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[Measurement of breast cancer dimensions: correlation between mammographic and macroscopic data].

In this study the maximum diameters of 48 mammary carcinomas of various istotype detected preoperatively on mammography are compared with the diameters of neoplasms diagnosed by the anatomo pathologist on macroscopic examination of the excised part. Statistical analysis has shown that for every mammographic value it is possible to infer the corresponding real macroscopic value by adding or subtracting 0.45 cm.

Breast Neoplasms↗

Whether mobility influences the osteometric features at the articular ends of the metacarpal bones.

A study of osteometric characteristics of first and third metacarpal bones has been undertaken with the object of assessing the relative role of mobility and length of metacarpal bones in relation to the dimensions of the head, mid-shaft and base of the respective bones. Observations regarding the length of metacarpal bones, dorsopalmar and side-to-side diameters of the head, mid-shaft and base and corresponding axial ratios have been recorded on fifty samples, each of first and third metacarpal bones. The data thus collected have been subjected to a statistical analysis, consisting of Students "t" test and Coefficient of Correlation and the inferences presented in relation to the relative importance of mobility and length in the determination of these characteristics. This analysis suggests that the shapes of the articular surfaces and the shaft cross-sectional area of the metacarpal bones are not correlated to the length but are associated with mobility.

Anthropometry↗

Time-related factors in the study of risks in animals and humans.

Data from epidemiological studies of humans exposed to potentially harmful substances are usually analyzed using methods that account for the dependence of risks on time-related factors such as age and follow-up period. Recently developed statistical procedures allow modeling of the age-specific risks as a function of dose as well as factors such as age at exposure, time since exposure, exposure duration, and dose rate. These procedures potentially allow more rigorous inferences and clearer understanding of the patterns of risk observed in epidemiological studies than has been available in the past. Statistical procedures that consider time-related factors can also be applied to laboratory animal data, providing information that is useful for the problems involved in extrapolating from animal studies to humans. By applying such procedures to data on exposure to the same substance in different species (including humans) or to different substances in the same species, better understanding of the relationship of risks across species and across substances can be achieved. In addition, such statistical procedures allow appropriate treatment of exposure that is accumulated over time and lead to improved understanding of patterns of risk over time. The approach is illustrated using data from a lifespan study of beagle dogs exposed to inhaled Pu.

Administration, Inhalation↗

Estimating degradation by a Wiener diffusion process subject to measurement error.

Most materials and components degrade physically before they fail. Engineering degradation tests are designed to measure these degradation processes. Measurements in the tests reflect the inherent randomness of degradation itself as well as measurement errors created by imperfect instruments, procedures and environments. This paper describes a statistical model for measured degradation data that takes both sources of variation into account. The degradation process in the model is taken to be a Wiener diffusion process. The measurement errors are assumed to be independent normal random outcomes that are independent of the degradation process. The paper describes inference procedures for the model and discusses some practical issues that must be considered in dealing with the statistical problem. A case study is presented.

Engineering↗

Motif prediction in ribosomal RNAs Lessons and prospects for automated motif prediction in homologous RNA molecules.

The traditional way to infer RNA secondary structure involves an iterative process of alignment and evaluation of covariation statistics between all positions possibly involved in basepairing. Watson-Crick basepairs typically show covariations that score well when examples of two or more possible basepairs occur. This is not necessarily the case for non-Watson-Crick basepairing geometries. For example, for sheared (trans Hoogsteen/Sugar edge) pairs, one base is highly conserved (always A or mostly A with some C or U), while the other can vary (G or A and sometimes C and U as well). RNA motifs consist of ordered, stacked arrays of non-Watson-Crick basepairs that in the secondary structure representation form hairpin or internal loops, multi-stem junctions, and even pseudoknots. Although RNA motifs occur recurrently and contribute in a modular fashion to RNA architecture, it is usually not apparent which bases interact and whether it is by edge-to-edge H-bonding or solely by stacking interactions. Using a modular sequence-analysis approach, recurrent motifs related to the sarcin-ricin loop of 23S RNA and to loop E from 5S RNA were predicted in universally conserved regions of the large ribosomal RNAs (16S- and 23S-like) before the publication of high-resolution, atomic-level structures of representative examples of 16S and 23S rRNA molecules in their native contexts. This provides the opportunity to evaluate the predictive power of motif-level sequence analysis, with the goal of automating the process for predicting RNA motifs in genomic sequences. The process of inferring structure from sequence by constructing accurate alignments is a circular one. The crucial link that allows a productive iteration of motif modeling and realignment is the comparison of the sequence variations for each putative pair with the corresponding isostericity matrix to determine which basepairs are consistent both with the sequence and the geometrical data.

Base Pairing↗

Rohling's Interpretive Method and inherent limitations on the flexibility of "flexible batteries".

