SEARCH · PubMed Health

Results for “bootstrap”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

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

At least 181 records · Page 10Linked to original sources

PubMed2001

A bootstrapped commingling analysis of platelet monoamine oxidase activity levels corrected for cigarette smoking.

Monoamine oxidase (MAO) activity levels have been suggested as a possible biological marker for alcohol dependence and abuse, as well as for schizophrenia and other psychiatric conditions. Using platelet MAO activities in the Collaborative Study on the Genetics of Alcoholism data set, we applied bootstrapping methods as a novel way to test for admixture in families. This bootstrapping involved resampling in family units and hypothesis testing of the resampled datasets for commingling in the distribution of MAO activity levels. Prior to commingling analysis, we used linear models to find covariates of greatest effect on MAO activity levels. While an alcoholism diagnosis was significant in men (n = 1151, P < 0.0001) and women (n = 1254, P = 0.0003), the effect lost significance after controlling for cigarette smoking, indicating alcoholism and smoking behavior to be highly confounded. When smoking histories were compared, former smokers had levels (mean = 7.1) closer to those who never smoked (mean = 7.0) than to current smokers (mean = 5.4). Furthermore, current daily smoking and time since smoking cessation were significantly related to MAO levels, indicating smoking probably has a direct effect on MAO levels, rather than the reverse. These results suggest that studies using MAO levels as a biological marker should consider smoking as an important covariate. Finally, admixture was found in MAO levels controlled for smoking and sex, possibly indicating a major genetic locus; this confirms previous evidence for admixture.

Biomarkers↗

PubMed2004

Clarification of the bootstrap percolation paradox.

We study the onset of the bootstrap percolation transition as a model of generalized dynamical arrest. Our results apply to two dimensions, but there is no significant barrier to extending them to higher dimensionality. We develop a new importance-sampling procedure in simulation, based on rare events around "holes", that enables us to access bootstrap lengths beyond those previously studied. By framing a new theory in terms of paths or processes that lead to emptying of the lattice we are able to develop systematic corrections to the existing theory and compare them to simulations. Thereby, for the first time in the literature, it is possible to obtain credible comparisons between theory and simulation in the accessible density range.

Journal Article↗

PubMed2007

Performance generalization in biometric authentication using joint user-specific and sample bootstraps.

Biometric authentication performance is often depicted by a detection error trade-off (DET) curve. We show that this curve is dependent on the choice of samples available, the demographic composition and the number of users specific to a database. We propose a two-step bootstrap procedure to take into account the three mentioned sources of variability. This is an extension to the Bolle et al.'s bootstrap subset technique. Preliminary experiments on the NIST2005 and XM2VTS benchmark databases are encouraging, e.g., the average result across all 24 systems evaluated on NIST2005 indicates that one can predict, with more than 75 percent of DET coverage, an unseen DET curve with eight times more users. Furthermore, our finding suggests that with more data available, the confidence intervals become smaller and, hence, more useful.

Algorithms↗

PubMed2005

Bootstrap tests for overdispersion in a zero-inflated Poisson regression model.

Ridout, Hinde, and Demétrio (2001, Biometrics 57, 219-223) derived a score test for testing a zero-inflated Poisson (ZIP) regression model against zero-inflated negative binomial (ZINB) alternatives. They mentioned that the score test using the normal approximation might underestimate the nominal significance level possibly for small sample cases. To remedy this problem, a parametric bootstrap method is proposed. It is shown that the bootstrap method keeps the significance level close to the nominal one and has greater power uniformly than the existing normal approximation for testing the hypothesis.

Biometry↗

PubMed1994

Bootstrap-corrected ADF test statistics in covariance structure analysis.

The asymptotically distribution-free (ADF) test statistic for covariance structure analysis (CSA) has been reported to perform very poorly in simulation studies, i.e. it leads to inaccurate decisions regarding the adequacy of models of psychological processes. It is shown in the present study that the poor performance of the ADF test statistic is due to inadequate estimation of the weight matrix (W = gamma -1), which is a critical quantity in the ADF theory. Bootstrap procedures based on Hall's bias reduction perspective are proposed to correct the ADF test statistic. It is shown that the bootstrap correction of additive bias on the ADF test statistic yields the desired tail behaviour as the sample size reaches 500 for a 15-variable-3-factor confirmatory factor-analytic model, even if the distribution of the observed variables is not multivariate normal and the latent factors are dependent. These results help to revive the ADF theory in CSA.

Factor Analysis, Statistical↗

PubMedDate not supplied

The use of bootstrap resampling to assess the uncertainty of cooper statistics.

