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Objectives: Data management and statistical analysis are integral parts of any research. But there is difficulty in cleaning, organizing, and maintaining data in a systematic and meaningful way for accurate analysis without any tool. Hence, there is a need to learn a tool such as SPSS, STATA, R, SAS and so many for understanding the data. This study focused on the impact of short-course training on STATA on healthcare eye professionals and found an improvement in their competency to use computer-based statistical software packages for data management and analysis and identify the major challenges for participants and suggestions to improve the training. Materials and Methods: We organized a 10-day short course on data management and data analysis using STATA. A pre and post-test evaluation was done every day to find the impact. The questionnaire covered several areas of research methodology from data management to statistics. Categorical data were described in frequency and percentage whereas continuous data was described in median and interquartile range (IQR). Wilcoxon signed-rank test was applied to find out the significant improvement in the knowledge. Results: The 10 days cumulative median score for pre- and post-test was 43 (IOR = 39-47.5) and 72.5 (IOR = 69-77) respectively and was statistically significant (P = 0.012). Male participants, younger age groups, experience, and prior knowledge were identified as major determinants to improve the knowledge and performance in the usage of STATA. We observed that advanced training in the usage of STATA is highly beneficial to people interested/involved in research to gain autonomy in data management and analysis. Conclusion: The evaluation of the training confirms that hands-on training in the use of STATA software does have a significant impact on the knowledge, self-efficacy, and attitude of the learner. Since Stata is very user-friendly, we recommend that researchers and medical students should be offered a comprehensive training course in STATA.

BackgroundCaesarean section (CS) is a considered to be a life-saving operative intervention for women and new-borns in certain antepartum and intrapartum conditions. Caesarean delivery may be accompanied by several complications including surgical site infections (SSI). However, there is a significant lack of uniformity in the administration of antibiotics for preventing surgical site infections (SSI) following Caesarean deliveries. The present study was conducted to determine the incidence of post CS SSI following the adoption of single-dose antibiotic prophylaxis as recommended by WHO at a tertiary care teaching hospital in Medchal, India. Also, to identify the risk factors of SSI and reported the bacteriological profiles and the antimicrobial sensitivity and resistance pattern of the culture positive isolatesMain objectivesTo estimate the incidence of surgical site infections (SSIs) according to CDC criteria following WHO-recommended single-dose antibiotic prophylaxis for Caesarean section at a tertiary care teaching hospital in Medchal, India.MethodsA prospective hospital-based study was conducted between June 2017 and December 2019, in which women who underwent Caesarean delivery were followed up for 30 days post-delivery. Clinical details were collected using a structured questionnaire, and participants were followed up weekly after discharge to document any signs and symptoms of SSI. Symptomatic patients were requested to come to the hospital for further investigation and treatment. Standard microbiological tests were conducted to detect microorganisms and their antibiotic sensitivity.ResultsThe study included 2,015 participants with a mean age of 24.1 years. The majority were multigravida (n=1,274, 63.2%) and underwent emergency Caesarean delivery (n=1,226, 60.8%). Of these, 92 participants (4.6%, 95% CI: 3.7% to 5.6%) developed surgical site infections, with 91 (98.9%) having superficial and 1 (1.1%) having a deep infection. Among those who developed an SSI, 84 (91.3%) did so during their hospital stay, while 8 (8.7%) developed an SSI at home. The adjusted relative risk (aRR) for developing an SSI was 2.5 (95% CI: 1.4 to 4.6; Power 99.9%) among obese women and 2.3 (95% CI: 1.1 to 4.7; Power 100%) among women aged 25 years or younger. Microbial growth was observed in 75.8% (n=50/66) samples. The most common organisms identified wereStaphylococcus aureus(n=23, 46.0%),Klebsiella sp. (n=13, 26.0%), andEscherichia coli(n=12, 24.0%).ConclusionGiven the low rate of SSI following Caesarean deliveries subjected to single-dose antibiotic prophylaxis and the increased risk noted with obesity, it is rationale to practice the latest recommendations of WHO including higher dose for obese patients, unless there is compelling evidence to do otherwise in any context.

This paper deals with the classical and Bayesian estimation of the discrete Teissier distribution with randomly censored data. We have obtained the maximum likelihood point and interval estimator for the unknown parameter. Under the squared error loss function, a Bayes estimator is also computed utilising informative and non-informative priors. Furthermore, an algorithm to generate randomly right-censored data from the proposed model is presented. The performance of various estimation approaches is compared through comprehensive simulation studies. Finally, the applicability of the suggested discrete model has been demonstrated using two real datasets. The results show that the suggested discrete distribution fits censored data adequately and can be used to analyse randomly right-censored data generated from various domains.

