Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

This study compared the performance of Poisson regression, negative binomial regression and generalized negative binomial regression models using road traffic crashes data collected from Ekiti state command of Federal Road Safety Commission (FRSC). The method of maximum likelihood estimation was employed to determine the parameter estimate for each models, the significance of each variable consider were examined. The criteria used in selecting the best model fitted are Akaike information criterion (AIC), Bayesian information criterion and Deviance. R software package was used in analysing the data. Firstly multicollinearity was tested to determine if there is high correlation between the predictor variables. Data were analysed and the result from the three model were compared using Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the deviance.The result from the three models were compared using AIC, BIC and Deviance, AIC values for the models are (414.79, 476.8 and 587.312), BIC values (490.8873, 495.59 and 589.321) and Deviance value (61.93, 66.927 and 69.927) respectively. Generalized negative binomial regression (GNBR) had the least value of AIC and BIC, thus consider as the best model for analyzing road traffic crashes data in Ekiti state, Nigeria. Having shown a smaller AIC, BIC and deviance values, the generalized negative binomial was consider a better fit model when analysing road traffic crashes in Ekiti state, Nigeria.

The objective of this study is to determine the significant predictors of endometrial cancer using accelerated failure time models (AFTM). We have demonstrated the applications of AFTM viz. Exponential, Weibull, Log-normal, Log-logistic, Gompertz, Gamma and Generalized Gamma AFTM, as an alternative of Cox proportional hazard model. Data for the analysis was collected from Acharya Harihar Post Graduate Institute of Cancer (AHPGIC), Cuttack, Odisha during the period 2016–20. Based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) value, the Weibull AFTM has been chosen as the best fitted AFT model. The predictors such as age, comorbidity, tumor size, isolated para-aortic and adnexa have been found as significant predictors (p-value < 0.05) to explain the survival of endometrial cancer patients. Hence, by optimizing different treatments, based on such prognostic factors plays an important role in managing endometrial cancer at an early stage.

Carcinoembryonic antigen (CEA) is a reliable tumor marker for the management and surveillance of colon cancer. However, limitations in previous studies have made it difficult to elucidate whether CEA should be established as a prognostic indicator. This study examines the association between elevated preoperative CEA levels and overall survival in colon cancer patients using a national population-based registry. Stage I-III colon cancer patients were identified from the 2004-2006 National Cancer Database. A multivariable Cox proportional hazards model was used to estimate the association between elevated CEA levels and overall survival after controlling for important patient, hospital, and tumor characteristics. A Monte Carlo Markov Chain was used to impute the large degree of missing CEA data. All models controlled for the propensity score in order to account for selection bias. A total of 137,381 patients met the inclusion criteria. Overall, 34% of patients had an elevated CEA level and 66% had a normal CEA level, with a median survival of 70 and 100months, respectively. Patients with an elevated CEA level had a 62% increase in the hazard of death (HR 1.62, 95% CI 1.53-1.74) compared with patients with a normal CEA level. Preoperative CEA was an independent predictor of overall survival across all stages. The results support recommendations to include CEA levels as another high-risk feature that clinicians can use to counsel patients on adjuvant chemotherapy, especially for stage II patients.

The Laplace distribution and its extensions have been widely utilized in statistical modelling due to their ability to capture real-world data characteristics such as skewness and heavy tails. This study evaluated the performance of the classical Laplace (L) distribution against three of its variants: the Transmuted Laplace (TL), Alternative Laplace (AL), and Asymmetric Laplace (ASL) distributions. While these extensions introduce additional parameters to enhance flexibility, their empirical performance remains a subject of interest. Using three datasets Rent prices, Voltage Drop, and Nigeria’s Unemployment Rate, this study assessed model fit based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Squared Error (MSE). Findings revealed that the standard Laplace (L) distribution consistently outperforms its counterparts. In the Rent dataset, it achieves the lowest AIC (613.636), BIC (609.2266), and a reasonable MSE (2343.761), whereas the TL and AL distributions yield significantly higher AIC and BIC values, and the ASL distribution demonstrates an extremely high MSE (9.34 × 10¹²), indicating poor fit. A similar trend is observed in the Voltage Drop dataset, where the L distribution records the lowest AIC (201.1564), BIC (197.7293), and MSE (132.7978), while TL and ASL show excessive model instability. In the Unemployment Rate dataset, the L distribution again provides the best fit, with an AIC of 349.7985, a BIC of 345.896, and a moderate MSE of 186.4666. On average, across all datasets, the L distribution remains the most robust model, with the lowest AIC (388.197), BIC (384.284), and MSE (887.6751). The AL distribution follows closely with an MSE of 888.9518 but exhibits significantly higher AIC (2426.027) and BIC (2424.071). The ASL distribution, while demonstrating moderate AIC (1443.016) and BIC (1448.885), suffers from poor predictive accuracy with an extremely high MSE (3.19E+12). The TL distribution performs the worst, with the highest AIC (34,686.77), BIC (20,112.08), and an MSE of 76,038.22, highlighting its instability. In conclusion, this study established that the standard Laplace (L) distribution provides the most reliable and accurate fit across diverse datasets. While alternative forms introduce additional flexibility, their increased complexity does not necessarily yield superior model performance. Future research should explore modifications to improve the parameter stability of Laplace extensions and investigate alternative estimation techniques to enhance predictive accuracy in real-world applications.

