991 resultados para Marginal model


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Reduced re'nal function has been reported with tenofovir disoproxil fumarate (TDF). It is not clear whether TDF co-administered with a boosted protease inhibitor (PI) leads to a greater decline in renal function than TDF co-administered with a non-nucleoside reverse transcriptase inhibitor (NNRTI).Methods: We selected ail antiretroviral therapy-naive patients in the Swiss HIV Cohort Study (SHCS) with calibrated or corrected serum creatinine measurements starting antiretroviral therapy with TDF and either efavirenz (EFV) or the ritonavir-boosted PIs, lopinavir (LPV/r) or atazanavir (ATV/r). As a measure of renal function, we used the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation to estimate the glomerular filtration rate (eGFR). We calculated the difference in eGFR over time between two therapies using a marginal model for repeated measures. In weighted analyses, observations were weighted by the product of their point of treatment and censoring weights to adjust for differences both in the sort of patients starting each therapy and in the sort of patients remaining on each therapy over time.Results: By March 2011, 940 patients with at least one creatinine measurement on a first therapy with either TDF and EFV (n=484), TDF and LPVlr (n=269) or TDF and ATV/r (n=187) had been followed for a median of 1. 7, 1.2 and 1.3 years, respectively. Table 1 shows the difference in average estimated GFR (eGFR) over time since starting cART for two marginal models. The first model was not adjusted for potential confounders; the second mode! used weights to adjust for confounders. The results suggest a greater decline in renal function during the first 6 months if TDF is used with a PI rather than with an NNRTI, but no further difference between these therapies after the first 6 months. TDF and ATV/r may lead to a greater decline in the first 6 months than TDF and LPVlr.Conclusions: TDF co-administered with a boosted PI leads to a greater de cline in renal function over the first 6 months of therapy than TDF co-administered with an NNRTI; this decline may be worse with ATV/r than with LPV/r.

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Background: Reduced re'nal function has been reported with tenofovir disoproxil fumarate (TDF). It is not clear whether TDF co-administered with a boosted protease inhibitor (PI) leads to a greater decline in renal function than TDF co-administered with a non-nucleoside reverse transcriptase inhibitor (NNRTI).Methods: We selected ail antiretroviral therapy-naive patients in the Swiss HIV Cohort Study (SHCS) with calibrated or corrected serum creatinine measurements starting antiretroviral therapy with TDF and either efavirenz (EFV) or the ritonavir-boosted PIs, lopinavir (LPV/r) or atazanavir (ATV/r). As a measure of renal function, we used the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation to estimate the glomerular filtration rate (eGFR). We calculated the difference in eGFR over time between two therapies using a marginal model for repeated measures. In weighted analyses, observations were weighted by the product of their point of treatment and censoring weights to adjust for differences both in the sort of patients starting each therapy and in the sort of patients remaining on each therapy over time.Results: By March 2011, 940 patients with at least one creatinine measurement on a first therapy with either TDF and EFV (n=484), TDF and LPVlr (n=269) or TDF and ATV/r (n=187) had been followed for a median of 1. 7, 1.2 and 1.3 years, respectively. Table 1 shows the difference in average estimated GFR (eGFR) over time since starting cART for two marginal models. The first model was not adjusted for potential confounders; the second mode! used weights to adjust for confounders. The results suggest a greater decline in renal function during the first 6 months if TDF is used with a PI rather than with an NNRTI, but no further difference between these therapies after the first 6 months. TDF and ATV/r may lead to a greater decline in the first 6 months than TDF and LPVlr.Conclusions: TDF co-administered with a boosted PI leads to a greater de cline in renal function over the first 6 months of therapy than TDF co-administered with an NNRTI; this decline may be worse with ATV/r than with LPV/r.

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In this work we propose and analyze nonlinear elliptical models for longitudinal data, which represent an alternative to gaussian models in the cases of heavy tails, for instance. The elliptical distributions may help to control the influence of the observations in the parameter estimates by naturally attributing different weights for each case. We consider random effects to introduce the within-group correlation and work with the marginal model without requiring numerical integration. An iterative algorithm to obtain maximum likelihood estimates for the parameters is presented, as well as diagnostic results based on residual distances and local influence [Cook, D., 1986. Assessment of local influence. journal of the Royal Statistical Society - Series B 48 (2), 133-169; Cook D., 1987. Influence assessment. journal of Applied Statistics 14 (2),117-131; Escobar, L.A., Meeker, W.Q., 1992, Assessing influence in regression analysis with censored data, Biometrics 48, 507-528]. As numerical illustration, we apply the obtained results to a kinetics longitudinal data set presented in [Vonesh, E.F., Carter, R.L., 1992. Mixed-effects nonlinear regression for unbalanced repeated measures. Biometrics 48, 1-17], which was analyzed under the assumption of normality. (C) 2009 Elsevier B.V. All rights reserved.

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The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

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Clustered data analysis is characterized by the need to describe both systematic variation in a mean model and cluster-dependent random variation in an association model. Marginalized multilevel models embrace the robustness and interpretations of a marginal mean model, while retaining the likelihood inference capabilities and flexible dependence structures of a conditional association model. Although there has been increasing recognition of the attractiveness of marginalized multilevel models, there has been a gap in their practical application arising from a lack of readily available estimation procedures. We extend the marginalized multilevel model to allow for nonlinear functions in both the mean and association aspects. We then formulate marginal models through conditional specifications to facilitate estimation with mixed model computational solutions already in place. We illustrate this approach on a cerebrovascular deficiency crossover trial.

