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At least 1,171 records · Page 65Linked to original sources
PubMed2004

Epidemiology of breastfeeding: advances and multidisciplinary applications.

Advancing understanding of the importance of breastfeeding (BF) has required advances in application of epidemiologic methods to BF research. This chapter considers, in the context of BF epidemiology, how different research designs complement each other to establish firmer causal inferences, the need for multidisciplinary collaborations, and the application of powerful analytical methods to strengthen epidemiologic studies. A recent global meta-analysis of 47 studies documenting the inverse association between lifetime BF duration and breast cancer risk illustrates the value of longitudinal (cohort) studies to establish the temporal sequence of events and to rule out differential recall biases that can threaten the internal validity of case-control studies. The discovery of the transmission of human immunodeficiency virus (HIV) via human milk has led epidemiologists to provide important hypotheses to be tested at the molecular and cellular level and in turn test hypotheses generated in laboratory studies. Application of powerful statistical methods to BF epidemiology has helped strengthen causal inferences. A retrospective analysis of the 1976--1977 Malaysian Family Life Survey found interactive multivariate statistical models to better understand how different socioeconomic and cultural contexts modify the influence of BF on child health. Further, a study conducted in Cebu City, Philippines, demonstrated how structural equation models can be used to test the pathways through which proximate (e.g., BF) and distal (e.g., socioeconomic status) determinants affect child health after adjusting for reverse causality (i.e., child health influencing maternal BF choice). For ethical reasons, BF randomized controlled trials (RCTs) have seldom been conducted. Recent RCTs examining the link between BF peer counseling and BF outcomes in diverse settings provides a unique opportunity to firmly establish the causal inferences regarding BF and the prevention of diarrheal diseases. The value of rapid assessment anthropologic methods to guide development of peer counseling interventions subsequently tested through RCTs is presented. This chapter also illustrates how Cox regression and logistic regression can be applied to BF research.

Breast Feeding↗
PubMed2003

Inference in spline-based models for multiple time-to-event data, with applications to a breast cancer prevention trial.

As part of the National Surgical Adjuvant Breast and Bowel Project, a controlled clinical trial known as the Breast Cancer Prevention Trial (BCPT) was conducted to assess the effectiveness of tamoxifen as a preventive agent for breast cancer. In addition to the incidence of breast cancer, data were collected on several other, possibly adverse, outcomes, such as invasive endometrial cancer, ischemic heart disease, transient ischemic attack, deep vein thrombosis and/or pulmonary embolism. In this article, we present results from an illustrative analysis of the BCPT data, based on a new modeling technique, to assess the effectiveness of the drug tamoxifen as a preventive agent for breast cancer. We extended the flexible model of Gray (1994, Spline-based test in survival analysis, Biometrics 50, 640-652) to allow inference on multiple time-to-event outcomes in the style of the marginal modeling setup of Wei, Lin, and Weissfeld (1989, Regression analysis of multivariate incomplete failure time data by modeling marginal distributions, Journal of the American Statistical Association 84, 1065-1073). This proposed model makes inference possible for multiple time-to-event data while allowing for greater flexibility in modeling the effects of prognostic factors with nonlinear exposure-response relationships. Results from simulation studies on the small-sample properties of the asymptotic tests will also be presented.

Analysis of Variance↗
PubMed2025

Exact model-free function inference using uniform marginal counts for null population.

