Kernel-Based Visual Hazard Comparison (kbVHC): a Simulation-Free Diagnostic for Parametric Repeated Time-to-Event Models

Goulooze, Sebastiaan C.; Välitalo, Pyry A. J.; Knibbe, Catherijne A. J.; Krekels, Elke H. J.

doi:10.1208/s12248-017-0162-9

Cited by 8 publications

(22 citation statements)

References 17 publications

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“…The degree of between-subject variability was set to a coefficient of variation of 75% in all scenarios, which corresponds with a variance of η of 0.45. h pop was set to either 0.05 or 0.005 h −1 , to simulate scenarios with either a relatively high or low number of events per subject, respectively. These values are expected to result in a low or high degree of shrinkage of the EBEs, as we have previously shown shrinkage to be associated to the amount of events per subject, likely because a lower amounts of events in an individual yields less information on an individual level (15).…”

Section: Rtte Simulation and Modelmentioning

confidence: 93%

Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools

Goulooze

Krekels

Hankemeier

et al. 2018

AAPS J

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During covariate modeling in pharmacometrics, computational time can be reduced by using a fast preselection tool to identify a subset of promising covariates that are to be tested with the more computationally demanding likelihood ratio test (LRT), which is considered to be the standard for covariate selection. There is however a lack of knowledge on best practices for covariate (pre)selection in pharmacometric repeated time-to-event (RTTE) models. Therefore, we aimed to systematically evaluate the performance of three covariate (pre)selection tools for RTTE models: the likelihood ratio test (LRT), the empirical Bayes estimates (EBE) test, and a novel Schoenfeld-like residual test. This was done in simulated datasets with and without a Btrue^time-constant covariate, and both in the presence and absence of high EBE shrinkage. In scenarios with a Btrue^covariate effect, all tools had comparable power to detect this effect. In scenarios without a Btrue^covariate effect, the false positive rates of the LRT and the Schoenfeld-like residual test were slightly inflated to 5.7% and 7.2% respectively, while the EBE test had no inflated false positive rate. The presence of high EBE shrinkage (> 40%) did not affect the performance of any of the covariate (pre)selection tools. We found the EBE test to be a fast and accurate tool for covariate preselection in RTTE models. The novel Schoenfeld-like residual test proposed here had a similar performance in the tested scenarios and might be applied more readily to time-varying covariates, such as drug concentration and dynamic biomarkers.

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Section: Rtte Simulation and Modelmentioning

confidence: 93%

Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools

Goulooze

Krekels

Hankemeier

et al. 2018

AAPS J

Self Cite

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“…Therefore, we used the kernel-based visual hazard comparison (kbVHC) as a model evaluation tool, which compares a kernel-based nonparametric hazard estimate of the data with the mean hazard of the repeated time-to-event model over time. 20 While the nonparametric hazard estimate may be smoother than the modelpredicted hazard, the model-predicted hazard should display a similar trend of the hazard over time with the nonparametric hazard estimate. CV target , the parameter that controls the smoothness of the nonparametric hazard estimate of the kbVHC, was set to 25%.…”

Section: Pharmacokinetic-pharmacodynamic Model Developmentmentioning

confidence: 98%

“…CV target , the parameter that controls the smoothness of the nonparametric hazard estimate of the kbVHC, was set to 25%. 20…”

Section: Pharmacokinetic-pharmacodynamic Model Developmentmentioning

confidence: 99%

“…Figure 4 shows the model evaluation of the final model using the kbVHC method, 20 in which the nonparametric hazard estimate (representing the observations) is compared with the mean hazard of the model (representing model predictions). The figure shows for the nonparametric hazard estimate of the data a drop in the hazard for morphine rescue between 12 and 18 hours, and an elevated hazard between 24 and 40 hours, which is the period after most morphine infusions are stopped.…”

