Smallest AIC Research Articles

Determinants of active trachoma among rural children aged 1–9 years old in Aw-Bare Wereda, Somali Region of Ethiopia: a cross-sectional study

IntroductionChronic and highly contagious, trachoma is a condition characterized by recurrent bacterial infection with ocular strains of Mycoplasma trachoma. It spreads through fingers, flies, and fomites, especially in situations where there is overcrowding. If untreated, the illness may result in blindness. Trachoma is an ancient disease and has previously been a significant public health problem in many areas of the world, including parts of Europe and North America. There are at least 400 million cases of active trachoma in the world, 8 million of which have resulted in blindness. Trachoma is a serious public health issue that is very common in Ethiopia. Therefore, the objective of this study is to identify the determinants of active trachoma among rural children aged 1–9 years old in Aw-bare woreda, Somali region of Ethiopia.MethodA cross-sectional community-based study involving children aged 1–9 who lived in six selected rural kebeles in the Awbare woreda Somali region and carried out using an ordinal logistic regression model. The study comprised 377 children in total. Our sample youngsters were chosen through a two-stage cluster sampling procedure. Then also chose our sample kebeles by simple random sampling. The main environmental, personal, and demographic factors that influenced the outcomes of active trachoma status were modeled using partial proportional odds modeling and descriptive statistics.ResultThe study showed that the prevalence of active trachoma was found to be 47.7%. The covariate secondary level of education of mother OR = 1.357; 95% CI (1.051, 1.75), P-value = 0.0192, Inside house cooking place of children family OR = 0.789:95% CI (0.687, 0.927), P-value = 0.0031, children stay at home OR = 2.203:95%CI (1.526, 3.473), P-value = 0.0057,rich income family OR = 1.335:95%CI(1.166,1.528),P-value = 0.0001,Amount of water fetched per day OR = 2.129,95%CI(1.780,2.547),P-Vaue = 0.0001 were significant effect on active trachoma. PPOM represents the best fit as it has the smallest AIC and BIC. It is also more parsimonious.ConclusionThe mother’s educational level, the location where the children spent the majority of their time indoors cooking, the fly density during the interview, the family’s income, the child’s age in years, the distance to the water source, the quantity of water fetched daily, and the number of people sharing a room have all been found to be significant predictors of the child’s active trachoma status. Thus, increasing maternal education, access to clean water, and socioeconomic position are all crucial measures in preventing trachoma. Preventing trachoma also involves reducing the number of kids in a room and enhancing activities linked to personal cleanliness, such as giving kids a thorough facial wash to remove debris and discharge from their eyes.

Development of a novel critical nitrogen concentration–cumulative transpiration curve for optimizing nitrogen management under varying irrigation conditions in winter wheat

Accurate nitrogen (N) nutrition diagnosis is essential for improving N use efficiency in crop production. The widely used critical N (Nc) dilution curve traditionally depends solely on agronomic variables, neglecting crop water status. With three-year field experiments with winter wheat, encompassing two irrigation levels (rainfed and irrigation at jointing and anthesis) and three N levels (0, 180, and 270 kg ha−1), this study aims to establish a novel approach for determining the Nc dilution curve based on crop cumulative transpiration (T), providing a comprehensive analysis of the interaction between N and water availability. The Nc curves derived from both crop dry matter (DM) and T demonstrated N concentration dilution under different conditions with different parameters. The equation Nc = 6.43T−0.24 established a consistent relationship across varying irrigation regimes. Independent test results indicated that the nitrogen nutrition index (NNI), calculated from this curve, effectively identifies and quantifies the two sources of N deficiency: insufficient N supply in the soil and insufficient soil water concentration leading to decreased N availability for root absorption. Additionally, the NNI calculated from the Nc-DM and Nc-T curves exhibited a strong negative correlation with accumulated N deficit (Nand) and a positive correlation with relative grain yield (RGY). The NNI derived from the Nc-T curve outperformed the NNI derived from the Nc-DM curve concerning its relationship with Nand and RGY, as indicated by larger R2 values and smaller AIC. The novel Nc curve based on T serves as an effective diagnostic tool for assessing winter wheat N status, predicting grain yield, and optimizing N fertilizer management across varying irrigation conditions. These findings would provide new insights and methods to improve the simulations of water-N interaction relationship in crop growth models.

