DROPOUTS IN LONGITUDINAL STUDIES: DEFINITIONS AND MODELS

In this paper, we consider statistical tests for inter-subject and total variabilities between treatments under crossover designs. Since estimators of variance components for inter-subject variability and total variability in crossover design are not independent, the usual F-test cannot be applied. Alternatively, we propose a test based on the concept of the extension of the modified large sample method to compare inter-subject variability and total variability between treatments under a 2×2mreplicated crossover design. An asymptotic power of the proposed test is derived. A sensitivity analysis is performed based on the asymptotic power to determine how the power changes with respect to various parameters such as inter-subject correlation and intra-class correlation. Also the two methods for sample size calculation for testing total variability under 2×4 crossover design are discussed. The method based on the Fisher–Cornish inversion shows better performance than the method based on the normal approximation. Several simulation studies were conducted to investigate the finite sample performance of the proposed test. Our simulation results show that the proposed test can control type I error satisfactorily.     

Bayesian information criterion for longitudinal and clustered data

When a number of models are fit to the same data set, one method of choosing the 'best' model is to select the model for which Akaike's information criterion (AIC) is lowest. AIC applies when maximum likelihood is used to estimate the unknown parameters in the model. The value of -2 log likelihood for each model fit is penalized by adding twice the number of estimated parameters. The number of estimated parameters includes both the linear parameters and parameters in the covariance structure. Another criterion for model selection is the Bayesian information criterion (BIC). BIC penalizes -2 log likelihood by adding the number of estimated parameters multiplied by the log of the sample size. For large sample sizes, BIC penalizes -2 log likelihood much more than AIC making it harder to enter new parameters into the model. An assumption in BIC is that the observations are independent. In mixed models, the observations are not independent. This paper develops a method for calculating the 'effective sample size' for mixed models based on Fisher's information. The effective sample size replaces the sample size in BIC and can vary from the number of subjects to the number of observations. A number of error models are considered based on a general mixed model including unstructured, compound symmetry.     

High-sensitivity C-reactive protein as a serum predictor of nonalcoholic fatty liver disease based on the Akaike Information Criterion scoring system in the general Japanese population

Background  High-sensitivity C-reactive protein (hs-CRP) has been developed and used as a marker to predict coronary vascular diseases in metabolic syndrome (MS). We investigated whether serum hs-CRP concentration was associated with nonalcoholic fatty liver disease (NAFLD) based on the Akaike Information Criterion (AIC) scoring system, using patients from the human dry dock program. Methods  From 2004 to 2005, 1254 subjects visited our human dry dock annual checkup program. We excluded from this study individuals with markers of viral hepatitis and those whose alcohol consumption was more than 20 g/week. Finally, 230 subjects (93 men and 137 women) were investigated. Serum hs-CRP concentrations were measured using a highly sensitive latex agglutination assay system. The AIC scoring system with the CATDAP-20 program was introduced to evaluate the parameters that are present frequently in NAFLD. Results  NAFLD was diagnosed by ultrasound sonography in 35.4% of the men and 18.9% of the women. High serum hs-CRP concentrations were observed in women with NAFLD (normal: NAFLD = 0.45:1.47 mg/l, P < 0.05). Body mass index (BMI), waist circumference, and body weight had the three lowest AIC score (P = 4.5e⁻¹⁹ to 2.6e⁻¹⁶). hs-CRP was the third lowest variable among the serum markers associated with NAFLD (P = 2.3e⁻⁶) In addition, the hs-CRP concentration was correlated strongly with triglyceride values in females with NAFLD and with fasting blood glucose, HbA1c, and waist/hip ratio in males with NAFLD (P < 0.05). Conclusions  The serum hs-CRP concentration was a strong predictor for NAFLD with a low AIC score and correlated with serum markers that indicated lipid and glucose metabolism.     

