SEARCH · Search PubMed

Results for “Statistics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,099 records · Page 61Linked to original sources

PubMed2004

LOR-OSEM: statistical PET reconstruction from raw line-of-response histograms.

Iterative statistical reconstruction methods are becoming the standard in positron emission tomography (PET). Conventional maximum-likelihood expectation-maximization (MLEM) and ordered-subsets (OSEM) algorithms act on data which have been pre-processed into corrected, evenly-spaced histograms; however, such pre-processing corrupts the Poisson statistics. Recent advances have incorporated attenuation, scatter and randoms compensation into the iterative reconstruction. The objective of this work was to incorporate the remaining pre-processing steps, including arc correction, to reconstruct directly from raw unevenly-spaced line-of-response (LOR) histograms. This exactly preserves Poisson statistics and full spatial information in a manner closely related to listmode ML, making full use of the ML statistical model. The LOR-OSEM algorithm was implemented using a rotation-based projector which maps directly to the unevenly-spaced LOR grid. Simulation and phantom experiments were performed to characterize resolution, contrast and noise properties for 2D PET. LOR-OSEM provided a beneficial noise-resolution tradeoff, outperforming AW-OSEM by about the same margin that AW-OSEM outperformed pre-corrected OSEM. The relationship between LOR-ML and listmode ML algorithms was explored, and implementation differences are discussed. LOR-OSEM is a viable alternative to AW-OSEM for histogram-based reconstruction with improved spatial resolution and noise properties.

Algorithms↗

PubMed2005

Effect of statistical uncertainties on Monte Carlo treatment planning.

This paper reviews the effect of statistical uncertainties on radiotherapy treatment planning using Monte Carlo simulations. We discuss issues related to the statistical analysis of Monte Carlo dose calculations for realistic clinical beams using various variance reduction or time saving techniques. We discuss the effect of statistical uncertainties on dose prescription and monitor unit calculation for conventional treatment and intensity-modulated radiotherapy (IMRT) based on Monte Carlo simulations. We show the effect of statistical uncertainties on beamlet dose calculation and plan optimization for IMRT and other advanced treatment techniques such as modulated electron radiotherapy (MERT). We provide practical guidelines for the clinical implementation of Monte Carlo treatment planning and show realistic examples of Monte Carlo based IMRT and MERT plans.

Computer Simulation↗

PubMed2006

Small nodule detectability evaluation using a generalized scan-statistic model.

In this paper is investigated the use of the scan statistic for evaluating the detectability of small nodules in medical images. The scan-statistic method is often used in applications in which random fields must be searched for abnormal local features. Several results of the detection with localization theory are reviewed and a generalization is presented using the noise nodule distribution obtained by scanning arbitrary areas. One benefit of the noise nodule model is that it enables determination of the scan-statistic distribution by using only a few image samples in a way suitable both for simulation and experimental setups. Also, based on the noise nodule model, the case of multiple targets per image is addressed and an image abnormality test using the likelihood ratio and an alternative test using multiple decision thresholds are derived. The results obtained reveal that in the case of low contrast nodules or multiple nodules the usual test strategy based on a single decision threshold underperforms compared with the alternative tests. That is a consequence of the fact that not only the contrast or the size, but also the number of suspicious nodules is a clue indicating the image abnormality. In the case of the likelihood ratio test, the multiple clues are unified in a single decision variable. Other tests that process multiple clues differently do not necessarily produce a unique ROC curve, as shown in examples using a test involving two decision thresholds. We present examples with two-dimensional time-of-flight (TOF) and non-TOF PET image sets analysed using the scan statistic for different search areas, as well as the fixed position observer.

Algorithms↗

PubMed1988

Statistical methods for the analysis of HIV-1 core polypeptide antigen data in clinical studies.

