How valuable are multiple treatment comparison methods in evidence-based health-care evaluation?

Objectives To compare the use of pair-wise meta-analysis methods to multiple treatment comparison (MTC) methods for evidence-based health-care evaluation to estimate the effectiveness and cost-effectiveness of alternative health-care interventions based on the available evidence. Methods Pair-wise meta-analysis and more complex evidence syntheses, incorporating an MTC component, are applied to three examples: 1) clinical effectiveness of interventions for preventing strokes in people with atrial fibrillation; 2) clinical and cost-effectiveness of using drug-eluting stents in percutaneous coronary intervention in patients with coronary artery disease; and 3) clinical and cost-effectiveness of using neuraminidase inhibitors in the treatment of influenza. We compare the two synthesis approaches with respect to the assumptions made, empirical estimates produced, and conclusions drawn. Results The difference between point estimates of effectiveness produced by the pair-wise and MTC approaches was generally unpredictable—sometimes agreeing closely whereas in other instances differing considerably. In all three examples, the MTC approach allowed the inclusion of randomized controlled trial evidence ignored in the pair-wise meta-analysis approach. This generally increased the precision of the effectiveness estimates from the MTC model. Conclusions The MTC approach to synthesis allows the evidence base on clinical effectiveness to be treated as a coherent whole, include more data, and sometimes relax the assumptions made in the pair-wise approaches. However, MTC models are necessarily more complex than those developed for pair-wise meta-analysis and thus could be seen as less transparent. Therefore, it is important that model details and the assumptions made are carefully reported alongside the results.

LC-MS/MS method for simultaneous analysis of uracil, 5,6-dihydrouracil, 5-fluorouracil and 5-fluoro-5,6-dihydrouracil in human plasma for therapeutic drug monitoring and toxicity prediction in cancer patients

The chemotherapeutic drug 5-fluorouracil (5-FU) is widely used for treating solid tumors. Response to 5-FU treatment is variable with 10-30% of patients experiencing serious toxicity partly explained by reduced activity of dihydropyrimidine dehydrogenase (DPD). DPD converts endogenous uracil (U) into 5,6-dihydrouracil (UH(2) ), and analogously, 5-FU into 5-fluoro-5,6-dihydrouracil (5-FUH(2) ). Combined quantification of U and UH(2) with 5-FU and 5-FUH(2) may provide a pre-therapeutic assessment of DPD activity and further guide drug dosing during therapy. Here, we report the development of a liquid chromatography-tandem mass spectrometry assay for simultaneous quantification of U, UH(2) , 5-FU and 5-FUH(2) in human plasma. Samples were prepared by liquid-liquid extraction with 10:1 ethyl acetate-2-propanol (v/v). The evaporated samples were reconstituted in 0.1% formic acid and 10 μL aliquots were injected into the HPLC system. Analyte separation was achieved on an Atlantis dC(18) column with a mobile phase consisting of 1.0 mm ammonium acetate, 0.5 mm formic acid and 3.3% methanol. Positively ionized analytes were detected by multiple reaction monitoring. The analytical response was linear in the range 0.01-10 μm for U, 0.1-10 μm for UH(2) , 0.1-75 μm for 5-FU and 0.75-75 μm for 5-FUH(2) , covering the expected concentration ranges in plasma. The method was validated following the FDA guidelines and applied to clinical samples obtained from ten 5-FU-treated colorectal cancer patients. The present method merges the analysis of 5-FU pharmacokinetics and DPD activity into a single assay representing a valuable tool to improve the efficacy and safety of 5-FU-based chemotherapy.

