Erratum to: Characterizing plasma albumin concentration changes in TB/HIV patients on anti retroviral and anti –tuberculosis therapy
© Bisaso et al.; licensee Springer. 2015
Received: 9 December 2014
Accepted: 10 December 2014
Published: 11 February 2015
The original article was published in In Silico Pharmacology 2014 2:3
We would like to retract the relation between ABCB1c.3435C > T gene mutation and baseline albumin secretion rate. This is because, even though the mutation in the gene was identified as a significant covariate with our data, we are still unable to biologically explain the relationship.
The final model equation (equation 7), referred to here as the “Simplified Solution” is not the direct solution of the differential equations preceding it. We would therefore like replace it with the unsolved differential equation below and show a comparison of the results of the two equations.
The equation was fit to the data in NONMEM version 7.2, using a differential equations solver specified by the ADVAN6 and TOL = 3 subroutines. The First Order Conditional Estimation with interaction (FOCEI) estimation method was used.
The Interaction term was not used in the previous analysis because it has been reported not to be useful when there are small number of observations per individual and therefore does not provide different results (Peter 2011).
The two models, i.e. the previously defined simplified solution and the unsolved differential equation above were compared with respect to fit, difference and precision of parameter estimates, goodness of fit, prediction bias and precision as well as length of runtimes given the same initial parameter estimates.
NONMEM estimated relative standard errors, percentage difference in parameter estimates, numerical predictive checks and other model comparison criteria for model1 and model2
The unsolved differential equation model
The previous simplified solution
% parameter difference
Relative standard errors
Q0_TB = 1(proportional increase in Q0 with TB)
Other model criteria
Measures of model prediction
The total run time (parameter estimation plus covariance) for the unsolved differential equations model above was more than three times that of the previously defined model and this ratio was higher during more intensive procedures like bootstrapping and stepwise covariate analysis.
We would like to change model equation to the unsolved differential equation model. The model provided here and the previously defined model differ mostly in one parameter, R (rate of change of albumin secretion) and the computation times, but the other outputs including parameters, predictions and goodness of fit plots were similar.
- Bisaso KR, Owen JS, Ojara FW, Namuwenge PM, Mugisha A, Mbuagbaw L, et al. Characterizing plasma albumin concentration changes in TB/HIV patients on anti retroviral and anti -tuberculosis therapy. In Silico Pharmacol. 2014; 2(1):3. doi:10.1186/s40203-014-0003-93.View ArticlePubMed CentralPubMedGoogle Scholar
- Peter LB. Nonlinear Mixed Effects Models: Theory. In: Peter LB, editor. Pharmacokinetic-Pharmacodynamic Modeling and Simulation. 2nd ed. New York: Springer; 2011: p. 256.Google Scholar
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