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Results for “Bootstrap resampling”

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PubMed2004

Randomization tests for ERP topographies and whole spatiotemporal data matrices.

In ERP studies, the comparison of topographies (multichannel measurements) or whole spatiotemporal data matrices (multichannel time series of measurements), the classical statistical tests very often cannot be used. It is argued that, for these comparisons, randomization tests are an excellent alternative. It is also argued that the randomization test is superior to another resampling method, the bootstrap, because exact probability statements (e.g., p values) can be made. A review is given of the literature on randomization tests designed for electrophysiological data. New randomization tests are presented and applied to two data sets, one coming from a psychopharmacological experiment and the other from an ERP experiment in visual word recognition.

Animals↗

PubMed2006

Toward objective and quantitative evaluation of imaging systems using images of phantoms.

The use of imaging phantoms is a common method of evaluating image quality in the clinical setting. These evaluations rely on a subjective decision by a human observer with respect to the faintest detectable signal(s) in the image. Because of the variable and subjective nature of the human-observer scores, the evaluations manifest a lack of precision and a potential for bias. The advent of digital imaging systems with their inherent digital data provides the opportunity to use techniques that do not rely on human-observer decisions and thresholds. Using the digital data, signal-detection theory (SDT) provides the basis for more objective and quantitative evaluations which are independent of a human-observer decision threshold. In a SDT framework, the evaluation of imaging phantoms represents a "signal-known-exactly/background-known-exactly" ("SKE/ BKE") detection task. In this study, we compute the performance of prewhitening and nonprewhitening model observers in terms of the observer signal-to-noise ratio (SNR) for these "SK E/BKE" tasks. We apply the evaluation methods to a number of imaging systems. For example, we use data from a laboratory implementation of digital radiography and from a full-field digital mammography system in a clinical setting. In addition, we make a comparison of our methods to human-observer scoring of a set of digital images of the CDMAM phantom available from the internet (EUREF-European Reference Organization). In the latter case, we show a significant increase in the precision of the quantitative methods versus the variability in the scores from human observers on the same set of images. As regards bias, the performance of a model observer estimated from a finite data set is known to be biased. In this study, we minimize the bias and estimate the variance of the observer SNR using statistical resampling techniques, namely, "bootstrapping" and "shuffling" of the data sets. Our methods provide objective and quantitative evaluation of imaging systems with increased precision and reduced bias.

Algorithms↗

PubMed2004

Utility of recombinant enzyme kinetics in prediction of human clearance: impact of variability, CYP3A5, and CYP2C19 on CYP3A4 probe substrates.

A systematic kinetic analysis of the metabolism of five benzodiazepines (low to high clearance compounds) was performed in CYP3A4, CYP3A5, and CYP2C19 baculovirus-expressed recombinant systems. The data obtained in the expression systems were scaled and compared with human liver microsomal predicted clearance and observed in vivo values, using either cytochrome P450 relative activity factors (RAFs) or the relative abundance approach. Interindividual variability, both in content (CYP3A4, CYP3A5) and activity (CYP3A4, CYP3A5, and CYP2C19), were incorporated in the clearance prediction by bootstrap analysis. These resampling Monte Carlo-based simulations were performed to justify any distribution assumptions in the generated range of the predicted clearance due to a limited sample size. This approach allowed extrapolation of the recombinant clearance data to specific population groups and investigation of the role of "minor" forms like CYP3A5 and CYP2C19 in comparison to the most prolific CYP3A4. The use of quinidine 3-hydroxylation and alprazolam 1'-hydroxylation as RAF markers for CYP3A4 and CYP3A5 activity, respectively, and the incorporation of variability improved the clearance prediction of the selected benzodiazepines (apart from flunitrazepam) to within 2-fold of the in vivo value. Clearance estimates from the immunoquantified protein levels were approximately 8-fold lower in comparison to the RAF approach. The differences observed in the benzodiazepine metabolite pathway ratios between CYP3A4 and CYP3A5, particularly for 1'- to 4-hydroxymidazolam and alprazolam, provided a useful measure of interindividual differences within the CYP3A family.

Algorithms↗

PubMed2005

On studentising and blocklength selection for the bootstrap on time series.

