scholarly journals Using Akaike’s information theoretic criterion in population analysis: a simulation study

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 71 ◽  
Author(s):  
Erik Olofsen ◽  
Albert Dahan
Keyword(s):  
Simulation Study ◽  
Interindividual Variability ◽  
Population Analysis ◽  
Selection Criterion ◽  
Population Data ◽  
Predictive Performance ◽  
Pharmacokinetic Data ◽  
Information Theoretic ◽  
Mixed Effects Modeling ◽  
Present Simulation

Akaike’s information-theoretic criterion for model discrimination (AIC) is often stated to “overfit”, i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, when no fixed-dimensional correct model exists, for example for pharmacokinetic data, AIC, or its bias-corrected version (AICc) might be the selection criterion of choice if the objective is to minimize prediction error. The present simulation study was designed to assess the behavior of AICc when applying it to the analysis of population data, for various degrees of interindividual variability. The simulation study showed that, at least in a relatively simple mixed effects modeling context, minimal mean AICc corresponded to best predictive performance even in the presence of large interindividual variability.

scholarly journals Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study

F1000Research ◽  
2014 ◽  
Vol 2 ◽  
pp. 71 ◽  
Author(s):  
Erik Olofsen ◽  
Albert Dahan
Keyword(s):  
Simulation Study ◽  
Prediction Error ◽  
Interindividual Variability ◽  
Predictive Performance ◽  
Performance Measure ◽  
Mixed Effects ◽  
Pharmacokinetic Data ◽  
Information Theoretic ◽  
Correct Model ◽  
Drug Concentrations

Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be the selection criterion of choice.We performed Monte Carlo simulations using a model of pharmacokinetic data (a power function of time) with the property that fits with common multi-exponential models can never be perfect - thus resembling the situation with real data. Prespecified models were fitted to simulated data sets, and AIC and AICc (the criterion with a correction for small sample sizes) values were calculated and averaged. The average predictive performances of the models, quantified using simulated validation sets, were compared to the means of the AICs. The data for fits and validation consisted of 11 concentration measurements each obtained in 5 individuals, with three degrees of interindividual variability in the pharmacokinetic volume of distribution.Mean AICc corresponded very well, and better than mean AIC, with mean predictive performance. With increasing interindividual variability, there was a trend towards larger optimal models, but with respect to both lowest AICc and best predictive performance. Furthermore, it was observed that the mean square prediction error itself became less suitable as a validation criterion, and that a predictive performance measure should incorporate interindividual variability.This simulation study showed that, at least in a relatively simple mixed effects modelling context with a set of prespecified models, minimal mean AICc corresponded to best predictive performance even in the presence of relatively large interindividual variability.

scholarly journals Using Akaike's information theoretic criterion in mixed-effects modeling of pharmacokinetic data: a simulation study

F1000Research ◽  
2015 ◽  
Vol 2 ◽  
pp. 71 ◽  
Author(s):  
Erik Olofsen ◽  
Albert Dahan
Keyword(s):  
Simulation Study ◽  
Prediction Error ◽  
Interindividual Variability ◽  
Predictive Performance ◽  
Performance Measure ◽  
Mixed Effects ◽  
Pharmacokinetic Data ◽  
Information Theoretic ◽  
Correct Model ◽  
Drug Concentrations

Akaike's information theoretic criterion for model discrimination (AIC) is often stated to "overfit", i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, with experimental pharmacokinetic data it may not be possible to identify the correct model, because of the complexity of the processes governing drug disposition. Instead of trying to find the correct model, a more useful objective might be to minimize the prediction error of drug concentrations in subjects with unknown disposition characteristics. In that case, the AIC might be the selection criterion of choice.We performed Monte Carlo simulations using a model of pharmacokinetic data (a power function of time) with the property that fits with common multi-exponential models can never be perfect - thus resembling the situation with real data. Prespecified models were fitted to simulated data sets, and AIC and AICc (the criterion with a correction for small sample sizes) values were calculated and averaged. The average predictive performances of the models, quantified using simulated validation sets, were compared to the means of the AICs. The data for fits and validation consisted of 11 concentration measurements each obtained in 5 individuals, with three degrees of interindividual variability in the pharmacokinetic volume of distribution.Mean AICc corresponded very well, and better than mean AIC, with mean predictive performance. With increasing interindividual variability, there was a trend towards larger optimal models, but with respect to both lowest AICc and best predictive performance. Furthermore, it was observed that the mean square prediction error itself became less suitable as a validation criterion, and that a predictive performance measure should incorporate interindividual variability.This simulation study showed that, at least in a relatively simple mixed-effects modelling context with a set of prespecified models, minimal mean AICc corresponded to best predictive performance even in the presence of relatively large interindividual variability.

