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A bootstrap-based test for establishing noninferiority in clinical trials.

A randomized, active-control clinical trial setting with the objective of testing noninferiority for a continuous response variable is considered. Noninferiority margin is based on the concept of preserving a certain fraction of the active control effect. Noninferiority is established if the ratio of the lower (upper) limit of the two-sided 95% confidence interval for the treatment difference to the estimated mean of the active control is greater (less) than a certain fraction. The nominal significance level is not maintained by the above confidence interval-based noninferiority test. We use bootstrapping to derive an accurate lower (upper) limit of the same confidence interval, which approximates the nominal significance level better and improves the power.

Computer Simulation↗

Robust and bootstrap testing procedures for bioequivalence.

A common problem encountered in bioequivalence studies is the presence of outliers. In this situation, the two one-sided t-tests proposed by Schuirmann fail to provide reasonable power for concluding bioequivalence. In contrast, our proposed 2 beta trimmed-t procedure has the following advantages: (1) it has higher efficiency for nonnormal symmetric distributions, (2) it is resistant to outliers, and (3) it is relatively easy to compute. Two bootstrap procedures introduced here provide further justification for the proposed trimmed t-test procedure. Results from Monte Carlo studies illustrate the power of the proposed procedures under various distributional assumptions for a 2 x 2 crossover trial.

Biological Availability↗

A non-parametric bootstrap approach for analysing the statistical properties of SPECT and PET images.

Knowledge of the statistical properties of reconstructed single photon emission computed tomography (SPECT) and positron emission tomography (PET) images would be helpful for optimizing acquisition and image processing protocols. We describe a non-parametric bootstrap approach to accurately estimate the statistical properties of SPECT or PET images whatever the noise properties in the projections and the reconstruction algorithm. Using analytical simulations and real PET data, this method is shown to accurately predict the statistical properties, including the variance and covariance, of reconstructed pixel values for both linear (filtered backprojection) and non-linear (ordered subset expectation maximization) reconstruction algorithms.

Algorithms↗

Maximum likelihood inference and bootstrap methods for plant organ growth via multi-phase kinetic models and their application to maize.

BACKGROUND AND AIMS: Fitting the parameters of models of plant organ growth is a means to investigate how environmental conditions affect plant architecture. The aim of this article is to evaluate some non-linear methods for fitting the parameters of multi-phase models of the kinetics of extension of plant organs such as laminae, sheaths and internodes. * METHODS: A set of computational procedures was developed allowing parameter-fitting of multi-phase models, using the maximum likelihood criterion, in which phases are identified with reference to ontogenic processes. Two bootstrap methods were compared to assess the precision of the estimates of fitted parameters, and of functions of these parameters such as the final leaf length, and the duration and rate of the rapid extension phase. Methods were applied to an experimental dataset, representing the kinetics of laminae, sheaths and internodes along the maize shoot, for two contrasting densities. * KEY RESULTS: A set of multi-phase models was proposed to describe the extension of laminae, sheaths and internodes along the shoot. The distinguishable phases differed between laminae, sheaths and internodes. For sheaths and laminae, but not for internodes, the same model could be fitted to all phytomers along the shoot. The variation of parameters along the shoot and between density treatments, as well as derived functions such as the durations of the phases of extension, are presented for laminae. It was the duration of the fast extension period, rather than its rate, which determined the difference in final length between treatments. * CONCLUSIONS: Such methods permit a large degree of objectivity and facilitate the analysis of such rather complicated but co-ordinated datasets. The work also illustrates some natural limitations of maximum likelihood methods, and viable ways of overcoming them by including a priori knowledge in the model fitting method are discussed.

Likelihood Functions↗

Gene expression analysis with the parametric bootstrap.

Recent developments in microarray technology make it possible to capture the gene expression profiles for thousands of genes at once. With this data researchers are tackling problems ranging from the identification of 'cancer genes' to the formidable task of adding functional annotations to our rapidly growing gene databases. Specific research questions suggest patterns of gene expression that are interesting and informative: for instance, genes with large variance or groups of genes that are highly correlated. Cluster analysis and related techniques are proving to be very useful. However, such exploratory methods alone do not provide the opportunity to engage in statistical inference. Given the high dimensionality (thousands of genes) and small sample sizes (often <30) encountered in these datasets, an honest assessment of sampling variability is crucial and can prevent the over-interpretation of spurious results. We describe a statistical framework that encompasses many of the analytical goals in gene expression analysis; our framework is completely compatible with many of the current approaches and, in fact, can increase their utility. We propose the use of a deterministic rule, applied to the parameters of the gene expression distribution, to select a target subset of genes that are of biological interest. In addition to subset membership, the target subset can include information about relationships between genes, such as clustering. This target subset presents an interesting parameter that we can estimate by applying the rule to the sample statistics of microarray data. The parametric bootstrap, based on a multivariate normal model, is used to estimate the distribution of these estimated subsets and relevant summary measures of this sampling distribution are proposed. We focus on rules that operate on the mean and covariance. Using Bernstein's Inequality, we obtain consistency of the subset estimates, under the assumption that the sample size converges faster to infinity than the logarithm of the number of genes. We also provide a conservative sample size formula guaranteeing that the sample mean and sample covariance matrix are uniformly within a distance epsilon > 0 of the population mean and covariance. The practical performance of the method using a cluster-based subset rule is illustrated with a simulation study. The method is illustrated with an analysis of a publicly available leukemia data set.

