Abstract:
BACKGROUND: Survival analysis is the most appropriate method of analysis for time-to-event data. The classical
accelerated failure-time model is a more powerful and interpretable model than the Cox proportional hazards model,
provided that model imposed distribution and homoscedasticity assumptions satisfied. However, most of the real data
are heteroscedastic which violates the fundamental assumption and consequently, the statistical inference could be
erroneous in accelerated failure-time modeling. The weighted least-squares estimation for the accelerated failure-time
model is an efficient semi-parametric approach for time-to-event data without the homoscedasticity assumption, which
is developed recently and not often utilized for real data analysis. Thus, this study was conducted to ascertain the better
performance of the weighted least-squares estimation method over the classical methods.
METHODS: We analyzed a REAL dataset on Antiretroviral Therapy patients we recently collected. We compared the
results from classical methods of estimation for the accelerated failure-time model with the results revealed from the
weighted least-squares estimation.
RESULTS: We found that the data are heteroscedastic and indicated that the weighted least-square method should be
used to analyze this data. The weighted least-squares estimation revealed more accurate, and efficient estimates of
covariates effect since its confidence intervals were shorter and it identified more significant covariates. Accordingly, the
survival of HIV positives was found to be significantly linked with age, weight, functional status, CD4 (Cluster of
Differentiation agent 4 glycoproteins), and clinical stages.
CONCLUSIONS: The weighted least-squares estimation performed the best in providing more significant effects and precise
estimates than the classical accelerated failure-time methods of estimation if data are heteroscedastic. Thus, we
recommend future researchers should utilize weighted least-squares estimation rather than the classical methods when
the homoscedasticity assumption is violated.