Background: In India, high mortality and morbidity rates of human rabies is observed. Hence, a structured surveillance system is yet to be put in place for public health discussion. At the tertiary care hospital and all public health centres, requirement of anti-rabies vaccine is needed in advance to predict the upcoming months coverage so that wastage of vaccine is minimum. Objective: To find a suitable model for forecasting the appropriate stock of anti-rabies vaccines to avoid shortage and over-supply at anti rabies clinic. Methods and Material: This was a record based cross sectional study, conducted at anti rabies clinic of Jawaharlal Nehru Medical College and Hospital, Ajmer. Data of used anti rabies vaccine was taken from immunization inventory during the period from 2017 to 2020. Time series analysis based on Holt-Winter and Box-Jenkins methods were carried out to predict the need of vaccine. Results: Study series was not stationary and stationarity was observed by taken difference in the observation between two consequent months. Residuals of the series were normally distributed and independent to each other. ARIMA(0, 1, 1) was the best model in comparison to Holt-Winter model for prediction because of low value of model selection criterion. The forecasted value for anti-rabies vaccine was done for the year 2021. Conclusions: The following study concluded that time series can be used as a tool to forecast anti-rabies vaccine coverage and will help the policy makers to formulate appropriate plans and strategies and improve the management of vaccination resources and inventory.

BACKGROUND Monitoring ocular morbidity among pediatric patients requires regular follow-up visits. We found that the follow-up rate was poor among children in our setting. Therefore, we intended to assess the effectiveness of 2 interventions—(1) counseling and (2) SMS text messaging and phone calls—to improve the follow-up rates. OBJECTIVE This study aimed to evaluate the effectiveness of 2 interventions, counseling and SMS and phone calls group, as well as a routine standard care for improving the follow-up rate of pediatric patients. METHODS A Nonrandomized, quasiexperimental design was used. Children (aged 0-16 years) with ocular conditions requiring at least 3 follow-up visits during the study period were included. A total of 264 participants were equally allocated to the 3 intervention groups of (1) counseling, (2) SMS and phone calls, and (3) routine standard care group. A 20-minute counseling session by a trained counselor with the provision of disease-specific leaflets were given to those in the counseling group. For the second intervention group, parents of children received an SMS text 3 days before and a phone call 1 day before their scheduled follow-up visits. Participants allocated for the routine standard care group were provided with the existing services with no additional counseling and reminders. Participants attending 3 follow-ups within 2 days of the scheduled visit date were considered compliant. The difference in and among the proportion of participants completing all 3 follow-up visits in each group was assessed. RESULTS The demographic characteristics of the participants were similar across the study groups. Only 3% (8/264) of participants completed all 3 follow-up visits, but overall compliance with the follow-up, as defined by the investigators, was found to be only 0.76% (2/264). There was no statistically significant difference in the proportion of follow-up between the intervention groups. However, the proportion of participants attending the first and second follow-ups, as well as the overall total number of follow-ups, was more in the SMS and phone-call group followed by the counseling group. CONCLUSIONS We did not find any evidence on the effectiveness of our interventions to improve the follow-up rate. The primary reason could be that this study was conducted during the COVID-19 pandemic. It could also be possible that the intensity of the interventions may have influenced the outcomes. A rigorously designed study during the absence of any lockdown restrictions is warranted to evaluate intervention effectiveness. The study also provides useful insights and highlights the importance of designing and systematically developing interventions for improving the follow-up rate and ensuring a continuum of care to children with visual disabilities in Nepal and similar contexts. CLINICALTRIAL ClinicalTrials.gov NCT04837534; https://clinicaltrials.gov/ct2/show/NCT04837534 INTERNATIONAL REGISTERED REPORT RR2-10.2196/31578

A vast majority of the countries are under economic and health crises due to the current epidemic of coronavirus disease 2019 (COVID-19). The present study analyzes the COVID-19 using time series, an essential gizmo for knowing the enlargement of infection and its changing behavior, especially the trending model. We consider an autoregressive model with a non-linear time trend component that approximately converts into the linear trend using the spline function. The spline function splits the series of COVID-19 into different piecewise segments between respective knots in the form of various growth stages and fits the linear time trend. First, we obtain the number of knots with their locations in the COVID-19 series to identify the transmission stages of COVID-19 infection. Then, the estimation of the model parameters is obtained under the Bayesian setup for the best-fitted model. The results advocate that the proposed model appropriately determines the location of knots based on different transmission stages and know the current transmission situation of the COVID-19 pandemic in a country.

In this paper, we develop an estimation procedure for an autoregressive model with polynomial time trend approximated by a spline function. Spline function has the advantage of approximating the no...