Section: Final Parameter Estimates Can Be Found Inmentioning

confidence: 99%

“…Figure 4. kbVHC of the final PK-PD model. A, Model evaluation of the final PK-PD model by visual comparison of the nonparametric hazard and the model-predicted hazard using the kbVHC 20. The solid red line depicts the mean of the model-predicted individual hazard estimates of all noncensored individuals while the black dashed line and the gray shaded area depict the kernel estimate of the hazard rate in the data and its 95% confidence interval.…”

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confidence: 99%

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Predicting Unacceptable Pain in Cardiac Surgery Patients Receiving Morphine Maintenance and Rescue Doses: A Model-Based Pharmacokinetic-Pharmacodynamic Analysis

Goulooze

Krekels

Saleh

et al. 2020

Anesthesia &Amp; Analgesia

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GLOSSARY AIC = Akaike information criterion; Chr.Analg = chronic use of analgesics; CI = confidence interval; EFF morphine = exponential effect of morphine concentration; ICU = intensive care unit; IQR = interquartile range; kbVHC = kernel-based visual hazard comparison; M3G = morphine-3-glucuronide; NRS = numerical rating scale; OPCIC = optimization of procedural pain control in intensive care unit patients; PK-PD = pharmacokinetic-pharmacodynamic; RSE = relative standard error; RTTE = repeated time-to-event; SNP = single nucleotide polymorphism; WDFY4 = WD repeat-and FYVE domain-containing protein 4 KEY POINTS• Question: Can morphine concentrations be related to the hazard of unacceptable pain following cardiac surgery using repeated time-to-event modeling? • Finding: A model was developed that predicts the hazard of unacceptable pain using the morphine concentration, time after surgery, and time of day. • Meaning: The low morphine concentrations that result from weaning may contribute to the high incidence of unacceptable pain following cardiac surgery.BACKGROUND: Optimal analgesic treatment following cardiac surgery is crucial for both patient comfort and successful postoperative recovery. While knowledge of both the pharmacokinetics and pharmacodynamics of analgesics is required to predict optimal drug dosing, models quantifying the pharmacodynamics are scarce. Here, we quantify the pharmacodynamics of morphine by modeling the need for rescue morphine to treat unacceptable pain in 118 patients after cardiac surgery. METHODS:The rescue morphine event data were analyzed with repeated time-to-event (RTTE) modeling using NONMEM. Postoperative pain titration protocol consisted of continuous morphine infusions (median duration 20.5 hours) with paracetamol 4 times daily and rescue morphine in case of unacceptable pain (numerical rating scale ≥4). RESULTS: Patients had a median age of 73 years (interquartile range [IQR]: 63-77) and median bodyweight of 80 kg (IQR: 72-90 kg). Most patients (55%) required at least 1 rescue morphine dose. The hazard for rescue morphine following cardiac surgery was found to be significantly influenced by time after surgery, a day/night cycle with a peak at 23:00 (95% confidence interval [CI], 19:35-02:03) each day, and an effect of morphine concentration with 50% hazard reduction at 9.3 ng•mL −1 (95% CI, 6.7-16). CONCLUSIONS:The pharmacodynamics of morphine after cardiac surgery was successfully quantified using RTTE modeling. Future studies can be used to expand the model to better predict morphine's pharmacodynamics on the individual level and to include the pharmacodynamics of other analgesics so that improved postoperative pain treatment protocols can be developed.

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Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

Wijk

Simonsson

2022

CPT Pharmacom & Syst Pharma

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Parametric time‐to‐event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time‐to‐event analysis starting with graphical exploration by Kaplan–Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel‐based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log‐normal, log‐logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time‐to‐event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time‐to‐event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time‐to‐event modeling.

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Kernel-Based Visual Hazard Comparison (kbVHC): a Simulation-Free Diagnostic for Parametric Repeated Time-to-Event Models

Cited by 8 publications

References 17 publications

Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools

Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools

Predicting Unacceptable Pain in Cardiac Surgery Patients Receiving Morphine Maintenance and Rescue Doses: A Model-Based Pharmacokinetic-Pharmacodynamic Analysis

Finding the right hazard function for time‐to‐event modeling: A tutorial and Shiny application

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