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Effect Parameters (effect + parameter)
Selected AbstractsOn the nature of modified composite electrical effect parametersJOURNAL OF PHYSICAL ORGANIC CHEMISTRY, Issue 10 2003Marvin Charton Abstract It is shown that a correlation of some property, reactivity or biological activity with pure parameters can also be carried out with composite parameters to produce a model with comparable statistics. Modified composite electrical effect parameters can be obtained from known composite electrical effect parameters by means of an algorithm such as ,X,,=,(,X + c)m in which ,X, is the modified composite electrical effect parameter (MCEEP), ,X the original composite electrical effect parameter, c a constant and m an exponent. MCEEPs were calculated from this equation with c equal to 2 and m ranging from ,4 to 4 when ,X is ,m or ,p, and from ,2 to 2 when it is ,p+. They were used to model 10 sets of chemical reactivities that had previously been correlated with the pure parameters ,1, ,d and ,e, which represent the localized (field), intrinsic delocalized (resonance) and electronic demand sensitivity electrical effects, respectively. In most cases both the pure and the modified composite parameters give comparable fits to the data as measured by the values of 100R2, in accord with the prediction. The composition of the MCEEPs was a linear function of m. The advantage in using pure parameters is that they are readily interpretable. Correlations with composite parameters can be interpreted only if their composition has been determined. These results provide an understanding of the way in which topological parameters work. Copyright © 2003 John Wiley & Sons, Ltd. [source] Use of instrumental variables in the presence of heterogeneity and self-selection: an application to treatments of breast cancer patientsHEALTH ECONOMICS, Issue 11 2007Anirban Basu Abstract Instrumental variable (IV) methods are widely used in the health economics literature to adjust for hidden selection biases in observational studies when estimating treatment effects. Less attention has been paid in the applied literature to the proper use of IVs if treatment effects are heterogeneous across subjects and individuals select treatments based on expected idiosyncratic gains or losses from treatments. In this paper we compare conventional IV analysis with alternative approaches that use IVs to estimate treatment effects in models with response heterogeneity and self-selection. Instead of interpreting IV estimates as the effect of treatment at an unknown margin of patients, we identify the marginal patients and we apply the method of local IVs to estimate the average treatment effect and the effect on the treated on 5-year direct costs of breast-conserving surgery and radiation therapy compared with mastectomy in breast cancer patients. We use a sample from the Outcomes and Preferences in Older Women, Nationwide Survey which is designed to be representative of all female Medicare beneficiaries (aged 67 or older) with newly diagnosed breast cancer between 1992 and 1994. Our results reveal some of the advantages and limitations of conventional and alternative IV methods in estimating mean treatment effect parameters. Copyright © 2007 John Wiley & Sons, Ltd. [source] Estimating treatment effects in randomized clinical trials with non-compliance: the impact of maternal smoking on birthweightHEALTH ECONOMICS, Issue 5 2001Barton H. Hamilton Abstract This paper assesses the causal impact of late-term (8th month) maternal smoking on birthweight using data from a randomized clinical trial, in which some women were encouraged not to smoke, while others were not. The estimation of treatment effects in this case is made difficult as a result of the presence of non-compliers, women who would not change their smoking status, regardless of the receipt of encouragement. Because these women are not at risk of changing treatment status, treatment effect distributions may be difficult to construct for them. Consequently, the paper focuses on obtaining the distribution of treatment impacts for the sub-set of compliers found in the data. Because compliance status is not observed for all subjects in the sample, a Bayesian finite mixture model is estimated that recovers the treatment effect parameters of interest. The complier average treatment effect implies that smokers give birth to infants weighing 348 g less than those of non-smokers, on average, although the 95% posterior density interval contains zero. The treatment effect is stronger for women who were moderate smokers prior to pregnancy, implying a birthweight difference of 430 g. However, the model predicts that only about 22% of the women in the sample were at risk of changing their smoking behaviour in response to encouragement to quit. Copyright © 2001 John Wiley & Sons, Ltd. [source] On the nature of modified composite electrical effect parametersJOURNAL OF PHYSICAL ORGANIC CHEMISTRY, Issue 10 2003Marvin Charton Abstract It is shown that a correlation of some property, reactivity or biological activity with pure parameters can also be carried out with composite parameters to produce a model with comparable statistics. Modified composite electrical effect parameters can be obtained from known composite electrical effect parameters by means of an algorithm such as ,X,,=,(,X + c)m in which ,X, is the modified composite electrical effect parameter (MCEEP), ,X the original composite electrical effect parameter, c a constant and m an exponent. MCEEPs were calculated from this equation with c equal to 2 and m ranging from ,4 to 4 when ,X is ,m or ,p, and from ,2 to 2 when it is ,p+. They were used to model 10 sets of chemical reactivities that had previously been correlated with the pure parameters ,1, ,d and ,e, which represent the localized (field), intrinsic delocalized (resonance) and electronic demand sensitivity electrical effects, respectively. In most cases both the pure and the modified composite parameters give comparable fits to the data as measured by the values of 100R2, in accord with the