The Experts below are selected from a list of 366 Experts worldwide ranked by ideXlab platform

John K Kruschke - One of the best experts on this subject based on the ideXlab platform.

  • Posterior Predictive Checks can and should be bayesian comment on gelman and shalizi philosophy and the practice of bayesian statistics
    British Journal of Mathematical and Statistical Psychology, 2013
    Co-Authors: John K Kruschke
    Abstract:

    Bayesian inference is conditional on the space of models assumed by the analyst. The Posterior distribution indicates only which of the available parameter values are less bad than the others, without indicating whether the best available parameter values really fit the data well. A Posterior Predictive Check is important to assess whether the Posterior predictions of the least bad parameters are discrepant from the actual data in systematic ways. Gelman and Shalizi (2012a) assert that the Posterior Predictive Check, whether done qualitatively or quantitatively, is non-Bayesian. I suggest that the qualitative Posterior Predictive Check might be Bayesian, and the quantitative Posterior Predictive Check should be Bayesian. In particular, I show that the ‘Bayesian p-value’, from which an analyst attempts to reject a model without recourse to an alternative model, is ambiguous and inconclusive. Instead, the Posterior Predictive Check, whether qualitative or quantitative, should be consummated with Bayesian estimation of an expanded model. The conclusion agrees with Gelman and Shalizi regarding the importance of the Posterior Predictive Check for breaking out of an initially assumed space of models. Philosophically, the conclusion allows the liberation to be completely Bayesian instead of relying on a non-Bayesian deus ex machina. Practically, the conclusion cautions against use of the Bayesian p-value in favour of direct model expansion and Bayesian evaluation.

Liangliang Wang - One of the best experts on this subject based on the ideXlab platform.

  • modeling conditional dependence between diagnostic tests a multiple latent variable model
    Statistics in Medicine, 2009
    Co-Authors: Nandini Dendukuri, Alula Hadgu, Liangliang Wang
    Abstract:

    Applications of latent class analysis in diagnostic test studies have assumed that all tests are measuring a common binary latent variable, the true disease status. In this article we describe a new approach that recognizes that tests based on different biological phenomena measure different latent variables, which in turn measure the latent true disease status. This allows for adjustment of conditional dependence between tests within disease categories. The model further allows for the inclusion of measured covariates and unmeasured random effects affecting test performance within latent classes. We describe a Bayesian approach for model estimation and describe a new Posterior Predictive Check for evaluating candidate models. The methods are motivated and illustrated by results from a study of diagnostic tests for Chlamydia trachomatis. Published in 2008 by John Wiley & Sons, Ltd.

Lewis B Sheiner - One of the best experts on this subject based on the ideXlab platform.

  • evaluating pharmacokinetic pharmacodynamic models using the Posterior Predictive Check
    Journal of Pharmacokinetics and Pharmacodynamics, 2001
    Co-Authors: Yoshitaka Yano, Stuart L Beal, Lewis B Sheiner
    Abstract:

    The Posterior Predictive Check (PPC) is a model evaluation tool. It assigns a value (p PPC ) to the probability that the value of a given statistic computed from data arising under an analysis model is as or more extreme than the value computed from the real data themselves. If this probability is too small, the analysis model is regarded as invalid for the given statistic. Properties of the PPC for pharmacokinetic (PK) and pharmacodynamic (PD) model evaluation are examined herein for a particularly simple simulation setting: extensive sampling of a single individual's data arising from simple PK/PD and error models. To test the performance characteristics of the PPC, repeatedly, “real” data are simulated and for a variety of statistics, the PPC is applied to an analysis model, which may (null hypothesis) or may not (alternative hypothesis) be identical to the simulation model. Five models are used here: (PK1) mono-exponential with proportional error, (PK2) biexponential with proportional error, (PK2e) biexponential with additive error, (PD1) E max model with additive error under the logit transform, and (PD2) sigmoid E max model with additive error under the logit transform. Six simulation/analysis settings are studied. The first three, (PK1/PK1), (PK2/PK2), and (PD1/PD1) evaluate whether the PPC has appropriate type-I error level, whereas the second three (PK2/PK1), (PK2e/PK2), and (PD2/PD1) evaluate whether the PPC has adequate power. For a set of 100 data sets simulated/analyzed under each model pair according to a stipulated extensive sampling design, the p PPC is computed for a number of statistics in three different ways (each way uses a different approximation to the Posterior distribution on the model parameters). We find that in general; (i) The PPC is conservative under the null in the sense that for many statistics, prob(p PPC ≤α)<α for small α. With respect to such statistics, this means that useful models will rarely be regarded incorrectly as invalid. A high correlation of a statistic with the parameter estimates obtained from the same data used to compute the statistic (a measure of statistical “sufficiency”) tends to identify the most conservative statistics. (ii) Power is not very great, at least for the alternative models we tested, and it is especially poor with “statistics” that are in part a function of parameters as well as data. Although there is a tendency for nonsufficient statistics (as we have measured this) to have greater power, this is by no means an infallible diagnostic. (iii) No clear advantage for one or another method of approximating the Posterior distribution on model parameters is found.