MOTIVATION: Recognizing cause-effect relationships is a fundamental inquiry in science. However, current causal inference methods often focus on directionality but not statistical significance. A ramification is chance patterns of uneven marginal distributions achieving a perfect directionality score. RESULTS: To overcome such issues, we design the uniform exact function test with continuity correction (UEFTC) to detect functional dependency between two discrete random variables. The null hypothesis is two variables being statistically independent. Unique from related tests whose null populations use observed marginals, we define the null population by an embedded uniform square. We also present a fast algorithm to accomplish the test. On datasets with ground truth, the UEFTC exhibits accurate directionality, low biases, and robust statistical behavior over alternatives. We found nonmonotonic response by gene TCB2 to beta-estradiol dosage in engineered yeast strains. In the human duodenum with environmental enteric dysfunction, we discovered pathology-dependent anti-co-methylated CpG sites in the vicinity of genes POU2AF1 and LSP1; such activity represents orchestrated methylation and demethylation along the same gene, unreported previously. The UEFTC has much improved effectiveness in exact model-free function inference for data-driven knowledge discovery. AVAILABILITY AND IMPLEMENTATION: An open-source R package "UniExactFunTest" implementing the presented uniform exact function tests is available via CRAN at doi: 10.32614/CRAN.package.UniExactFunTest. Computer code to reproduce figures can be found in supplementary file "UEFTC-main.zip."

Algorithms↗
PubMed2005

The rank ordering of genotypic fitness values predicts genetic constraint on natural selection on landscapes lacking sign epistasis.

Sewall Wright's genotypic fitness landscape makes explicit one mechanism by which epistasis for fitness can constrain evolution by natural selection. Wright distinguished between landscapes possessing multiple fitness peaks and those with only a single peak and emphasized that the former class imposes substantially greater constraint on natural selection. Here I present novel formalism that more finely partitions the universe of possible fitness landscapes on the basis of the rank ordering of their genotypic fitness values. In this report I focus on fitness landscapes lacking sign epistasis (i.e., landscapes that lack mutations the sign of whose fitness effect varies epistatically), which constitute a subset of Wright's single peaked landscapes. More than one fitness rank ordering lacking sign epistasis exists for L > 2 (where L is the number of interacting loci), and I find that a highly statistically significant effect exists between landscape membership in fitness rank-ordering partition and two different proxies for genetic constraint, even within this subset of landscapes. This statistical association is robust to population size, permitting general inferences about some of the characteristics of fitness rank orderings responsible for genetic constraint on natural selection.

Biological Evolution↗
PubMedDate not supplied

Outcomes from stroke rehabilitation in Veterans Affairs rehabilitation units: detecting and correcting for selection bias.

This paper addresses the issue of statistical selection bias in multivariate models of functional gain estimated from observational data. Stroke patients from 20 high-volume Veterans Affairs Medical Centers (VAMCs) with acute and subacute inpatient rehabilitation treatment units were observed. Their gains in overall, motor, and cognitive functional status were measured with the use of the Functional Independence Measure (FIM). In estimating multivariate models of FIM gain during rehabilitation using these observational data, we found statistically significant evidence of selection bias, along with considerable differences in inferences between standard multivariate analyses and our selectivity-corrected models. Our results demonstrate the importance of detecting and correcting for statistical selection bias when one uses nonexperimental data to study gains in functional status.

Activities of Daily Living↗
PubMed2002

Teaching Bayesian reasoning: an evaluation of a classroom tutorial for medical students.

How likely is a diagnosis, given a particular medical test result? This probability can be determined by using Bayes's rule; however, previous research has shown that doctors often experience problems with Bayesian inferences. These findings illustrate the need to teach statistical reasoning in medical education. A new method of teaching Bayesian reasoning is representation learning: the key idea is to instruct medical students how to translate probability information into a representation that is easier to process, namely natural frequencies. This approach was implemented in a one-hour classroom tutorial to evaluate its effectiveness in this setting and compared with a traditional rule-learning approach. Evaluation took place two months after training by testing students' ability to correctly solve a Bayesian inference task with information represented as probabilities. While both approaches improved performance, almost three times as many students were able to profit from representation training as opposed to rule training.

Adult↗
PubMed1998

Receptor noise as a determinant of colour thresholds.

