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At least 487 records · Page 27Linked to original sources
PubMed2005

Bioequivalence evaluation of two brands of lisinopril tablets (Lisotec and Zestril) in healthy human volunteers.

The bioequivalence of two brands of lisinopril 20 mg tablets was demonstrated in 28 healthy human volunteers after a single oral dose in a randomized cross-over study, conducted at ACDIMA Center for Bioequivalence and Pharmaceutical Studies, Amman, Jordan. Reference (Zestril, AstraZeneca, UK) and test (Lisotec, Julphar, UAE) products were administered to fasting volunteers on 2 treatment days separated by a 2-week washout period; blood samples were collected at specified time intervals, and the plasma was separated and analysed for lisinopril using a validated LC-MS/MS method at ACDIMA Laboratory. The pharmacokinetic parameters AUC(0-t), AUC(0- proportional), C(MAX), T(MAX), T(1/2) and the elimination rate constant were determined from the plasma concentration-time profiles for both formulations and were compared statistically to evaluate bioequivalence between the two brands, using the statistical modules recommended by the FDA. The analysis of variance (ANOVA) did not show any significant difference between the two formulations and 90% confidence intervals fell within the acceptable range for bioequivalence. Based on these statistical inferences it was concluded that the two brands exhibited comparable pharmacokinetic profiles and that Julphar's Lisotec is bioequivalent to Zestril of AstraZeneca, UK.

Adolescent↗
PubMed2006

Estimating biomarker-based HIV incidence using prevalence data in high risk groups with missing outcomes.

The novel two-step serologic sensitive/less sensitive testing algorithm for detecting recent HIV seroconversion (STARHS) provides a simple and practical method to estimate HIV-1 incidence using cross-sectional HIV seroprevalence data. STARHS has been used increasingly in epidemiologic studies. However, the uncertainty of incidence estimates using this algorithm has not been well described, especially for high risk groups or when missing data is present because a fraction of sensitive enzyme immunoassay (EIA) positive specimens are not tested by the less sensitive EIA. Ad hoc methods used in practice provide incorrect confidence limits and thus may jeopardize statistical inference. In this report, we propose maximum likelihood and Bayesian methods for correctly estimating the uncertainty in incidence estimates obtained using prevalence data with a fraction missing, and extend the methods to regression settings. Using a study of injection drug users participating in a drug detoxification program in New York city as an example, we demonstrated the impact of underestimating the uncertainty in incidence estimates using ad hoc methods. Our methods can be applied to estimate the incidence of other diseases from prevalence data using similar testing algorithms when missing data is present.

Bayes Theorem↗
PubMed2003

Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies.

Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particularly in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02 cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process.

Algorithms↗
PubMed2004

Maximum-likelihood estimation of haplotype frequencies in nuclear families.

The importance of haplotype analysis in the context of association fine mapping of disease genes has grown steadily over the last years. Since experimental methods to determine haplotypes on a large scale are not available, phase has to be inferred statistically. For individual genotype data, several reconstruction techniques and many implementations of the expectation-maximization (EM) algorithm for haplotype frequency estimation exist. Recent research work has shown that incorporating available genotype information of related individuals largely increases the precision of haplotype frequency estimates. We, therefore, implemented a highly flexible program written in C, called FAMHAP, which calculates maximum likelihood estimates (MLEs) of haplotype frequencies from general nuclear families with an arbitrary number of children via the EM-algorithm for up to 20 SNPs. For more loci, we have implemented a locus-iterative mode of the EM-algorithm, which gives reliable approximations of the MLEs for up to 63 SNP loci, or less when multi-allelic markers are incorporated into the analysis. Missing genotypes can be handled as well. The program is able to distinguish cases (haplotypes transmitted to the first affected child of a family) from pseudo-controls (non-transmitted haplotypes with respect to the child). We tested the performance of FAMHAP and the accuracy of the obtained haplotype frequencies on a variety of simulated data sets. The implementation proved to work well when many markers were considered and no significant differences between the estimates obtained with the usual EM-algorithm and those obtained in its locus-iterative mode were observed. We conclude from the simulations that the accuracy of haplotype frequency estimation and reconstruction in nuclear families is very reliable in general and robust against missing genotypes.

Algorithms↗
PubMed1994

Genetic analysis combining path analysis with regressive models: the BETA path model of polygenic and familial environmental transmission.

We have extended the class D regressive model for the purpose of combined path and segregation analyses by incorporating the BETA path model. We have done this by expressing correlations among residuals from major genotype (RMGs) of family members under the class D regressive model as functions of path coefficients under the BETA path model. The likelihood function under the combined model was factorized into a product of conditional densities, which is dominated by bivariate normal densities. Statistical inferences under the combined model are analogous to those under the class D regressive model.

