The Impact of Censored Observations on Model Fit and Structural Model Discrimination in Nonlinear Mixed Effects Modelling when using Different Estimation Algorithms
(English)Manuscript (preprint) (Other academic)
Missing data due to censored observations is a common problem in nonlinear mixed effects modelling of clinical data. The aim of this study was to investigate how the estimated model parameters and the discrimination of correct structural model were affected by different patterns of censored observations and to investigate if there were any differences in these statistics when using different estimation algorithms to fit the models. Simulations generated data for 400 individuals with six observations per individual using a one-compartment model. Observations (62%) were censored according to three different missing data mechanisms. A one-compartment and a two-compartment model were fitted to the data using six different estimation algorithms.The performance of the algorithms was evaluated in a stochastic simulations and estimations study where 200 data sets were simulated. The algorithms were compared according to bias and precision of parameter estimates and according to the type I error rate in the evaluation of structural model. The EM algorithms, especially the importance sampling algorithms (IMP and IMPMAP), gave unbiased and precise parameter estimates as long as data were missing completely at random or missing at random, while the gradient based algorithms (especially FO and FOCE) experienced some problems with biased estimates under these missing data mechanisms. The type I error rate was not elevated when using any of the algorithms as long as the missing data mechanism was not missing not at random.
missing data, missing dependent variable, missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR), bias, precision, type I error
IdentifiersURN: urn:nbn:se:uu:diva-224097OAI: oai:DiVA.org:uu-224097DiVA: diva2:715318