Levels of HIV-1 core polypeptide were assessed in serum and in lymphocyte cultures obtained from ARC and AIDS patients enrolled in a prospectively randomized, placebo-controlled study of zidovudine (AZT). Because these data have special features uncharacteristic of most laboratory data, a comprehensive account of statistical methods appropriate for their analysis is contained in this paper. Standard methods are described for the analysis of HIV-1 antigen in serum collected repeatedly over time in the same individual. For the analysis of lymphocyte culture data, more sophisticated statistical techniques based on nonparametric survival analysis methods are proposed. Using microcomputer software available upon request and developed to implement this statistical procedure, a significant decline in lymphocyte HIV-1 virus expression was noted between pretreatment and 3 months after the initiation of therapy among AZT-treated patients (p = 0.0017) that was not seen in placebo-treated patients (p = 0.25). Statistically significant between-group differences were also noted in the change from baseline at 3 months in HIV-1 antigen serum data (p = 0.040). We conclude that HIV-1 core polypeptide is an important measure of the antiretroviral activity of AZT and that the demonstrated clinical efficacy of AZT relative to placebo parallels its antiretroviral effect.

AIDS-Related Complex↗

PubMed1998

Statistics of large-scale sequence searching.

MOTIVATION: Database search programs such as FASTA, BLAST or a rigorous Smith-Waterman algorithm produce lists of database entries, which are assumed to be related to the query. The computation of statistical significance of similarity scores is well established for single pairs of sequences and using purely random models. However, the multi-trial context of a database search poses new problems. The credibility of a certain score obtained in a database search decreases with the amount of data that is compared. To improve p-value computation for database search experiments, statistical properties of the databases, such as the distribution of sequence length and effects induced by frequently repeated sequence patterns, need to be taken into account. RESULTS: We investigated the SWISS-PROT protein database Release 31.0 running extensive simulations of database searches. A discrepancy is observed between the theoretical predictions and the empirical distribution. To correct for this, we evaluate the statistical significance of scores in the context of a database search by a contrasting semi-random model. This model enhances purely random models by one additional parameter reflecting individual statistical properties of real databases. We call this parameter the effective size of the database. CONTACT: r.spang@dkfz-heidelberg.de;m.vingron@dkfz-hei del berg.de

Computational Biology↗

PubMed2002

Statistical analysis of high-density oligonucleotide arrays: a multiplicative noise model.

MOTIVATION: High-density oligonucleotide arrays (GeneChip, Affymetrix, Santa Clara, CA) have become a standard research tool in many areas of biomedical research. They quantitatively monitor the expression of thousands of genes simultaneously by measuring fluorescence from gene-specific targets or probes. The relationship between signal intensities and transcript abundance as well as normalization issues have been the focus of much recent attention (Hill et al., 2001; Chudin et al., 2002; Naef et al., 2002a). It is desirable that a researcher has the best possible analytical tools to make the most of the information that this powerful technology has to offer. At present there are three analytical methods available: the newly released Affymetrix Microarray Suite 5.0 (AMS) software that accompanies the GeneChip product, the method of Li and Wong (LW; Li and Wong, 2001), and the method of Naef et al. (FN; Naef et al., 2001). The AMS method is tailored for analysis of a single microarray, and can therefore be used with any experimental design. The LW method on the other hand depends on a large number of microarrays in an experiment and cannot be used for an isolated microarray, and the FN method is particular to paired microarrays, such as resulting from an experiment in which each 'treatment' sample has a corresponding 'control' sample. Our focus is on analysis of experiments in which there is a series of samples. In this case only the AMS, LW, and the method described in this paper can be used. The present method is model-based, like the LW method, but assumes multiplicative not additive noise, and employs elimination of statistically significant outliers for improved results. Unlike LW and AMS, we do not assume probe-specific background (measured by the so-called mismatch probes). Rather, we assume uniform background, whose level is estimated using both the mismatch and perfect match probe intensities. RESULTS: We present a new method for GeneChip analysis, based on a statistical model with multiplicative noise. We demonstrated that this method yields results superior to those obtained by the Affymetrix Microarray Suite 5.0 software and to those obtained by the model-based method of Li and Wong (Li and Wong, 2001). The present method eliminates the hard-to-interpret negative expression indices, and the binary 'presence' calls (present or absent) are replaced by the statistical significance (p-value) of gene expression. We have found that thresholding the p-values at the (0.1)(16)-level produces about the same number of 'present' calls as the AMS software. By testing our method on a pair of replicate GeneChips (hybridized with the same cRNA), we found that 95.6% of data points lie within the 1.25-fold interval. In other words, our method had a 4.4% type I error rate at the 1.25-fold level. The error rate of the LW method was 15%, and that of the AMS method was 29%. There were no points outside the 2-fold interval with the present method. Analysis of variance (ANOVA) of another experiment with multiple replicates shows that this reduction of variance is not accompanied by a corresponding reduction of signal. On the contrary, the signal-to-noise ratio (as measured by the distribution of F-statistics) of the present method is on average 3.4-times better than that of AMS, and 1.4-times better than that of Li and Wong.