Binomial Distribution↗
PubMed2004

Use of unphased multilocus genotype data in indirect association studies.

It is usually assumed that detection of a disease susceptability gene via marker polymorphisms in linkage disequilibrium with it is facilitated by consideration of marker haplotypes. However, capture of the marker haplotype information requires resolution of gametic phase, and this must usually be inferred statistically. Recently, we questioned the value of the marker haplotype information, and suggested that certain analyses of multivariate marker data, not based on haplotypes explicitly and not requiring resolution of gametic phase, are often more powerful than analyses based on haplotypes. Here, we review this work and assess more carefully the situations in which our conclusions might apply. We also relate these analyses to alternative approaches to haplotype analysis, namely those based on haplotype similarity and those inspired by cladistics.

Genetic Markers↗
PubMed2002

Using larger dimensional signal subspaces to increase sensitivity in fMRI time series analyses.

It has been explained previously how using large dimensional signal-subspaces can reduce/eliminate bias in the estimated fMRI response (Burock and Dale [2000]: Hum Brain Mapp 11:249-260). It has also been explained how one can project this less biased estimate onto a one-dimensional subspace of interest (Burock and Dale [2000]: Hum Brain Mapp 11:249-260). In cases where there are multiple, correlated characterized response components per event type, separately projecting the full hemodynamic response onto one-dimensional subspaces of interest can lead to bias. We present an approach for both estimating the full hemodynamic response and obtaining from it unbiased estimates of effects of theoretical interest (in the context of ordinary least-squares estimation). The latter estimates are identical to those obtained by projecting the original data into the space defined by the (possibly multi-dimensional) effects of theoretical interest, but the ensuing statistical inference can be more sensitive. Hum.

Bias↗
PubMed2006

Nonlinear local electrovascular coupling. I: A theoretical model.

Here we present a detailed biophysical model of how brain electrical and vascular dynamics are generated within a basic cortical unit. The model was obtained from coupling a canonical neuronal mass and an expandable vasculature. In this proposal, we address several aspects related to electroencephalographic and functional magnetic resonance imaging data fusion: (1) the impact of the cerebral architecture (at different physical levels) on the observations; (2) the physiology involved in electrovascular coupling; and (3) energetic considerations to gain a better understanding of how the glucose budget is used during neuronal activity. The model has three components. The first is the canonical neural mass model of three subpopulations of neurons that respond to incoming excitatory synaptic inputs. The generation of the membrane potentials in the somas of these neurons and the electric currents flowing in the neuropil are modeled by this component. The second and third components model the electrovascular coupling and the dynamics of vascular states in an extended balloon approach, respectively. In the first part we describe, in some detail, the biophysical model and establish its face validity using simulations of visually evoked responses under different flickering frequencies and luminous contrasts. In a second part, a recursive optimization algorithm is developed and used to make statistical inferences about this forward/generative model from actual data.

Brain↗
PubMed2007

Nonlinear local electrovascular coupling. II: From data to neuronal masses.

In the companion article a local electrovascular coupling (LEVC) model was proposed to explain the continuous dynamics of electrical and vascular states within a cortical unit. These states produce certain mesoscopic reflections whose discrete time series can be reconstructed from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). In this article we develop a recursive optimization algorithm based on the local linearization (LL) filter and an innovation method to make statistical inferences about the LEVC model from both EEG and fMRI data, i.e., to estimate the unobserved states and the unknown parameters of the model. For a better understanding, the LL filter is described from a Bayesian point of view, providing the particulars for the case of hybrid data (e.g., EEG and fMRI), which could be sampled at different rates. The dynamics of the exogenous synaptic inputs going into the cortical unit are also estimated by introducing a set of Gaussian radial basis functions. In order to study the dynamics of the electrical and vascular states in the striate cortex of humans as well as their local interrelationships, we applied this algorithm to EEG and fMRI recordings obtained concurrently from two subjects while passively observing a radial checkerboard with a white/black pattern reversal. The EEG and fMRI data from the first subject was used to estimate the electrical/vascular states and parameters of the LEVC model in V1 for a 4.0 Hz reversion frequency. We used the EEG data from the second subject to investigate the changes in the dynamics of the electrical states when the frequency of reversion is varied from 0.5-4.0 Hz. Then we made use of the estimated electrical states to predict the effects on the vasculature that such variations produce.

Bayes Theorem↗
PubMed2025

S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators.

