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Table 3 The regression models obtained after parameter estimation using Bayesian Estimation (BE), Least Squares Estimation (LSE), and Maximum Likelihood Estimation (MLE) for the Serial Reconstruction Unit (SRU) model, Poisson model, Lyman model, Logit model, and Logistic model. The R-squared (R2) statistic is utilized to assess the degree of fit of the regression models to the actual data

From: A comparative study of different parameter estimation methods for predictive models of Normal Tissue Complication Probability (NTCP) of radiation-induced temporal lobe injury following intensity-modulated radiotherapy in nasopharyngeal carcinoma

  

SRU

Poisson

Lyman

Logit

Logistic

Data-A

BE

0.963

0.953

0.959

0.977

0.951

LSE

0.977

0.986

0.907

0.963

0.920

MLE

0.977

0.843

0.949

0.940

0.731

Data-B

BE

0.806

0.958

0.742

0.864

0.918

LSE

0.762

0.697

0.679

0.671

0.956

MLE

0.536

0.857

0.868

0.501

0.780

Data-C

BE

0.962

0.915

0.964

0.946

0.971

LSE

0.837

0.916

0.876

0.890

0.923

MLE

0.915

0.896

0.897

0.853

0.559