Survival Curves Projection and Benefit Time Points Estimation using a New Statistical Method - Pages 28-40

Toni Monleón-Getino

https://doi.org/10.6000/1929-6029.2020.09.04

Published: 9 May 2020

Abstract: Survival analysis concerns the analysis of time-to-event data and it is essential to study in fields such as oncology, the survival function, S(t), calculation is usually used, but in the presence of competing risks (presence of competing events), is necessary introduce other statistical concepts and methods, as is the Cumulative incidence function CI(t). This is defined as the proportion of subjects with an event time less than or equal to. The present study describe a methodology that enables to obtain numerically a shape of CI(t) curves and estimate the benefit time points (BTP) as the time (t) when a 90, 95 or 99% is reached for the maximum value of CI(t). Once you get the numerical function of CI(t), it can be projected for an infinite time, with all the limitations that it entails. To do this task the R function Weibull.cumulative.incidence() is proposed. In a first step these function transforms the survival function (S(t)) obtained using the Kaplan–Meier method to CI(t). In a second step the best fit function of CI(t) is calculated in order to estimate BTP using two procedures, 1) Parametric function: estimates a Weibull growth curve of 4 parameters by means a non-linear regression (nls) procedure or 2) Non parametric method: using Local Polynomial Regression (LPR) or LOESS fitting. Two examples are presented and developed using Weibull.cumulative.incidence() function in order to present the method. The methodology presented will be useful for performing better tracking of the evolution of the diseases (especially in the case of the presence of competitive risks), project time to infinity and it is possible that this methodology can help identify the causes of current trends in diseases like cancer. We think that BTP points can be important in large diseases like cardiac illness or cancer to seek the inflection point of the disease, treatment associate or speculate how is the course of the disease and change the treatments at those points. These points can be important to take medical decisions furthermore.

Keywords: Survival function, projection, Weibull growth curve, non linear regression.

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Inference Procedures on the Ratio of Modified Generalized Poisson Distribution Means: Applications to RNA_SEQ Data - Pages 41-49

M.M. Shoukri and Maha Al-Eid

https://doi.org/10.6000/1929-6029.2020.09.05

Published: 4 June 2020

Abstract: The Poisson and the Negative Binomial distributions are commonly used as analytic tools to model count data. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial has a variance larger than the mean and therefore is appropriate to model over-dispersed count data. The Generalized Poisson Distribution is becoming a popular alternative to the Negative Binomial. We have considered inference procedures on a modified form of this distribution when two samples are available from two independent populations and the target effect size of interest is the ratio of the two population means. The statistical objective is to construct confidence limits on the ratio. We first test the presence of over dispersion and derive several estimators in the single sample situation. When two samples are available, our interest is focused on the estimation of an effect size measured by the ratio of the respective population means. We have compared two methods; namely the Fieller’s and the delta methods in terms of coverage probabilities. We have illustrated the methodologies on published genomic datasets.

Keywords: Overdispersion, Parameter orthogonality, Fieller’s theorem, Mixed estimator, Delta method, Coverage probabilities.

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The Effect of the Health Personnel Exposed to the Attack of Patients and Relatives on the Perception of Aggression  - Pages 50-58

Mahmut Kilic

https://doi.org/10.6000/1929-6029.2020.09.06

Published: 26 October 2020

Abstract: Purpose: The aim of the study is to evaluate the effect of health personnel's exposure to the violence of patients and relatives on the perception of aggression.

Materials and Methods: This cross-sectional study was conducted in 2015 among health personnel who are in contact with patients and their relatives working in health institutions in Yozgat city center. The study was completed with 358 people who agreed to participate in the study with verbal consent. The data were collected through the Perception of Aggression Scale (POAS), the socio-demographic form and a form that evaluates the health personnel being attacked. In the analysis of the data, univariate tests and multivariate regression analyzes were used.

