Likelihood-based approaches to modeling demand for medical care

We review recent likelihood-based approaches to modeling demand for medical care. A semi-nonparametric model along the lines of Cameron and Johansson's Poisson polynomial model, but using a negative binomial baseline model, is introduced. We apply these models, as well a semiparametric Poisson, hurdle semiparametric Poisson, and finite mixtures of negative binomial models to six measures of health care usage taken from the Medical Expenditure Panel survey. We conclude that most of the models lead to statistically similar results, both in terms of information criteria and conditional and unconditional prediction. This suggests that applied researchers may not need to be overly concerned with the choice of which of these models they use to analyze data on health care demand.

Calibration of CES functions for real-world multisectoral modeling

We show how to calibrate CES production and utility functions when indirect taxation affecting inputs and consumption is present. These calibrated functions can then be used in computable general equilibrium models. Taxation modifies the standard calibration procedures since any taxed good has two associated prices and a choice of reference value units has to be made. We also provide an example of computer code to solve the calibration of CES utilities under two alternate normalizations. To our knowledge, this paper fills a methodological gap in the CGE literature.

Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference.

Restriction site-associated DNA sequencing (RADseq) provides researchers with the ability to record genetic polymorphism across thousands of loci for nonmodel organisms, potentially revolutionizing the field of molecular ecology. However, as with other genotyping methods, RADseq is prone to a number of sources of error that may have consequential effects for population genetic inferences, and these have received only limited attention in terms of the estimation and reporting of genotyping error rates. Here we use individual sample replicates, under the expectation of identical genotypes, to quantify genotyping error in the absence of a reference genome. We then use sample replicates to (i) optimize de novo assembly parameters within the program Stacks, by minimizing error and maximizing the retrieval of informative loci; and (ii) quantify error rates for loci, alleles and single-nucleotide polymorphisms. As an empirical example, we use a double-digest RAD data set of a nonmodel plant species, Berberis alpina, collected from high-altitude mountains in Mexico.

Impact of genetic SLC28 transporter and ITPA variants on ribavirin 21 serum level, hemoglobin drop and therapeutic response in patients with HCV infection

Background: T reatment o f chronic hepatitis C i s evolving, a nd direct acting antivirals ( DAAs) are now a dded to p egylated interferon-α ( Peg- INF-α) and ribavirin (RBV) for the treatment o f hepatitis C v irus ( HCV) genotype 1 infection. DAAs c ause d ifferent side effects and can even worsen RBV induced hemolytic anemia. T herefore, identifying host genetic d eterminants of R BV bioavailability and therapeutic e fficacy will remain crucial for individualized treatment. Recent d ata showed associations between R BV induced h emolytic anemia and genetic polymorphisms o f concentrative nucleoside transporters s uch as C NT3 (SLC28A3) and i nosine t riphosphatase (ITPA). T o analyze t he association of genetic variants of SLC28 transporters and ITPA with RBV induced hemolytic anemia and treatment o utcome. Methods: I n our study, 173 patients f rom t he S wiss Hepatitis C C ohort Study and 2 2 patients from Swiss Association for the Study of the Liver study 24 (61% HCV g enotype 1, 3 9% genotypes 2 o r 3) were analyzed for SLC28A2 single nucleotide p olymorphism (SNP) rs11854484, SLC28A3 rs56350726 and SLC28A3 rs10868138 as well as ITPA SNPs rs1127354 and rs7270101. RBV serum levels during treatment were measured in 49 patients. Results: SLC28A2 r s11854484 genotype TT was associated with significantly higher dosage- and body weight-adjusted RBV levels as compared to genotypes TC and CC (p=0.04 and p=0.02 at weeks 4 and 8, respectively). ITPA SNPs rs1127354 and rs7270101 were associated with h emolytic a nemia both in genotype as w ell as i n allelic a nalyses. SLC28A3 rs56350726 genotype TT (vs. AT/AA, RR=2.1; 95% CI 1.1-4.1) as well as the T allele (vs. A; RR=1.8, 95% CI 1.1-3.2) were associated with increased SVR rates. The combined analysis of overall ITPA activity and SLC28 v ariants together revealed n o significant a dditive effects on either treatment-related anemia or SVR. Conclusions: T he newly identified association between RBV serum levels a nd SLC28A2 rs11854484 genotype as well as the replicated association of ITPA and SLC28A3 g enetic p olymorphisms w ith RBV induced hemolytic anemia and treatment r esponse underpin the need for further studies on host genetic d eterminants of R BV bioavailability and therapeutic e fficacy f or individualized treatment of chronic hepatitis C.

Ab initio modeling and molecular dynamics simulation of the alpha 1b-adrenergic receptor activation.

This work describes the ab initio procedure employed to build an activation model for the alpha 1b-adrenergic receptor (alpha 1b-AR). The first version of the model was progressively modified and complicated by means of a many-step iterative procedure characterized by the employment of experimental validations of the model in each upgrading step. A combined simulated (molecular dynamics) and experimental mutagenesis approach was used to determine the structural and dynamic features characterizing the inactive and active states of alpha 1b-AR. The latest version of the model has been successfully challenged with respect to its ability to interpret and predict the functional properties of a large number of mutants. The iterative approach employed to describe alpha 1b-AR activation in terms of molecular structure and dynamics allows further complications of the model to allow prediction and interpretation of an ever-increasing number of experimental data.

Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score--the CoLaus Study.

AIMS/HYPOTHESIS: Several susceptibility genes for type 2 diabetes have been discovered recently. Individually, these genes increase the disease risk only minimally. The goals of the present study were to determine, at the population level, the risk of diabetes in individuals who carry risk alleles within several susceptibility genes for the disease and the added value of this genetic information over the clinical predictors. METHODS: We constructed an additive genetic score using the most replicated single-nucleotide polymorphisms (SNPs) within 15 type 2 diabetes-susceptibility genes, weighting each SNP with its reported effect. We tested this score in the extensively phenotyped population-based cross-sectional CoLaus Study in Lausanne, Switzerland (n = 5,360), involving 356 diabetic individuals. RESULTS: The clinical predictors of prevalent diabetes were age, BMI, family history of diabetes, WHR, and triacylglycerol/HDL-cholesterol ratio. After adjustment for these variables, the risk of diabetes was 2.7 (95% CI 1.8-4.0, p = 0.000006) for individuals with a genetic score within the top quintile, compared with the bottom quintile. Adding the genetic score to the clinical covariates improved the area under the receiver operating characteristic curve slightly (from 0.86 to 0.87), yet significantly (p = 0.002). BMI was similar in these two extreme quintiles. CONCLUSIONS/INTERPRETATION: In this population, a simple weighted 15 SNP-based genetic score provides additional information over clinical predictors of prevalent diabetes. At this stage, however, the clinical benefit of this genetic information is limited.