The simultaneous role of an alveolus as flow mixer and flow feeder for the deposition of inhaled submicron particles

In an effort to understand the fate of inhaled submicron particles in the small sacs, or alveoli, comprising the gas-exchange region of the lung, we calculated the flow in three-dimensional (3D) rhythmically expanding models of alveolated ducts. Since convection toward the alveolar walls is a precursor to particle deposition, it was the goal of this paper to investigate the streamline maps' dependence upon alveoli location along the acinar tree. On the alveolar midplane, the recirculating flow pattern exhibited closed streamlines with a stagnation saddle point. Off the midplane we found no closed streamlines but nested, funnel-like, spiral, structures (reminiscent of Russian nesting dolls) that were directed towards the expanding walls in inspiration, and away from the contracting walls in expiration. These nested, funnel-like, structures were surrounded by air that flowed into the cavity from the central channel over inspiration and flowed from the cavity to the central channel over expiration. We also found that fluid particle tracks exhibited similar nested funnel-like spiral structures. We conclude that these unique alveolar flow structures may be of importance in enhancing deposition. In addition, due to inertia, the nested, funnel-like, structures change shape and position slightly during a breathing cycle, resulting in flow mixing. Also, each inspiration feeds a fresh supply of particle-laden air from the central channel to the region surrounding the mixing region. Thus, this combination of flow mixer and flow feeder makes each individual alveolus an effective mixing unit, which is likely to play an important role in determining the overall efficiency of convective mixing in the acinus.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Kinetic assessment of persistent halogenated xenobiotics in cell culture models: comparison of mono- and poly-halogenated compounds

We evaluated the suitability of single and multiple cell type cultures as model systems to characterise cellular kinetics of highly lipophilic compounds with potential ecotoxicological impact. Confluent mono-layers of human skin fibroblasts, rat astrocytoma C6 cells, non-differentiated and differentiated mouse 3T3 cells were kept in culture medium supplemented with 10% foetal calf serum. For competitive uptake experiments up to four different cell types, grown on glass sectors, were exposed for 3h to (14)C-labelled model compounds, dissolved either in organic solvents or incorporated into unilamellar lecithin liposomes. Bromo-, or chloro-benzenes, decabromodiphenylether (DBP), and dichlorodiphenyl ethylene (DDE) were tested in rather high concentration of 20 microM. Cellular toxicity was low. Compound levels were related to protein, DNA, and triglyceride contents. Cellular uptake was fast and dependent on physico-chemical properties of the compounds (lipophilicity, molecular size), formulation, and cell type. Mono-halogenated benzenes showed low and similar uptake levels (=low accumulation compounds). DBP and DDE showed much higher cellular accumulations (=high accumulation compounds) except for DBP in 3T3 cells. Uptake from liposomal formulations was mostly higher than if compounds were dissolved in organic solvents. The extent of uptake correlated with the cellular content of triglycerides, except for DBP. Uptake competition between different cell types was studied in a sectorial multi-cell culture model. For low accumulation compounds negligible differences were found among C6 cells and fibroblasts. Uptake of DDE was slightly and that of DBP highly increased in fibroblasts. Well-defined cell culture systems, especially the sectorial model, are appropriate to screen for bioaccumulation and cytotoxicity of (unknown) chemical entities in vitro.

MULTIPLE DISEASES IN CARRIER PROBABILITY ESTIMATION: ACCOUNTING FOR SURVIVING ALL CANCERS OTHER THAN BREAST AND OVARY IN BRCAPRO

Mendelian models can predict who carries an inherited deleterious mutation of known disease genes based on family history. For example, the BRCAPRO model is commonly used to identify families who carry mutations of BRCA1 and BRCA2, based on familial breast and ovarian cancers. These models incorporate the age of diagnosis of diseases in relatives and current age or age of death. We develop a rigorous foundation for handling multiple diseases with censoring. We prove that any disease unrelated to mutations can be excluded from the model, unless it is sufficiently common and dependent on a mutation-related disease time. Furthermore, if a family member has a disease with higher probability density among mutation carriers, but the model does not account for it, then the carrier probability is deflated. However, even if a family only has diseases the model accounts for, if the model excludes a mutation-related disease, then the carrier probability will be inflated. In light of these results, we extend BRCAPRO to account for surviving all non-breast/ovary cancers as a single outcome. The extension also enables BRCAPRO to extract more useful information from male relatives. Using 1500 familes from the Cancer Genetics Network, accounting for surviving other cancers improves BRCAPRO’s concordance index from 0.758 to 0.762 (p = 0.046), improves its positive predictive value from 35% to 39% (p < 10−6) without impacting its negative predictive value, and improves its overall calibration, although calibration slightly worsens for those with carrier probability < 10%. Copyright c 2000 John Wiley & Sons, Ltd.

