Statistical Inference under Censored Data for the New Exponential-X Fréchet Distribution: Simulation and Application to Leukemia Data

In reliability studies, the best fitting of lifetime models leads to accurate estimates and predictions, especially when these models have nonmonotone hazard functions. For this purpose, the new Exponential-X Fréchet (NEXF) distribution that belongs to the new exponential-X (NEX) family of distributions is proposed to be a superior fitting model for some reliability models with nonmonotone hazard functions and beat the competitive distribution such as the exponential distribution and Frechet distribution with two and three parameters. So, we concentrated our effort to introduce a new novel model. Throughout this research, we have studied the properties of its statistical measures of the NEXF distribution. The process of parameter estimation has been studied under a complete sample and Type-I censoring scheme. The numerical simulation is detailed to asses the proposed techniques of estimation. Finally, a Type-I censoring real-life application on leukaemia patient’s survival with a new treatment has been studied to illustrate the estimation methods, which are well fitted by the NEXF distribution among all its competitors. We used for the fitting test the novel modified Kolmogorov–Smirnov (KS) algorithm for fitting Type-I censored data.

Behaviour of the Monotone Single Index Model Under Repeated Measurements

The generalized linear model is an important method in the statistical toolkit. The isotonic single index model can be thought of as a further generalization whereby the link function is assumed to be monotone non-decreasing as opposed to known and fixed. Such a shape constraint is quite natural in many statistical problems, and is fulfilled by the usual generalized linear models. In this paper we consider inference in this model in the setting where repeated measurements of predictor values and associated responses are observed. This setting is encountered in medical studies and is very different from the one considered in the classical monotone single index model studied in the literature. Here, we use nonparametric maximum likelihood estimation to infer the unknown regression vector and link function. We present a detailed study of finite and asymptotic properties of this estimator and propose goodness-of-fit tests for the model. Through an extended simulation study, we show that the model has competitive predictive performance. We illustrate our estimation approach using a Leukemia data set.

#24: Neutropenic Enterocolitis in the Pediatric Patient with Hematological Cancer at Centro Medico Nacional 20 de Noviembre, from June 2019 to May 2020

Background Neutropenic Enterocolitis is a life-threatening condition which occurs in patients presenting neutropenia (absolute neutrophil count <500 / mm3), where secondary to the use of chemotherapy there is an aggressive destruction of tumor cells, which alters the rapid replication phase, in which different types of epithelia are also involved, this is why they decrease their turnover rate, evolving into injury in the intestinal mucosa, especially at the level of the terminal ileus and cecum, although it can affect any part of the intestine. Clinically manifested by fever, pain, and abdominal distension; it is more frequently associated with hematological cancer, although it can occur in other types of cancer. CT is the Gold Standard for diagnosis. Ultrasonography may also be useful, however, this diagnostic tool is operator dependent so its sensitivity and specificity decrease. Medical treatment is usually sufficient, but surgical intervention may be necessary in patients with perforation or deterioration. Methods The incidence of Neutropenic Enterocolitis cases in the pediatric population was identified by means of a descriptive, cross-sectional and retrospective study. We used clinical records of patients admitted with a diagnosis of leukemia. Data analysis was carried out by means of a non-probability sampling for convenience, creating the database in electronic system followed by the data analysis obtained by the JASP software version 0.13.1.0. Results we took into account a total of 1019 patients with leukemia, from which 95.58 % (n=974) were ALL (Acute Lymphoblastic Leukemia) and 4.41 % (n=45) AML (Acute Myeloblastic Leukemia); the Neutropenic Enterocolitis diagnostic, gave us 49 files, from which we eliminated 12 with a different diagnosis, obtaining a total sample of 37 clinical records. The most affected population was the group of between 10–17 years with an incidence of 51.35 %, the most common type of hematologic cancer was ALL representing 86.4 % of the cases, from which 40.54 % were in the induction phase of treatment when they started with the clinical symptoms of neutropenic enterocolitis, being fever, abdominal pain and diarrhea the most common symptoms. The diagnosis was made based on clinical presentation, and radiology tests being abdominal ultrasound the most common diagnostic tool. The most efficacious treatment because there were no complications and there was no need for escalation, was Piperacillin/Tazobactam, followed by Meropenem. Conclusions Neutropenic Enterocolitis was more frequently diagnosed in patients with ALL and in those who were receiving the induction phase of treatment; a total of 28 % presented with sepsis or septic shock. The antibiotic scheme Piperacillin/Tazobactam suffered less modifications unlike Cefepime/Metronidazole which had to be scaled to carbapenem and just 10.81 % of the patients had the Gold Standard diagnostic tool (Abdominal Computed Tomography).

RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification

In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

A wide variety of 1) parametric regression models and 2) co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. While both methodological schemes have their own well-known benefits, little is known about their synergistic potential. Our study introduces their methodological fusion that cross-exploits the strengths of individual approaches via a built-in information-sharing mechanism. This fusion is theoretically based on certain trait-conditioned dependency patterns between two genes depending on their role in the underlying parametric model. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a parametric sense, and 2) exploits the underlying parametric model itself in the estimation process. To also account for the substantial amount of intrinsic noise and collinearities, often entailed by expression data, a tailored co-expression measure is introduced along with this framework to alleviate related computational problems. A remarkable advance over the reference methods in simulated scenarios substantiate the method’s high-efficiency. As proof-of-concept, this synergistic approach is successfully applied in survival analysis, with acute myeloid leukemia data, further highlighting the framework’s versatility and broad practical relevance.

Melatonin as a Potential Multitherapeutic Agent

Melatonin (N-acetyl-5-methoxytryptamine, MEL) is a hormone produced by the pineal gland that was discovered many years ago. The physiological roles of this hormone in the body are varied. The beneficial effects of MEL administration may be related to its influence on mitochondrial physiology. Mitochondrial dysfunction is considered an important factor in various physiological and pathological processes, such as the development of neurodegenerative and cardiovascular diseases, diabetes, various forms of liver disease, skeletal muscle disorders, and aging. Mitochondrial dysfunction induces an increase in the permeability of the inner membrane, which leads to the formation of a permeability transition pore (mPTP) in the mitochondria. The long-term administration of MEL has been shown to improve the functional state of mitochondria and inhibit the opening of the mPTP during aging. It is known that MEL is able to suppress the initiation, progression, angiogenesis, and metastasis of cancer as well as the sensitization of malignant cells to conventional chemotherapy and radiation therapy. This review summarizes the studies carried out by our group on the combined effect of MEL with chemotherapeutic agents (retinoic acid, cytarabine, and navitoclax) on the HL-60 cells used as a model of acute promyelocytic leukemia. Data on the effects of MEL on oxidative stress, aging, and heart failure are also reported.

Transmuted Topp-Leone Weibull Lifetime Distribution: Statistical Properties and Different Method of Estimation

In this work we focus on proposing a new lifetime Weibull type model called the transmuted Topp-Leone Weibull and studying its properties. We derive some new bivariate and multivariate transmuted Topp-Leone Weibull versions using “Farlie Gumbel Morgenstern (FGM) Copula”, “modified FGM Copula”, “Clayton Copula” and “Renyi's entropy Copula”. The estimation of its unknown parameters is carried out by considering different method of estimation. The statistical performances of all methods are studied by two real data sets and a numerical Monte Carlo simulation. The Cramer-Von Mises method is the best method for modeling the carbon fibers data. The maximum likelihood method is the best method for modeling the Leukemia data, however all other methods performed well.