Algorithms↗

PubMed2005

On the power for linkage detection using a test based on scan statistics.

We analyze some aspects of scan statistics, which have been proposed to help for the detection of weak signals in genetic linkage analysis. We derive approximate expressions for the power of a test based on moving averages of the identity by descent allele sharing proportions for pairs of relatives at several contiguous markers. We confirm these approximate formulae by simulation. The results show that when there is a single trait-locus on a chromosome, the test based on the scan statistic is slightly less powerful than that based on the customary allele sharing statistic. On the other hand, if two genes having a moderate effect on a trait lie close to each other on the same chromosome, scan statistics improve power to detect linkage.

Chromosome Mapping↗

PubMed1991

Statistical power in single subject trials.

A controlled single subject trial compares the efficacy of a new treatment with a control treatment in an individual patient. The treatments are administered in a double-blind, randomized, multi-crossover sequence of periods. During the trial response measures are obtained from each treatment period and form the basis for the statistical evaluation. Similar to the situation in clinical trials using groups of patients the statistical power is dependent on sample size, variability of responses, magnitude of the differential treatment effect and the level of statistical significance. In addition, the randomization procedure is of importance and power estimations show that a pairwise random allocation of treatment periods is more powerful than an unrestricted randomization. Since a single subject trial is a time consuming approach, the total number of treatment periods, the sample size, is restricted in order to make such trials feasible. Accordingly, less rigorous statistical requirements and power must be accepted. The consequence is an increased risk of both Type I and II errors. However, in comparison with the trial and error approach frequently applied in clinical practice, the controlled single subject trial may improve the certainty of therapeutic decisions in the individual patient.

Clinical Trials as Topic↗

PubMed2000

Do-it-yourself statistics: A computer-assisted likelihood approach to analysis of data from genetic crosses.

Graduate school programs in genetics have become so full that courses in statistics have often been eliminated. In addition, typical introductory statistics courses for the "statistics user" rather than the nascent statistician are laden with methods for analysis of measured variables while genetic data are most often discrete numbers. These courses are often seen by students and genetics professors alike as largely irrelevant cookbook courses. The powerful methods of likelihood analysis, although commonly employed in human genetics, are much less often used in other areas of genetics, even though current computational tools make this approach readily accessible. This article introduces the MLIKELY.PAS computer program and the logic of do-it-yourself maximum-likelihood statistics. The program itself, course materials, and expanded discussions of some examples that are only summarized here are available at http://www.unisi. it/ricerca/dip/bio_evol/sitomlikely/mlikely.h tml.

Animals↗

PubMed2000

A new statistic for detecting genetic differentiation.

A new statistic for detecting genetic differentiation of subpopulations is described. The statistic can be calculated when genetic data are collected on individuals sampled from two or more localities. It is assumed that haplotypic data are obtained, either in the form of DNA sequences or data on many tightly linked markers. Using a symmetric island model, and assuming an infinite-sites model of mutation, it is found that the new statistic is as powerful or more powerful than previously proposed statistics for a wide range of parameter values.

Chi-Square Distribution↗

PubMed2000

Measuring morbidity: disease counts, binary variables, and statistical power.

