Impute and Analyze Data with Observations Below the Limit of
Quantification
c(
person("Vahid","Nassiri", email = "vahid.nassiri@openanalytics.eu", role = "cre"),
person("Helen", "Barnett", email= "h.barnett@lancaster.ac.uk", role = "aut"),
person("Helena", "Geys", email= "HGEYS@its.jnj.com", role = "aut"),
person("Tom", "Jacobs", email= "TJACOBS1@its.jnj.com", role = "aut"),
person("Thomas", "Jaki", email= "t.jaki@lancaster.ac.uk", role = "aut"))
Methods for non-compartmental
pharmacokinetic analysis with observations below the
limit of quantification (BLOQ) are implemented as
described in Barnett, Helen Yvette.
"Optimizing pharmacokinetic studies utilizing
microsampling." PhD diss., Lancaster University, 2017.
(available online:
<http://eprints.lancs.ac.uk/89163/1/2017barnettphd.pdf>).
It includes estimating the area under the concentrations
versus time curve (AUC) and its standard error using two
approaches: direct estimation using censored maximum
likelihood, also by first imputing the BLOQ's
using various methods, then compute AUC and its standard
error using imputed data.
Vahid Nassiri [cre],
Helen Barnett [aut],
Helena Geys [aut],
Tom Jacobs [aut],
Thomas Jaki [aut]

Impute and Analyze Data with BLOQ Observations
It includes estimating the area under the concentrations
versus time curve (AUC) and its standard error for data with
Below the Limit of Quantification (BLOQ) observations. Two
approaches are implemented: direct estimation using censored maximum
likelihood, also by first imputing the BLOQ's
using various methods, then compute AUC and its standard
error using imputed data. Technical details can found in
Barnett, Helen Yvette, Helena Geys, Tom Jacobs, and Thomas Jaki.
"Methods for Non-Compartmental Pharmacokinetic Analysis With Observations
Below the Limit of Quantification." Statistics in Biopharmaceutical
Research (2020): 1-12.
(available online:
<https://www.tandfonline.com/doi/full/10.1080/19466315.2019.1701546>).

Daniel Brunner (aut), Constantin Weiser (ctr), Andre Romahn (ctr)
Provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.
Rcpp (>= 0.12.7), mvQuad, ucminf, numDeriv, randtoolbox