For independent data, non-parametric bootstrap is realised by resampling the data with replacement. This approach fails for dependent data such as time series. If the data generating process is at least stationary and mixing, the blockwise bootstrap by drawing subsamples or blocks of the data saves the concept. For the blockwise bootstrap a blocklength has to be selected. We propose a method for selecting the optimal blocklength. To improve the finite size properties of the blockwise bootstrap, studentised statistics is considered. If the statistic can be represented as a smooth function model this studentisation can be approximated efficiently. The studentised blockwise bootstrap method is applied for testing hypotheses on medical time series.

Algorithms↗

PubMed2005

Incorporating gene-specific variation when inferring and evaluating optimal evolutionary tree topologies from multilocus sequence data.

Because of the increase of genomic data, multiple genes are often available for the inference of phylogenetic relationships. The simple approach for combining multiple genes from the same taxon is to concatenate the sequences and then ignore the fact that different positions in the concatenated sequence came from different genes. Here, we discuss two criteria for inferring the optimal tree topology from data sets with multiple genes. These criteria are designed for multigene data sets where gene-specific evolutionary features are too important to ignore. One criterion is conventional and is obtained by taking the sum of log-likelihoods over all genes. The other criterion is obtained by dividing the log-likelihood for a gene by its sequence length and then taking the arithmetic mean over genes of these ratios. A similar strategy could be adopted with parsimony scores. The optimal tree is then declared to be the one for which the sum or the arithmetic mean is maximized. These criteria are justified within a two-stage hierarchical framework. The first level of the hierarchy represents gene-specific evolutionary features, and the second represents site-specific features for given genes. For testing significance of the optimal topology, we suggest a two-stage bootstrap procedure that involves resampling genes and then resampling alignment columns within resampled genes. An advantage of this procedure over concatenation is that it can effectively account for gene-specific evolutionary features. We discuss the applicability of the two-stage bootstrap idea to the Kishino-Hasegawa test and the Shimodaira-Hasegawa test.

Biological Evolution↗

PubMed2001

Mitochondrial versus nuclear gene sequences in deep-level mammalian phylogeny reconstruction.

Both mitochondrial and nuclear gene sequences have been employed in efforts to reconstruct deep-level phylogenetic relationships. A fundamental question in molecular systematics concerns the efficacy of different types of sequences in recovering clades at different taxonomic levels. We compared the performance of four mitochondrial data sets (cytochrome b, cytochrome oxidase II, NADH dehydrogenase subunit I, 12S rRNA-tRNA-16S rRNA) and eight nuclear data sets (exonic regions of alpha-2B adrenergic receptor, aquaporin, ss-casein, gamma-fibrinogen, interphotoreceptor retinoid binding protein, kappa-casein, protamine, von Willebrand Factor) in recovering deep-level mammalian clades. We employed parsimony and minimum-evolution with a variety of distance corrections for superimposed substitutions. In 32 different pairwise comparisons between these mitochondrial and nuclear data sets, we used the maximum set of overlapping taxa. In each case, the variable-length bootstrap was used to resample at the size of the smaller data set. The nuclear exons consistently performed better than mitochondrial protein and rRNA-tRNA coding genes on a per-residue basis in recovering benchmark clades. We also concatenated nuclear genes for overlapping taxa and made comparisons with concatenated mitochondrial protein-coding genes from complete mitochondrial genomes. The variable-length bootstrap was used to score the recovery of benchmark clades as a function of the number of resampled base pairs. In every case, the nuclear concatenations were more efficient than the mitochondrial concatenations in recovering benchmark clades. Among genes included in our study, the nuclear genes were much less affected by superimposed substitutions. Nuclear genes having appropriate rates of substitution should receive strong consideration in efforts to reconstruct deep-level phylogenetic relationships.

Animals↗

PubMed2026

An individualized nomogram for predicting progression-free survival in systemic anaplastic large cell lymphoma: a multicenter, retrospective, and internally validated study.

