Some statistical methods in analysis of single and multiple events with application to infant mortality data.
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Date
2020
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Abstract
The time to event analysis or survival analysis aims at making inferences on the time elapsed
between the recruitment of subjects or the onset of observations, until the occurrence of
some event of interest. Methods used in general statistical analysis, in particular in regression
analysis, are not directly applicable to time to event data due to covariate correlation, censoring
and truncation. While analysing time to event data, medical statistics adopts mainly nonparametric
methods due to difficulty in finding the adequate distribution of the phenomenon
under study.
This study reviews non-parametric classical methods of time to event analysis namely Aalen
Additive Hazards Model (AAHM) trough counting and martingale processes, Cox Proportional
Hazard Model (CPHM) and Cox-Aalen Hazards Model (CAHM) with application to the infant
mortality at Kigali University Teaching Hospital (KUTH) in Rwanda. Proportional hazards
assumption (PHA) was checked by assessing Kaplan-Meier estimates of survival functions per
groups of covariates. Multiple events models were also reviewed and a model suitable to the
dataset was selected. The dataset comprises 2117 newborns and socio-economic and clinical
covariates for mothers and children. Two events per subject were modeled namely, the death
and the occurrence of at least one of the conditions that may also cause long term death to
infants.
To overcome the instability of models (also known as checking consistence of models) and
potential small sample size, re-sampling was applied to both CPHM and appropriate multiple
events model. The popular non-parametric re-sampling methods namely bootstrap and jackknife
for the available covariates were conducted and then re-sampled models were compared
to the non-re-sampled ones.
The results in different models reveal significant and non-significant covariates, the relative risk
and related standard error and confidence intervals per covariate. Among the results, it was
found that babies from under 20 years old mothers were at relatively higher risk and therefore,
pregnancy of under 20 years old mothers should be avoided. It was also found that an infant’s
abnormality in weight and head increases the risk of infant mortality, clinically recommended
ways of keeping pregnancy against any cause of infant abnormality were then recommended.
Description
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.