Mediation analysis is widely utilized in neuroscience to investigate the role of brain image phenotypes in the neurological pathways from genetic exposures to clinical outcomes. However, it is still difficult to conduct mediation analyses with whole genome-wide exposures and brain subcortical shape mediators due to several challenges including (i) large-scale genetic exposures, that is, millions of single-nucleotide polymorphisms (SNPs); (ii) nonlinear Hilbert space for shape mediators; and (iii) statistical inference on the direct and indirect effects. To tackle these challenges, this paper proposes a genome-wide mediation analysis framework with brain subcortical shape mediators. First, to address the issue caused by the high dimensionality in genetic exposures, a fast genome-wide association analysis is conducted to discover potential genetic variants with significant genetic effects on the clinical outcome. Second, the square-root velocity function representations are extracted from the brain subcortical shapes, which fall in an unconstrained linear Hilbert subspace. Third, to identify the underlying causal pathways from the detected SNPs to the clinical outcome implicitly through the shape mediators, we utilize a shape mediation analysis framework consisting of a shape-on-scalar model and a scalar-on-shape model. Furthermore, the bootstrap resampling approach is adopted to investigate both global and spatial significant mediation effects. Finally, our framework is applied to the corpus callosum shape data from the Alzheimer's Disease Neuroimaging Initiative.

Humans↗
PubMedDate not supplied

Uneven copper distribution in the human newborn liver.

The pattern of copper distribution in human newborn liver was investigated by histochemical methods (rhodamine, orcein and rubeanic acid) and by atomic absorption spectroscopy. A significant correlation (p less than 0.005) was found between the degree of histochemical positivity and the copper concentration found by atomic absorption spectroscopy. In the majority of the 30 livers examined (first group), the copper concentration was much higher than that of normal adult liver, although exhibiting striking individual differences. No correlation between the copper content and sex, body weight or gestational age was found. From a second group of five livers, longitudinal tissue slices 0.5 cm thick were partitioned into regular blocks of about 0.5 gm, which were individually analyzed by atomic absorption spectroscopy. Copper appeared unevenly distributed within each liver, with marked differences even between adjacent blocks. However, a consistent tendency of copper to accumulate in the left lobe more than in the right one was evident. Five additional blocks, one for each liver, were further partitioned into 10 small specimens of a final size (0.05 gm), comparable to that of a needle biopsy. Even at this sampling level, consisting of tissue fragments taken from a small tissue area, the copper concentration appeared quite irregularly distributed. These findings may be considered for two different aspects: (a) the biological implications of the pattern of copper accumulation in different lobar and lobular liver compartments and (b) the statistical inference, for diagnostic purposes, of the mean liver copper content from measurements of single percutaneous biopsy specimens.

Age Factors↗
PubMed1993

Viral infections and chemical exposures as risk factors for hepatocellular carcinoma in Vietnam.

A case-control study investigating risk factors for hepatocellular carcinoma (HCC) was conducted in Hanoi, in the north of Vietnam, between 1989 and 1992. Male cases of HCC (152) diagnosed in 2 hospitals were included. Hospital controls (241) admitted mainly to abdominal surgery departments were frequency-matched to cases for sex, age, hospital and place of residence (Hanoi, province). Odds ratios adjusted for matching variables and other potential confounders were estimated using unconditional logistic regression, or exact non-parametric statistical inference when numbers were small. Positivity for hepatitis B surface antigen (HBsAg) was the main risk factor for HCC in this sample. Five subjects (3 cases, 2 controls) had been infected by hepatitis C virus (HCV), and none of them were carriers of HBsAg, giving an OR of 38 associated with HCV infection among HBsAG-negative subjects. Alcohol drinking was associated with HCC and interacted with HBsAg positivity. Agricultural use of organophosphorous pesticides (30 liters/year or more) and military service in the south of Vietnam for 10 years or more were also associated with an increased risk of HCC. This study confirms the major role played by HBV infection and its association with HCC in south-east Asia. It also suggests how other factors such as alcohol consumption or exposure to chemicals may interact with HBV infection.

2,4,5-Trichlorophenoxyacetic Acid↗
PubMedDate not supplied

Anxious symptomatic volunteers in the Twin Cities: sociodemographic description and MMPI psychodiagnosis.

This paper underscores the need for true sample selection before statistical inference of any value can be made. Its findings suggest that the sample selection strategy of symptomatic volunteers can produce samples of anxious subjects with remarkable demographic similarity across the country. Anxiety as a constitutional rather than pathognomonic symptom is illustrated by the heterogeneity of its psychometric evaluation in our volunteers. Expecting a drug to safely relieve the anxiety of all anxious symptomatic volunteers is akin to expecting a drug to safely relieve the headaches of all headache sufferers. We feel that careful psychodiagnosis remains the best sample selection strategy.

Adolescent↗
PubMed2005

An introduction to medical statistics for health care professionals: Hypothesis tests and estimation.