OBJECTIVES: To develop an individualized nomogram for predicting disease progression risk in systemic anaplastic large cell lymphoma (sALCL). METHODS: Independent predictors of progression-free survival (PFS) were identified using Cox regression in a multicenter retrospective cohort of 109 sALCL patients (2010-2022). These were incorporated into a three-factor nomogram, evaluated via bootstrapped internal validation (1000 resamples), ROC analysis, C-index, decision curve analysis (DCA), and clinical impact curve (CIC). RESULTS: A total of 29 PFS events occurred during a median follow-up of 31 months. Multivariable modelling selected serum β2-microglobulin elevation, extranodal disease, and front-line chemotherapy choice (CHOP versus CHOPE or BV+CHP) as autonomous progression drivers. Upon internal bootstrap validation, the nomogram yielded strong prognostic accuracy, achieving AUCs of 0.81, 0.85 and 0.87 for 1-, 3- and 5-year progression-free survival, alongside a corrected C-index of 0.779 (95% CI: 0.699 - 0.861). Calibration plots showed close agreement between predicted and observed outcomes, while DCA confirmed superior net clinical benefit versus conventional IPI or Ann Arbor stratification across multiple decision thresholds. CONCLUSION: This first sALCL-specific nomogram integrates clinical and treatment variables to provide personalized PFS risk estimation. While internally validated, this exploratory, observation-based tool requires external validation and recalibration in prospective cohorts before clinical implementation.

Humans↗

PubMed2005

Dispersion dimorphism in human populations.

The aim of this paper is to verify the existence of dispersion (or variability) dimorphism in several anthropometric traits, i.e., some skeletal, muscular, and adipose dimensions, and to provide an evaluation of this scarcely considered aspect of sex differences. This research focuses on two human samples from two different European regions, Sardinia (Italy) and the Basque Country (Spain). Twenty-three anthropometric measurements were taken. A first qualitative analysis was carried out comparing the profiles of the coefficients of variation of each variable in both sexes. Secondly, the equality of variability was verified with different tests. In the normal case, Student's t-test, as proposed by Sokal and Braumann ([1980] Syst. Zool. 29:50-63), was applied. In the more general case, it was necessary to resort to resampling techniques. A suitable bootstrap test was constructed and the results were compared, when possible, with those obtained by other methods. The measurements showed parallel trends in the two populations. In particular, subcutaneous skinfolds showed significant dispersion dimorphism, with the greatest coefficients of variation in men. We suggest that this result was linked to stronger canalization in female dimensions related to the reproductive function and influenced by sociocultural factors. We also suggest defining dispersion dimorphism, whose existence is confirmed by the results presented in this paper, as a descriptive pattern of phenotype variability in both sexes that can be specific of a given class of anthropometric traits.

Anthropometry↗

PubMed2006

Characterization of Escherichia coli isolates from different fecal sources by means of classification tree analysis of fatty acid methyl ester (FAME) profiles.

Microbial source tracking (MST) methods need to be rapid, inexpensive and accurate. Unfortunately, many MST methods provide a wealth of information that is difficult to interpret by the regulators who use this information to make decisions. This paper describes the use of classification tree analysis to interpret the results of a MST method based on fatty acid methyl ester (FAME) profiles of Escherichia coli isolates, and to present results in a format readily interpretable by water quality managers. Raw sewage E. coli isolates and animal E. coli isolates from cow, dog, gull, and horse were isolated and their FAME profiles collected. Correct classification rates determined with leaveone-out cross-validation resulted in an overall low correct classification rate of 61%. A higher overall correct classification rate of 85% was obtained when the animal isolates were pooled together and compared to the raw sewage isolates. Bootstrap aggregation or adaptive resampling and combining of the FAME profile data increased correct classification rates substantially. Other MST methods may be better suited to differentiate between different fecal sources but classification tree analysis has enabled us to distinguish raw sewage from animal E. coli isolates, which previously had not been possible with other multivariate methods such as principal component analysis and cluster analysis.

Animals↗

PubMed1994

Statistical analysis of synaptic transmission: model discrimination and confidence limits.

