The study recognized the worth of understanding the how’s of handling censoring and censored data in survival analysis and the potential biases it might cause if researchers fail to identify and handle the concepts with utmost care. We systematically reviewed the concepts of censoring and how researchers have handled censored data and brought all the ideas under one umbrella. The review was done on articles written in the English language spanning from the late fifties to the present time. We googled through NCBI, PubMed, Google scholar and other websites and identified theories and publications on the research topic. Revelation was that censoring has the potential of biasing results and reducing the statistical power of analyses if not handled with the appropriate techniques it requires. We also found that, besides the four main approaches (complete-data analysis method; imputation approach; dichotomizing the data; the likelihood-based approach) to handling censored data, there were several other innovative approaches to handling censored data. These methods include censored network estimation; conditional mean imputation method; inverse probability of censoring weighting; maximum likelihood estimation; Buckley-Janes least squares algorithm; simple multiple imputation strategy; filter algorithm; Bayesian framework; β -substitution method; search-and-score-hill-climbing algorithm and constraint-based conditional independence algorithm; frequentist; Markov chain Monte Carlo for imputed data; quantile regression; random effects hierarchical Cox proportional hazards; Lin’s Concordance Correlation Coefficient; classical maximum likelihood estimate. We infer that the presence of incomplete information about subjects does not necessarily mean that such information must be discarded, rather they must be incorporated into the study for they might carry certain relevant information that holds the key to the understanding of the research. We anticipate that through this review, researchers will develop a deeper understanding of this concept in survival analysis and select the appropriate statistical procedures for such studies devoid of biases.

Introduction:Various psychosocial studies on health related lifestyles lay emphasis on the fact that the perception one has of himself as being at risk of HIV/AIDS infection was a necessary condition for preventive behaviors to be adopted. Hierarchical Multiple Regression models was used to examine the relationship between eight independent variables and one dependent variable to isolate predictors which have significant influence on behavior and sexual practices.Methods:A Cross-sectional design was used for the study. Structured close-ended interviewer-administered questionnaire was used to collect primary data. Multistage stratified technique was used to sample views from 380 students from Takoradi Polytechnic, Ghana. A Hierarchical multiple regression model was used to ascertain the significance of certain predictors of sexual behavior and practices.Results:The variables that were extracted from the multiple regression were; for the constant; β=14.202, t=2.279, p=0.023, variable is significant; for the marital status; β=0.092, t=1.996, p<0.05, variable is significant; for the knowledge on AIDs; β= 0.090, t=1.996, p<0.05, variable is significant; for the attitude towards HIV/AIDs; β=0.486, t=10.575, p<0.001, variable is highly significant. Thus, the best fitting model for predicting behavior and sexual practices was a linear combination of the constant, one’s marital status, knowledge on HIV/AIDs and Attitude towards HIV/AIDs.,Y (Behavior and sexual practices) = β0 + β1 (Marital status) + β2 (Knowledge on HIV AIDs issues) + β3 (Attitude towards HIV AIDs issues)β0, β1, β2 and β3 are respectively 14.201, 2.038, 0.148 and 0.486; the higher the better.Conclusions:Attitude and behavior change education on HIV/AIDs should be intensified in the institution so that students could adopt better lifestyles.

Problem statement:This study focused on statistical analysis of risk factors of the major disease among residents of STMA. The objectives of the study were to find the predominant diseases affecting residents in the catchments area. The study was also meant to identify risk factors associated with the incidence of the predominant disease. Approach: Purposive sampling technique was used to select residents from the target population. A structured questionnaire was used to obtain information from the sampling units. The data was analyzed using measures of risk, principal component analysis and chi-square tests. Results: Malaria (68%) topped the list of predominant diseases. It was also found that (44%) of those who had been diagnosed of malaria disease lived around swampy areas. The absolute risk value (78%) suggested that people living around the swampy area were at risk of infection by malaria parasites. The absolute risk value of 62% for dwellers of non-swampy areas indicated that aside the environmental factors there are other factors which were associated with the incidence of malaria. The principal component analysis results showed that; diets, social and educational status affect the incidence of malaria. The p-value of the chi-square test on two risk factors (environment and ones family history) were also significant, indicating that there was an association between the incidence of malaria in the catchments area and the identified risk factors. This result confirmed earlier researches that sought to investigate the relationship between diseases and environmental factors. Conclusion/Recommendations: It was revealed that apart from environmental factors and family history; diets, social status and educational background play an important role in the health of the residents. It was recommended that STMA takes the appropriate measures to prevent 6 people from the risk factor at any case.

