| 1. |
- Maksimovic, M., et al.
(författare)
-
First observations and performance of the RPW instrument on board the Solar Orbiter mission
- 2021
-
Ingår i: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 656
-
Tidskriftsartikel (refereegranskat)abstract
- The Radio and Plasma Waves (RPW) instrument on the ESA Solar Orbiter mission is designed to measure in situ magnetic and electric fields and waves from the continuum up to several hundred kHz. The RPW also observes solar and heliospheric radio emissions up to 16 MHz. It was switched on and its antennae were successfully deployed two days after the launch of Solar Orbiter on February 10, 2020. Since then, the instrument has acquired enough data to make it possible to assess its performance and the electromagnetic disturbances it experiences. In this article, we assess its scientific performance and present the first RPW observations. In particular, we focus on a statistical analysis of the first observations of interplanetary dust by the instrument's Thermal Noise Receiver. We also review the electro-magnetic disturbances that RPW suffers, especially those which potential users of the instrument data should be aware of before starting their research work.
|
|
| 2. |
- Maksimovic, M., et al.
(författare)
-
The Solar Orbiter Radio and Plasma Waves (RPW) instrument
- 2020
-
Ingår i: Astronomy and Astrophysics. - : EDP SCIENCES S A. - 0004-6361 .- 1432-0746. ; 642
-
Tidskriftsartikel (refereegranskat)abstract
- The Radio and Plasma Waves (RPW) instrument on the ESA Solar Orbiter mission is described in this paper. This instrument is designed to measure in-situ magnetic and electric fields and waves from the continuous to a few hundreds of kHz. RPW will also observe solar radio emissions up to 16 MHz. The RPW instrument is of primary importance to the Solar Orbiter mission and science requirements since it is essential to answer three of the four mission overarching science objectives. In addition RPW will exchange on-board data with the other in-situ instruments in order to process algorithms for interplanetary shocks and type III langmuir waves detections.
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Nyberg, Joakim, 1978-, et al.
(författare)
-
Edoxaban Exposure-Response Analysis and Clinical Utility Index Assessment in Patients With Symptomatic Deep-Vein Thrombosis or Pulmonary Embolism
- 2016
-
Ingår i: CPT. - : Wiley. - 2163-8306. ; 5:4, s. 222-232
-
Tidskriftsartikel (refereegranskat)abstract
- Edoxaban exposure-response relationships from the phase III study evaluating edoxaban for prevention and treatment of venous thromboembolism (VTE) in patients with acute deep vein thrombosis (DVT) and/or pulmonary embolism (PE) were assessed by parametric time-to-event analysis. Statistical significant exposure-response relationships were recurrent VTE with hazard ratio (HR) based on average edoxaban concentration at steady state (C-av) (HRCav) 50.98 (i.e., change in the HR with every 1 ng/mL increase of C-av); the composite of recurrent DVT and nonfatal PE with HRC(av)50.99; and the composite of recurrent DVT, nonfatal PE, and all-cause mortality HRC(av)50.98, and all death using maximal edoxaban concentration (C-max) with HR (C-max) 50.99. No statistical significant exposure-response relationships were found for clinically relevant bleeding or major adverse cardiovascular event. Results support the recommendation of once-daily edoxaban 60 mg, and a reduced 30 mg dose in patients with moderate renal impairment, body weight <= 60 kg, or use of P-glycoprotein inhibitors verapamil or quinidine.
|
|
| 7. |
- Baverel, Paul G, et al.
