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PubMed2002

Functional imaging with low-resolution brain electromagnetic tomography (LORETA): a review.

This paper reviews several recent publications that have successfully used the functional brain imaging method known as LORETA. Emphasis is placed on the electrophysiological and neuroanatomical basis of the method, on the localization properties of the method, and on the validation of the method in real experimental human data. Papers that criticize LORETA are briefly discussed. LORETA publications in the 1994-1997 period based localization inference on images of raw electric neuronal activity. In 1998, a series of papers appeared that based localization inference on the statistical parametric mapping methodology applied to high-time resolution LORETA images. Starting in 1999, quantitative neuroanatomy was added to the methodology, based on the digitized Talairach atlas provided by the Brain Imaging Centre, Montreal Neurological Institute. The combination of these methodological developments has placed LORETA at a level that compares favorably to the more classical functional imaging methods, such as PET and fMRI.

Brain↗

PubMedDate not supplied

Changes in serum catecholamine levels in patients who are brain dead.

Prospective blood samplings from 15 patients admitted with a Glasgow Coma Score of less than 7 were obtained to observe and compare epinephrine, norepinephrine, and dopamine serum levels in patients with brain injury before, after, and in the absence of brain death. Nine of the patients developed or were admitted after brain death. Wide variations in catecholamine blood levels over time were documented, and subgroup analysis precluded useful statistical comparison or inference of the data. The data are presented therefore as descriptive observations only. No apparent differences were noted between similarly injured patients in whom brain death did not develop and patients before brain death or between patients with penetrating versus nonpenetrating brain injury. Brain death was preceded by hypertension and corresponding elevations in serum catecholamine levels in one patient with complete data. Catecholamine levels appeared to fall after brain death in most patients. Only minimal changes in myocardial histology were present in three donor hearts, and the two transplanted hearts functioned satisfactorily. Serum catecholamine measurement or monitoring does not provide a precise method of determining potential injury to the donor heart before or after brain death. Other experimental data and clinical observation indicate that some hearts may be injured in the donor during the evolution of brain death. Pharmacologic intervention may prevent such injury in experimental animals but must be used before brain death is induced. Such interventions should be studied in selected human donors before brain death to determine whether cardiac function is improved in the donor or recipient.

Adult↗

PubMedDate not supplied

HIV evolutionary dynamics within and among hosts.

The HIV evolutionary processes continuously unfold, leaving a measurable footprint in viral gene sequences. A variety of statistical models and inference techniques have been developed to reconstruct the HIV evolutionary history and to investigate the population genetic processes that shape viral diversity. Remarkably different population genetic forces are at work within and among hosts. Population-level HIV phylogenies are mainly shaped by selectively neutral epidemiologic processes, implying that genealogy-based population genetic inference can be useful to study the HIV epidemic history. Such evolutionary analyses have shed light on the origins of HIV, and on the epidemic spread of viral variants in different geographic locations and in different populations. The HIV genealogies reconstructed from within-host sequences indicate the action of selection pressure. In addition, recombination has a significant impact on HIV genetic diversity. Accurately quantifying both the adaptation rate and the population recombination rate of HIV will contribute to a better understanding of immune escape and drug resistance. Characterizing the impact of HIV transmission on viral genetic diversity will be a key factor in reconciling the different population genetic processes within and among hosts.

Adaptation, Biological↗

PubMed1998

Statistical methods for assessing differential vaccine protection against human immunodeficiency virus types.

The human immunodeficiency virus type 1 (HIV-1) is extremely diverse. In assessing the utility of an HIV-1 vaccine, an important issue is the possibility of differential protection. We discuss statistical methods of inferring how the vaccine efficacy may vary with viral type from data that would be collected from a randomized, double-blind, placebo-controlled preventive vaccine efficacy trial. Detailed characterization of virus isolated from individuals infected during the trial will be available. We focus on the highly simplified case in which the viral characteristics are summarized by a single feature, which may be nominal, or a scalar quantity that represents distance between the isolate and the prototype virus or viruses used in the vaccine preparation. We consider discrete categorical and continuous response models for this quantity and identify models whose parameters can be interpreted as log ratios of strain-specific relative risks of infection in a prospective model for HIV-1 exposure and transmission. Methods of inference are described for the multinomial logistic regression (MLR) model for discrete categorical response, and a new semiparametric model which can be viewed as a continuous analog of the MLR model is introduced. The methods are illustrated by application to HIV-1 and hepatitis B vaccine trial data.

AIDS Vaccines↗

PubMed2004

Expression-based monitoring of transcription factor activity: the TELiS database.