Journal Article↗

Confidence intervals of evolutionary distances between sequences and comparison with usual approaches including the bootstrap method.

Two methods are commonly employed for evaluating the extent of the uncertainty of evolutionary distances between sequences: either some estimator of the variance of the distance estimator, or the bootstrap method. However, both approaches can be misleading, particularly when the evolutionary distance is small. We propose using another statistical method which does not have the same defect: interval estimation. We show how confidence intervals may be constructed for the Jukes and Cantor (1969) and Kimura two-parameter (1980) estimators. We compare the exact confidence intervals thus obtained with the approximate intervals derived by the two previous methods, using artificial and biological data. The results show that the usual methods clearly underestimate the variability when the substitution rate is low and when sequences are short. Moreover, our analysis suggests that similar results may be expected for other evolutionary distance estimators.

Algorithms↗

A simple method for classifying genes and a bootstrap test for classifications.

A new simple method for classifying genes is proposed based on Klastorin's method. This method classifies genes into monophyletic groups which are made distinct from each other by evolutionary changes. The method is applicable as long as the phylogenetic tree of genes is obtained. There is a fast algorithm for obtaining the classification. A bootstrap test of a classification is also presented. As an example, we classified opsin genes. The classification obtained by this method is the same as the previous classification based on the function of opsins.

Algorithms↗

Directed rigidity and bootstrap percolation in 1+1 dimensions.

We study directed rigidity percolation (equivalent to directed bootstrap percolation) on three different lattices: square, triangular, and augmented triangular. The first two of these display a first-order transition at p=1, while the augmented triangular lattice shows a continuous transition at a nontrivial p(c). On the augmented triangular lattice we find, by extensive numerical simulation, that the the directed rigidity percolation transition belongs to the same universality class as the directed percolation. The same conclusion is reached by studying its surface critical behavior, i.e., the spreading of rigidity from finite clusters close to a nonrigid wall. Near the discontinuous transition at p=1 on the triangular lattice, we are able to calculate the finite-size behavior of the density of rigid sites analytically. Our results are confirmed by numerical simulation.

Journal Article↗

Correlation function bootstrapping in quantum chaotic systems.

We discuss a general and efficient approach for "bootstrapping" short-time correlation data in chaotic or complex quantum systems to obtain information about long-time dynamics and stationary properties, such as the local density of states. When the short-time data are sufficient to identify an individual quantum system, we obtain a systematic approximation for the spectrum and wave functions. Otherwise, we obtain statistical properties, including wave function intensity distributions, for an ensemble of all quantum systems sharing the given short-time correlations. The results are valid for open or closed systems, and are stable under perturbation of the short-time input data. Numerical examples include quantum maps and two-dimensional anharmonic oscillators.

Journal Article↗

k-core (bootstrap) percolation on complex networks: critical phenomena and nonlocal effects.

We develop the theory of the -core (bootstrap) percolation on uncorrelated random networks with arbitrary degree distributions. We show that the -core percolation is an unusual, hybrid phase transition with a jump emergence of the k-core as at a first order phase transition but also with a critical singularity as at a continuous transition. We describe the properties of the -core, explain the meaning of the order parameter for the k-core percolation, and reveal the origin of the specific critical phenomena. We demonstrate that a so-called "corona" of the k-core plays a crucial role (corona is a subset of vertices in the k-core which have exactly neighbors in the -core). It turns out that the k-core percolation threshold is at the same time the percolation threshold of finite corona clusters. The mean separation of vertices in corona clusters plays the role of the correlation length and diverges at the critical point. We show that a random removal of even one vertex from the k-core may result in the collapse of a vast region of the k-core around the removed vertex. The mean size of this region diverges at the critical point. We find an exact mapping of the k-core percolation to a model of cooperative relaxation. This model undergoes critical relaxation with a divergent rate at some critical moment.

Journal Article↗

Hysteresis in the random-field Ising model and bootstrap percolation.

We study hysteresis in the random-field Ising model with an asymmetric distribution of quenched fields, in the limit of low disorder in two and three dimensions. We relate the spin flip process to bootstrap percolation, and show that the characteristic length for self-averaging L small star, filled increases as exp[exp(J/Delta)] in 2D, and as exp(exp[exp(J/Delta)]) in 3D, for disorder strength Delta much less than the exchange coupling J. For system size 1< infinity for both square and cubic lattices. For lattices with coordination number 3, the limiting magnetization shows no jump, and h(coer) tends to J.