Inferences about mechanisms at one particular stage of a visual pathway may be made from psychophysical thresholds only if the noise at the stage in question dominates that in the others. Spectral sensitivities, measured under bright conditions, for di-, tri-, and tetrachromatic eyes from a range of animals can be modelled by assuming that thresholds are set by colour opponency mechanisms whose performance is limited by photoreceptor noise, the achromatic signal being disregarded. Noise in the opponency channels themselves is therefore not statistically independent, and it is not possible to infer anything more about the channels from psychophysical thresholds. As well as giving insight into mechanisms of vision, the model predicts the performance of colour vision in animals where physiological and anatomical data on the eye are available, but there are no direct measurements of perceptual thresholds. The model, therefore, is widely applicable to comparative studies of eye design and visual ecology.

Animals↗
PubMed2003

Regression analysis of incomplete medical cost data.

The accumulation of medical cost over time for each subject is an increasing stochastic process defined up to the instant of death. The stochastic structure of this process is complex. In most applications, the process can only be observed at a limited number of time points. Furthermore, the process is subject to right censoring so that it is unobservable after the censoring time. These special features of the medical cost data, especially the presence of death and censoring, pose major challenges in the construction of plausible statistical models and the development of the corresponding inference procedures. In this paper, we propose several classes of regression models which formulate the effects of possibly time-dependent covariates on the marginal mean of cost accumulation in the presence of death or on the conditional means of cost accumulation given specific survival patterns. We then develop estimating equations for these models by combining the approach of generalized estimating equations for longitudinal data with the inverse probability of censoring weighting technique. The resultant estimators are shown to be consistent and asymptotically normal with simple variance estimators. Simulation studies indicate that the proposed inference procedures behave well in practical situations. An application to data taken from a large cancer study reveals that the Medicare enrollees who are diagnosed with less aggressive ovarian cancer tend to accumulate medical cost at lower rates than those with more aggressive disease, but tend to have higher lifetime costs because they live longer.

Aged↗
PubMedDate not supplied

Engineering in software testing: statistical testing based on a usage model applied to medical device development.

When a population is too large for exhaustive study, as is the case for all possible uses of a software system, a statistically correct sample must be drawn as a basis for inferences about the population. A Markov chain usage model is an engineering formalism that represents the population of possible uses for which a product is to be tested. In statistical testing of software based on a Markov chain usage model, the rich body of analytical results available for Markov chains provides numerous insights that can be used in both product development and test planing. A usage model is based on specifications rather than code, so insights that result from model building can inform product decisions in the early stages of a project when the opportunity to prevent problems is the greatest. Statistical testing based on a usage model provides a sound scientific basis for quantifying the reliability of software.

Equipment Safety↗
PubMed2006

In search of functional association from time-series microarray data based on the change trend and level of gene expression.

BACKGROUND: The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. RESULTS: In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC) based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological interactions, including 443 known protein interactions and some known cell cycle related regulatory interactions. It should be emphasized that the overlapping of gene pairs detected by the three methods is normally not very high, indicating a necessity of combining the different methods in search of functional association of genes from time-series data. For a p-value threshold of 1E-5 the percentage of process-identity and function-similarity gene pairs among the shared part of the three methods reaches 60.2% and 55.6% respectively, building a good basis for further experimental and functional study. Furthermore, the combined use of methods is important to infer more complete regulatory circuits and network as exemplified in this study. CONCLUSION: The TC method can significantly augment the current major methods to infer functional linkages and biological network and is well suitable for exploring temporal relationships of gene expression in time-series data.

Algorithms↗
PubMed2004

The footprints of visual attention during search with 100% valid and 100% invalid cues.

