Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments

Background. Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. Method. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). Result. The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Conclusion. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.

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POS0889 CO PRESCRIPTION OF ANTI-ACID THERAPY REDUCES THE BIO AVAILABILITY OF MYCOPHENOLATE MOFETIL IN SYSTEMIC SCLEROSIS PATIENTS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3898 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 700.2-701

Author(s):

G. Alex ◽

S. K C ◽

D. Reachel Varghese ◽

S. Babu A S ◽

R. Reji ◽

...

Keyword(s):

Mycophenolate Mofetil ◽

Systemic Sclerosis ◽

Area Under Curve ◽

The Other ◽

Linear Mixed Effect Model ◽

Mixed Effect Model ◽

Mixed Effect ◽

Transplant Patients ◽

Effect Model ◽

The Mean

Background:Mycophenolate mofetil (MMF) is an effective treatment option for systemic sclerosis (SSC). However, many patients require co administration of proton pump inhibitors (PPI) or H2 receptor blockers (HRB) because of significant gastrointestinal manifestations in SSC. Co-treatment with PPI or HRB have shown to be associated with reduced drug exposure in post-transplant patients.1, 2 There is scarcity of data among patients with SSC. We evaluated the drug concentration of MMF over 12 hours of exposure and assessed the impact of ranitidine and PPI in twenty patients with SSC.Objectives:To assess the effect of esomeprazole or ranitidine on the bioavailability of MMF in SSC patients who are on a stable dose of MMF.Methods:Twenty SSC patients, who were on a stable dose of MMF (1.5-3 g) for the past 3 months were selected for the study after obtaining informed written consent. All patients were given either MMF (without PPI or HRB), MMF + esomeprazole, MMF + ranitidine for one month each. At the end of each month, EDTA plasma samples were collected at various time points including 0, 1/2, 1, 1½, 2, 2½, 3, 4, 5, 6, 8 and 12 hours following drug administration to determine the 12-hour area under curve (AUC) of mycophenolic acid (MPA) levels. Estimation of MPA levels was carried out using reverse phase high performance liquid chromatography (HPLC). Total gastrointestinal score was calculated at the end of each month using UCLA Scleroderma Clinical Trial Consortium GIT 2.0 scoring. To compare the mean AUC, linear mixed effect model was fit by considering treatment as the fixed effect and subject as the random effect. MMF was set as the reference treatment for the other three treatments and these were analysed together using Linear mixed effect model.Results:All patients were females with mean age of 45 years. Addition of either ranitidine or esomeprazole significantly reduced the mean AUC and C max of the MMF over 12-hour time period. On the other hand, PPI or HRB helped in reduction of the total GI score at the end of 1 month. Details of pharmacokinetics are depicted in the table 1.Table 1.Pharmacokinetics and GI score with MMF in combination with PPI / HRBMMFMMF+ RMMF + EpAUCmean (95% CI)67.97 (62.73, 73.20)53.04 (44.80, 61.27)45.69 (41.10, 50.28)<0.001*T- MAXmean (95% CI)42.00 (33.60, 50.40)46.50 (32.48, 60.52)79.50 (58.99, 100.01)<0.001*C-MAXmean (95% CI)29.61(26.74, 32.48)15.14 (11.32, 18.97)12.62 (10.58, 14.66)<0.001*Mean GI scoremean (95% CI)0.28 (0.15,0.40)0.19 (0.09, 0.30)0.14 (0.06,0.23)0.009AUC, area under curve Mycophenolic acid; C-MAX, maximum concentration of MPA in 12 hours following MMF; CI confidence interval;Mean GI score, UCLA Scleroderma Clinical Trial Consortium GIT 2.0 scoring; MMF, mycophenolate mofetil; MMF+E, mycophenolate mofetil + esomeprazole; MMF+R, mycophenolate mofetil+ ranitidine;*p value < 0.05 considered as significantConclusion:As co administration of PPI or HRB can significantly reduce the bioavailability of MMF in patients with systemic sclerosis. To avoid therapeutic failure of MMF drug level monitoring is essential when these agents re prescribed with MMF.References:[1]Schaier M, Scholl C, Scharpf D, Hug F, Bönisch-Schmidt S, Dikow R, et al. Proton pump inhibitors interfere with the immunosuppressive potency of mycophenolate mofetil. Rheumatology (Oxford, England). 2010;49:2061–7.[2]Rissling O, Glander P, Hambach P, Mai M, Brakemeier S, Klonower D, et al. No relevant pharmacokinetic interaction between pantoprazole and mycophenolate in renal transplant patients: a randomized crossover study. British Journal of Clinical Pharmacology. 2015;80:1086–96.Disclosure of Interests:None declared

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Concord begets concord: A Bayesian model of nominal concord typology

Proceedings of the Linguistic Society of America ◽

10.3765/plsa.v6i1.4988 ◽

2021 ◽

Vol 6 (1) ◽

pp. 541

Author(s):

Kyle Mahowald ◽

Dan Jurafsky ◽

Mark Norris

Keyword(s):