(författare)
-
Predictive performance of internal and external validation procedures
-
Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Purpose: To compare estimates of predictive performance between internal (IV) and external data-splitting (EV) validation procedures. Methods: Datasets of different study size (n=6, 12, 24, 48, 96, 192, or 384 individuals) were simulated from a one compartment, first-order absorption, pharmacokinetic model and both parametric (FOCE), and nonparametric (NONP) parameter estimates were obtained in NONMEM. From these, three different validation procedures (IV, EV, and a population validation (PV)) were undertaken by means of numerical predictive checks (NPCs) to provide estimates of predictive performance, the PV procedure serving as a reference to assess performance of IV and EV. The predictive performance of NONP versus FOCE estimates was further assessed. Results: Estimates of predictive performance for predicting the median of the population distribution had in general significantly lower imprecision for IV than EV, with little bias for both procedures. For small study sizes, n=6-12 (FOCE) or n=6-24 (NONP), the tails of the population distribution were significantly more biased with IV than EV, but similar imprecision was obtained. The predictive performance for FOCE was similar or superior to that of NONP. Conclusions: Data-splitting is inferior to IV when evaluating predictive models to retain sufficient precision both in predictions and in estimates of predictive performance.
|
|
| 8. |
|
|
| 9. |
- Karlsson, Kristin C., et al.
(författare)
-
A population pharmacokinetic model of gabapentin developed in nonparametric adaptive grid and nonlinear mixed effects modeling
- 2009
-
Ingår i: Therapeutic Drug Monitoring. - 0163-4356 .- 1536-3694. ; 31:1, s. 86-94
-
Tidskriftsartikel (refereegranskat)abstract
- Gabapentin is used in analgesic treatment of neuropathic pain, and large interindividual variation has been observed in the pharmacokinetics (PK) of the drug. The aim of this study was to develop a population PK model for gabapentin appropriate for monitoring patients with neuropathic pain and for individualizing their dose regimens. Steady-state serum concentrations of gabapentin, distributed over a dosage interval, were obtained from 16 adult patients. Data were analyzed with an iterative 2-stage Bayesian and a nonparametric adaptive grid algorithm (NPAG) (USC*PACK) and with nonlinear mixed effects modeling (NONMEM). Compartmental population models for gabapentin PK were developed in NPAG and NONMEM using creatinine clearance and body weight as covariates. Bioavailability was included in the models as a function of dose by using a hyperbolic function derived from data previously reported in the literature. The mean population parameter estimates from the final NPAG model predicted individual serum concentrations reasonably well. The models developed in NONMEM provided additional information about the relevance of the various possible covariates and also allowed for further evaluation by simulation from the model. The population PK model may be utilized in the MM-USCPACK monitoring software (MM: multiple model dosage design) for predicting and achieving individually optimized steady-state serum concentrations of gabapentin.
|
|
| 10. |
- Karlsson, Kristin C., et al.
(författare)
-
Modeling subpopulations with the $MIXTURE subroutine in NONMEM : finding the individual probability of belonging to a subpopulation for the use in model analysis and improved decision making
- 2009
-
Ingår i: AAPS Journal. - : Springer. - 1550-7416. ; 11:1, s. 148-154
-
Tidskriftsartikel (refereegranskat)abstract
- In nonlinear mixed effects modeling using NONMEM, mixture models can be used for multimodal distributions of parameters. The fraction of individuals belonging to each of the subpopulations can be estimated, and the most probable subpopulation for each patient is output (MIXEST(k)). The objective function value (OFV) that is minimized is the sum of the OFVs for each patient (OFV(i)), which in turn is the sum across the k subpopulations (OFV(i,k)). The OFV(i,k) values can be used together with the total probability in the population of belonging to subpopulation k to calculate the individual probability of belonging to the subpopulation (IP(k)). Our objective was to explore the information gained by using IP(k) instead of or in addition to MIXEST(k) in the analysis of mixture models. Two real data sets described previously by mixture models as well as simulations were used to explore the use of IP(k) and the precision of individual parameter values based on IP(k) and MIXEST(k). For both real data-based mixture models, a substantial fraction (11% and 26%) of the patients had IP(k) values not close to 0 or 1 (IP(k) between 0.25 and 0.75). Simulations of eight different scenarios showed that individual parameter estimates based on MIXEST were less precise than those based on IP(k), as the root mean squared error was reduced for IP(k) in all scenarios. A probability estimate such as IP(k) provides more detailed information about each individual than the discrete MIXEST(k). Individual parameter estimates based on IP(k) should be preferable whenever individual parameter estimates are to be used as study output or for simulations.
|
|