Efficiency of the Sieved-NPMLE in CAR-Missing Data Models

We consider nonparametric missing data models for which the censoring mechanism satisfies coarsening at random and which allow complete observations on the variable X of interest. W show that beyond some empirical process conditions the only essential condition for efficiency of an NPMLE of the distribution of X is that the regions associated with incomplete observations on X contain enough complete observations. This is heuristically explained by describing the EM-algorithm. We provide identifiably of the self-consistency equation and efficiency of the NPMLE in order to make this statement rigorous. The usual kind of differentiability conditions in the proof are avoided by using an identity which holds for the NPMLE of linear parameters in convex models. We provide a bivariate censoring application in which the condition and hence the NPMLE fails, but where other estimators, not based on the NPMLE principle, are highly inefficient. It is shown how to slightly reduce the data so that the conditions hold for the reduced data. The conditions are verified for the univariate censoring, double censored, and Ibragimov-Has'minski models.

Estimating Multiple Tumor Transition Rates Based on Data from Survival/Sacrifice Experiments

In this paper, we focus on the model for two types of tumors. Tumor development can be described by four types of death rates and four tumor transition rates. We present a general semi-parametric model to estimate the tumor transition rates based on data from survival/sacrifice experiments. In the model, we make a proportional assumption of tumor transition rates on a common parametric function but no assumption of the death rates from any states. We derived the likelihood function of the data observed in such an experiment, and an EM algorithm that simplified estimating procedures. This article extends work on semi-parametric models for one type of tumor (see Portier and Dinse and Dinse) to two types of tumors.

Enantioselective analysis of ketamine and its metabolites in equine plasma and urine by CE with multiple isomer sulfated beta-CD

CE with multiple isomer sulfated beta-CD as the chiral selector was assessed for the simultaneous analysis of the enantiomers of ketamine and metabolites in extracts of equine plasma and urine. Different lots of the commercial chiral selector provided significant changes in enantiomeric ketamine separability, a fact that can be related to the manufacturing variability. A mixture of two lots was found to provide high-resolution separations and interference-free detection of the enantiomers of ketamine, norketamine, dehydronorketamine, and an incompletely identified hydroxylated metabolite of norketamine in liquid/liquid extracts of the two body fluids. Ketamine, norketamine, and dehydronorketamine could be unambiguously identified via HPLC fractionation of urinary extracts and using LC-MS and LC-MS/MS with 1 mmu mass discrimination. The CE assay was used to characterize the stereoselectivity of the compounds' enantiomers in the samples of five ponies anesthetized with isoflurane in oxygen and treated with intravenous continuous infusion of racemic ketamine. The concentrations of the ketamine enantiomers in plasma are equal, whereas the urinary amount of R-ketamine is larger than that of S-ketamine. Plasma and urine contain higher S- than R-norketamine levels and the mean S-/R-enantiomer ratios of dehydronorketamine in plasma and urine are lower than unity and similar.

Semiparametric Estimation of Models for Natural Direct and Indirect Effects

In recent years, researchers in the health and social sciences have become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of an exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Natural direct and indirect effects are of particular interest as they generally combine to produce the total effect of the exposure and therefore provide insight on the mechanism by which it operates to produce the outcome. A semiparametric theory has recently been proposed to make inferences about marginal mean natural direct and indirect effects in observational studies (Tchetgen Tchetgen and Shpitser, 2011), which delivers multiply robust locally efficient estimators of the marginal direct and indirect effects, and thus generalizes previous results for total effects to the mediation setting. In this paper we extend the new theory to handle a setting in which a parametric model for the natural direct (indirect) effect within levels of pre-exposure variables is specified and the model for the observed data likelihood is otherwise unrestricted. We show that estimation is generally not feasible in this model because of the curse of dimensionality associated with the required estimation of auxiliary conditional densities or expectations, given high-dimensional covariates. We thus consider multiply robust estimation and propose a more general model which assumes a subset but not all of several working models holds.

A Pseudolikelihood Approach for Simultaneous Analysis of Array Comparative Genomic Hybridizations (aCGH)

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based Comparative Genomic Hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and across hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and across hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure, and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and through analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present.