Logistic Smooth Transition Autoregressive (LSTAR) and Exponential Smooth Transition Autoregressive (ESTAR) Methods in Predicting the Exchange Rate of Farmers in Lampung Province, Indonesia

There are many time series forecasting techniques, one of which is Smooth Transition Autoregressive (STAR). STAR is an extension of the autoregressive model for nonlinear time series data. The STAR model consists of the Logistic STAR (LSTAR) model and the Exponential STAR (ESTAR) model. The aim of this research is to compare which model is more suitable for predicting farmer exchange rates in Lampung Province, Indonesia. The results of this research show that the ESTAR model outperforms the LSTAR model based on a smaller AIC.

Modified traditional TNM staging of pyriform sinus and hypopharyngeal and laryngeal cancer based on lymph node ratio and its clinical significance: a population-based study combined with external validation.

To evaluate the application value of a new TLNRM staging prediction model based on lymph node ratio (LNR) in patients with Pyriform Sinus and Hypopharyngeal and Laryngeal cancer (PHLC). A total of 2,257 patients with pathologically diagnosed PHLC from 2004 through 2019 were collected from the SEE database for analysis. The N staging of AJCC was replaced by LNR, and we compared the differences in patient prognosis and judgment ability between the new TLNRM staging and the 8th edition TNM staging. At the same time, data from 1,094 people in our hospital were included for external verification and validation. We selected four cutoff points based on LNR and reclassified N staging into five groups (LNR1-5). Compared to the traditional TNM staging (8th edition), the new TLNRM staging showed a statistically significant 5-year OS difference. The decision curve showed that the new TLNRM staging had a higher net benefit for different decision thresholds than the traditional TNM staging system's prediction line. The smaller AIC and BIC suggested that the new staging system had a higher sensitivity to prognosis evaluation compared to the traditional staging system. TLNRM stage III patients can benefit from radiotherapy, while TLNRM IVA and IVB patients can benefit from chemoradiotherapy. The same conclusion has been drawn from external validation data from our center. Compared with the traditional 8th edition AJCC staging system, the new TLNRM staging system has advantages in predicting the staging and prognosis of PHLC patients, and can independently guide postoperative chemoradiotherapy in patients.

Survival Prognostic Factors of Male Breast Cancer Using Appropriate Survival Analysis for Small Sample Size: Three Center Experience

Introduction: Breast cancer in men is a rare disease that has been increasing in recent decades. Identifying factors influencing the survival rate of these patients is particularly important considering the small sample size. The aim of this study was to present the results of the conventional Cox- LASSO method and compare it with the newer refined generalized log-rank (RGLR) method for analyzing survival data with a small sample size. Methods: Available information related to men with breast cancer referred to 3 treatment centers in the country (Iran) between 2012 and 2020 were reviewed. Cox-LASSO and RGLR models were fitted on the data. The analyzes were done using R.4.1.2 software and the significance level of 0.05 was considered. Results: About 60% of the conflicts are reported on the left side. About 53% of men have been diagnosed at a low stage. The tumor size of 75% of the patients was between 2 and 4.3. Most patients have received modified radical mastectomy (MRM) treatment and adjuvant radiotherapy. 80% of patients had received chemotherapy and most had received anthracycline-taxane base. According to Akaike's criterion, RGLR model (AIC=289.32) was better than Cox-LASSO (AIC=314.76) model. Results of RGLR model indicated that, age (p-value= 0.038, HR >50 vs <50 = 6.75, 95% CI: 2.70–17.30), left laterality (p-value = 0.019, HR left vs right = 3.45, 95% CI: 1.48–8.02), larger tumor size (p-value=0.033, HR T2 vs T1 = 3.70, 95% CI: 2.92–6.68; HR T3 vs T1 = 4.34, 95% CI: 3.17–5.95), higher tumor grades (p-value<0.001, HR grade 2 or 3 vs grade1 = 8.67, 95% CI: 5.10–14.71), are influential factors decreasing male breast cancer patient’s survival. Conclusion: Although the results of the two existing models in the field of small sample size survival analysis (Cox-LASSO and RGLR) are close to each other, the RGLR model has performed better than the Cox-LASSO. With smaller AIC and SE of parameter estimation, RGLR model was choose compared to Cox-LASSO model.