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We extended the standard neoclassical model of investment for the case of an open economy. Our model shows that risk premium not only creates a wedge between the marginal product of capital across countries but also reduces an economy`s savings rate. A riskier market thus presents a lower income per capita, ceteris paribus. Our empirical analysis, from 1950 to 2003, lends support to the conclusion that both risk and the correction for output price to investment ratio help to explain the differentials.

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This paper presents a method for estimating the posterior probability density of the cointegrating rank of a multivariate error correction model. A second contribution is the careful elicitation of the prior for the cointegrating vectors derived from a prior on the cointegrating space. This prior obtains naturally from treating the cointegrating space as the parameter of interest in inference and overcomes problems previously encountered in Bayesian cointegration analysis. Using this new prior and Laplace approximation, an estimator for the posterior probability of the rank is given. The approach performs well compared with information criteria in Monte Carlo experiments. (C) 2003 Elsevier B.V. All rights reserved.

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Background: Organs from the so-called marginal donors have been used with a significant higher risk of primary non function than organs retrieved from the optimal donors. We investigated the early metabolic changes and blood flow redistribution in splanchnic territory in an experimental model that mimics marginal brain-dead (BD) donor. Material/Methods: Ten dogs (21.3 +/- 0.9 kg), were subjected to a brain death protocol induced by subdural balloon inflation and observed for 30 min thereafter without ally additional interventions. Mean arterial and intracranial pressures, heart rate, cardiac output (CO), portal vein and hepatic artery blood flows (PVBF and HABF, ultrasonic flowprobe), and O(2)-derived variables were evaluated. Results: An increase in arterial pressure, CO, PVBF and HABF was observed after BD induction. At the end, an intense hypotension with normalization in CO (3.0 +/- 0.2 VS. 2.8 +/- 2.8 L/min) and PVBF (687 +/- 114 vs. 623 +/- 130 ml/min) was observed, whereas HABF (277 33 vs. 134 28 ml/min, p<0.005) remained lower than baseline values. Conclusions: Despite severe hypotension induced by sudden increase of intracranial pressure, the systemic and splanchnic blood flows were partially preserved without signs of severe hypoperfusion (i.e. hyperlactatemia). Additionally, the HABF was mostly negatively affected in this model of marginal BD donor. Our data suggest that not only the cardiac output, but the intrinsic hepatic microcirculatory mechanism plays a role in the hepatic blood flow control after BD.

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Construction of hydroelectric dams in tropical regions has been contributing significantly to forest fragmentation. Alterations at edges of forest fragments impact plant communities that suffer increases in tree damage and dead, and decreases in seedling recruitment. This study aimed to test the core-area model in a fragmented landscape caused by construction of a hydroelectric power plant in the Brazilian Amazon. We studied variations in forest structure between the margin and interiors of 17 islands of 8-100 hectares in the Tucuruí dam reservoir, in two plots (30 and >100m from the margin) per island. Mean tree density, basal area, seedling density and forest cover did not significantly differ between marginal and interior island plots. Also, no significant differences were found in liana density, dead tree or damage for margin and interior plots. The peculiar topographic conditions associated with the matrix habitat and shapes of the island seem to extend edge effects to the islands' centers independently of the island size, giving the interior similar physical microclimatic conditions as at the edges. We propose a protocol for assessing the ecological impacts of edge effects in fragments of natural habitat surrounded by induced (artificial) edges. The protocol involves three steps: (1) identification of focal taxa of particular conservation or management interest, (2) measurement of an "edge function" that describes the response of these taxa to induced edges, and (3) use of a "Core-Area Model" to extrapolate edge function parameters to existing or novel situations.

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Besley (1988) uses a scaling approach to model merit good arguments in commodity tax policy. In this paper, I question this approach on the grounds that it produces 'wrong' recommendations--taxation (subsidisation) of merit (demerit) goods--whenever the demand for the (de)merit good is inelastic. I propose an alternative approach that does not suffer from this deficiency, and derive the ensuing first and second best tax rules, as well as the marginal cost expressions to perform tax reform analysis.

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Actual tax systems do not follow the normative recommendations of yhe theory of optimal taxation. There are two reasons for this. Firstly, the informational difficulties of knowing or estimating all relevant elasticities and parameters. Secondly, the political complexities that would arise if a new tax implementation would depart too much from current systems that are perceived as somewhat egalitarians. Hence an ex-novo overhaul of the tax system might just be non-viable. In contrast, a small marginal tax reform could be politically more palatable to accept and economically more simple to implement. The goal of this paper is to evaluate, as a step previous to any tax reform, the marginal welfare cost of the current tax system in Spain. We do this by using a computational general equilibrium model calibrated to a point-in-time micro database. The simulations results show that the Spanish tax system gives rise to a considerable marginal excess burden. Its order of magnitude is of about 0.50 money units for each additional money unit collected through taxes.

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This paper contributes to the on-going empirical debate regarding the role of the RBC model and in particular of technology shocks in explaining aggregate fluctuations. To this end we estimate the model’s posterior density using Markov-Chain Monte-Carlo (MCMC) methods. Within this framework we extend Ireland’s (2001, 2004) hybrid estimation approach to allow for a vector autoregressive moving average (VARMA) process to describe the movements and co-movements of the model’s errors not explained by the basic RBC model. The results of marginal likelihood ratio tests reveal that the more general model of the errors significantly improves the model’s fit relative to the VAR and AR alternatives. Moreover, despite setting the RBC model a more difficult task under the VARMA specification, our analysis, based on forecast error and spectral decompositions, suggests that the RBC model is still capable of explaining a significant fraction of the observed variation in macroeconomic aggregates in the post-war U.S. economy.