The predictive abilities of two-group classification models (CMs) are often expressed in terms of their Cooper statistics. These statistics are often reported without any indication of their uncertainty, making it impossible to judge whether the predicted classifications are significantly better than the predictions made by a different CM, or whether the predictive performance of the CM exceeds predefined performance criteria in a statistically significant way. Bootstrap resampling routines are reported that provide a means of expressing the uncertainty associated with Cooper statistics. The usefulness of the bootstrapping routines is illustrated by constructing 95% confidence intervals for the Cooper statistics of four alternative skin-corrosivity tests (the rat skin transcutaneous electrical resistance assay, EPISKIN, Skin(2) and CORROSITEX), and four two-step sequences in which each in vitro test is used in combination with a physicochemical test for skin corrosion based on pH measurements.

Animal Testing Alternatives↗

PubMedDate not supplied

Probabilistic sensitivity analysis incorporating the bootstrap: an example comparing treatments for the eradication of Helicobacter pylori.

Decision-analytic models are frequently used to evaluate the relative costs and benefits of alternative therapeutic strategies for health care. Various types of sensitivity analysis are used to evaluate the uncertainty inherent in the models. Although probabilistic sensitivity analysis is more difficult theoretically and computationally, the results can be much more powerful and useful than deterministic sensitivity analysis. The authors show how a Monte Carlo simulation can be implemented using standard software to perform a probabilistic sensitivity analysis incorporating the bootstrap. The method is applied to a decision-analytic model evaluating the cost-effectiveness of Helicobacter pylori eradication. The necessary steps are straightforward and are described in detail. The use of the bootstrap avoids certain difficulties encountered with theoretical distributions. The probabilistic sensitivity analysis provided insights into the decision-analytic model beyond the traditional base-case and deterministic sensitivity analyses and should become the standard method for assessing sensitivity.

Anti-Bacterial Agents↗

PubMed2000

Testing treatment effects in repeated measures designs: trimmed means and bootstrapping.

Non-normality and covariance heterogeneity between groups affect the validity of the traditional repeated measures methods of analysis, particularly when group sizes are unequal. A non-pooled Welch-type statistic (WJ) and the Huynh Improved General Approximation (IGA) test generally have been found to be effective in controlling rates of Type I error in unbalanced non-spherical repeated measures designs even though data are non-normal in form and covariance matrices are heterogeneous. However, under some conditions of departure from multisample sphericity and multivariate normality their rates of Type I error have been found to be elevated. Westfall and Young's results suggest that Type I error control could be improved by combining bootstrap methods with methods based on trimmed means. Accordingly, in our investigation we examined four methods for testing for main and interaction effects in a between- by within-subjects repeated measures design: (a) the IGA and WJ tests with least squares estimators based on theoretically determined critical values; (b) the IGA and WJ tests with least squares estimators based on empirically determined critical values; (c) the IGA and WJ tests with robust estimators based on theoretically determined critical values; and (d) the IGA and WJ tests with robust estimators based on empirically determined critical values. We found that the IGA tests were always robust to assumption violations whether based on least squares or robust estimators or whether critical values were obtained through theoretical or empirical methods. The WJ procedure, however, occasionally resulted in liberal rates of error when based on least squares estimators but always proved robust when applied with robust estimators. Neither approach particularly benefited from adopting bootstrapped critical values. Recommendations are provided to researchers regarding when each approach is best.

Humans↗

PubMed2000

Estimation of population profiles of two strains of the fly Megaselia scalaris (Diptera: Phoridae) by bootstrap simulation.

Based on experimental population profiles of strains of the fly Megaselia scalaris (Phoridae), the minimal number of sample profiles was determined that should be repeated by bootstrap simulation process in order to obtain a confident estimation of the mean population profile and present estimations of the standard error as a precise measure of the simulations made. The original data are from experimental populations founded with SR and R4 strains, with three replicates, which were kept for 33 weeks by serial transfer technique in a constant temperature room (25 +/- 1.0 degrees C). The variable used was population size and the model adopted for each profile was a stationary stochastic process. By these simulations, the three experimental population profiles were enlarged so as to determine minimum sample size. After sample size was determined, bootstrap simulations were made in order to calculate confidence intervals and to compare the mean population profiles of these two strains. The results show that with a minimum sample size of 50, stabilization of means begins.

Animals↗

PubMed2002

Geographical information systems and bootstrap aggregation (bagging) of tree-based classifiers for Lyme disease risk prediction in Trentino, Italian Alps.