Spatial modeling and variability analysis of soil and crop canopy coverage has been accomplished using aerial multispectral images. Multispectral imagery was acquired using an MS-4100 multispectral camera at different flight altitudes over a 40 ha cotton field. After the acquired images were geo-registered and processed, spatial relationships between the aerial images and ground-based soil conductivity and NDVI (normalized difference vegetation index) measurements were estimated and compared using two spatial analysis approaches (model-driven spatial regression and data-driven geostatistics) and one non-spatial approach (multiple linear regression). Comparison of the three approaches indicated that OLS (ordinary least squares) solutions from multiple linear regression models performed worst in modeling ground-based soil conductivity and NDVI with high AIC (Akaike information criterion) (-668.3 to 2980) and BIC (Bayesian information criterion) (-642.4 to 3006) values. Spatial regression and geostatistics performed much better in modeling soil conductivity, with low AIC (2698 to 2820) and BIC (2732 to 2850) values. For modeling ground-based NDVI, the AIC and BIC values were -681.7 and -652.1, respectively, for spatial error regression and -679.8 and -646.5, respectively, for geostatistics, which were only moderate improvements over OLS (-668.3 and -642.4). Validation of the geostatistical models indicated that they could predict soil conductivity much better than the corresponding multiple linear regression models, with lower RMSE (root mean squared error) values (0.096 to 0.186, compared to 0.146 to 0.306). Results indicated that the aerial images could be used for spatial modeling and prediction, and they were informative for spatial prediction of ground soil and canopy coverage variability. The methods used for this study could help deliver baseline data for crop monitoring with remote sensing and establish a procedure for general crop management.

Analysis of color differences in stained contemporary esthetic dental materials

Poverty is one of the main problems in regional economic development that requires in depth statistical analysis to understand the factors that influence it. This study aims to model the spatial dependence of the percentage of poor people in South Sulawesi Province in 2024 by considering the influence of the Human Development Index (HDI) and the Open Unemployment Rate (OUR). The analysis was conducted using three spatial regression model approaches, namely the Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Autoregressive Moving Average Model (SARMA). The selection of the best model was based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. The results of the analysis show that the SARMA model has the smallest AIC and BIC values (AIC = 102.6484 and BIC = 109.7167), so it was selected as the best model to explain the variation in poverty between regions in South Sulawesi. Parameter estimates in the SARMA model show that HDI has a significant negative effect on the percentage of poor people ( ; ), while OUR has no significant effect ( ; ). In addition, there is spatial dependence in both the dependent variable ( ) and the error component ( ), indicating the existence of interrelated poverty between regions. Thus, the results of this study confirm that an increase in HDI plays an important role in reducing poverty levels in South Sulawesi, and indicate the need for a development policy approach that considers spatial interrelationships between regions.

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.