COMPARATIVE STUDY IN ADDRESSING MULTICOLLINEARITY USING LOCALLY COMPENSATED RIDGE-GEOGRAPHICALLY WEIGHTED REGRESSION (LCR-GWR) AND GEOGRAPHICALLY WEIGHTED LASSO (GWL)

In spatial data, multicollinearity and spatial heterogeneity are often encountered simultaneously. To overcome the problem of heterogeneity in spatial data, GWR method can be used but this method can only overcome heterogeneity but not multicollinearity. Therefore, another method is needed to overcome multicollinearity in spatial data. The purpose of this study is to look at the ability of LCR-GWR and GWL methods to overcome multicollinearity problems simultaneously. The best method is determined by the results of the study which has smaller AIC and RMSE values. The results showed that the GWL method has lower AIC and RMSE values compared to the LCR-GWR model. Therefore, it can be said that GWL is better able to overcome multicollinearity and spatial heterogeneity in Income data compared to LCR-GWR.

Establishment of a nomogram prediction model using common preoperative indicators for early weight loss after laparoscopic sleeve gastrectomy

Objectives: To construct a nomogram prediction model using common preoperative indicators for early weight loss (EWL) 1 year after laparoscopic sleeve gastrectomy (LSG). Methods: Relevant data of obese patients who had undergone LSG from January 2015 to May 2022 in Fujian Medical University Union Hospital and Quanzhou First Hospital Affiliated Fujian Medical University were analyzed. Patients with a history of major abdominal surgery, severe gastroesophageal reflux disease, pregnancy within 1 year after surgery, or who were lost to follow-up were excluded, resulting in a total of 200 patients in the study (190 from Fujian Medical University Union Hospital and 10 from Quanzhou First Hospital Affiliated Fujian Medical University). The participants were 51 men and 149 women of a mean age 29.9±8.2 years and a body mass index (BMI) 38.7±6.5 kg/m2. All patients in this group underwent standardized LSG procedure. Achieving ideal weight (BMI≤25 kg/m2) 1 year after LSG was defined as goal of EWL. Logistic regression analyses were performed to identify factors that independently influenced EWL. These factors were incorporated into the nomogram model. Receiver operating characteristic (ROC) curves (the larger the area under the curve [AUC], the better the predictive ability and accuracy of the model), likelihood ratio test (higher likelihood ratio indicates greater model homogeneity), decision curve analysis (higher net benefit indicates a better model), Akaike information criterion (AIC; smaller AIC indicates a better model), and Bayesian information criterion (BIC; smaller BIC indicates a better model) were used to validate the predictive ability of the column line diagram model. Results: In this study of 200 obese patients who underwent LSG surgery, 136 achieved EWL goal, whereas the remaining 64 did not. The rate of EWL goal achievement of the entire group was 68.0%. Compared with patients who did not achieve EWL goal, those who did had lower BMI, alanine transaminase, aspartate transaminase, triglycerides, and higher cholesterol. Additionally, the proportion of female was higher and the proportions of patients with fatty liver and hypertension lower in those who achieved EWL goal (all P<0.05). Univariate and multivariate logistic regression analysis revealed that preoperative BMI (OR=0.852, 95%CI: 0.796-0.912, P<0.001), alanine transaminase (OR=0.992, 95%CI: 0.985-0.999, P=0.024), presence of fatty liver (OR=0.185, 95%CI: 0.038-0.887, P=0.035) and hypertension (OR=0.374, 95%CI: 0.144-0.969, P=0.043) were independently associated with failure to achieve EWL goal. Cholesterol (OR=1.428, 95%CI: 1.052-1.939, P=0.022) was independently associated with achieving EWL goal. We used the above variables to establish an EWL nomogram model. ROC analysis, the likelihood ratio test, decision curve analysis, and AIC all revealed that the predictive value of the model was better than that of BMI alone (nomogram model vs. BMI: area under the curve 0.840 vs. 0.798, P=0.047; likelihood ratio: 58.785 vs. 36.565, AIC: 193.066 vs. 207.063, BIC: 212.856 vs. 213.660). Conclusion: Our predictive model is more accurate in predicting EWL after LSG compared with using BMI.

