TITLE:
Exploring the Impact of Factors Affecting the Lifespan of HIVs/AIDS Patient’s Survival: An Investigation Using Advanced Statistical Techniques
AUTHORS:
Christiana I. Ezeilo, Edith U. Umeh, Daniel C. Osuagwu, Chrisogonus K. Onyekwere
KEYWORDS:
Chris-Jerry Distribution, Power Chris-Jerry Distribution, Cox Proportional Hazard, Aalen’s Model, Factors Affecting HIV/AIDS Patients, CD4 Counts of HIV/AIDS Patients
JOURNAL NAME:
Open Journal of Statistics,
Vol.13 No.4,
August
23,
2023
ABSTRACT: This study investigates the impact of various
factors on the lifespan and diagnostic time of HIV/AIDS patients using advanced
statistical techniques. The Power Chris-Jerry (PCJ) distribution is applied to
model CD4 counts of patients, and the goodness-of-fit test confirms a strong
fit with a p-value of 0.6196. The PCJ distribution is found to be the best fit
based on information criteria (AIC and BIC) with the smallest negative
log-likelihood, AIC, and BIC values. The study uses datasets from St. Luke
hospital Uyo, Nigeria, containing HIV/AIDS diagnosis date, age, CD4 count,
gender, and opportunistic infection dates. Multiple linear regression is
employed to analyze the relationship between these variables and HIV/AIDS
diagnostic time. The results indicate that age, CD4 count, and opportunistic
infection significantly impact the diagnostic time, while gender shows a
nonsignificant relationship. The F-test confirms the model's overall
significance, indicating the factors are good predictors of HIV/AIDS diagnostic
time. The R-squared value of approximately 72% suggests that administering
antiretroviral therapy (ART) can improve diagnostic time by suppressing the
virus and protecting the immune system. Cox proportional hazard modeling is
used to examine the effects of predictor variables on patient survival time.
Age and CD4 count are not significant factors in the hazard of HIV/AIDS
diagnostic time, while opportunistic infection is a significant predictor with
a decreasing effect on the hazard rate. Gender shows a strong but nonsignificant
relationship with decreased risk of death. To address the violation of the
assumption of proportional hazard, the study employs an assumption-free
alternative, Aalen’s model.
In the Aalen model, all predictor variables except age and gender are statistically
significant in relation to HIV/AIDS diagnostic time. The findings provide
valuable insights into the factors influencing diagnostic time and survival of
HIV/AIDS patients, which can inform interventions aimed at reducing
transmission and improving early diagnosis and treatment. The Power Chris-Jerry
distribution proves to be a suitable fit for modeling CD4 counts, while
multiple linear regression and survival analysis techniques provide insights
into the relationships between predictor variables and diagnostic time. These
results contribute to the understanding of HIV/AIDS patient outcomes and can
guide public health interventions to enhance early detection, treatment, and
care.