A study was aimed to estimate the seroprevalence and assess the potential risk factors of brucellosis among sheep and goats in Gujarat from 2013 to 2018. Atotal of 2445 sera (goats=1373 and sheep=1072) were subjected to Rose Bengal plate test (RBPT) and indirect enzyme-linked immunosorbent assay (iELISA). A total of seven important risk factors or predictor variables were identified and variables with p<0.10 were retained for univariate logistic regression analysis. The apparent and true seroprevalence were 6.70 and 7.84% in goats and 13.24 and 15.50% in sheep, respectively. In goats, univariate logistic regression analysis affirmed that only two factors i.e., history of disease (odds ratio, OR- 5.03) and larger flock size (OR-3.03) had significant (p<0.001) association with the seroprevalence of brucellosis. In addition to these two, location (OR- 2.71) was also significantly associated (P<0.001) with seroprevalence of brucellosis in sheep. Least Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values (625.61 and 677.86) as indicators of best correlation were obtained when breed, sex, history of disease and flock size were used as predictor variables for goats. In sheep, least AIC and BIC values (688.55 and 728.36) were obtained with the model incorporating breed, history of disease, region of sampling and flock size as predictors. To conclude, in goats and sheep, three common factors viz., breed, history and flock size were significantly associated with seroprevalence of brucellosis.

Background: Stroke is one of the major public health problems worldwide. Physical and mental health data of stroke survivors are often expressed in proportions. Therefore, the Beta Regression models family for data between zero and one will be appropriate.&#x0D; Objectives: To identify a suitable model and the likely risk factors of physical and mental health of stroke survivors.&#x0D; Method: Secondary data of stroke survivors from two tertiary health Institutions in Ogun State, Nigeria, were analysed. Inflated Beta (BEINF) and Inflated-at-one-Beta (BEINF1) models were compared using Deviance (DEV), Akaike Information Criterion (AIC) and Bayesian Information Criteria (BIC) for model selection. The model with minimum DEV, AIC and BIC was considered to be better.&#x0D; Results: The deviance (-86.0604,), AIC (-46.0604) and BIC (6.4391) values of the BEINF1 model for physical health and the deviance (-20.1217), AIC (19.8783) and BIC (72.3778) values of BEINF1 model for mental health were smaller than BEINF models. Therefore, BEINF1 was the better model to identify the health risk factors of stroke survivors. Age, marital status, diastolic blood pressure, disability duration and systolic blood pressure had a significant association with physical health, while BMI had a significant positive association with mental health. &#x0D; Conclusion: The beta-inflated-at-one (BEINF1) model is suitable for identifying health risk factors of stroke survivors when the outcome variable is a proportion. Both demographic and clinical characteristics were significantly associated with the health of stroke survivors. This study would assist researchers in knowing the appropriate model for analysing proportion or percentage response variables.

BackgroundWith the aging of the population, colorectal surgeons will have to face more elderly colorectal cancer (CRC) patients in the future. We aim to analyze independent risk factors affecting overall survival in elderly (age ≥65 years) patients with stage II–III CRC and construct a nomogram to predict patient survival.MethodsA total of 3,016 elderly CRC patients with stage II–III were obtained from the SEER database. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analyses were used to screen independent prognostic factors, and a survival prediction nomogram was constructed based on the results. The consistency index (C-index), decision curve analysis (DCA), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to compare the predictive ability between the nomogram and tumor-node-metastasis (TNM) stage system. All patients were classified into high-risk and low-risk groups based on risk scores calculated by nomogram. The Kaplan-Meier method was used to compare the survival differences between two groups.ResultsThe 3- and 5-year area under the curve (AUC) values of the prediction nomogram model were 76.6% and 74.8%, respectively. The AIC, BIC, and C-index values of the nomogram model were 6,032.502, 15,728.72, and 0.707, respectively, which were better than the TNM staging system. Kaplan-Meier survival analysis showed a significant survival difference between high-risk and low-risk groups (P<0.0001).ConclusionsWe constructed a prediction nomogram for stage II–III elderly CRC patients by combining pre-treatment carcinoembryonic antigen (CEA) levels, which can accurately predict patient survival. This facilitates clinicians to accurately assess patient prognosis and identify high-risk patients to adopt more aggressive and effective treatment strategies.

Kenya is one of the countries in the world with a good quantity of wind. This makes the country to work on technologies that can help in harnessing the wind with a vision of achieving a total capacity of 2GW of wind energy by 2030. The objective of this research is to find the best three-parameter wind speed distribution for examining wind speed using the maximum likelihood fitting technique. To achieve the objective, the study used hourly wind speed data collected for a period of three years (2016 – 2018) from five sites within Narok County. The study examines the best distributions that the data fits and then conducted a suitability test of the distributions using the Kolmogorov-Smirnov test. The distribution parameters were fitted using maximum likelihood technique and model comparison test conducted using Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values with the decision rule that the best distribution relies on the distribution with the smaller AIC and BIC values. The research showed that the best distribution is the gamma distribution with the shape parameter of 2.071773, scale parameter of 1.120855, and threshold parameter of 0.1174. A conclusion that gamma distribution is the best three-parameter distribution for examining the Narok country wind speed data.