Peramalan nilai tukar rupiah terhadap dollar Amerika menggunakan model ARIMA

The exchange rate of the Rupiah against the currencies of other countries is one of the factors in identifying the condition of an economic condition. The value exchange rate can be strengthened or weakened at any time, according to the country's situation. Fluctuations of the Rupiah exchange rate could affect domestic inflation. Therefore, it is necessary to plan a good policy, considering the Rupiah's movement in the future. One of the currencies that affect the world's economic conditions is the United States Dollar. This study forecasts the Rupiah's exchange rate against the United States Dollar (USD). Data is obtained from Bank Indonesia, with the period of the data used from January 2018 to August 2022. One method of forecasting time series data is the Autoregressive Moving Average (ARIMA) method. The best model is selected based on the smallest AIC and BIC values. Based on the analysis of model selection, the ARIMA(1, 0, 1) model is the best model for forecasting the exchange rate of the Rupiah against the US Dollar. The results of the prediction of the Rupiah exchange rate show that the exchange rate of the Rupiah against the US Dollar has increased and decreased, which was not significant. Keywords: ARIMA, exchange rate, forecasting, time seriesMSC2020: 62M10

Comparison non-linear growth models to reflect body weight and body measurements of pre-weaning Kejobong goat

This study aimed to compare two growth models that reflect body weight and body measurement of pre-weaning Kejobong goat. A total data records of 35 heads of Kejobong goat observed for 14 weeks in five variables were used in this study. Two non-linear growth models (Gompertz and Logistic model) were applied for analysis of growth to compare growth performance by Non-Linear Mixed Model. Result showed Gompertz models has smaller AIC, BIC and σ2e value than those Logistic models. The growth curve models based on Gompertz were: y = 23.94 exp (-1.43 exp−0.01764*age) for body weight; y = 61.72 exp (-0.5 exp–0.01805*age) for wither weight; y = 13.12 exp (-0.35 exp−0.026*age) for chest width; y = 55.72 exp (-0.50 exp −0.01805*age) for hip height and y = 52.30 exp (-0.50 exp-0.01805*age) for hearth girth. It conclude that Based AIC, BIC and σ2e value, Gompertz model is the most fitted growth model for reflecting body weight and body measurement of pre-weaning Kejobong goat than those Logistic model.

A new lymph node ratio-based staging system for rectosigmoid cancer: a retrospective study with external validation.

This study evaluated the clinical value of a new American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging prediction model based on lymph node ratio (LNR) in rectosigmoid cancer (RSC). The analysis included 1444 patients with nonmetastatic RSC diagnosed pathologically between 2010 and 2016 who were collected from the National Cancer Institute Surveillance, Epidemiology, and Results database. The AJCC N-stage was redefined according to the LNR cutoff point, and the ability of the new staging system to predict prognosis was compared with that of the AJCC TNM staging system. Data from 739 patients from our hospital were used for external validation. According to the number of examined lymph nodes and LNR, the N stage was divided into five groups (LNR0-5). The 5-year OS of patients divided according to the new T lymph node ratio M (TLNRM) staging into stage I (T1LNR1, T1LNR2), IIA (T1LNR3, T2LNR1, T2LNR2, T2LNR3, T1LNR4, T3LNR1), IIB (T2LNR4), IIC (T3LNR2, T4a LNR1, T1LNR5), IIIA (T3LNR3, T2LNR5, T4b LNR1, T4a LNR2, T3LNR4), IIIB (T3LNR5, T4a LNR3, T4a LNR4, T4b LNR2), and IIIC (T4b LNR3, T4a LNR5, T4b LNR4, T4b LNR5) was significantly different ( P <0.05). Decision curve analysis showed that the net income of the new TLNRM staging system for different decision thresholds was higher than the prediction line of the traditional eighth TNM staging system. The smaller Akaike information criterion and Bayesian information suggested that the new staging system had a higher sensitivity for predicting prognosis than the traditional staging system. TLNRM II and III patients benefited from adjuvant chemotherapy, while adjuvant chemotherapy did not improve the prognosis of TNM II patients. These findings were confirmed by the external validation data. The new TLNRM staging system was superior to the eighth edition AJCC staging system for staging and predicting the prognosis of patients with RSC and may become an effective tool in clinical practice.

