Survival quantile regression Cox proportional Hazard model Accelerated failure time model Log-rank test Survival function and censoring. Coronavirus 2019 (Covid-19) cases in Rivers State, Nigeria are on the increase day by day. It became imperative to investigate the survival rate of covid-19 patients in this state. The survival quantile regression was applied assuming right censoring to estimate the effect of age, sex, fever, anosmia, comorbidity, and cough on the survival time of patients. The results show that on admission into the hospital the survival time of the patients depended on the age and the presence of anosmia, comorbidity, and fever. By the mid survival period only anosmia and fever were seen to be significant but at the 75th quantile comorbidity was also seen to be significant along with fever and anosmia. The result also shows that having fever is associated with longer stay in the hospital based on the size of the effect at different quantiles. We also noticed that though the effect of anosmia and comorbidity were significant at the 25th and 75th quantile the sizes of the effects were minimal, but comorbidity was seen to have a bigger effect than anosmia. Comparing the survival time of groups, the results showed that males and females have the same survival time and patients with and without comorbidity equally have the same survival time. Patients without fever, anosmia and cough had a shorter survival time than those that had fever, anosmia, and cough. We then concluded that fever, comorbidity, and anosmia are the major factors that affect the survival time of covid-19 patients in Rivers State, Nigeria. Contribution/Originality: This study is one of the very few studies that have investigated the effect of different covariates at different points on the distribution of the survival time of Covid-19 patients in Rivers State, Nigeria.
The traditional frequentist quantile regression makes minimal assumptions that accommodate errors that are not normal given that the response variable (y) is continuous even in Bayesian framework. However inference on these models where y is not continuous proves to be challenging particularly when the response variable is an ordinal data. This paper portrays the idea of Bayesian quantile estimation on ordinal data. This method utilizes the latent variable inferential framework. Estimation was done using Markov chain Monte Carlo simulation with Gibbs sampler where the cut points were set ahead of time and remained fixed all through the analysis. The method was applied in a mental health study of University undergraduate students. Investigations of the model exemplify the practical utility of Bayesian ordinal quantile models. In this paper we were able to investigate the mental health state of undergraduate students at different points in the distribution of their ages. Our findings show that the age of the students has a significant effect on their mental health. The results revealed that at 25 th , 50 th and 75 th quantiles the ages had a negative effect on their mental health while at the 95 th quantile the effect was positive. This study was able to show that older undergraduate students are more mentally equipped to withstand the stress of higher learning in the University.Contribution/Originality: The paper's primary contribution is to apply Bayesian ordinal quantile regression to mental health analysis. The study utilized the Gibbs sampler with fixed cut-points. It portrayed insight to the effect of age on the mental health of undergraduate students at different points on the age distribution. of response variable y, that is Qy( |x), where is the quantile with interval 0 < < 1. Here, the quantile of y
This paper investigates three MICE methods: Predictive Mean Matching (PMM), Quantile Regression-based Multiple Imputation (QR-basedMI) and Simple Random Sampling Imputation (SRSI) at imputation numbers 5, 15, 20 and 30 with 5% and 20% missing values, to ascertain the one that produces imputed values that best matches the observed values and compare the model fit based on the AIC and MSE. The results show that; QR-basedMI produced more imputed values that didn’t match the observed, SRSI produced imputed values that match the observed values better as the number of imputations increases while PMM produced imputed values that matched the observed at all number of imputations and missingness considered. The model fit results for 5% missingness showed that QR-basedMI produced the best results in terms of MSE except for M=15, while AIC results showed that PMM produced best result for M= 5, QR-basedMI produced best results for M=15 and for M=20 and 30 SRSI produced the best results. The model fit results for 20% missingness shows that PMM produced the best results at all the number of imputations considered for both AIC and MSE except the AIC at M=15 where SRSI was seen to produce the best results. It is concluded that in comparison, the PMM is most suited when missingness is 20% but for 5% missingness the model fit is best with QR-basedMI.
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