This study was conducted to compare random regression models for third order Ali Schaeffer (AS), Wilmink (W) and Legendre polynomials (L) on estimation of genetic parameters for first lactation milk yield in Jersey cows. For this aim, data used in this study were 6387 official milk yield records from monthly recording of 686 first lactations between 1996 and 2011 in Karakoy Agricultural State Farm, Samsun (Turkey). In this study, (co)variance components, heritability for first lactation test day milk yields (TDMY) and genetic correlations among these TDMYs were estimated by using DFREML statistical package under DXMRR option. To compare the models, -2LogL, Akaike’s information criterion (AIC), Bayesian information criterion (BIC), Residual variances (RV) and Log likelihood values were used. Heritabilities (0.08 to 0.28), additive genetic correlations (0.68 to 0.99) and phenotypic correlations (0.21 to 0.66) were estimated by AS(4,4) random regression model which had the lowest AIC and BIC values. As a result, it was decided that the AS(4,4) random regression model can be used for management decisions and genetic evaluation of Jersey cows for milk production.

Joint models for longitudinal and survival data have now become increasingly popular in clinical trials or other studies for assessing a treatment effect while accounting for longitudinal measures such as patient-reported outcomes or tumor response. Most studies in the existing literature primarily focus on reducing the bias and improving efficiency in the estimate of the treatment effect in the joint modeling of survival and longitudinal data. Global fit indices such as Akaike information criterion (AIC) or Bayesian information criterion (BIC) can be used to assess the overall fit of the joint model. However, these indices do not provide separate assessments of each component of the joint model. In this chapter, we develop new model assessment criteria using a novel decomposition of AIC and BIC (i.e., AIC = AIC\(_\textrm{Surv}\) + AIC\(_\textrm{Long} | \textrm{Surv}\) and BIC = BIC\(_\textrm{Surv}\) + BIC\(_\textrm{Long} | \textrm{Surv}\)) to assess the contribution of the survival data to the model fit of the longitudinal data. We apply the proposed methodology to the analysis of a real dataset from a cancer clinical trial in mesothelioma.

After tumor resection, a preventive diverting loop ileostomy creation is a routine surgical procedure to prevent anastomotic leakage and infections and to preclude secondary surgeries. Despite its benefits, several studies have proposed potential complications that extend the disease course by impairing the feasibility of adjuvant chemotherapy and adherence. The aim of this study was to evaluate the impact of ileostomy complications on the adherence to adjuvant treatment and overall survival (OS) of colon cancer (CC) patients. Retrospective, observational study. Patients diagnosed with colon adenocarcinoma were treated between January 2010 and December 2020 at the National Cancer Institute in Mexico. χ2 and t-test, Kaplan-Meier, log-rank, and Cox regression. Statistical significance differences were assessed when p was bilaterally < 0.05. The most frequent complications of loop-derived ileostomy were hydro-electrolytic dehydration (50%), acute kidney injury (AKI) (26%), grade 1-2 diarrhea (28%), and grade 3-4 diarrhea (21%) (p = 0.001). Patients with complete chemotherapy did not reach the median OS. In contrast, the median OS for patients with non-complete chemotherapy was 56months (p = 0.023). Additionally, 5-year OS reached to 100% in the early restitution group, 85% in the late restitution group, and 60% in the non-restitution group (p = 0.016). Finally, AKI (p = 0.029; 95% confidence interval (CI) 3.348 [1.133-9.895]), complete chemotherapy (p = 0.028; 95% CI 0.376 [0.105-0.940]), and reversed ileostomy (p = 0.001; 95% CI 0.125 [0.038-0.407]) remained as predictors of overall survival for patients with CC treated with a loop ileostomy. Our results emphasize the early stoma reversal restitution as a safe and feasible alternative to prevent severe complications related to ileostomies which improve chemotherapy adherence and overall survival of colon cancer patients. This is one of the pioneer studies analyzing the impact of ileostomy on treatment adherence and outcome of Latin American patients with colon cancer. Retrospective study No. 2021/045, in April 2021.