In this article, we develop Bayesian estimation procedure for estimating the stress strength reliability R = P [X > Y ] when X (strength) and Y (stress) are the inverse Chen random variables. First, we study some statistical properties of the inverse Chen distribution such as quantiles, mode, stochastic ordering, entropy measure, order statistics and stress strength reliability. Then, we estimate the stress strength parameters and R using maximum likelihood and Bayesian estimations. A symmetric (squared error loss) and an asymmetric (entropy loss) loss functions are considered for Bayesian estimation under the assumption of gamma prior. Since, joint posterior distribution of the model parameters and R involve multiple integrations and have complex form. So, we do not get analytical solution without using any numerical techniques. Therefore, we propose to use Lindley’s approximation and Markov chain Monte Carlo techniques for Bayesian computation. A simulation study is carried out for the proposed Bayes estimators of unknown parameters and compared with the maximum likelihood estimator on the basis of mean squared error. Finally, an empirical illustration based on failure time data is presented to demonstrate the applicability of inverse Chen stress strength model.

This paper proposes a new panel autoregressive model named as merged panel autoregressive (M-PAR) model that explains the desired inferences of merger and acquisition (M&A) concept. Bayesian analys...

This paper provides a Bayesian setup for multiple regimes threshold autoregressive model with possible break points. A full conditional posterior distribution is obtained for all model parameters with considering suitable prior information. Threshold and break point variables do not attain standard form distributions. To compute posterior distributions, we apply the Gibbs sampler with the Metropolis-Hastings algorithm. A variety of loss functions are considered for optimizing the risk associated with each parameter. For empirical evidence, simulation study and real data illustration are carried out.

This paper deals with the problem of modelling time series data with structural breaks occur at multiple time points that may result in varying order of the model at every structural break. A flexible and generalized class of Autoregressive (AR) models with multiple structural breaks is proposed for modelling in such situations. Estimation of model parameters are discussed in both classical and Bayesian frameworks. Since the joint posterior of the parameters is not analytically tractable, we employ a Markov Chain Monte Carlo method, Gibbs sampling to simulate posterior sample. To verify the order change, a hypotheses test is constructed using posterior probability and compared with that of without breaks. The methodologies proposed here are illustrated by means of simulation study and a real data analysis.

This article presents a novel discrete distribution with a single parameter, called the discrete Teissier distribution. It is noted that this model, with one parameter, offers a high degree of fitting flexibility as it is capable of modelling equi-, over-, and under-dispersed, positive and negative skewed, and increasing failure rate datasets. In this article, we have explored its numerous essential distributional features such as recurrence relation, moments, generating function, index of dispersion, coefficient of variation, entropy, survival and hazard rate functions, mean residual life and mean past life functions, stress-strength reliability, order statistics, and infinite divisibility. The classical point estimators have been developed using the method of maximum likelihood, method of moment, and least-squares estimation, whilst an interval estimation based on Fisher���s information has also been presented. Finally, the applicability of the suggested discrete model has been demonstrated using two complete real datasets.

The spread of coronavirus disease 2019 (COVID-19) in various countries varies in different manners, depending on the demographic and climatic conditions. This needs expediting better strategies to combat the novel coronavirus and also the emergence of new viral strains in the future. Our in-depth analysis suggests the spread of COVID-19 cases worldwide, the timeline of spread among most affected countries as of September 3, 2020. Evaluation of the climatic conditions in 2-week interval is carried out. The study recorded most of the countries getting affected by COVID-19 belong to the range of 25–35°C maximum temperature, 20–30°C minimum temperature, and 60%–80% relative humidity. To get a more generalized view on the spread, the study explored the temperature distribution of affected countries and chronicled in both minimum and maximum temperatures so that the number of COVID-19 cases mostly follows a lognormal distribution. In different ranges of relative humidity, not a single distribution is obtained to explain the spread of COVID-19 cases worldwide. A comparison between theoretic and descriptive parameters is also done to support the spread of COVID-19.

