Mixture and Non-Mixture Cure Models for the survival analysis of SARS-CoV-2 patients in Khyber Pakhtunkhwa, Pakistan.

Objective: To examine the potential difference in survival and risk of death between asymptomatic and symptomatic SARS-CoV-2 patients, controlled by age and gender for all the attendance in hospitals of Khyber Pakhtunkhwa (KP), Pakistan.

Methods: In this retrospective study, the medical records of 6273 SARS-CoV-2 patients admitted to almost all hospitals in Khyber Pakhtunkhwa during the first wave of the coronavirus outbreak from March to June 2020 were analysed. The effects of gender, age, and being symptomatic on the survival of SARS-CoV-2 patients were assessed using cure-survival models as opposed to the conventional Cox proportional hazards model.

A robust cusum control chart for median absolute deviation based on trimming and winsorization.

Statistical quality control is concerned with the analysis of production and manufacturing processes. Control charts are process control techniques, commonly applied to observe and control deviations. Shewhart control charts are very sensitive and used for large shifts based on the basic assumption of normality.

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction.

Bayesian AEWMA control chart under ranked set sampling with application to reliability engineering.

The article introduces a novel Bayesian AEWMA Control Chart that integrates different loss functions (LFs) like the square error loss function and Linex loss function under an informative prior for posterior and posterior predictive distributions, implemented across diverse ranked set sampling (RSS) designs. The main objective is to detect small to moderate shifts in the process mean, with the average run length and standard deviation of run length serving as performance measures. The study employs a hard bake process in semiconductor production to demonstrate the effectiveness of the proposed chart, comparing it with existing control charts through Monte Carlo simulations.

Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model.

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic.

Robust model selection using the out-of-bag bootstrap in linear regression.

Outlying observations have a large influence on the linear model selection process. In this article, we present a novel approach to robust model selection in linear regression to accommodate the situations where outliers are present in the data. The model selection criterion is based on two components, the robust conditional expected prediction loss, and a robust goodness-of-fit with a penalty term.

Regression analysis of hydro-meteorological variables for climate change prediction: A case study of Chitral Basin, Hindukush region.

In the present study, hydro-meteorological variables of Chitral Basin in Hindukush region of Pakistan were studied to predict the changes in climatic components such as temperature, precipitation, humidity and river flow based on observed data from 1990 to 2019. Uncertainties in climate change projection were studied using various statistical methods, such as trend variability analysis via stationarity test and validation of regression assumptions prior to fitting of regression estimates. Also, multiple regression models were estimated for each hydro-meteorological variables for the given 30 years of observed data.

Robust proportional overlapping analysis for feature selection in binary classification within functional genomic experiments.

In this paper, a novel feature selection method called Robust Proportional Overlapping Score (RPOS), for microarray gene expression datasets has been proposed, by utilizing the robust measure of dispersion, i.e., Median Absolute Deviation (MAD).

Regulatory genes identification within functional genomics experiments for tissue classification into binary classes via machine learning techniques.

Objective: The aim of this study is to filter out the most informative genes that mainly regulate the target tissue class, increase classification accuracy, reduce the curse of dimensionality, and discard redundant and irrelevant genes.

Method: This paper presented the idea of gene selection using bagging sub-forest (BSF). The proposed method provided genes importance grounded on the idea specified in the standard random forest algorithm.

Forecasting COVID-19 in Pakistan.

Objectives: Forecasting epidemics like COVID-19 is of crucial importance, it will not only help the governments but also, the medical practitioners to know the future trajectory of the spread, which might help them with the best possible treatments, precautionary measures and protections. In this study, the popular autoregressive integrated moving average (ARIMA) will be used to forecast the cumulative number of confirmed, recovered cases, and the number of deaths in Pakistan from COVID-19 spanning June 25, 2020 to July 04, 2020 (10 days ahead forecast).

Methods: To meet the desire objectives, data for this study have been taken from the Ministry of National Health Service of Pakistan's website from February 27, 2020 to June 24, 2020.

The analysis of risk factors associated with tonsillitis in district Mardan, Pakistan.

Objective: To assess the risk factors associated with tonsillitis.

Methods: The cross-sectional study was conducted at Mardan Medical Complex and District Headquarter Hospital, Mardan, Pakistan, from January to June 2018, and comprised tonsillitis patients. Data was collected using a questionnaire which included different risk factors like age 1-10 years, gender, residential area, dietary habit etc.

Neighborhood networks and program participation.

We investigate whether social interactions among pregnant women can lead to increased Medicaid participation within this population. Using geographically fine vital statistics data, we exploit variation in Medicaid use among recently pregnant mothers, within small neighborhoods, to study the impact on participation among currently pregnant women. Women are more likely to use Medicaid benefits while pregnant including prenatal care, when previously pregnant women on their census block also received similar benefits.

Risk factors associated with childhood asthma in District Mardan, Pakistan.

Objective: To estimate the prevalence of asthma in children aged <10 years, and to identify important risk factors for asthma..

Methods: The case-control study was conducted at Mardan Medical Complex and District Head Quarters Hospital, Mardan, Pakistan, from June to September 2017.

Sample size issues in multilevel logistic regression models.

Educational researchers, psychologists, social, epidemiological and medical scientists are often dealing with multilevel data. Sometimes, the response variable in multilevel data is categorical in nature and needs to be analyzed through Multilevel Logistic Regression Models. The main theme of this paper is to provide guidelines for the analysts to select an appropriate sample size while fitting multilevel logistic regression models for different threshold parameters and different estimation methods.

Terrorism and early childhood health outcomes: Evidence from Pakistan.

Objective: To study how the recent rise in terrorist activity affects health of children exposed to violence.

Method: Using spatial and temporal variation in terrorist attacks in Pakistan, combined with a fixed effect strategy at various levels, we identify the causal effect of terrorist activity on height, weight, and health behaviors of children.

Results: A one-standard deviation increased intensity of attack, defined as number of fatalities per attack, leads to approximately 5 more children per 1000 being stunted if attacks occur during gestation and between 12 and 19 more children per 1000 being stunted if attacks occur post birth.

Bayesian Analysis of Three-Parameter Frechet Distribution with Medical Applications.

The medical data are often filed for each patient in clinical studies in order to inform decision-making. Usually, medical data are generally skewed to the right, and skewed distributions can be the appropriate candidates in making inferences using Bayesian framework. Furthermore, the Bayesian estimators of skewed distribution can be used to tackle the problem of decision-making in medicine and health management under uncertainty.

Rules for resolving Mendelian inconsistencies in nuclear pedigrees typed for two-allele markers.

Gene-mapping studies, regularly, rely on examination for Mendelian transmission of marker alleles in a pedigree as a way of screening for genotyping errors and mutations. For analysis of family data sets, it is, usually, necessary to resolve or remove the genotyping errors prior to consideration. At the Center of Inherited Disease Research (CIDR), to deal with their large-scale data flow, they formalized their data cleaning approach in a set of rules based on PedCheck output.

Unit Root Testing and Estimation in Nonlinear ESTAR Models with Normal and Non-Normal Errors.

Exponential Smooth Transition Autoregressive (ESTAR) models can capture non-linear adjustment of the deviations from equilibrium conditions which may explain the economic behavior of many variables that appear non stationary from a linear viewpoint. Many researchers employ the Kapetanios test which has a unit root as the null and a stationary nonlinear model as the alternative. However this test statistics is based on the assumption of normally distributed errors in the DGP.