Human performance during visual search typically improves when spatial cues indicate the possible target locations. In many instances, the performance improvement is quantitatively predicted by a Bayesian or quasi-Bayesian observer in which visual attention simply selects the information at the cued locations without changing the quality of processing or sensitivity and ignores the information at the uncued locations. Aside from the general good agreement between the effect of the cue on model and human performance, there has been little independent confirmation that humans are effectively selecting the relevant information. In this study, we used the classification image technique to assess the effectiveness of spatial cues in the attentional selection of relevant locations and suppression of irrelevant locations indicated by spatial cues. Observers searched for a bright target among dimmer distractors that might appear (with 50% probability) in one of eight locations in visual white noise. The possible target location was indicated using a 100% valid box cue or seven 100% invalid box cues in which the only potential target locations was uncued. For both conditions, we found statistically significant perceptual templates shaped as differences of Gaussians at the relevant locations with no perceptual templates at the irrelevant locations. We did not find statistical significant differences between the shapes of the inferred perceptual templates for the 100% valid and 100% invalid cues conditions. The results confirm the idea that during search visual attention allows the observer to effectively select relevant information and ignore irrelevant information. The results for the 100% invalid cues condition suggests that the selection process is not drawn automatically to the cue but can be under the observers' voluntary control.

Attention↗
PubMed1999

Functional genomics: learning to think about gene expression data.

Three recent studies of gene expression patterns in whole cells provide examples of the inferences one can make from this type of information. They also provide examples of the non-traditional types of reasoning we will need to use to make such inferences.

Data Interpretation, Statistical↗
PubMed1999

Marginally specified logistic-normal models for longitudinal binary data.

Likelihood-based inference for longitudinal binary data can be obtained using a generalized linear mixed model (Breslow, N. and Clayton, D. G., 1993, Journal of the American Statistical Association 88, 9-25; Wolfinger, R. and O'Connell, M., 1993, Journal of Statistical Computation and Simulation 48, 233-243), given the recent improvements in computational approaches. Alternatively, Fitzmaurice and Laird (1993, Biometrika 80, 141-151), Molenberghs and Lesaffre (1994, Journal of the American Statistical Association 89, 633-644), and Heagerty and Zeger (1996, Journal of the American Statistical Association 91, 1024-1036) have developed a likelihood-based inference that adopts a marginal mean regression parameter and completes full specification of the joint multivariate distribution through either canonical and/or marginal higher moment assumptions. Each of these marginal approaches is computationally intense and currently limited to small cluster sizes. In this manuscript, an alternative parameterization of the logistic-normal random effects model is adopted, and both likelihood and estimating equation approaches to parameter estimation are studied. A key feature of the proposed approach is that marginal regression parameters are adopted that still permit individual-level predictions or contrasts. An example is presented where scientific interest is in both the mean response and the covariance among repeated measurements.

Biometry↗
PubMed2002

The footprints of visual attention in the Posner cueing paradigm revealed by classification images.

In the Posner cueing paradigm, observers' performance in detecting a target is typically better in trials in which the target is present at the cued location than in trials in which the target appears at the uncued location. This effect can be explained in terms of a Bayesian observer where visual attention simply weights the information differently at the cued (attended) and uncued (unattended) locations without a change in the quality of processing at each location. Alternatively, it could also be explained in terms of visual attention changing the shape of the perceptual filter at the cued location. In this study, we use the classification image technique to compare the human perceptual filters at the cued and uncued locations in a contrast discrimination task. We did not find statistically significant differences between the shapes of the inferred perceptual filters across the two locations, nor did the observed differences account for the measured cueing effects in human observers. Instead, we found a difference in the magnitude of the classification images, supporting the idea that visual attention changes the weighting of information at the cued and uncued location, but does not change the quality of processing at each individual location.

Attention↗
PubMed2003

Posterior probability maps and SPMs.

This technical note describes the construction of posterior probability maps that enable conditional or Bayesian inferences about regionally specific effects in neuroimaging. Posterior probability maps are images of the probability or confidence that an activation exceeds some specified threshold, given the data. Posterior probability maps (PPMs) represent a complementary alternative to statistical parametric maps (SPMs) that are used to make classical inferences. However, a key problem in Bayesian inference is the specification of appropriate priors. This problem can be finessed using empirical Bayes in which prior variances are estimated from the data, under some simple assumptions about their form. Empirical Bayes requires a hierarchical observation model, in which higher levels can be regarded as providing prior constraints on lower levels. In neuroimaging, observations of the same effect over voxels provide a natural, two-level hierarchy that enables an empirical Bayesian approach. In this note we present a brief motivation and the operational details of a simple empirical Bayesian method for computing posterior probability maps. We then compare Bayesian and classical inference through the equivalent PPMs and SPMs testing for the same effect in the same data.