Results: Of the health personnel, 81.6% of them stated that they were exposed to the violence of the patients and their relatives during their professional career and 37.7% during the last 12 months. In the regression analysis, the perception of functional aggression was higher in those working in university hospitals, and lower in physicians (p <0.05). Dysfunctional aggression perception was lower in medical secretaries, family health center staff, and university hospital staff (p <0.05). No significant relationship was found between the perception of aggression and age, gender, education level, professional experience (years), and their exposure to attack during the past 12 months (p> 0.05).

Conclusion: Health personnel are of the opinion that the aggressive behavior of the patients does not correspond to the situation they are in and there is no acceptable excuse for such behaviors.

Keywords: Health Personnel, Exposure to Violence, Aggression, Perception.

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Estimation of Parent-Sib Correlations for Quantitative Traits Using the Linear Mixed Regression Model: Applications to Arterial Blood Pressures Data Collected From Nuclear Families  - Pages 59-68

Maha Al-Eid, Sarah AL-Gahtani and Mohamed M. Shoukri

https://doi.org/10.6000/1929-6029.2020.09.07

Published: 03 November 2020

Abstract: A fundamental question in quantitative genetics is whether observed variation in the phenotypic values of a particular trait is due to environmental or to biological factors. Proportion of variations attributed to genetic factors is known as heritability of the trait. Heritability is a concept that summarizes how much of the variation in a trait is due to variation in genetic factors. Often, this term is used in reference to the resemblance between parents and their offspring. In this context, high heritability implies a strong resemblance between parents and offspring with regard to a specific trait, while low heritability implies a low level of resemblance. While many applications measure the offspring resemblance to their parents using the mid-parental value of a quantitative trait of interest as an input parameter, others focus on estimating maternal and paternal heritability. In this paper we address the problem of estimating parental heritability using the nuclear family as a unit of analysis. We derive moment and maximum likelihood estimators of parental heritability, and test their equality using the likelihood ratio test, the delta method. We also use Fieller’s interval on the ratio of parental heritability to address the question of bioequivalence. The methods are illustrated on published arterial blood pressures data collected from nuclear families.

Keywords: Genetic epidemiology, Familial correlations, Heritability, Linear Mixed normal models, Maximum Likelihood estimation, Estimating Ratio of parameters, Bootstrap Confidence interval.

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Relationship between Pretreatment Serum Albumin Levels with the Risk of Malignant Pleural Mesothelioma  - Pages 69-82

Sabyasachi Mukherjee

https://doi.org/10.6000/1929-6029.2020.09.08

Published: 31 December 2020

Abstract: Background: Malignant Pleural Mesothelioma (MPM) is a very rare and aggressive form of cancer. Recently it was found that pretreatment Serum Albumin (SA), the main circulating protein in blood is a significant prognostic factor for MPM patients. The objective of this present article is to show the relationship between pretreatment Serum albumin (SA) levels with the risk of MPM.

Methods: Generalized additive model (GAM), an advanced regression analysis method has been introduced here to find this mathematical relationship between the response variable (SA) and the cofactors.

Results: The main determinates of SA are identified - asbestos exposure, hemoglobin, disease diagnosis status (patients having MPM) are the factors having significant association with SA, whereas duration of asbestos exposure, duration of disease symptoms, total protein (TP), Pleural lactic dehydrogenise (PLD), pleural protein (PP), pleural glucose (PG) and C-reactive protein (CRP) are the significant continuous variables for SA. The non-parametric estimation part of this model shows Lactate dehydrogenase (LDH) and Glucose level are the significant smoothing terms. Additionally it is also found that, second and third order interactions between cofactors are highly significant for SA.

Conclusions: The findings of this present work can conclude that - serum albumin may play the role of a very significant prognostic factor for MPM disease and it has been established here through mathematical modeling. Few of the findings are already been exist in MPM research literature whereas some of the findings are completely new in the literature.

Keywords: Malignant Pleural Mesothelioma, Serum albumin, Gamma distribution, Generalized additive model, Probabilistic Modeling.

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