Procedures for discriminating between competing statistical models of synaptic transmission, and for providing confidence limits on the parameters of these models, have been developed. These procedures were tested against simulated data and were used to analyze the fluctuations in synaptic currents evoked in hippocampal neurones. All models were fitted to data using the Expectation-Maximization algorithm and a maximum likelihood criterion. Competing models were evaluated using the log-likelihood ratio (Wilks statistic). When the competing models were not nested, Monte Carlo sampling of the model used as the null hypothesis (H0) provided density functions against which H0 and the alternate model (H1) were tested. The statistic for the log-likelihood ratio was determined from the fit of H0 and H1 to these probability densities. This statistic was used to determine the significance level at which H0 could be rejected for the original data. When the competing models were nested, log-likelihood ratios and the chi 2 statistic were used to determine the confidence level for rejection. Once the model that provided the best statistical fit to the data was identified, many estimates for the model parameters were calculated by resampling the original data. Bootstrap techniques were then used to obtain the confidence limits of these parameters.

Algorithms↗

PubMed2001

Evaluation of dissolution profiles using principal component analysis.

The performance of principal component analysis (PCA) for the evaluation of dissolution profiles is examined and compared with other methods such as the similarity factor and the calculation of the area under the curve. Both simulated and real data from the pharmaceutical industry are used. The PCA scores plots of the dissolution curves provide information about the between- and within-batch variations. Differences in level or shape can be observed in the first two principal components (PCs). Irrelevant irregularities, which have a strong influence on the similarity factor, are neglected in PC1/PC2. To detect outliers in a set of dissolution curves, PCA was preferred above Hotelling's T2 test. In general, PCA is found to be a useful technique to examine dissolution data visually, but however, it does not contain criteria to decide if batches are similar or not. This can be done by combining PCA with the resampling with replacement or bootstrap method to construct confidence limits.

Solubility↗

PubMed2005

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data.

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap 632+ and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to approximately 96% correct classification rates with less than 10% of the original features.

Adult↗

PubMed2004

A likelihood-based approach to estimating and testing for isolation by distance.

Simple regression of genetic similarities between pairs of populations on their corresponding geographic distances is frequently used to detect the presence of isolation by distance (IBD). However, these pairwise values are obviously not independent and there is no parametric procedure for estimating and testing for the IBD intercepts and slopes based on standard regression theory. Nonparametric tests, such as the Mantel test, and resampling techniques, such as bootstrapping, have been exploited with limited success. Here, I describe a likelihood-based analysis to allow for simultaneously detecting patterns of correlated residuals and estimating and testing for the presence of IBD. It is shown, through the analysis of two molecular datasets in pine species, that different covariance structures of the residuals exist. More over, the likelihood ratio tests under these covariance structures are less sensitive to the presence of IBD than the Mantel test and the simple regression analysis but more sensitive than the bootstrap and jackknife samples over independent populations or population pairs. Because the likelihood analysis directly models and accounts for nonindependence of residuals, it should legitimately detect the presence of IBD, thereby allowing for accurate inferences about evolutionary and demographic processes influencing the extent and patterns of IBD.

Analysis of Variance↗

PubMedDate not supplied

Characterizing the variability of system kernel and input estimates.

The identification of the input to, or kernels of, a system using nonparametric representations and least-squares estimation is becoming increasingly popular. Nonparametric representations avoid making a priori assumptions about the input or having detailed knowledge about the system, and only need to guarantee known general characteristics (for example, positivity), which are obtained through the imposition of constraints on the estimates. An often overlooked problem is how to characterize the variability of the estimates so obtained. This problem is caused by the presence of constraints--and/or the nonlinearities of the estimates, or the complexity of the (regression based) estimation algorithms used--which make standard methods of estimating variability incorrect. In this article we investigate the use of a resampling technique called the "bootstrap" to obtain the desired estimates of variability. We present real data analysis demonstrating the approach, and through simulations we test the performance of a novel bootstrap technique obtaining confidence bands for the estimated functions.

Algorithms↗

PubMed2002

Cost effectiveness of paclitaxel/cisplatin compared with cyclophosphamide/cisplatin in the treatment of advanced ovarian cancer in Belgium.