The risk of exposure to Lyme disease in the province of Trento, Italian Alps, was predicted through the analysis of the distribution of Ixodes ricinus (L.) nymphs infected with Borrelia burgdorferi s.l. with a model based on bootstrap aggregation (bagging) of tree-based classifiers within a geographical information system (GIS). Data on L ricinus density assessed by dragging the vegetation in 438 sites during 1996 were cross-correlated with the digital cartography of a GIS, which included the variables altitude, exposure and slope, substratum, vegetation type and roe deer density. Ticks were more abundant at altitudes below 1,300 m a.s.l., in the presence of limestone and vegetation cover with thermophile deciduous forests and high densities of roe deer. A bootstrap aggregation procedure (bagging) was used to produce a model for the prediction of tick occurrence, the accuracy of which was tested on actual tick counts assessed by a further dragging campaign carried out during 1997 to determine infection prevalence and resulted in average 77%. Other tests of the model were made on additional and independent data sets. The prevalence of infection with Borrelia burgdorferi s.l, determined by polymerase chain reaction on 2,208 nymphs collected by random dragging in 245 transects selected within eight areas where the model predicted the occurrence of I. ricinus during 1997, was 17.5% and was positively correlated to tick abundance and roe deer density. These findings were used to relate the output of the bagged model (probability of tick occurrence) to the density of infected nymphs through a stepwise model selection procedure and thus to produce a GIS digital map of the probability distribution of infected nymphs in the Province of Trento at high resolution scale (50 by 50-m cell resolution). The application of the bagging procedure increased the accuracy of the prediction made by a single classification tree, a well-known classification method for the analysis of epidemiological data.

Animals↗

PubMed2001

Group-based measurement strategies in exposure assessment explored by bootstrapping.

OBJECTIVES: The precision of mean exposure to pushing was examined in 2 occupational groups using various combinations of the number of workers and measurements per worker. METHODS: The frequency and duration of pushing of the 2 occupational groups was assessed using onsite observation. All data were divided into successive periods of 30 minutes of observation. The precision of the group mean exposure to pushing was expressed by 90% confidence intervals obtained by bootstrapping. The effect on the confidence interval of varying numbers of workers and numbers of periods per worker was examined. RESULTS: For both occupational groups there was little precision to be gained when >10 workers were observed. Within the maximum number of workers used in the bootstrap simulations, it appeared that, beyond 10 workers, the confidence intervals decreased by <5% for every worker that was added, when each worker was observed at least 8 periods of 30 minutes. If workers were observed exactly 4 periods of 30 minutes per worker, an additional 4 workers were required to compensate for the loss of precision. An unbalanced strategy with approximately 8 periods of 30 minutes per worker hardly decreased the precision of the group mean, however. CONCLUSIONS: The precision of the group-based mean exposure to pushing is influenced by the number of workers observed and by the number of repeated measurements per worker. In the planning of measurement strategies, it is advisable to account for possible sources of variance in advance and to assess the exposure variability.

Adult↗

PubMed2001

Random assignment of available cases: bootstrap standard errors and confidence intervals.

A frequently used experimental design in psychological research randomly divides a set of available cases, a local population, between 2 treatments and then applies an independent-samples t test to either test a hypothesis about or estimate a confidence interval (CI) for the population mean difference in treatment response. C. S. Reichardt and H. F. Gollob (1999) established that the t test can be conservative for this design-yielding hypothesis test P values that are too large or CIs that are too wide for the relevant local population. This article develops a less conservative approach to local population inference, one based on the logic of B. Efron's (1979) nonparametric bootstrap. The resulting randomization bootstrap is then compared with an established approach to local population inference, that based on randomization or permutation tests. Finally, the importance of local population inference is established by reference to the distinction between statistical and scientific inference.

Confidence Intervals↗

PubMed2001

Exploring among-site rate variation models in a maximum likelihood framework using empirical data: effects of model assumptions on estimates of topology, branch lengths, and bootstrap support.

We have investigated the effects of different among-site rate variation models on the estimation of substitution model parameters, branch lengths, topology, and bootstrap proportions under minimum evolution (ME) and maximum likelihood (ML). Specifically, we examined equal rates, invariable sites, gamma-distributed rates, and site-specific rates (SSR) models, using mitochondrial DNA sequence data from three protein-coding genes and one tRNA gene from species of the New Zealand cicada genus Maoricicada. Estimates of topology were relatively insensitive to the substitution model used; however, estimates of bootstrap support, branch lengths, and R-matrices (underlying relative substitution rate matrix) were strongly influenced by the assumptions of the substitution model. We identified one situation where ME and ML tree building became inaccurate when implemented with an inappropriate among-site rate variation model. Despite the fact the SSR models often have a better fit to the data than do invariable sites and gamma rates models, SSR models have some serious weaknesses. First, SSR rate parameters are not comparable across data sets, unlike the proportion of invariable sites or the alpha shape parameter of the gamma distribution. Second, the extreme among-site rate variation within codon positions is problematic for SSR models, which explicitly assume rate homogeneity within each rate class. Third, the SSR models appear to give severe underestimates of R-matrices and branch lengths relative to invariable sites and gamma rates models in this example. We recommend performing phylogenetic analyses under a range of substitution models to test the effects of model assumptions not only on estimates of topology but also on estimates of branch length and nodal support.