MOTIVATION: In microarray studies it is often of interest to identify upstream transcription control pathways mediating observed changes in gene expression. The Transcription Element Listening System (TELiS) combines sequence-based analysis of gene regulatory regions with statistical prevalence analyses to identify transcription-factor binding motifs (TFBMs) that are over-represented among the promoters of up- or down-regulated genes. Efficiency is maximized by decomposing the problem into two steps: (1) a priori compilation of prevalence matrices specifying the number of putative binding sites for a variety of transcription factors in promoters from all genes assayed by a given microarray, and (2) real-time statistical analysis of pre-compiled prevalence matrices to identify TFBMs that are over- or under-represented in promoters of differentially expressed genes. The interlocking JAVA applications namely, PromoterScan and PromoterStats carry out these tasks, and together constitute the TELiS database for reverse inference of transcription factor activity. RESULTS: In two validation studies, TELiS accurately detected in vivo activation of NF-kappaB and the Type I interferon system by HIV-1 infection and pharmacologic activation of the glucocorticoid receptor in peripheral blood mononuclear cells. The population-based statistical inference underlying TELiS out-performed conventional statistical tests in analytic sensitivity, with parametric studies demonstrating accurate identification of transcription factor activity from as few as 20 differentially expressed genes. TELiS thus provides a simple, rapid and sensitive tool for identifying transcription control pathways mediating observed gene expression dynamics.

Algorithms↗

PubMed2000

Statistics in ophthalmic research: two eyes, one eye or the mean?

BACKGROUND: Ophthalmic data, while different among individuals, are usually similar between fellow eyes of the same individual. This study was designed to illustrate alternative approaches to account for the correlation between fellow eyes. This is important for making inferences using data from both eyes. METHODS: With the use of a real data set from a population-based study, we described the distribution of intraocular pressure (IOP) by estimating the mean and standard deviation (SD) and evaluated the potential risk factors of higher IOP based on the regression method. The units of observation studied were of both eyes, right eye only, left eye only, the eyes with higher IOP and the mean value of both eyes. Furthermore, the generalized estimating equation (GEE) method was used to account for the correlation between fellow eyes in the regression analysis. Results and inferences from the different approaches were compared. RESULTS: The analysis included all the eyes, providing the largest sample size and unbiased estimates of the mean and SDs. There were some discrepancies among different approaches in the regression analysis. The GEE method simultaneously evaluated the effects of both eyes, and increased precision and enhanced inferences. CONCLUSIONS: Inconsistent results among different ophthalmic studies result from variations in not only study design and courses but also statistical methods. Making the best use of appropriate statistical techniques, which account for between eye correlation, provides valid statistical inferences.

Blood Pressure↗

PubMedDate not supplied

Risk factors, confounding, and the illusion of statistical control.

When experimental designs are premature, impractical, or impossible, researchers must rely on statistical methods to adjust for potentially confounding effects. Such procedures, however, are quite fallible. We examine several errors that often follow the use of statistical adjustment. The first is inferring a factor is causal because it predicts an outcome even after "statistical control" for other factors. This inference is fallacious when (as usual) such control involves removing the linear contribution of imperfectly measured variables, or when some confounders remain unmeasured. The converse fallacy is inferring a factor is not causally important because its association with the outcome is attenuated or eliminated by the inclusion of covariates in the adjustment process. This attenuation may only reflect that the covariates treated as confounders are actually mediators (intermediates) and critical to the causal chain from the study factor to the study outcome. Other problems arise due to mismeasurement of the study factor or outcome, or because these study variables are only proxies for underlying constructs. Statistical adjustment serves a useful function, but it cannot transform observational studies into natural experiments, and involves far more subjective judgment than many users realize.

Bias↗

PubMed2005

An alternative to null-hypothesis significance tests.

The statistic p(rep) estimates the probability of replicating an effect. It captures traditional publication criteria for signal-to-noise ratio, while avoiding parametric inference and the resulting Bayesian dilemma. In concert with effect size and replication intervals, p(rep) provides all of the information now used in evaluating research, while avoiding many of the pitfalls of traditional statistical inference.

Bayes Theorem↗

PubMed2006

Accurate haplotype inference for multiple linked single-nucleotide polymorphisms using sibship data.

Sibships are commonly used in genetic dissection of complex diseases, particularly for late-onset diseases. Haplotype-based association studies have been advocated as powerful tools for fine mapping and positional cloning of complex disease genes. Existing methods for haplotype inference using data from relatives were originally developed for pedigree data. In this study, we proposed a new statistical method for haplotype inference for multiple tightly linked single-nucleotide polymorphisms (SNPs), which is tailored for extensively accumulated sibship data. This new method was implemented via an expectation-maximization (EM) algorithm without the usual assumption of linkage equilibrium among markers. Our EM algorithm does not incur extra computational burden for haplotype inference using sibship data when compared with using unrelated parental data. Furthermore, its computational efficiency is not affected by increasing sibship size. We examined the robustness and statistical performance of our new method in simulated data created from an empirical haplotype data set of human growth hormone gene 1. The utility of our method was illustrated with an application to the analyses of haplotypes of three candidate genes for osteoporosis.

