves were used for graphical presentation. A multitude of patient-specific factors showed a statistical impact in univariate analysis (transfusion, age, gender, diabetes, pT, pN, L, V, Pn, G, UICC, adjuvant therapy, free microvascular transplant, preoperative hemoglobin level). Transfusion status and pN stage were the only variables that showed a significant correlation to M+ in the multivariate Cox model. The hazard ratios for the occurrence of M+ were 2.42 for RBC transfusions and 2.99 for pN+. Administration of RBC transfusions was identified as a significant prognostic parameter for the occurrence of distant metastases after surgical treatment of OSCC. Hence, the administration of RBC transfusions should be considered carefully in the perioperative management.

Symmetry Analysis of the Uncertain Alternative Box-Cox Regression Model

The asymmetry of residuals about the origin is a severe issue in estimating a Box-Cox transformed model. In the framework of uncertainty theory, there are such theoretical issues regarding the least-squares estimation (LSE) and maximum likelihood estimation (MLE) of the linear models after the Box-Cox transformation on the response variables. Heretofore, only weighting methods for least-squares analysis have been available. This article proposes an uncertain alternative Box-Cox model to alleviate the asymmetry of residuals and avoid λ tending to negative infinity for uncertain LSE or uncertain MLE. Such symmetry of residuals about the origin is reasonable in applications of experts’ experimental data. The parameter estimation method was given via a theorem, and the performance of our model was supported via numerical simulations. According to the numerical simulations, our proposed ‘alternative Box-Cox model’ can overcome the problems of a grossly underestimated lambda and the asymmetry of residuals. The estimated residuals neither deviated from zero nor changed unevenly, in clear contrast to the LSE and MLE for the uncertain Box-Cox model downward biased residuals. Thus, though the LSE and MLE are not applicable on the uncertain Box-Cox model, they fit the uncertain alternative Box-Cox model. Compared with the uncertain Box-Cox model, the issue of a systematically underestimated λ is not likely to occur in our uncertain alternative Box-Cox model. Both the LSE and MLE can be used directly without constructing a weighted estimation method, offering better performance in the asymmetry of residuals.