Animals↗

PubMed1992

[Jackknife and bootstrap].

The jackknife and the bootstrap are two non parametric methods which provide estimates- of the bias and the variance of an estimator, without any assumption about its statistical distribution. The jackknife is based on the observation of the estimator for subsamples, generally of size n-1, obtained from the original sample. The bootstrap is based on the observation of the estimator on size n samples drawn from the original sample. The two methods are presented, their principle is illustrated through their application to simple examples and to more complex epidemiological problems.

Bias↗

PubMed2006

An empirical examination of the standard errors of maximum likelihood phylogenetic parameters under the molecular clock via bootstrapping.

The molecular clock theory has greatly enlightened our understanding of macroevolutionary events. Maximum likelihood (ML) estimation of divergence times involves the adoption of fixed calibration points, and the confidence intervals associated with the estimates are generally very narrow. The credibility intervals are inferred assuming that the estimates are normally distributed, which may not be the case. Moreover, calculation of standard errors is usually carried out by the curvature method and is complicated by the difficulty in approximating second derivatives of the likelihood function. In this study, a standard primate phylogeny was used to examine the standard errors of ML estimates via the bootstrap method. Confidence intervals were also assessed from the posterior distribution of divergence times inferred via Bayesian Markov Chain Monte Carlo. For the primate topology under evaluation, no significant differences were found between the bootstrap and the curvature methods. Also, Bayesian confidence intervals were always wider than those obtained by ML.

Animals↗

PubMed1987

Bootstrapped potential circadian harbingers if not determinants of cardiovascular risk.

Among 12 endocrine variables in blood from clinically healthy adult women sampled systematically around the clock and the year, discriminant analysis methods have singled out certain hormones in certain seasons as classifiers for a high or low risk of developing diseases associated with a high circadian rhythm-adjusted mean (midline estimating statistic of rhythm, MESOR, M) of blood pressure, i.e., risk of M-hypertension (RMH). Before extending the labor intensive, costly data base, showing circadian changes with RMH, we reanalyzed available data by circadian bootstrapping, complementing earlier circannual bootstrapping. Differences in circadian M for aldosterone in all four seasons and for TSH in spring and summer (the only seasons checked), but not for the cortisol M checked in spring and summer, are validated, as are differences in circadian amplitude for TSH in spring and summer and aldosterone in spring. Identification of classifiers provides cost-effective, time-specified endocrine checks complementing the targeted automatic monitoring of blood pressure as part of a system of chronobioengineering for health maintenance.

Adolescent↗

PubMed1995

Correction for covariate measurement error in generalized linear models--a bootstrap approach.

A two-phase bootstrap method is proposed for correcting covariate measurement error. Two data sets are needed: validation data for approximating the measurement model and data with a response variable. Bootstrap samples from both the data sets validation data are taken. Parameter estimates of the generalized linear model are calculated using expectations of the measurement model from the validation data as explanatory variables. The method is compared through simulation in logistic regression with the correction method proposed by Rosner, Willet, and Spiegelman (1991, Statistics in Medicine 8, 1051-1069). A real data example is also presented.

Age Factors↗

PubMed1994

Neural net-bootstrap hybrid methods for prediction of complications in patients implanted with artificial heart valves.

A novel hybrid methodology for prediction of valve related complications in patients with implanted artificial heart valves is discussed. Artificial neural networks provided a mechanism for prediction of postoperative valve-related deaths based on preoperative patient information and valve parameters. Then bootstrap methodology was applied for estimating prediction errors and maximizing prediction accuracy. Data from a clinical trial with 10 years of follow-up on 789 patients implanted with Carpentier-Edwards Pericardial Bioprosthesis were used. A random subset of the data was reserved for validation of the final outcome. The remaining patients' records were repeatedly divided into two groups, using resampling strategy provided by the bootstrap methodology. One of the groups was used for training the neural net and the other one for testing the trained network and determining error rates. Patient information, such as sex, age, NYHA class and anticoagulation therapy, as well as valve parameters, such as size and the date of implant were used as the network inputs. Calculated error rates were then used for assessing the distribution of the error, further optimization of the neural network, and constructing confidence intervals for the error rates. Thus, reliable statistical estimation was obtained on the prediction accuracy. Additionally this new hybrid methodology allowed us to optimize the neural network even further, raising the accuracy of prediction to 78%.

Bioprosthesis↗