Background Monitoring ocular morbidity among pediatric patients requires regular follow-up visits. We found that the follow-up rate was poor among children in our setting. Therefore, we intended to assess the effectiveness of 2 interventions—(1) counseling and (2) SMS text messaging and phone calls—to improve the follow-up rates. Objective This study aimed to evaluate the effectiveness of 2 interventions, counseling and SMS and phone calls group, as well as a routine standard care for improving the follow-up rate of pediatric patients. Methods A Nonrandomized, quasiexperimental design was used. Children (aged 0-16 years) with ocular conditions requiring at least 3 follow-up visits during the study period were included. A total of 264 participants were equally allocated to the 3 intervention groups of (1) counseling, (2) SMS and phone calls, and (3) routine standard care group. A 20-minute counseling session by a trained counselor with the provision of disease-specific leaflets were given to those in the counseling group. For the second intervention group, parents of children received an SMS text 3 days before and a phone call 1 day before their scheduled follow-up visits. Participants allocated for the routine standard care group were provided with the existing services with no additional counseling and reminders. Participants attending 3 follow-ups within 2 days of the scheduled visit date were considered compliant. The difference in and among the proportion of participants completing all 3 follow-up visits in each group was assessed. Results The demographic characteristics of the participants were similar across the study groups. Only 3% (8/264) of participants completed all 3 follow-up visits, but overall compliance with the follow-up, as defined by the investigators, was found to be only 0.76% (2/264). There was no statistically significant difference in the proportion of follow-up between the intervention groups. However, the proportion of participants attending the first and second follow-ups, as well as the overall total number of follow-ups, was more in the SMS and phone-call group followed by the counseling group. Conclusions We did not find any evidence on the effectiveness of our interventions to improve the follow-up rate. The primary reason could be that this study was conducted during the COVID-19 pandemic. It could also be possible that the intensity of the interventions may have influenced the outcomes. A rigorously designed study during the absence of any lockdown restrictions is warranted to evaluate intervention effectiveness. The study also provides useful insights and highlights the importance of designing and systematically developing interventions for improving the follow-up rate and ensuring a continuum of care to children with visual disabilities in Nepal and similar contexts. Trial Registration ClinicalTrials.gov NCT04837534; https://clinicaltrials.gov/ct2/show/NCT04837534 International Registered Report Identifier (IRRID) RR2-10.2196/31578

The emphasis of conventional survival research methods has historically been on the occurrence of failures over time. The lack of knowledge of related observed and unobserved covariates during the study of such events can have negative consequences. Frailty models are a viable option for investigating the impact of unobserved covariates in this context. In this article, we suppose that frailty multiplies the hazard rate. As a useful method to ensure the effect of unobserved heterogeneity, we propose weighted Lindley (WL) frailty models with generalized Weibull (GW) and generalized log-logistic-II (GLL2) as the baseline distributions. The Bayesian paradigm of Markov Chain Monte Carlo (MCMC) methodology is used to estimate the model parameters. Subsequently, model comparisons are performed via Bayesian comparison techniques. The popular kidney data set is considered to illustrate the results. It is shown that the new models perform better than those based on gamma and inverse Gaussian frailty distributions.

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19 Indian data using various models. Based on the collected COVID-19 outbreak data, we try to find the propagation rule of this outbreak disease and predict the outbreak situations in India. For India data, the time series model gives the best results in the form of predication as compared to other models for all variables of COVID-19. For new cases, new deaths, total cases and total deaths, the best fitted ARIMA models are as follows: ARIMA(0,2,3), ARIMA(0,1,1), ARIMA(0,2,0) and ARIMA(0,2,1). Based on time series analysis, we predict all variables for next month and conclude that the predictive value of Indian COVID-19 data of total cases is more than 20 lakhs with more than 43 thousand total deaths. The present chapter recommended that a comparison between various predictive models provide the accurate and better forecast value of the COVID-19 outbreak for all study variables.

All transmission disease depends on the transmission opportunity or medium like humans in COVID-19. Due to globalization and regular movement of people from one country to another, spread of COVID 19 reached to 208 countries till May 10, 2020. For any society health is major concern for humanity as well as administration. Any pandemic is declared as and when it reached at a particular severity level and control vice versa. So, we have continued the daily COVID 19 cases analysis and segregated till May 10, 2020. We have included at least 25 countries for the analysis purpose due to limitation of number of observations in the analysis. Maximum number of day’s data available for China is for 100 days, followed by Iran for 81 days, minimum number of days data is for 16 days for Western Sahara and Tajikistan.

COVID-19 is an infectious disease, growth of which depends upon the linked stages of the epidemic, the average number of people one person can infect and the time it takes for those people to become infectious themselves. We have studied the COVID-19 time series to understand the growth behaviour of COVID-19 cases series. A structural break occurs in the COVID-19 series at the change time form one stage to another. We have performed the structural break analysis of data available for 207 countries till April 20, 2020. There are 42 countries which have recorded five breaks in COVID cases series. This means that these countries are in the sixth stage of growth transmission and show a downward pattern in reporting in the daily cases, whereas countries with two and three breaks, record the rapid growth pattern in the daily cases. From this study, we conclude that the more the breaks in the series, there is more possibility to determine the constant or decreasing rate of daily cases. It is well fitted using lognormal distribution as this distribution is archived at its highest peak after some period and then suddenly it decreases at a longer time period. This can be seen in various countries like China, Australia, New Zealand and so on.