Risk factors of pneumonia among elderly with robust Poisson regression - A study on mimic III data

Introduction and Aim: Pneumonia is a common and serious illness among the elderly. Early identification of the risk factors for pneumonia is essential for improving the survival outcomes among elderly. The present study aimed to identify an optimal regression approach to determine the risk factors for pneumonia among elderly patients. Materials and Methods: The present study utilized data from the Medical Information Mart for Intensive Care (MIMIC III) to evaluate the use of alternative generalized linear models to identify the risk factors for pneumonia. The regression model with the smallest AIC, BIC and SE was considered as the appropriate regression model for the data. Robust Poisson model was considered the best fit for the current data as it had the lowest AIC, BIC and standard error compared to other regression models. Results: Variables such as BMI, renal failure, hypertension, diabetes and asthma were identified as the significant risk factors for pneumonia. The risk of pneumonia was found to be significantly higher in the underweight category of BMI [RRadj=1.70; 95% CI=1.38, 2.08]; diabetic patients [RRadj =1.29; 95% CI=1.03, 1.61); asthmatic patients [RRadj =1.35; 95% CI=1.15, 1.58] and patients with renal failure [RRadj =1.16; 95% CI= 1.05, 1.29]. Conclusion: Among various binary regression models, Poisson regression with robust variance (sandwich Poisson regression) provided unbiased estimates of the relationship. In the present study, variables such as BMI, renal failure, diabetics, hypertension and asthma were identified as the significant risk factors for pneumonia in the elderly using robust Poisson regression.

Forecasting The Share Price of PT Merdeka Copper Gold Tbk By Using Arch-Garch Model

This study aims to obtain a forecasting model and compare PT Merdeka Copper Gold Tbk share price using the time series method, ARCH-GARCH model. The data used is historical data for the period December 2021 – December 2022. The initial steps are the stationarity test, identifying the ARIMA model, and checking the heteroscedasticity effect of the best ARIMA model. Then from this model, identify the ARCH-GARCH model. After the model has been formed, compare those models that have been assumed by using the smallest AIC and SBC values and checking the model's heteroscedasticity effect. The last step is forecasting for January 2023 using GARCH (1,0). The equation is .

Nonlinear modeling of growth and study of a new characterization point applied to Timahdit breed sheep under uncertain paternity

The objectives of this study are to determine which model, among those of Brody, Logistic, Gompertz, Von Bertalanffy and Richards, is the most appropriate to describe the growth of this breed and to choose a point for this model which would be closest to the slaughter weight which have to be used as a selection criterion. Also, in preparation for genetic evaluation under uncertain paternity, Henderson's Average Numerator Relationship Matrix method was used and an R code was developed for the creation of the inverse matrix A which was replaced the pedigree in the animal model. To do this, 64,282 observations corresponding to 12,944 animals, collected between 2009 and 2016, were analyzed. The Von Bertalanffy function had the smallest AIC, BIC, and deviance criteria values, indicating better data representation for both sexes. Considering the average slaughter live weight of 29.4kg in the study area, the new point of characterization named f(tbm), later than the inflection point of the growth curve, is closer to the commercial target for females intended for regular slaughter supplying butchers and for animals of both sexes intended for religious festivals. Therefore, it would be appropriate to consider this point as a selection criterion for this breed. The developed R code will be integrated into a free R package allowing the estimation of the genetic parameters of the traits of the Von Bertalanffy model.