Two-compartment dispersion model for analysis of organ perfusion system of drugs by fast inverse laplace transform (FILT)

A dispersion model developed in Chromatographic theory is applied to the analysis of the elution profile in the liver perfusion system of experimental animals. The equation for the dispersion model with the linear nonequilibrium partition between the perfusate and an organ tissue is derived in the Laplace-transformed form, and the fast inverse Laplace transform (FILT) is introduced to the pharmacokinetic field for the manipulation of the transformed equation. By the analysis of the nonlinear least squares method associated with FILT, this model (two-compartment dispersion model) is compared to the model with equilibrium partition between the perfusate and the liver tissue (one-compartment dispersion model) for the outflow curves of ampicillin and oxacillin from the rat liver. The model estimation by Akaike's information criterion (AIC) suggests that the two-compartment dispersion model is more proper than the one-compartment dispersion model to mathematically describe the local disposition of these drugs in the perfusion system. The blood space in the liver, V_B, and the dispersion number D_N are estimated at 1.30 ml (±0.23 SD) and 0.051 (±0.023 SD), respectively, both of which are independent of the drugs. The efficiency number, R_N, of ampicillin is 0.044 (±0.049 SD) which is significantly smaller than 0.704 (±0.101 SD) of oxacillin. The parameters in the two-compartment dispersion model are correlated to the recovery ratio, F_H, mean transit time, ¯t_H, and the relative variance, ²/¯t_H ², of the elution profile of drugs from the rat liver.Notation A Cross-sectional area of the blood space - C(t, z) Concentration of drug (one-compartment dispersion model) - C(s, z) Laplace transform of C(t, z) - C ₁(t, z) Concentration of drug in blood space (two-compartment dispersion model) - C ₂(t, z) Concentration of drug in the liver tissue (two-compartment dispersion model) - C ₁ (s, z) Laplace transform ofC ₁(t, z) - D Axial or longitudinal dispersion coefficient - D _c(=D· A ²) Corrected dispersion coefficient - D _N Dispersion number - f _I(t) Input function with respect tot - f_I(z) Input function with respect toz - F_I(s) Laplace transform of f_I(t) - f_s(t) System weight function with respect tot - f_s(z) System weight function with respect to z - F_H Recovery ratio - k Partition ratio (distribution ratio) - k₁₂, k₂₁ Forward and backward partition rate constant in the central elimination two-compartment dispersion model - k ₁₂ ^p ,k ₂₁ ^p Forward and backward partition rate constant in the peripheral elimination two-compartment dispersion model - k_e Elimination (or irreversible transfer) rate constant - k _e ^p Elimination rate constant in peripheral elimination model - K_H Distribution constant - L Length of blood space in liver - M Amount of drug injected - m Coefficient related to the injected amount - p_h Mass transfer coefficient from perfusate to hepatic tissue - Q Flow rate of perfusate - R_N Efficiency number - s Laplace variable - t Time - ¯ t_H Mean transit time - Linear flow velocity of the perfusate - V _B(= L·A) Blood volume (sum of the sinusoid volume and the space of Disse) - v_h Apparent volume of distribution - V _H Anatomical volume of liver tissue - z Axial coordinate in the liver - (t) Delta function - Volume ratio of the anatomical liver tissue to the blood space - ² Variance of transit time - ²/¯t _H ² Relative dispersion to transit time - Partial derivatives     

A Change-Point Regression Approach for Efficacy Evaluation of Dietary Supplements

In clinical trials for dietary supplements and functional foods, the study population tends to be a mixture of healthy subjects and those who are not so healthy but are not definitely diseased (called “borderline subjects”). For such heterogeneous populations, the t-test and ANCOVA method often fail to provide the desired treatment efficacy. We propose an alternative approach for the efficacy evaluation of dietary supplements and functional foods based on a change-point linear regression model. The model does not require the assumption of a constant treatment effect and provides clinically interpretable results. By employing the AIC-based profile likelihood method, inferences can be made easily using standard statistical software. The proposed method was applied to the Garcinia study data, and the merit of the method was demonstrated by comparing it with traditional methods.     