OBJECTIVE: To assess the economic impact of two polychemotherapy regimens for patients with advanced ovarian cancer from the perspective of the Belgian health insurance and financing system. DESIGN: An economic evaluation was integrated in an intergroup randomised controlled trial (EORTC 55931) in which patients were randomised to receive the new treatment of paclitaxel and cisplatin or the standard therapy of cyclophosphamide and cisplatin. Data on the use of medical resources were collected prospectively for the 231 European Organization for Research and Treatment of Cancer (EORTC) patients in the trial and costs were valued by using unit prices. The outcome for the economic evaluation was mean survival time as determined by the so-called restricted means method, with the time point of restriction fixed by statistical criteria. A correction of censoring of the cost data collected in the trial was also performed. MAIN OUTCOME MEASURES AND RESULTS: The paclitaxel and cisplatin group experienced a statistically significant improvement in mean survival time of 4 months, which was associated with an increase in the average total cost per patient of 6795 euros (EUR; 1998 values), when costs were assessed over the same period as the gain in mean survival time. This corresponds to a point estimate of the incremental cost-effectiveness ratio of EUR20 385 per life-year gained. The impact of uncertainty was assessed by using a bias-corrected and accelerated bootstrap method with 5000 resamples, and the final results of the analysis are expressed in terms of a cost-effectiveness acceptability curve. CONCLUSIONS: The present economic evaluation has shown that the substitution of paclitaxel for cyclophosphamide in the chemotherapy regimen for women with advanced ovarian cancer leads to a significant improvement in patient survival, which is associated with an increase in costs for the Belgian health insurance system.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed2006

Comparison of bootstrap approaches for estimation of uncertainties of DTI parameters.

Bootstrap is an empirical non-parametric statistical technique based on data resampling that has been used to quantify uncertainties of diffusion tensor MRI (DTI) parameters, useful in tractography and in assessing DTI methods. The current bootstrap method (repetition bootstrap) used for DTI analysis performs resampling within the data sharing common diffusion gradients, requiring multiple acquisitions for each diffusion gradient. Recently, wild bootstrap was proposed that can be applied without multiple acquisitions. In this paper, two new approaches are introduced called residual bootstrap and repetition bootknife. We show that repetition bootknife corrects for the large bias present in the repetition bootstrap method and, therefore, better estimates the standard errors. Like wild bootstrap, residual bootstrap is applicable to single acquisition scheme, and both are based on regression residuals (called model-based resampling). Residual bootstrap is based on the assumption that non-constant variance of measured diffusion-attenuated signals can be modeled, which is actually the assumption behind the widely used weighted least squares solution of diffusion tensor. The performances of these bootstrap approaches were compared in terms of bias, variance, and overall error of bootstrap-estimated standard error by Monte Carlo simulation. We demonstrate that residual bootstrap has smaller biases and overall errors, which enables estimation of uncertainties with higher accuracy. Understanding the properties of these bootstrap procedures will help us to choose the optimal approach for estimating uncertainties that can benefit hypothesis testing based on DTI parameters, probabilistic fiber tracking, and optimizing DTI methods.

Brain↗

PubMed2003

Novel versus unsupported clades: assessing the qualitative support for clades in MRP supertrees.

Matrix representation with parsimony (MRP) supertree construction has been criticized because the supertree may specify clades that are contradicted by every source tree contributing to it. Such unsupported clades may also occur using other supertree methods; however, their incidence is largely unknown. In this study, I investigated the frequency of unsupported clades in both simulated and empirical MRP supertrees. Here, I propose a new index, QS, to quantify the qualitative support for a supertree and its clades among the set of source trees. Results show that unsupported clades are very rare in MRP supertrees, occurring most often when there are few source trees that all possess the same set of taxa. However, even under these conditions the frequency of unsupported clades was <0.2%. Unsupported clades were absent from both the Carnivora and Lagomorpha supertrees, reflecting the use of large numbers of source trees for both. The proposed QS indices are correlated broadly with another measure of quantitative clade support (bootstrap frequencies, as derived from resampling of the MRP matrix) but appear to be more sensitive. More importantly, they sample at the level of the source trees and thus, unlike the bootstrap, are suitable for summarizing the support of MRP supertree clades.

Algorithms↗

PubMed2000

Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.

Since the early 1980s, a bewildering array of methods for constructing bootstrap confidence intervals have been proposed. In this article, we address the following questions. First, when should bootstrap confidence intervals be used. Secondly, which method should be chosen, and thirdly, how should it be implemented. In order to do this, we review the common algorithms for resampling and methods for constructing bootstrap confidence intervals, together with some less well known ones, highlighting their strengths and weaknesses. We then present a simulation study, a flow chart for choosing an appropriate method and a survival analysis example.

Algorithms↗