Model Comparison of Covid-19 in Lampung Indonesia

The COVID-19 has been pandemic in the world and has resulted in so many deaths due to being infected by the virus, therefore this study aims to determine a suitable model in estimating the number of deaths due to being infected by the COVID-19. This study focus in Lampung, Indonesia. The analysis of the death number was using three methods, there waspoisson regression, negative binomial regression (NBR), and generalized poisson regression (GPR). From the results, three predictor variables have significant effect to the model, there was positive cases of COVID-19, number of poor people, and life expectancy, while population density per km2 has no significant effect. The best estimation model has smallest AIC and BIC values, and the poisson regression method is the best among other methods.&#x0D; Keywords: Poisson Regression, NBR, GPR, COVID-19

A Conway–Maxwell–Poisson-Binomial AR(1) Model for Bounded Time Series Data

Binomial autoregressive models are frequently used for modeling bounded time series counts. However, they are not well developed for more complex bounded time series counts of the occurrence of n exchangeable and dependent units, which are becoming increasingly common in practice. To fill this gap, this paper first constructs an exchangeable Conway-Maxwell-Poisson-binomial (CMPB) thinning operator and then establishes the Conway-Maxwell-Poisson-binomial AR (CMPBAR) model. We establish its stationarity and ergodicity, discuss the conditional maximum likelihood (CML) estimate of the model's parameters, and establish the asymptotic normality of the CML estimator. In a simulation study, the boxplots illustrate that the CML estimator is consistent and the qqplots show the asymptotic normality of the CML estimator. In the real data example, our model takes a smaller AIC and BIC than its main competitors.

Modeling Geographically Weighted Negative Binomial Regression (GWNBR) on Stunting Incidence in Malang Regency

Discrete data on the response variable can be analyzed using poisson regression. The assumption of equidispersion in poisson regression must be fulfilled, but in practice there are many problems of overdispersion. The negative binomial regression model is used to overcome the problem of overdispersion, but this model is global while in some cases each location has different characteristics. Therefore, a method that considers the effects of spatial heterogeneity is needed. If the response variable is discrete data that is overdispersed and includes spatial effects, a model called Geographically Weighted Negative Binomial Regression (GWNBR) is developed. The GWNBR method can be applied in the health sector, such as in stunting. The prevalence of stunting in Malang Regency is still quite high, there is 25.7%. By conducting the GWNBR test, 385 models were obtained, one of them is Tulungrejo Village with factors influencing the incidence of stunting, namely access to permanent healthy latrines, access to posyandu, exclusive breastfeeding, population density and community empowerment. From three weights used, namely the Adaptive Gaussian Kernel, Adaptive Bisquare Kernel and Adaptive Tricube Kernel, the best model was obtained from the Adaptive Bisquare Kernel weighting with the smallest AIC is -211.3763.

Geographically Weighted Negative Binomial Regression Modeling using Adaptive Kernel on the Number of Maternal Deaths during Childbirth

The standard model that is used for count data is Poisson Regression. In fact, most of the count data is overdispersed, which means that the response variable has greater variance than the mean. So the Poisson Regression cannot be used because overdispersion can cause inaccurate parameter estimators. One of the most widely used methods to overcome overdispersion is Negative Binomial Regression. If there are spatial effects such as spatial heterogeneity that are taken into Negative Binomial model, the appropriate method to analyze is Geographically Weighted Negative Binomial Regression (GWNBR). A spatial weighting matrix is required in the GWNBR model. In this study, three weighting functions were used, that is Adaptive Gaussian Kernel, Adaptive Bisquare Kernel, and Adaptive Tricube Kernel. From the three weighting functions, a model will be formed and the best model will be selected based on the smallest AIC. Count data used in this study is maternal deaths during childbirth in West Java Province, which is the highest case in Indonesia. The results of the analysis show that based on the smallest AIC, the best modeling in maternal deaths during childbirth in West Java is the GWNBR model using the Adaptive Gaussian Kernel weight. The results of the best model were obtained from three groups based on the predictor variables that had a significant effect.

