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and Var[νˆ ] = 4σ + l ) (l −2(1 + c . Similarly, when σˆ ≤ 0.04, νˆ = δˆ + σˆ − 0.04(c ) (7.12) is an estimate for the constant-scaled metric in accordance with FDA Guidance (2001) using a REML UN model. This estimate is asymptoti-cally normally distributed and unbiased with E[νˆ ] = δ +σ −σ − 0.04(c ) and Var[νˆ ] = 4σ . To assess PBE we ‘plug-in’ estimates of δ and the variance components and calculate the upper bound of an asymptotic 90% confidence interval. If this upper bound is below zero we declare that PBE has been shown. Using the code in Appendix B and the data in Section 7.2, we obtain the value −1.90 for log(AUC) and the value −0.95 for log(Cmax). As both of these are below zero, we can declare that T and R are PBE. 7.5.2 PBE using a replicate design Here we fit the same REML UN model as defined in Section 7.4. Let νˆ = δˆ + σˆ + σˆ − (1 + c )(σˆ + σˆ ) (7.13) be an estimate for the reference-scaled metric in accordance with FDA Guidance (2001) when (σˆ + σˆ > 0.04 and using a REML UN model. Then, this estimate is asymptotically normally distributed, un-biased with E[νˆ ] = δ +σ − (1 + c ) and has variance of Var[νˆ ] = 4σ + l + (1 + c ) (l )+ 2l −2(1+c −2(1 + c + 2(1 + c ) (l ) When σˆ + σˆ ≤ 0.04, let νˆ = δˆ + σˆ + σˆ − (σˆ + σˆ )− 0.04(c ) (7.14) be an estimate for the constant-scaled metric in accordance with FDA
DOI link for and Var[νˆ ] = 4σ + l ) (l −2(1 + c . Similarly, when σˆ ≤ 0.04, νˆ = δˆ + σˆ − 0.04(c ) (7.12) is an estimate for the constant-scaled metric in accordance with FDA Guidance (2001) using a REML UN model. This estimate is asymptoti-cally normally distributed and unbiased with E[νˆ ] = δ +σ −σ − 0.04(c ) and Var[νˆ ] = 4σ . To assess PBE we ‘plug-in’ estimates of δ and the variance components and calculate the upper bound of an asymptotic 90% confidence interval. If this upper bound is below zero we declare that PBE has been shown. Using the code in Appendix B and the data in Section 7.2, we obtain the value −1.90 for log(AUC) and the value −0.95 for log(Cmax). As both of these are below zero, we can declare that T and R are PBE. 7.5.2 PBE using a replicate design Here we fit the same REML UN model as defined in Section 7.4. Let νˆ = δˆ + σˆ + σˆ − (1 + c )(σˆ + σˆ ) (7.13) be an estimate for the reference-scaled metric in accordance with FDA Guidance (2001) when (σˆ + σˆ > 0.04 and using a REML UN model. Then, this estimate is asymptotically normally distributed, un-biased with E[νˆ ] = δ +σ − (1 + c ) and has variance of Var[νˆ ] = 4σ + l + (1 + c ) (l )+ 2l −2(1+c −2(1 + c + 2(1 + c ) (l ) When σˆ + σˆ ≤ 0.04, let νˆ = δˆ + σˆ + σˆ − (σˆ + σˆ )− 0.04(c ) (7.14) be an estimate for the constant-scaled metric in accordance with FDA
and Var[νˆ ] = 4σ + l ) (l −2(1 + c . Similarly, when σˆ ≤ 0.04, νˆ = δˆ + σˆ − 0.04(c ) (7.12) is an estimate for the constant-scaled metric in accordance with FDA Guidance (2001) using a REML UN model. This estimate is asymptoti-cally normally distributed and unbiased with E[νˆ ] = δ +σ −σ − 0.04(c ) and Var[νˆ ] = 4σ . To assess PBE we ‘plug-in’ estimates of δ and the variance components and calculate the upper bound of an asymptotic 90% confidence interval. If this upper bound is below zero we declare that PBE has been shown. Using the code in Appendix B and the data in Section 7.2, we obtain the value −1.90 for log(AUC) and the value −0.95 for log(Cmax). As both of these are below zero, we can declare that T and R are PBE. 7.5.2 PBE using a replicate design Here we fit the same REML UN model as defined in Section 7.4. Let νˆ = δˆ + σˆ + σˆ − (1 + c )(σˆ + σˆ ) (7.13) be an estimate for the reference-scaled metric in accordance with FDA Guidance (2001) when (σˆ + σˆ > 0.04 and using a REML UN model. Then, this estimate is asymptotically normally distributed, un-biased with E[νˆ ] = δ +σ − (1 + c ) and has variance of Var[νˆ ] = 4σ + l + (1 + c ) (l )+ 2l −2(1+c −2(1 + c + 2(1 + c ) (l ) When σˆ + σˆ ≤ 0.04, let νˆ = δˆ + σˆ + σˆ − (σˆ + σˆ )− 0.04(c ) (7.14) be an estimate for the constant-scaled metric in accordance with FDA
ABSTRACT