OBJECTIVES: This study compares the use of the binary disease variables with counts of the same conditions in models of self-rated health to better understand the advantages and disadvantages of each approach. In particular, the analysis seeks to determine if statistical power is adequate for the binary variable approach. METHODS: Morbidity measures from adults in 2 large national surveys were used in both cross-sectional and longitudinal analyses. RESULTS: Although differences across the approaches are modest, the binary variable approach offers greater explanatory power and slightly higher R2 values. Despite these advantages, statistical power is insufficient in some cases, especially for conditions that are relatively rare and/or that manifest modest differences on the outcome variable. DISCUSSION: Statistical power estimates are advisable when using the binary variable approach, especially if the list of diseases and health conditions is extensive. Although a simple count of diseases may be useful in some research applications, separate counts for serious and nonserious conditions should be more useful in many research projects while avoiding the risk of inadequate statistical power.

Adult↗

PubMed1987

The interaction of statistical significance, biology of dose-response and test design in the assessment of genotoxicity data.

The most useful function of statistical significance in genetic toxicology is to separate weakly positive from negative data. To achieve this objective, the experimental system, design and analysis must be considered as a whole and not as separate entities. Assumptions underlying statistical methods should be checked for applicability to the biology of the test. The number and arrangement of samples and dose levels must be organized to suit the selected statistical method. The number of observations in the test and in each sample will depend on the smallest increase of biological importance which should be detected as significant (at a selected value for alpha error) and the maximum acceptable chance of failing to detect this increase as significant (beta error). Generally, a few dose levels and a high degree of replication are desirable with a repeat test if practical. In the analyses of data it is particularly important to adapt the statistics if there are multiple comparisons. Even after correct determination of significance, interpretation can be difficult because of possible experimental artefacts and chance errors plus the impossibility of proving a negative result.

Data Interpretation, Statistical↗

PubMed1983

Statistical characterization of nucleic acid sequence functional domains.

It has long been recognized that various genome classes were distinguishable on the basis of base composition and nearest neighbor frequencies. In addition Grantham et al. (8) have recently presented evidence that these distinctions are preserved at the level of codon usage. As discussed in this report it is now clear that these and related statistics can uniquely characterize the various functional domains of the genome. In particular peptide coding, intervening segments, structural RNA coding and mitochondrial domains of the vertebrate genome are uniquely characterizable. The statistical measures not only reflect understood functional differences among these domains but suggest others. The ability of these simple statistics of nucleic acid sequences to reflect so much of the encoded complex pattern information and/or effects of selective constraints is somewhat surprising. Here, we investigated the statistical measures most distinctive of the various domains and then linked them to our current understandings in so far as possible.

Animals↗

PubMed1994

Statistical significance testing in the American Journal of Epidemiology, 1970-1990.

Despite persistent controversy over statistical significance testing and the obligation for all researchers to adopt some position on the issue, until now, practices of epidemiologists have not been examined thoroughly. Articles in the American Journal of Epidemiology around the years 1970, 1980, and 1990 were evaluated and divided into infectious disease epidemiology, cancer epidemiology, and cardiovascular disease epidemiology, with a minimum of 25 articles per topic area and time period. Presentation of significance test results and confidence intervals was evaluated in the abstracts, results text, and results tables, as was the degree of reliance on significance testing in the discussion section. Significance testing grew markedly in infectious disease epidemiology and was consistently high in cardiovascular disease and cancer epidemiology. Confidence intervals were virtually absent in 1970 and became ubiquitous (particularly in cancer epidemiology) by 1990, when the most common practice was to provide confidence intervals in results tables and to emphasize statistical significance tests in results text. Reliance on statistical significance tests in interpretation grew steadily in infectious disease, sustained a high level in cardiovascular disease, and declined after 1980 in cancer epidemiology. At present, dual presentation of confidence intervals and statistical test results is dominant.

Cardiovascular Diseases↗

PubMed2000

The use of nonparametric statistics in quantitative electron microscopy.

Parametric statistical methods assume samples that have a normal distribution and representative sample sizes (i.e. n >20). Quantitative electron microscopy is inherently restricted to small sample sizes and a priori there is no way to know if the expression of the ligand being studied has a normal distribution. Thus to make statistical inferences based on data generated by quantitative electron microscopy using parametric methods may not be justified. Nonparametric statistical methods offer a tool for the evaluation of data that do not meet the criteria for analysis by parametric methods. In this report I show the utility of using nonparametric statistical methods for the analysis of data generated by quantitative electron microscopy.