ANALYSIS OF THE VACCINATION'S IMPACT ON THE INCREASE IN COVID-19’S DAILY NEW AND RECOVERED CASES USING THE VECTOR AUTOREGRESSIVE (VAR) MODEL (CASE STUDY: WEST KALIMANTAN)

One of the efforts to suppress the increasing number of COVID-19 cases is the government's provision of a COVID-19 vaccine. This study examines the effect of the number of people who have been vaccinated, the first dose of vaccine, on the addition of new cases and cured cases. The three variables were analysed simultaneously using the help of the Vector Autoregressive (VAR) model. The data is on the number of new, recovered cases and people vaccinated per day from January 13 to December 30, 2021, in West Kalimantan Province. The main steps in this study are order identification, parameter estimation, and interpretation of the results. In this study, the order selection of the VAR model is limited to a maximum of the fourth order. Parameter estimation uses the Ordinary Least Square (OLS) method from several possible orders. Furthermore, the model selection is based on the smallest AIC and BIC values. The result is that the second-order VAR model has the smallest AIC and BIC values, so this model is said to be the best model. The interpretation of the equation obtained is that 74.1% of the factors adding new cases, the number of people being vaccinated, and the addition of cured cases at one and two last times affect the addition of new cases on that day. Meanwhile, the addition of new cases today was only influenced by 42.2% by new cases, the number of people being vaccinated, and the addition of recovered cases in the previous one and two days.

An Extension of the Mixed Effects Model that Includes Correlation within Group Errors

Mixed effect models allow for a great deal of flexibility in defining random outcomes, but they limit within-group errors to independent disbursed random variables with a zero mean and constant variance. In addition to its random outcomes for the implied structure, this model is expanded in this paper by including within-group correlated errors. We demonstrate how to accurately predict the model's parameters using a marginal maximum probability (ML) method. The model's accuracy is demonstrated by a real-world example. Additionally, we provide several instructions for correlation systems to represent serial and spatial correlation. Finally, we define how to combine variance features and correlation systems to flexibly model the within-group variance-covariance shape. We also discuss how the lme function can be used to maintain the prolonged linear mixed effects model. And, when compared to other models, the exponential spatial correlation version has the smallest AIC and BIC, making it appear to be the best within-group correlation model.

Binary Logistic Regression Model of Stroke Patients: A Case Study of Stroke Centre Hospital in Makassar

This paper aimed to determine factors that affect significantly types of stroke for stroke patients in Dadi Stroke Center Hospital. The binary logistic regression model was used to analyze the association between the types of stroke and some covariates namely age, sex, total cholesterol, blood sugar level, and history of diseases (hypertension/stroke/diabetes mellitus). Maximum Likelihood Estimation was used to estimate parameters. Combinations of covariates were compared using goodness-of-fit measures. Comparisons were made in the context of a case study, namely stroke patients (2017-2020). The results showed that a binary logistic model combining the history of diseases and blood sugar level provided the most suitable model as it has the smallest AIC and covariates included are statistically significant. The coefficient estimation of the history of diseases variable is -0.92402 with an odds ratio value exp(-0.92402)=0.4. This means that stroke patients who have a history of diseases experience a reduction of 60% in the odds of having a hemorrhagic stroke compared to stroke patients that do not have a history of diseases. In other words, stroke patients who have a history of diseases tend to have a non-hemorrhagic stroke. Furthermore, the coefficient estimation of blood sugar level is 0.74395 with an odds ratio value exp(0.74395)=2. It means that stroke patients who do not have normal blood sugar levels tend to have a hemorrhagic stroke 2 times greater than stroke patients with normal blood sugar levels. A history of diseases and blood sugar level were factors that significantly affect the types of stroke.