scholarly journals Improved selection of participants in genetic longevity studies: family scores revisited

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mar Rodríguez-Girondo ◽  
Niels van den Berg ◽  
Michel H. Hof ◽  
Marian Beekman ◽  
Eline Slagboom
Keyword(s):  
Missing Data ◽  
The Netherlands ◽  
Family Size ◽  
Simulation Study ◽  
Missing Values ◽  
Selection Criterion ◽  
Limited Success ◽  
Main Challenge ◽  
Historical Sample ◽  
Selection Of

Abstract Background Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data. Methods We discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN). Results Empirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score . Conclusions Model-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC.

scholarly journals The peril of adjusting for baseline when using change as a predictor

2019 ◽  
Author(s):  
Kimmo Sorjonen ◽  
Daniel Falkstedt ◽  
Bo Melin ◽  
Michael Ingre
Keyword(s):  
High Risk ◽  
Sample Size ◽  
Simulation Study ◽  
Strong Correlation ◽  
High Reliability ◽  
Large Sample Size ◽  
Present Simulation ◽  
Very High ◽  
Time Point

Some studies have analyzed the effect of a predictor measured at a later time point (X1), or of the X1-X0 difference, while adjusting for the predictor measured at baseline (X0), on some outcome Y of interest. The present simulation study shows that, if used to analyze the effect of change in X on Y, there is a high risk for this analysis to produce type 1-errors, especially with a strong correlation between true X and Y, when X0 and X1 are not measured with very high reliability, and with a large sample size. These problems are not encountered if analyzing the unadjusted effect of the X1-X0 difference on Y instead, and as this effect exhibits power on par with the adjusted effect it seems as the preferable method when using change between two measurement points as a predictor.

scholarly journals Model-Based Meta-analysis of Rifampicin Exposure and Mortality in Indonesian Tuberculous Meningitis Trials

2019 ◽  
Vol 71 (8) ◽  
pp. 1817-1823 ◽  
Author(s):  
Elin M Svensson ◽  
Sofiati Dian ◽  
Lindsey Te Brake ◽  
Ahmad Rizal Ganiem ◽  
Vycke Yunivita ◽  
...  
Keyword(s):  
Partition Coefficient ◽  
Tuberculous Meningitis ◽  
Meta Analysis ◽  
Antimicrobial Treatment ◽  
Pharmacokinetic Data ◽  
Maximal Effect ◽  
Risk Of Death ◽  
Mixed Effects Modeling ◽  
Optimal Dosing ◽  
The Relationship

Abstract Background Intensified antimicrobial treatment with higher rifampicin doses may improve outcome of tuberculous meningitis, but the desirable exposure and necessary dose are unknown. Our objective was to characterize the relationship between rifampicin exposures and mortality in order to identify optimal dosing for tuberculous meningitis. Methods An individual patient meta-analysis was performed on data from 3 Indonesian randomized controlled phase 2 trials comparing oral rifampicin 450 mg (~10 mg/kg) to intensified regimens including 750–1350 mg orally, or a 600-mg intravenous infusion. Pharmacokinetic data from plasma and cerebrospinal fluid (CSF) were analyzed with nonlinear mixed-effects modeling. Six-month survival was described with parametric time-to-event models. Results Pharmacokinetic analyses included 133 individuals (1150 concentration measurements, 170 from CSF). The final model featured 2 disposition compartments, saturable clearance, and autoinduction. Rifampicin CSF concentrations were described by a partition coefficient (5.5%; 95% confidence interval [CI], 4.5%–6.4%) and half-life for distribution plasma to CSF (2.1 hours; 95% CI, 1.3–2.9 hours). Higher CSF protein concentration increased the partition coefficient. Survival of 148 individuals (58 died, 15 dropouts) was well described by an exponentially declining hazard, with lower age, higher baseline Glasgow Coma Scale score, and higher individual rifampicin plasma exposure reducing the hazard. Simulations predicted an increase in 6-month survival from approximately 50% to approximately 70% upon increasing the oral rifampicin dose from 10 to 30 mg/kg, and predicted that even higher doses would further improve survival. Conclusions Higher rifampicin exposure substantially decreased the risk of death, and the maximal effect was not reached within the studied range. We suggest a rifampicin dose of at least 30 mg/kg to be investigated in phase 3 clinical trials.