  • a population pharmacokinetic pharmacodynamic analysis of repeated measures time to event pharmacodynamic responses the antiemetic effect of ondansetron
    Journal of Pharmacokinetics and Biopharmaceutics, 1999
    Co-Authors: Eugene H Cox, Christine Veyratfollet, Stuart L Beal, Eliane Fuseau, Saraswati Kenkare, Lewis B Sheiner
    Abstract:

    This paper presents and illustrates methodology for specifying, estimating, and evaluating a Predictive model for repeated measures time-to-event responses. The illustrative example specifies a model of the antiemetic effect vs. concentration relationship for the 5-HT3 antagonist ondansetron in the human ipecac model for emesis. A key part of this model is a time-dependent log hazard function for emesis that is increased by ipecac administration and decreased by ondansetron concentration. The model is fit using an approximate maximum likelihood method. The data consist of the time free of emeses and, for those individuals with emetic episodes, the time(s) of the episode(s). Model evaluation is accomplished using residual plots adapted to time-to-event data and a "Posterior Predictive Check" wherein observed data statistics are compared to those obtained from data simulated from the fitted model. The ondansetron concentration required to obtain a 50% reduction in the hazard of emesis is estimated to be 1.4 +/- 0.2 ng/ml, and the rate constant for elimination of ipecac-induced hazard is 1.5 +/- 0.2 hr-1.

Jeroen K Vermunt - One of the best experts on this subject based on the ideXlab platform.

  • Assessing Model Fit in Latent Class Analysis when Asymptotics do not hold
    2016
    Co-Authors: Jeroen K Vermunt
    Abstract:

    The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values are not valid when the sample size is not large and/or the analysed contingency table is sparse. Another problem is that for various other conceivable global and local fit measures, asymptotic distributions are not readily available. An alternative way to obtain the p-value for the statistic of interest is by constructing its empirical reference distribution using resampling techniques such as the parametric bootstrap or the Posterior Predictive Check (PPC). In the current paper, we show how to apply the parametric bootstrap and two versions of the PPC to obtain empirical p-values for a number of commonly used global and local fit statistics within the context of L

  • assessing model fit in latent class analysis when asymptotics do not hold
    Methodology: European Journal of Research Methods for The Behavioral and Social Sciences, 2015
    Co-Authors: Geert H Van Kollenburg, Joris Mulder, Jeroen K Vermunt
    Abstract:

    The application of latent class (LC) analysis involves evaluating the LC model using goodness-of-fit statistics. To assess the misfit of a specified model, say with the Pearson chi-squared statistic, a p-value can be obtained using an asymptotic reference distribution. However, asymptotic p-values are not valid when the sample size is not large and/or the analyzed contingency table is sparse. Another problem is that for various other conceivable global and local fit measures, asymptotic distributions are not readily available. An alternative way to obtain the p-value for the statistic of interest is by constructing its empirical reference distribution using resampling techniques such as the parametric bootstrap or the Posterior Predictive Check (PPC). In the current paper, we show how to apply the parametric bootstrap and two versions of the PPC to obtain empirical p-values for a number of commonly used global and local fit statistics within the context of LC analysis. The main difference between the PPC ...

Nicholas H. G. Holford - One of the best experts on this subject based on the ideXlab platform.

  • analysis of population pharmacokinetic data using nonmem and winbugs
    Journal of Biopharmaceutical Statistics, 2004
    Co-Authors: Stephen B. Duffull, Carl M J Kirkpatrick, Bruce Green, Nicholas H. G. Holford
    Abstract:

    ABSTRACT The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration–time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the Posterior distribution of the log-likelihood and a Posterior Predictive Check. The use of WinBUGS s...

  • analysis of population pharmacokinetic data using nonmem and winbugs
    Journal of Biopharmaceutical Statistics, 2004
    Co-Authors: Stephen B. Duffull, Carl M J Kirkpatrick, Bruce Green, Nicholas H. G. Holford
    Abstract:

    The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the Posterior distribution of the log-likelihood and a Posterior Predictive Check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the Posterior Predictive Check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.