Algorithms↗

PubMed2004

hp-DPI: Helicobacter pylori database of protein interactomes--embracing experimental and inferred interactions.

We implemented a statistical model into our protein interaction database for validation of two-hybrid assays of Helicobacter pylori, and prediction of putative protein interactions not yet discovered experimentally. To present the enormous amount of experimental and inferred protein interaction networking maps, the H.pylori Database of Protein Interactomes (hp-DPI) is developed with a succinct yet comprehensive visualization tool integrated with annotation from Genbank, GO, and KEGG. hp-DPI is first built with, but not limited to, H.pylori protein interactions and is expected to naturally include other organisms' protein interacting relationships in the future.

Algorithms↗

PubMed1990

Pooled inference across sexes for the in vivo micronucleus assay.

The Japanese Environmental Mutagen Society has investigated the extent of sex differences in the in vivo micronucleus assay (Sutou et al., 1986). In light of their findings, this manuscript reexamines the statistical analysis of the assay. A test statistic which pools the inference over sexes is introduced. The sensitivity of this statistic is examined in comparison with the more traditional procedure of analysis within each sex. The impact of extra-Poisson variation among animals on the validity and sensitivity of the test procedures is also examined.

Animals↗

PubMed2003

Incorporating biological knowledge into evaluation of causal regulatory hypotheses.

Biological data can be scarce and costly to obtain. The small number of samples available typically limits statistical power and makes reliable inference of causal relations extremely difficult. However, we argue that statistical power can be increased substantially by incorporating prior knowledge and data from diverse sources. We present a Bayesian framework that combines information from different sources and we show empirically that this lets one make correct causal inferences with small sample sizes that otherwise would be impossible.

Algorithms↗

PubMed1996

Comparison of partial citrate synthase gene (gltA) sequences for phylogenetic analysis of Bartonella species.

Nucleotide base sequence data were obtained for a 940-bp fragment of the citrate synthase-encoding gene (gltA) of representatives of the eight validly described Bartonella species and seven uncharacterized Bartonella strains obtained from small mammals. Complete 16S rRNA gene sequences were also determined for the uncharacterized strains, and these sequences revealed that each strain had a unique sequence which was very similar to the sequences of the previously recognized Bartonella species. A comparison of the gltA sequences of the different Bartonella species revealed that the levels of similarity between sequences were 83.8 to 93.5%, whereas comparisons of sequences obtained from different strains of the same species revealed that the levels of similarity were more than 99.8%. One of the uncharacterized strains had a gltA sequence that matched the sequence of Bartonella elizabethae, three uncharacterized strains had sequences which were more than 99.6% similar to each other (but less than 93.5% similar to any other sequence), and the remaining three uncharacterized strains each exhibited less than 93.5% sequence similarity to other Bartonella species or isolates. Phylogenetic trees were inferred from multiple alignments of both gltA and 16S ribosomal DNA (rDNA) sequences. Whereas the proposed intra-Bartonella architecture of trees inferred from 16S rDNA sequence data by using both distance matrix and parsimony methods had virtually no statistical support, the trees inferred from the gltA sequence data contained four well-supported lineages in the genus. The gltA-derived phylogeny appears to be more useful than the phylogeny derived from 16S rDNA sequence data for investigating the evolutionary relationships of Bartonella species, and the validity of the lineages identified by the gltA analysis is discussed in this paper.

Bartonella↗

PubMed2000

Detection of disease genes by use of family data. I. Likelihood-based theory.

We present a class of likelihood-based score statistics that accommodate genotypes of both unrelated individuals and families, thereby combining the advantages of case-control and family-based designs. The likelihood extends the one proposed by Schaid and colleagues (Schaid and Sommer 1993, 1994; Schaid 1996; Schaid and Li 1997) to arbitrary family structures with arbitrary patterns of missing data and to dense sets of multiple markers. The score statistic comprises two component test statistics. The first component statistic, the nonfounder statistic, evaluates disequilibrium in the transmission of marker alleles from parents to offspring. This statistic, when applied to nuclear families, generalizes the transmission/disequilibrium test to arbitrary numbers of affected and unaffected siblings, with or without typed parents. The second component statistic, the founder statistic, compares observed or inferred marker genotypes in the family founders with those of controls or those of some reference population. The founder statistic generalizes the statistics commonly used for case-control data. The strengths of the approach include both the ability to assess, by comparison of nonfounder and founder statistics, the potential bias resulting from population stratification and the ability to accommodate arbitrary family structures, thus eliminating the need for many different ad hoc tests. A limitation of the approach is the potential power loss and/or bias resulting from inappropriate assumptions on the distribution of founder genotypes. The systematic likelihood-based framework provided here should be useful in the evaluation of both the relative merits of case-control and various family-based designs and the relative merits of different tests applied to the same design. It should also be useful for genotype-disease association studies done with the use of a dense set of multiple markers.