Frequentist Model-averaged Estimators and Tests for Univariate Twin Models

Parameter estimates from analyses of univariate twin data usually do not reflect the uncertainty due to the model selection phase of the data analysis. To address the effect of model selection uncertainty on parameter estimates, we introduce frequentist model-averaged estimators for univariate twin data analysis that use information-theoretic criteria to assign model weights. We conduct simulation studies to examine the performance of model-averaged estimators of additive genetic variance, and for tests for additive genetic variance based on model-averaged estimators. In simulation studies with small or moderate sample sizes, model-averaged estimators of additive genetic variance typically have lower mean-squared error than either (i) estimators from individual twin models, or (ii) estimators obtained from a decision procedure where the best-fitting model from likelihood-ratio testing is used to estimate additive genetic variance. For each sample size simulated, bootstrap tests based on model-averaged estimators have higher power to detect additive genetic variance than currently-used tests in most cases.Edited by Stacey Cherny     

An algorithm for the treatment of type 2 diabetes in Latin America

Diabetes is a principal and growing health concern in Latin America, accounting for significant mortality and morbidities. Large, randomized, prospective trials of various interventional therapies in patients with both type 1 and type 2 diabetes have demonstrated that reductions in hyperglycaemia and management of diabetes-related risk factors can significantly reduce the micro- and macrovascular complications of diabetes. Therefore, patients with type 2 diabetes will benefit from more aggressive treatment regimens to help decrease the occurrence and rate of progression of diabetic complications. Given the many complexities of diabetes management, it is often difficult for general practice physicians to stay abreast of emerging treatment strategies and therapies. Owing to the high prevalence of type 2 diabetes in Latin America, the majority of patients with diabetes are treated by generalists rather than specialists. This article was intended to assist physicians and other healthcare professionals in developing and using effective treatment strategies to stem the growing epidemic of diabetes and its complications in Latin America.     

Preclinical factors influencing the relative contributions of Phase I and II enzymes to the metabolism of the experimental anti-cancer drug 5,6-dimethylxanthenone-4-acetic acid

It is important to determine the relative contribution of each metabolic pathway (f(p)) and of enzymes to the net metabolism of a drug. The aim of this study was to investigate, using a human liver bank, the f(p) of the anti-cancer drug 5,6-dimethylxanthenone-4-acetic acid (DMXAA) and the effects of various inhibitors and inducers on f(p). The mean apparent K(m) and V(max) values (N=14) were 21+/-5 microM and 0.04+/-0.02 nmol/min/mg, respectively, for 6-methylhydroxylation, and 143+/-79 microM and 0.71+/-0.52 nmol/min/mg, respectively, for acyl glucuronidation in human liver microsomes. 6-Methylhydroxylation and acyl glucuronidation contributed 26 and 74%, respectively, to DMXAA metabolism at 5 microM; values were 7 and 93% at 350 microM DMXAA. There was a significant relationship between the ratio of metabolic activity by Phase II and I reactions (R(II/I)) and uridine diphosphate glucuronosyltransferase (UGT2B7) protein level (r=0.605, P=0.022), whereas a reverse correlation between R(II/I) and cytochrome P450 (CYP1A) protein level was observed (r=-0.540, P=0.046). Various compounds inhibited either DMXAA glucuronidation or 6-methylhydroxylation, or both pathways. Pretreatment of rats with beta-naphthoflavone, but not phenobarbitone and cimetidine, increased the percentage of the contribution by 6-methylhydroxylation to 17% from 4% of control at 5 microM DMXAA. Our results indicate that the f(p) of DMXAA is subject to substrate concentration, inhibition, induction, and the protein levels of enzymes that biotransform DMXAA. However, clinical studies are important to verify the conclusions drawn from in vitro data.