Bayesian Model ◽

Functional Role ◽

Mixed Effect Model ◽

Mixed Effect ◽

Quantitative Evidence ◽

Effect Model ◽

Different Types ◽

Future Work ◽

Number Case

Nominal concord is a phenomenon whereby nominal modifiers (e.g., adjectives, demonstratives, numerals) agree with their nominals along various dimensions (e.g., gender, number, case, definiteness). Here, drawing on a rich and typologically diverse database of nominal concord (Norris 2020), we build a Bayesian mixed effect model of nominal concord. Specifically, we consider two competing hypotheses regarding the statistical relationship between different types of concord within a language: (1) concord begets concord: the presence of some type of concord in a language makes it more likely that it has other types of concord vs. (2) a little concord goes a long way: if a language has some kind of concord, it is less likely to have other types of concord. We present evidence strongly in favor of the first hypothesis, that concord begets concord. Languages with nominal concord tend to have concord in more than one place and of more than one type. Using posterior draws from our model, we also provide quantitative evidence for a number of the tendencies described by Norris (2019a). Future work will build on this model to understand the functional role of nominal concord in language systems, how it evolves, and how it co-evolves with other typological features.

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Covariance Generalized Linear Models: An Approach for Quantifying Uncertainty in Tree Stem Taper Modeling

Forest Science ◽

10.1093/forsci/fxab037 ◽

2021 ◽

Author(s):

Luan Demarco Fiorentin ◽

Wagner Hugo Bonat ◽

Allan Libanio Pelissari ◽

Sebastião do Amaral Machado ◽

Saulo Jorge Téo

Keyword(s):

Covariance Matrix ◽

Confidence Intervals ◽

Generalized Linear Models ◽

Linear Models ◽

Mixed Effect Model ◽

Mixed Effect ◽

Stem Taper ◽

Effect Model ◽

The Mean ◽

Tree Stem

Abstract A natural dependence among diameters measured within-tree is expected in taper data due to the hierarchical structure. The aim of this paper was to introduce the covariance generalized linear model (CGLM) framework in the context of forest biometrics for Pinus taeda stem form modeling. The CGLMs are based on marginal specification, which requires a definition of the mean and covariance components. The tree stem mean profiles were modeled by a nonlinear segmented model. The covariance matrix was built considering four strategies of linear combinations of known matrices, which expressed the variance or correlations among observations. The first strategy modeled only the variance of the diameters over the stem as a function of covariates, the second modeled correlation among observations, the third was defined based on a random walk model, the fourth was based on a structure similar to a mixed-effect model with a marginal specification, and the fourth was a traditional mixed-effect model. Mean squared error and bias showed that the approaches were similar for describing the mean profile for fitting and validation dataset. However, uncertainties expressed by confidence intervals of the relative diameters were significant and related to the matrix covariance structures of the CGLMs. Study Implications: We proposed stem taper modeling based on a new class of statistical models. Covariance generalized linear models allow quantification of the stem dynamic by using a nonlinear model. Uncertainty estimates are performed on a covariance matrix given by a linear combination of known matrices. The matrices enable modeling of the nonconstant variance as well as the several correlation patterns, resulting in a framework more flexible and robust than traditional approaches usually applied for stem taper modeling. For practical purposes, uncertainty modeling can improve forest management planning, because the production limits by timber assortments are more reliable due to the confidence intervals derived from an appropriate uncertainty analysis.

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Evidence of a range expansion in sunfish from 47 years of coastal sightings

Marine Biology ◽

10.1007/s00227-021-04005-8 ◽

2022 ◽

Vol 169 (2) ◽

Author(s):

Olga Lyashevska ◽

Deirdre Brophy ◽

Steve Wing ◽

David G. Johns ◽

Damien Haberlin ◽

...

Keyword(s):

Range Expansion ◽

Sea Surface ◽

Linear Mixed Effect Model ◽

Mixed Effect Model ◽

Mixed Effect ◽

Ocean Sunfish ◽

Science Projects ◽

Effect Model ◽

The Mean

AbstractAlmost nothing is known about the historical abundance of the ocean sunfish. Yet as an ecologically and functionally important taxa, understanding changes in abundance may be a useful indicator of how our seas are responding to anthropogenic changes including overfishing and climate change. Within this context, sightings from a coastal bird observatory (51.26$$^\circ$$ ∘ N, 9.30$$^\circ$$ ∘ W) over a 47 year period (from April to October 1971–2017) provided the first long-term index of sunfish abundance. Using a general linear mixed effect model with a hurdle to deal with imperfect detectability and to model trends, a higher probability of detecting sunfish was found in the 1990s and 2000s. Continuous Plankton Recorder (CPR) phytoplankton color indices and the annual mean position of the 13 $$^{\circ }$$ ∘ C sea surface isotherm were significantly correlated with the probability of detecting sunfish. An increase in siphonophore abundance (as measured by the CPR) was also documented. However, this increase occurred 10–15 years after the sunfish increase and was not significantly correlated with sunfish abundance. Our results suggest that the observed increase in sunfish sightings is evidence of a range expansion because it was significantly correlated with the mean position of the 13 $$^{\circ }$$ ∘ C isotherm which moved northwards by over 200 km. Furthermore, the observed increase in sunfish occured 10 years before sunfish sightings are documented in Icelandic and Norwegian waters, and was concurrent with well-known range expansions for other fish species during the 1990s. This study demonstrates how sustained citizen science projects can provide unique insights on the historical abundance of this enigmatic species.

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