Alleles↗

PubMed2004

On the logic of hypothesis testing in functional imaging.

Statistics is nowadays the customary language of functional imaging. It is common to express an experimental setting as a set of null hypotheses over complex models and to present results as maps of p-values derived from sophisticated probability distributions. However, the growing interest in the development of advanced statistical algorithms is not always paralleled by similar attention to how these techniques may regiment the ways in which users draw inferences from their data. This article investigates the logical bases of current statistical approaches in functional imaging and probes their suitability to inductive inference in neuroscience. The frequentist approach to statistical inference is reviewed with attention to its two main constituents: Fisherian "significance testing" and Neyman-Pearson "hypothesis testing". It is shown that these conceptual systems, which are similar in the univariate testing case, dissociate into two quite different methods of inference when applied to the multiple testing problem, the typical framework of functional imaging. This difference is explained with reference to specific issues, like small volume correction, which are most likely to generate confusion in the practitioner. Further insight into this problem is achieved by recasting the multiple comparison problem into a multivariate Bayesian formulation. This formulation introduces a new perspective where the inferential process is more clearly defined in two distinct steps. The first one, inductive in form, uses exploratory techniques to acquire preliminary notions on the spatial patterns and the signal and noise characteristics. The (smaller) set of likely spatial patterns generated is then tested with newer data and a more rigorous multiple hypothesis testing technique (deductive step).

Algorithms↗

PubMed1996

A review of the statistical analysis used in papers published in Clinical Radiology and British Journal of Radiology.

Statistical analysis such as significance testing have become essential features of published medical studies. This has resulted in an increased frequency with which statistics are used, making the interpretation of scientific publications more difficult. There is an extensive array of tests and techniques. The aim of this study is to identify which statistical tests are used in radiology publications. All major articles published in Clinical Radiology and British Journal of Radiology in one year were reviewed. The frequency of statistical methods used was as follows: no statistical method or descriptive statistics only 103 (47%), one type of statistical method 67 (31%), and two or more methods 47 (22%). Statistics dealing with basic inference, decisions, contingency tables or correlation/regression techniques were found in 124 (53%) in which a procedure had been used. Advanced statistics including receiver operating characteristics (ROC), odds ratio, regression techniques, multiway ANOVA, and nonparametric ANOVA studies accounted for only 41 (19%) in which a procedure had been used. We conclude that descriptive analysis and basic statistical techniques account for most of the statistical tests reported. Physicians should concentrate on improving their understanding of basic statistics but advice should be sought from professionals in the fields of biostatistics and epidemiology as to whether the use of more advanced techniques would be more appropriate.

Humans↗

PubMed1998

On the statistical analysis of allelic-loss data.

This paper concerns the statistical analysis of certain binary data arising in molecular studies of cancer. In allelic-loss experiments, tumour cell genomes are analysed at informative molecular marker loci to identify deleted chromosomal regions. The resulting binary data are used to infer properties of putative suppressor genes, genes involved in normal cell cycling. Various factors can complicate this inference, including background loss of heterozygosity, spatial (that is, within chromosome) dependence of the binary responses, non-informativeness of markers, covariates such as protein levels or tumour histology, heterogeneity of cells within tumours, and measurement error. We focus on the first three factors, discussing methods for statistical inference that separate background loss from significant loss. We outline the extension to other inferences, such as comparison questions and the relationship to covariates. Using characteristic features of tumourigenesis, we present a framework for the stochastic modelling of allelic-loss data, and build models within this framework; in particular, we propose a simple model that has chromosome breaks at locations of a Poisson process, and preferential selection cells with inactivated suppressor genes. We illustrate these methods on allelic-loss data from induced rat mammary tumours and human bladder cancers.

Animals↗

PubMed2000

Statistical issues in tumor marker studies.

CONTEXT: Inferences from tumor marker studies are complicated by a variety of statistical concerns, which can make proper interpretation of results difficult. This article focuses on important issues that should be addressed when designing, conducting, and analyzing tumor marker studies. OBJECTIVE: To highlight the importance of considering statistical significance, risk ratios, statistical power, reproducibility, multiple testing, confirmatory studies, and missing data in the design of marker studies used for prognosis. RESULTS: Suggestions are provided for more effectively conducting marker studies. These include more careful attention to adequacy of the number of subjects for a marker study and improved documentation and standardization of assay methods. The importance of complete reporting of study results and description of the statistical analysis methods used is also emphasized. CONCLUSION: Cooperation among clinicians, laboratory scientists, and statisticians will be required to conduct statistically sound tumor marker studies and to facilitate prioritizing markers for new and confirmatory studies in an environment of limited patient and specimen resources.

Biomarkers, Tumor↗