This article is the second in a series of three that will give health care professionals (HCPs) a sound introduction to medical statistics (Thomas, 2004). The objective of research is to find out about the population at large. However, it is generally not possible to study the whole of the population and research questions are addressed in an appropriate study sample. The next crucial step is then to use the information from the sample of individuals to make statements about the wider population of like individuals. This procedure of drawing conclusions about the population, based on study data, is known as inferential statistics. The findings from the study give us the best estimate of what is true for the relevant population, given the sample is representative of the population. It is important to consider how accurate this best estimate is, based on a single sample, when compared to the unknown population figure. Any difference between the observed sample result and the population characteristic is termed the sampling error. This article will cover the two main forms of statistical inference (hypothesis tests and estimation) along with issues that need to be addressed when considering the implications of the study results.

Journal Article↗
PubMed2005

How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS.

There has been a recent growth in the use of Bayesian methods in medical research. The main reasons for this are the development of computer intensive simulation based methods such as Markov chain Monte Carlo (MCMC), increases in computing power and the introduction of powerful software such as WinBUGS. This has enabled increasingly complex models to be fitted. The ability to fit these complex models has led to MCMC methods being used as a convenient tool by frequentists, who may have no desire to be fully Bayesian. Often researchers want 'the data to dominate' when there is no prior information and thus attempt to use vague prior distributions. However, with small amounts of data the use of vague priors can be problematic. The results are potentially sensitive to the choice of prior distribution. In general there are fewer problems with location parameters. The main problem is with scale parameters. With scale parameters, not only does one have to decide the distributional form of the prior distribution, but also whether to put the prior distribution on the variance, standard deviation or precision. We have conducted a simulation study comparing the effects of 13 different prior distributions for the scale parameter on simulated random effects meta-analysis data. We varied the number of studies (5, 10 and 30) and compared three different between-study variances to give nine different simulation scenarios. One thousand data sets were generated for each scenario and each data set was analysed using the 13 different prior distributions. The frequentist properties of bias and coverage were investigated for the between-study variance and the effect size. The choice of prior distribution was crucial when there were just five studies. There was a large variation in the estimates of the between-study variance for the 13 different prior distributions. With a large number of studies the choice of prior distribution was less important. The effect size estimated was not biased, but the precision with which it was estimated varied with the choice of prior distribution leading to varying coverage intervals and, potentially, to different statistical inferences. Again there was less of a problem with a larger number of studies. There is a particular problem if the between-study variance is close to the boundary at zero, as MCMC results tend to produce upwardly biased estimates of the between-study variance, particularly if inferences are based on the posterior mean. The choice of 'vague' prior distribution can lead to a marked variation in results, particularly in small studies. Sensitivity to the choice of prior distribution should always be assessed.

Anti-Bacterial Agents↗
PubMedDate not supplied

The detection of adverse reactions to therapeutic drugs.

The risk that a drug newly introduced into medical use will occasionally cause adverse reactions is neither negligible nor totally avoidable. Only well organized systems of monitoring can bring early detection and appropriate action. These in turn require either detailed supervision or spontaneous reporting. The paper is concerned with statistical inference from reports spontaneously submitted, and its logical limitations; it discusses strengths and weaknesses of the UK system, the detection process, and automated signalling.

Drug Information Services↗
PubMed1994

Fitting multivariate polynomial growth curves in two-period crossover designs.

We discuss the statistical analysis of data from two clinical trials using crossover designs. In both studies, response was observed repeatedly over time in each treatment period. The first study involves repeated measurements of a single response variable whereas the second involves bivariate response. Methods are described for fitting polynomial growth curves to achieve data reduction in a two-stage approach to the analysis of crossover designs. Thus, a multivariate parametric analysis frequently can be conducted even when the sample sizes are somewhat small as is the case in many crossover designs. Hypotheses that are usually of interest in crossover designs can be tested in the second stage of the analysis. Methods for testing the multivariate general linear hypothesis as a basis for statistical inference in such problems are discussed.

Allium↗
PubMed1995

Logistic regression with incompletely observed categorical covariates--investigating the sensitivity against violation of the missing at random assumption.

Missing values in the covariates are a widespread complication in the statistical inference of regression models. The maximum likelihood principle requires specification of the distribution of the covariates, at least in part. For categorical covariates, log-linear models can be used. Additionally, the missing at random assumption is necessary, which excludes a dependence of the occurrence of missing values on the unobserved covariate values. This assumption is often highly questionable. We present a framework to specify alternative missing value mechanisms such that maximum likelihood estimation of the regression parameters under a specified alternative is possible. This allows investigation of the sensitivity of a single estimate against violations of the missing at random assumption. The possible results of a sensitivity analysis are illustrated by artificial examples. The practical application is demonstrated by the analysis of two case-control studies.

ABO Blood-Group System↗