Journal Article↗

Validation of neoclassical bootstrap current models in the edge of an H-mode plasma.

Analysis of the parallel electric field E(parallel) evolution following an L-H transition in the DIII-D tokamak indicates the generation of a large negative pulse near the edge which propagates inward, indicative of the generation of a noninductive edge current. Modeling indicates that the observed E(parallel) evolution is consistent with a narrow current density peak generated in the plasma edge. Very good quantitative agreement is found between the measured E(parallel) evolution and that expected from neoclassical theory predictions of the bootstrap current.

Journal Article↗

Assessing blood flow control through a bootstrap method.

In order to assess blood flow control, the relationship between blood pressure and blood flow can be modeled by linear filters. We present a bootstrap method, which allows the statistical analysis of an index of blood flow control that is obtained from constrained system identification using an established set of pre-defined filters.

Algorithms↗

A refined bootstrap method for estimating the Zernike polynomial model order for corneal surfaces.

Following our previous work on optimal modeling of corneal surfaces with Zernike polynomials, we have developed a refined bootstrap-based procedure which improves the accuracy of the previous method. We show that for normal corneas, the optimal number of Zernike terms usually corresponds to the fourth or fifth radial order expansion of Zernike polynomials. On the other hand, for distorted corneas such as those encountered in keratoconus or in surgically altered cases, the estimated model was found to be up to three radial orders higher than for normal corneas.

Algorithms↗

Image transform bootstrapping and its applications to semantic scene classification.

The performance of an exemplar-based scene classification system depends largely on the size and quality of its set of training exemplars, which can be limited in practice. In addition, in nontrivial data sets, variations in scene content as well as distracting regions may exist in many testing images to prohibit good matches with the exemplars. Various boosting schemes have been proposed in machine learning, focusing on the feature space. We introduce the novel concept of image-transform bootstrapping using transforms in the image space to address such issues. In particular, three major schemes are described for exploiting this concept to augment training, testing, and both. We have successfully applied it to three applications of increasing difficulty: sunset detection, outdoor scene classification, and automatic image orientation detection. It is shown that appropriate transforms and meta-classification methods can be selected to boost performance according to the domain of the problem and the features/classifier used.

Algorithms↗

Bootstrap choice of estimators in parametric and semiparametric families: an extension of EIC.

Ishiguro, Sakamoto, and Kitagawa (1997, Annals of the Institute of Statistical Mathematics 49, 411-434) proposed EIC as an extension of Akaike criterion (AIC); the idea leading to EIC is to correct the bias of the log-likelihood, considered as an estimator of the Kullback-Leibler information, using bootstrap. We develop this criterion for its use in multivariate semiparametric situations, and argue that it can be used for choosing among parametric and semiparametric estimators. A simulation study based on aregression model shows that EIC is better than its competitors although likelihood cross-validation performs nearly as well except for small sample size. Its use is illustrated by estimating the mean evolution of viral RNA levels in a group of infants infected by HIV.

Age Factors↗

A bootstrap assessment of variability in pedigree reconstruction based on genetic markers.

The problem of assessing the variability in pedigree reconstruction using DNA markers is considered for the special case of single generation samples with no parents present. Error in pedigree reconstruction is measured through a metric imposed on the space of partitions of the individuals into family groups. A confidence set can therefore be taken to be a neighborhood of a point estimate, analogous to the estimation of a parameter in Euclidean space. The coverage probability is estimated using bootstrap techniques. Although the distributional properties of the sample depend on the population genotype frequencies, these are in practice usually unknown. Confidence sets conditioned on a statistic approximately sufficient for these frequencies are compared with confidence sets obtained by substituting frequency estimates directly into the sampling distribution. In two simulation studies, the difference is found to be of some consequence.

Animals↗

Penalized partial likelihood regression for right-censored data with bootstrap selection of the penalty parameter.

The Cox proportional hazards model is often used for estimating the association between covariates and a potentially censored failure time, and the corresponding partial likelihood estimators are used for the estimation and prediction of relative risk of failure. However, partial likelihood estimators are unstable and have large variance when collinearity exists among the explanatory variables or when the number of failures is not much greater than the number of covariates of interest. A penalized (log) partial likelihood is proposed to give more accurate relative risk estimators. We show that asymptotically there always exists a penalty parameter for the penalized partial likelihood that reduces mean squared estimation error for log relative risk, and we propose a resampling method to choose the penalty parameter. Simulations and an example show that the bootstrap-selected penalized partial likelihood estimators can, in some instances, have smaller bias than the partial likelihood estimators and have smaller mean squared estimation and prediction errors of log relative risk. These methods are illustrated with a data set in multiple myeloma from the Eastern Cooperative Oncology Group.

Antineoplastic Agents↗