Algorithms↗
PubMed2004

Sensitivity of score tests for zero-inflation in count data.

In many biomedical applications, count data have a large proportion of zeros and the zero-inflated Poisson regression (ZIP) model may be appropriate. A popular score test for zero-inflation, comparing the ZIP model to a standard Poisson regression model, was given by van den Broek. Similarly, for count data that exhibit extra zeros and are simultaneously overdispersed, a score test for testing the ZIP model against a zero-inflated negative binomial alternative was proposed by Ridout, Hinde and Demétrio. However, these test statistics are sensitive to anomalous cases in the data, and incorrect inferences concerning the choice of model may be drawn. In this paper, diagnostic measures are derived to assess the influence of observations on the score statistics. Two examples that motivated the application of zero-inflated regression models are considered to illustrate the importance of sensitivity analysis of the zero-inflation tests.

Accidents, Occupational↗
PubMed2004

Comparative evaluation of a new effective population size estimator based on approximate bayesian computation.

We describe and evaluate a new estimator of the effective population size (N(e)), a critical parameter in evolutionary and conservation biology. This new "SummStat" N(e) estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N(e). Simulations of a Wright-Fisher population with known N(e) show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, and N(e) values. We also address the paucity of information about the relative performance of N(e) estimators by comparing the SummStat estimator to two recently developed likelihood-based estimators and a traditional moment-based estimator. The SummStat estimator is the least biased of the four estimators compared. In 32 of 36 parameter combinations investigated using initial allele frequencies drawn from a Dirichlet distribution, it has the lowest bias. The relative mean square error (RMSE) of the SummStat estimator was generally intermediate to the others. All of the estimators had RMSE > 1 when small samples (n = 20, five loci) were collected a generation apart. In contrast, when samples were separated by three or more generations and N(e) < or = 50, the SummStat and likelihood-based estimators all had greatly reduced RMSE. Under the conditions simulated, SummStat confidence intervals were more conservative than the likelihood-based estimators and more likely to include true N(e). The greatest strength of the SummStat estimator is its flexible structure. This flexibility allows it to incorporate any potentially informative summary statistic from population genetic data.

Bayes Theorem↗
PubMed2003

A CART-based approach to discover emerging patterns in microarray data.

MOTIVATION: Cancer diagnosis using gene expression profiles requires supervised learning and gene selection methods. Of the many suggested approaches, the method of emerging patterns (EPs) has the particular advantage of explicitly modeling interactions among genes, which improves classification accuracy. However, finding useful (i.e. short and statistically significant) EP is typically very hard. METHODS: Here we introduce a CART-based approach to discover EPs in microarray data. The method is based on growing decision trees from which the EPs are extracted. This approach combines pattern search with a statistical procedure based on Fisher's exact test to assess the significance of each EP. Subsequently, sample classification based on the inferred EPs is performed using maximum-likelihood linear discriminant analysis. RESULTS: Using simulated data as well as gene expression data from colon and leukemia cancer experiments we assessed the performance of our pattern search algorithm and classification procedure. In the simulations, our method recovers a large proportion of known EPs while for real data it is comparable in classification accuracy with three top-performing alternative classification algorithms. In addition, it assigns statistical significance to the inferred EPs and allows to rank the patterns while simultaneously avoiding overfit of the data. The new approach therefore provides a versatile and computationally fast tool for elucidating local gene interactions as well as for classification. AVAILABILITY: A computer program written in the statistical language R implementing the new approach is freely available from the web page http://www.stat.uni-muenchen.de/~socher/

Algorithms↗