Most economic time series, such as GDP, real exchange rate and banking series are irregular by nature as they may be affected by a variety of discrepancies, including political changes, policy reforms, import-export market instability, etc. When such changes entail serious consequences for time series modelling, various researchers manage this problem by applying a structural break. Thus, the aim of this paper is to develop a generalised structural break time series model. The paper discusses a panel autoregressive model with multiple breaks present in all parameters, i.e. in the autoregressive coefficient and mean and error variance, which is a generalisation of various sub-models. The Bayesian approach is applied to estimate the model parameters and to obtain the highest posterior density interval. Strong evidence is observed to support the Bayes estimator and then it is compared with the maximum likelihood estimator. A simulation experiment is conducted and an empirical application on the SARRC association’s GDP per capita time series is used to illustrate the performance of the proposed model. This model is also extended to a temporary shift model.

This paper explores the effect of multiple structural breaks to estimate the parameters and test the unit root hypothesis in panel data time series model under Bayesian perspective. These breaks are present in both mean and error variance at the same time point. We obtain Bayes estimates for different loss function using conditional posterior distribution, which is not coming in a closed form, and this is approximately explained by Gibbs sampling. For hypothesis testing, posterior odds ratio is calculated and solved via Monte Carlo Integration. The proposed methodology is illustrated with numerical examples.

The Muth distribution and its derivation have been used to construct numerous statistical models in recent years, with applications in a variety of fields. In this paper, we use the inverse scheme to introduce the inverse power Muth distribution. It thus constitutes a new three-parameter heavy-tailed lifetime distribution belonging to the family of inverse distributions, which does not appear to have received adequate attention in the literature. We naturally call it inverse power Muth distribution. Two complementary parts compose the article. The first part aims to determine the main statistical properties of the inverse power Muth distribution, such as the shape behavior of the probability density and hazard rate functions, the expression of the quantile function and the related quantities, and some moment measures. The second part is devoted to its practical aspects, with a focus on its modeling capabilities. We examine the estimation of the model parameters via several well-established methods, including classical and Bayesian estimation methods. Then, we illustrate the flexibility and potential usefulness of the inverse power Muth model by means of a simulation study and two real datasets. A fair investigation reveals that it can outperform existing and comparable three-parameter models also based on the inverse scheme.

The idea about structural break in unit root hypothesis under time series model had received great amount of attention over many last decades. The importance of structural break in the mean had been comprehensively studied by Perron [15], Perron and Vogelsang [17], Zivot and Andrews [25] etc. This had also studied in considering of break in variance by Kim et al. [9], Cook [6], Kumar et al. [11] etc. There is sufficient contribution regarding break in mean and variance individually but both are equally important and this was little explored by Bai [1] for panel data and Meligkotsidou et al. [14] for univariate time series. In present paper, we are extending this on panel dataAR(1) time series model under Bayesian framework. Posterior odds ratio has been derived for various models with and without break in mean, variance and both in consideration of unit root hypothesis. A simulation as well as an empirical analysis is also carried out to get more generalized view on the model under study.

In this article, we proposed one parameter, inverse Lindley distribution, with its fundamental properties such as quantiles, mode, stochastic ordering, entropy, and stress–strength reliability. The proposed distribution has upside-down bathtub shape for its failure rate function. The estimation of stress–strength reliability has been approached by both classical and Bayesian methods. Under Bayesian set-up, both non-informative (Jeffrey) and informative (gamma) priors are considered under symmetric (squared error) and asymmetric (entropy) loss functions. The Lindley’s approximation method is used for Bayesian computation. The performances of the estimators have been compared in terms of their mean squared errors using simulated samples. Two real data-sets, representing the survival times of head and neck cancer patients, are considered for demonstrating the applicability of the proposed model.

A variable may be affected by some associated variables which may influence the estimation and testing procedures and also not much important to model separately, such types of variables are called covariates. The present paper dealt the covariate autoregressive (C-AR(1)) time series model with structural break in mean and variance under Bayesian framework. Parameters of the model have been estimated considering appropriate prior assumptions and compared with maximum likelihood estimator. A simulation study has been carried out to validate the theoretical results, and then the same implemented on the monthly REER time series of SAARC countries. Both studies, empirical and simulation justify our findings. A unit root hypothesis is also tested for the model under study and gets satisfactory result.Keywords: Autoregressive Model; Bayesian Inference; Covariate; Structural break; Gibbs Sampler; Posterior Probability