prediction. The composition of the MCEEPs was a linear function of m. The advantage in using pure parameters is that they are readily interpretable. Correlations with composite parameters can be interpreted only if their composition has been determined. These results provide an understanding of the way in which topological parameters work. Copyright © 2003 John Wiley & Sons, Ltd. [source] An application of hierarchical regression in the investigation of multiple paternal occupational exposures and neuroblastoma in offspring,AMERICAN JOURNAL OF INDUSTRIAL MEDICINE, Issue 5 2001Anneclaire J. De Roos MPH Abstract Background We used hierarchical regression to study the effects of 46 paternal occupational exposures on the incidence of neuroblastoma in offspring. Methods The study population included 405 cases and 302 controls. The effect of each exposure was estimated using both conventional maximum likelihood and hierarchical regression. Results Using hierarchical regression, overall precision was greatly enhanced compared to the conventional analysis. In addition, adjustment of effect estimates based on prespecified prior distributions of the true effect parameters allowed a more consistent interpretation across the entire panel of exposures. Estimates for several metals and solvents were shrunk close to the null value, whereas estimates for several thinner solvents, diesel fuel, solders, wood dust, and grain dust remained moderately elevated. Conclusions Hierarchical regression may mitigate some of the problems of the conventional approach by controlling for correlated exposures, enhancing the precision of estimates, and providing some adjustment of estimates based on prior knowledge. Am. J. Ind. Med. 39:477,486, 2001. © 2001 Wiley-Liss, Inc. [source] Bayesian Inference in Semiparametric Mixed Models for Longitudinal DataBIOMETRICS, Issue 1 2010Yisheng Li Summary We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of a cubic smoothing spline, and the random effect distribution using a normal distribution and alternatively a nonparametric Dirichlet process (DP) prior. When the random effect distribution is assumed to be normal, we propose a uniform shrinkage prior (USP) for the variance components and the smoothing parameter. When the random effect distribution is modeled nonparametrically, we use a DP prior with a normal base measure and propose a USP for the hyperparameters of the DP base measure. We argue that the commonly assumed DP prior implies a nonzero mean of the random effect distribution, even when a base measure with mean zero is specified. This implies weak identifiability for the fixed effects, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function. We propose an adjustment using a postprocessing technique. We show that under mild conditions the posterior is proper under the proposed USP, a flat prior for the fixed effect parameters, and an improper prior for the residual variance. We illustrate the proposed approach using a longitudinal hormone dataset, and carry out extensive simulation studies to compare its finite sample performance with existing methods. [source] Population pharmacokinetic analysis of varenicline in adult smokersBRITISH JOURNAL OF CLINICAL PHARMACOLOGY, Issue 5 2009Patanjali Ravva WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT? , Several clinical pharmacology studies have characterized the pharmacokinetics of varenicline in young adult and elderly smokers and subjects with impaired renal function. , Varenicline pharmacokinetics is linear over the recommended dose range. , Varenicline total clearance is linearly related to its renal clearance. , Both are progressively reduced as renal function declines, which results in a progressive increase in varenicline systemic exposure and prolonged half-life. WHAT THIS STUDY ADDS? , This work provides an integrated model-based analysis of varenicline pharmacokinetics across multiple studies in the target patient population. , The model describes the impact of patient-specific covariates, such as renal function, and provides a rationale for dose adjustment. , The resulting model also provides a means to predict individual-specific drug exposures to clinical responses in subsequent analyses. AIMS To characterize the population pharmacokinetics of varenicline and identify factors leading to its exposure variability in adult smokers. METHODS Data were pooled from nine clinical studies consisting of 1878 subjects. Models were developed to describe concentration,time profiles across individuals. Covariates were assessed using a full model approach; parameters and bootstrap 95% confidence intervals (CI) were estimated using nonlinear mixed effects modelling. RESULTS A two-compartment model with first-order absorption and elimination best described varenicline pharmacokinetics. The final population parameter estimates (95% CI) were: CL/F, 10.4 l h,1 (10.2, 10.6); V2/F, 337 l (309, 364); V3/F, 78.1 l (61.9, 98.9); Q/F, 2.08 l h,1 (1.39, 3.79); Ka, 1.69 h,1 (1.27, 2.00); and Alag, 0.43 h (0.37, 0.46). Random interindividual variances were estimated for Ka[70% coefficient of variation (CV)], CL/F (25% CV), and V2/F (50% CV) using a block covariance matrix. Fixed effect parameters were precisely estimated [most with % relative standard error < 10 and all with % relative standard error < 25], and a visual predictive check indicated adequate model performance. CL/F decreased from 10.4 l h,1 for a typical subject with normal renal function (CLcr = 100 ml min,1) to 4.4 l h,1 for a typical subject with severe renal impairment (CLcr = 20 ml min,1), which corresponds to a 2.4-fold increase in daily steady-state exposure. Bodyweight was the primary predictor of variability in volume of distribution. After accounting for renal function, there was no apparent effect of age, gender or race on varenicline pharmacokinetics. CONCLUSIONS Renal function is the clinically important factor leading to interindividual variability in varenicline exposure. A dose reduction to 1 mg day,1, which is half the recommended dose, is indicated for subjects with severe renal impairment. [source] | ||||||||||