On the predictive performance of two Bayesian joint models: a simulation study

2020 ◽  
pp. 1-10
Author(s):  
Jingyun Yang ◽  
Rong Li ◽  
Peirong Xiang ◽  
Jingyi Hu ◽  
Wenjin Lu ◽  
...  
Keyword(s):  
Simulation Study ◽  
Predictive Performance ◽  
Joint Models

Explaining Interindividual Variability of Docetaxel Pharmacokinetics and Pharmacodynamics in Asians Through Phenotyping and Genotyping Strategies

2002 ◽  
Vol 20 (17) ◽  
pp. 3683-3690 ◽  
Author(s):  
Boon-Cher Goh ◽  
Soo-Chin Lee ◽  
Ling-Zhi Wang ◽  
Lu Fan ◽  
Jia-Yi Guo ◽  
...  
Keyword(s):  
Interindividual Variability ◽  
Performance Status ◽  
Regulatory Region ◽  
Area Under The Curve ◽  
Serum Levels ◽  
Pharmacokinetic Data ◽  
Data Sets ◽  
Pharmacokinetics And Pharmacodynamics ◽  
C3435t Polymorphism ◽  
And Performance

PURPOSE: To explain the variability of docetaxel pharmacokinetics through study of CYP3A phenotype and genotype, and MDR1 genotype. PATIENTS AND METHODS: We studied the pharmacokinetics and pharmacodynamics of docetaxel in patients in whom it was indicated and who had not received known CYP3A4 substrates. Midazolam was administered intravenously to these patients at least 2 days before docetaxel treatment, and systemic clearances of both drugs were correlated. Patients were characterized for polymorphisms in the CYP3A4 promoter region, CYP3A5, and the C3435T polymorphism of MDR1. RESULTS: Thirty-two patients were enrolled, of whom 31 had full pharmacokinetic data sets. Docetaxel clearance correlated with midazolam clearance, body-surface area, serum albumin, and performance status. Docetaxel and midazolam clearances were normally distributed. In multiple linear regression analyses, midazolam clearance and performance status were the only significant covariates of docetaxel clearance, and the area under the curve of docetaxel, serum levels of alpha-1-acid glycoprotein, and ALT were significant predictors of nadir neutrophil count. No polymorphisms were detected in the 5′ regulatory region of CYP3A4. Nine patients of 25 studied were homozygous for the CYP3A5*3 genotype, and had lower mean clearance of midazolam but not docetaxel. The T/T genotype at the C3435T of MDR1, which is associated with reduced P-glycoprotein function, was found in eight of 27 patients. CONCLUSION: Midazolam may be used as a probe drug for CYP3A activity to predict docetaxel clearances, hence reducing interindividual variability. Homozygotes for CYP3A5*3 and C3435T of MDR1 are common in our population, and their effects on pharmacokinetics of relevant substrates should be studied further.

scholarly journals How green the urban development units in Sofia are: Earth observation and population time series analysis

2021 ◽  
Vol 44 ◽  
pp. 25-37
Author(s):  
Eugenia Sarafova
Keyword(s):  
Urban Development ◽  
Population Analysis ◽  
Population Data ◽  
Earth Observation ◽  
Decision Making Process ◽  
Green Areas ◽  
Green Vegetation ◽  
Per Capita ◽  
Sentinel 2

Over the last decades, the pressure that people and their activities put on the environment has increased. Green areas in many cities are diminishing in size due to urbanization, which inevitably leads to a decrease in quality of life. This study uses remote sensing (RS) data for Sofia, Bulgaria, for a period of nearly four decades, analyzing the dynamics of NDVI of the urban development units (UDUs). Statistics for NDVI per were calculated for each UDU for eleven dates in the following years: 1987, 1990, 1992, 1993, 1996, 2000, 2001, 2002, 2011, 2015, and 2020.  An estimate was made of the amount of green vegetation per capita, similar to other coefficients used for population analysis. NDVI profiles for major urban parks showed differences for the studied period. Sentinel-2 data for 2020 was used for visualization of the current situation, in combination with detailed population data for all UDUs. The obtained data will help the decision-making process for the development of UDUs, while the methodology can be applied in any other city worldwide.

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