Weighted Least Squares Method for the Accelerated Failure Time Model with Auxiliary Covariates

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.

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Accelerated Failure Time Model with Weighted Least-Squares Estimation: Application on Survival of HIV Positives

10.21203/rs.3.rs-123624/v1 ◽

2020 ◽

Author(s):

Yesuf Abdela Mustefa ◽

Ding-Geng Chen

Keyword(s):

Least Squares ◽

Failure Time ◽

Weighted Least Squares ◽

Least Squares Estimation ◽

Accelerated Failure Time Model ◽

Accelerated Failure Time ◽

Time Model ◽

Weighted Least Squares Estimation ◽

Accelerated Failure ◽

Failure Time Model

Abstract BackgroundSurvival 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 distributional and homoscedasticity assumptions satisfied. However, most of the real data are heteroscedastic which violate the fundamental assumption and consequently, the statistical inference could be erroneous in accelerated failure time modeling. Weighted least squares estimation for 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, the study was conducted to ascertain the predictive performance of weighted least squares estimation method over the classical methods.MethodsWe analyzed a sample of 203 real Antiretroviral Therapy dataset. We compared the results from clasical methods of estimation for accelerated failure time model with the results revealed from the weighted least squares estimation.ResultsWe found that the data are heteroscedastic. The weighted least squares estimation revealed more accurate, and efficient estimates of covariates effect. It also detected more significant covariates. Accordingly, survival of HIV positives varies with age, weight, functional status, CD4 percent, and clinical stages.ConclusionsThe weighted least squares estimation performed best in predicting the survival of HIV patients. Thus, we recommend future researchers should utilize weighted least squares estimation rather than the classical methods when the homoscedasticity assumption is violated.

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