This study has provided a starting point for defining and working with Cox models in respect of multivariate modeling. In medical researches, there may be situations, where several risk factors potentially affect patient prognosis, howbeit, only one or two might predict patient's predicament. In seeking to find out which of the risk factors contribute the most to the survival times of

Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though there are numerous statistical methods available for use in analysis, the extent of their understanding and ease of using these tools for analysis is limited. This study has twofold purpose: firstly, literature on categorical data commonly used in research was reviewed; next, we reported the results of a survey we designed and executed. Categorical data was collected via questionnaire and analyzed to serve as a backbone of the robustness of categorical data. Several conjectures about the independence of the socio-economic variables and e-commence were tested. Some of the factors influencing patronage of e-commerce were identified. It is clear from the literature that as one's academic qualification improves, there is an associated improvement in their preference for e-commerce, but the results revealed otherwise. Size of family was found to influence e-commerce. Both income and social status positively affected patronage in e-commerce. Gender also appeared to affect patronage in e-commerce. 62.3% of staff had patronized e-commerce. This shows that e-commerce patronage was gradually increasing. It is therefore our considered view that policy documents regulating and monitoring the use of e-commerce be developed to increase e-commerce participation across the globe. It is also recommended that the bottlenecks which obstruct patronage in e-commence be addressed so that a lot more staff will develop a positive attitude towards e-commerce.

In this paper, Label Setting Algorithm and Dynamic Programming Algorithm had been critically examined in determining the shortest path from one source to a destination. Shortest path problems are for finding a path with minimum cost from one or more origin (s) to one or more destination(s) through a connected network. A network of ten (10) cities (nodes) was employed as a numerical example to compare the performance of the two algorithms. Both algorithms arrived at the optimal distance of 11 km, which corresponds to the paths 1&rarr;4&rarr;5&rarr;8&rarr;10 ,1&rarr;3&rarr;5&rarr;8&rarr;10 , 1&rarr;2&rarr;6&rarr;9&rarr;10&nbsp; and&nbsp; 1&rarr;4&rarr;6&rarr;9&rarr;10 . Thus, the problem has multiple shortest paths. The computational results evince the outperformance of Dynamic Programming Algorithm, in terms of time efficiency, over the Label Setting Algorithm. Therefore, to save time, it is recommended to apply Dynamic Programming Algorithm to shortest paths and other applicable problems over the Label-Setting Algorithm.

The outbreak of COVID-19 infection and its effects have not spared any economy on the globe. The fourth variant has just announced its appearance with its high death toll and impact on economic activities. The basic reproductive number R 0 , which measures the transmission potential of an infectious disease, is extremely important in the study of epidemiology. The main purpose of this study was to derive R 0 and assess the stability of the model around its equilibrium points. The motivation was to simulate the effect of COVID-19 on the demand for fashion products and how its application has impacted the COVID-19 pandemic. A five-compartment susceptible-infection-recovery-susceptible-based model was formulated in an integrated environment with application of fashion-based personal protective equipment (FPPEs) and government policy regulation, using ordinary differential equations. Solution techniques included a mix of qualitative analysis and simulations with data from various publications on COVID-19. The study revealed that the disease-free equilibrium was both locally and globally asymptotically stable (LAS and GAS) for R 0 ≤ 1 , while the disease-endemic equilibrium was both LAS and GAS for R 0 ≥ 1 . As the demand for FPPEs increases, R 0 decreases, and vice versa. The sensitivity analysis indicated that R 0 was very sensitive to the rate of application of FPPEs. This confirms the significance of high demand for FPPEs in reducing the transmission of COVID-19 infection. Again, the pandemic has had both positive and negative impacts on the demand for fashion products; however, the negative impact outweighed the positive impact. Another discovery was that government policy stringency was significant in increasing demand for FPPEs. The sensitivity analyses suggested prioritization of FPPEs application together with all recommended PPEs. We recommend inter alia that FPPEs be used together with other nonpharmaceutical interventions. Operators in the fashion industry must be dynamic in adjusting to the new trends of taste for fashion products. Finally, governments should maintain high policy stringency.

A recurrent event remains the outcome variable of interest in many biometric studies. Recurrent events can be explained as events of defined interest that can occur to same person more than once during the study period. This study presents an overview of different pertinent recurrent models for analyzing recurrent events. Aims: To introduce, compare, evaluate and discuss pros and cons of four models in analyzing recurrent events, so as to validate previous findings in respect of the superiority or appropriateness of these models. Study Design:  A comparative studies based on simulation of recurrent event models applied to a tertiary data on cancer studies.  Methodology: Codes in R were implemented for simulating four recurrent event models, namely; The Andersen and Gill model; Prentice, Williams and Peterson models; Wei, Lin and Weissferd; and Cox frailty model. Finally, these models were applied to analyze the first forty subjects from a study of Bladder Cancer Tumors. The data set contained the first four repetitions of the tumor for each patient, and each recurrence time was recorded from the entry time of the patient into the study. An isolated risk interval is defined by each time to an event or censoring. Results: The choice and usage of any of the models lead to different conclusions, but the choice depends on: risk intervals; baseline hazard; risk set; and correlation adjustment or simplistically, type of data and research question. The PWP-GT model could be used if the research question is focused on whether treatment was effective for the  event since the previous event happened. However, if the research question is designed to find out whether treatment was effective for the  event since the start of treatment, then we could use the PWP- TT. The AG model will be adequate if a common baseline hazard could be assumed, but the model lacks the details and versatility of the event-specific models. The WLW model is very suitable for data with diverse events for the same person, which underscores a potentially different baseline hazard for each type. Conclusion: PWP-GT has proven to be the most useful model for analyzing recurrent event data.