Animals↗

PubMed2003

Statistical analysis of nonmonotonic dose-response relationships: research design and analysis of nasal cell proliferation in rats exposed to formaldehyde.

Statistical analyses of nonmonotonic dose-response curves are proposed, experimental designs to detect low-dose effects of J-shaped curves are suggested, and sample sizes are provided. For quantal data such as cancer incidence rates, much larger numbers of animals are required than for continuous data such as biomarker measurements. For example, 155 animals per dose group are required to have at least an 80% chance of detecting a decrease from a 20% incidence in controls to an incidence of 10% at a low dose. For a continuous measurement, only 14 animals per group are required to have at least an 80% chance of detecting a change of the mean by one standard deviation of the control group. Experimental designs based on three dose groups plus controls are discussed to detect nonmonotonicity or to estimate the zero equivalent dose (ZED), i.e., the dose that produces a response equal to the average response in the controls. Cell proliferation data in the nasal respiratory epithelium of rats exposed to formaldehyde by inhalation are used to illustrate the statistical procedures. Statistically significant departures from a monotonic dose response were obtained for time-weighted average labeling indices with an estimated ZED at a formaldehyde dose of 5.4 ppm, with a lower 95% confidence limit of 2.7 ppm. It is concluded that demonstration of a statistically significant bi-phasic dose-response curve, together with estimation of the resulting ZED, could serve as a point-of departure in establishing a reference dose for low-dose risk assessment.

Administration, Inhalation↗

PubMed1995

Analysis of statistical tests to compare visual analog scale measurements among groups.

BACKGROUND: A common type of study performed by anesthesiologists determines the effect of an intervention on pain reported by groups of patients. The goal of this study was to evaluate the effectiveness of t, analysis of variance (ANOVA), Mann-Whitney, and Kruskal-Wallis tests to compare visual analog scale (VAS) measurements between two or among three groups of patients. These results may be particularly helpful during the design of studies that measure pain with a VAS. METHODS: One VAS measurement was obtained from each of 480 nulliparous women in labor who were receiving oxytocin (149), nalbuphine (159), or epidural bupivacaine (172). Multiple simulated samples were then drawn from these data. These simulated samples were used in computer simulations of clinical trials comparing VAS measurements among groups. t and ANOVA tests were performed before and after an arcsin transformation was used, to make the data closer to a normal distribution. VAS measurements were also compared after they were divided into five ranked categories. RESULTS: The statistical distributions of VAS measurements were not normal (P < 10(-7)). Arcsin transformation made the distributions closer to normal distributions. Nevertheless, no statistical test incorrectly suggested that a difference existed among groups, when there was no difference, more often than the expected rate. t or ANOVA tests had a slightly greater statistical power than the other tests to detect differences among groups. Because arcsin transformation both decreased differences among means and reduced the variance to a lesser extent, it decreased power to detect differences among groups. Statistical power to detect differences among groups was not less for a five-category VAS than for a continuous VAS. CONCLUSIONS: We conclude that t and ANOVA, without an accompanying arcsin transformation, are good tests to find differences in VAS measurements among groups.

Analgesia, Epidural↗

PubMed1996

The seven deadly sins of statistical analysis.

In a pedantic but playful way, we discuss some common errors in the use of 'statistical analysis' that are regularly observed in our professional plastic surgical literature. The seven errors we discuss are (1) the use of parametric analysis of ordinal data; (2) the inappropriate use of parametric analysis in general; (3) the failure to consider the possibility of committing type II statistical error; (4) the use of unmodified t-tests for multiple comparisons; (5) the failure to employ analysis of covariance, multivariate regression, nonlinear regression, and logistical regression when indicated; (6) the habit of reporting standard error instead of standard deviation; and (7) the underuse or overuse of statistical consultation. Confidence and common sense are advocated as a means to balance statistical significance with clinical importance.

Humans↗