Miller and Rohling (2001) proposed a 24-step algorithm, the Rohling Interpretive Method (RIM), for quantitative interpretation of results from flexible neuropsychological test batteries. We believe that the RIM as presented in that paper has several conceptual problems, including (a) a failure to distinguish "statistically significant" from pathological differences, (b) an assumption that declines in specific abilities can be inferred when a particular test score deviates from an estimate of general premorbid ability, and (c) confusion between the standard deviation associated with individual test scores versus that of a composite of those scores. As an alternative, we suggest the value of developing and using co-normed comprehensive neuropsychological test batteries from which test users might select subsets of tests.

Adult↗

Geographic structure of mitochondrial and nuclear gene polymorphisms in Australian green turtle populations and male-biased gene flow.

The genetic structure of green turtle (Chelonia mydas) rookeries located around the Australian coast was assessed by (1) comparing the structure found within and among geographic regions, (2) comparing microsatellite loci vs. restriction fragment length polymorphism analyses of anonymous single copy nuclear DNA (ascnDNA) loci, and (3) comparing the structure found at nuclear DNA markers to that of previously analyzed mitochondrial (mtDNA) control region sequences. Significant genetic structure was observed over all regions at both sets of nuclear markers, though the microsatellite data provided greater resolution in identifying significant genetic differences in pairwise tests between regions. Inferences about population structure and migration rates from the microsatellite data varied depending on whether statistics were based on the stepwise mutation or infinite allele model, with the latter being more congruent with geography. Estimated rates of gene flow were generally higher than expected for nuclear DNA (nDNA) in comparison to mtDNA, and this difference was most pronounced in comparisons between the northern and southern Great Barrier Reef (GBR). The genetic data combined with results from physical tagging studies indicate that the lack of nuclear gene divergence through the GBR is likely due to the migration of sGBR turtles through the courtship area of the nGBR population, rather than male-biased dispersal. This example highlights the value of combining comparative studies of molecular variation with ecological data to infer population processes.

Alleles↗

Bayesian monitoring of phase II trials in cancer chemoprevention.

Early randomized Phase II cancer chemoprevention trials which assess short-term biological activity are critical to the decision process to advance to late Phase II/Phase III trials. We have adapted published Bayesian interim analysis methods (Spiegelhalter et al., J. R. Statist. Soc A, 1994; 157: 357-416) which give greater flexibility and simplicity of inference to the monitoring of randomized controlled Phase II trials using intermediate endpoints. The Bayesian stopping rule is designed to stop the trial more quickly when the evidence suggests ineffectiveness rather than when it suggests biological activity, thus allowing resources to be concentrated on those agents that show the most promise in this early stage of testing. We investigate frequentist performance characteristics of the proposed method through simulation of randomized placebo controlled trials with a growth factor intermediate end-point using mean and variance values derived from the literature. Simulation results show expected error rates and trial size similar to other commonly used group sequential methods for this setting. These results suggest that the Bayesian approach to interim analysis is well suited for monitoring small randomized controlled Phase II chemoprevention trials for early detection of either inactive or promising agents.

Antineoplastic Agents↗

Algorithms for inferring haplotypes.

Haplotype phase information in diploid organisms provides valuable information on human evolutionary history and may lead to the development of more efficient strategies to identify genetic variants that increase susceptibility to human diseases. Molecular haplotyping methods are labor-intensive, low-throughput, and very costly. Therefore, algorithms based on formal statistical theories were shown to be very effective and cost-efficient for haplotype reconstruction. This review covers 1) population-based haplotype inference methods: Clark's algorithm, expectation-maximization (EM) algorithm, coalescence-based algorithms (pseudo-Gibbs sampler and perfect/imperfect phylogeny), and partition-ligation algorithm implemented by a fully Bayesian model (Haplotyper) or by EM (PLEM); 2) family-based haplotype inference methods; 3) the handling of genotype scoring uncertainties (i.e., genotyping errors and raw two-dimensional genotype scatterplots) in inferring haplotypes; and 4) haplotype inference methods for pooled DNA samples. The advantages and limitations of each algorithm are discussed. By using simulations based on empirical data on the G6PD gene and TNFRSF5 gene, I demonstrate that different algorithms have different degrees of sensitivity to various extents of population diversities and genotyping error rates. Future development of statistical algorithms for addressing haplotype reconstruction will resort more and more to ideas based on combinatorial mathematics, graphical models, and machine learning, and they will have profound impacts on population genetics and genetic epidemiology with the advent of the human HapMap.

Algorithms↗

Estimating the frequency of events that cause multiple-nucleotide changes.

Existing mathematical models of DNA sequence evolution assume that all substitutions derive from point mutations. There is, however, increasing evidence that larger-scale events, involving two or more consecutive sites, may also be important. We describe a model, denoted SDT, that allows for single-nucleotide, doublet, and triplet mutations. Applied to protein-coding DNA, the SDT model allows doublet and triplet mutations to overlap codon boundaries but still permits data to be analyzed using the simplifying assumption of independence of sites. We have implemented the SDT model for maximum-likelihood phylogenetic inference and have applied it to an alignment of mammalian globin sequences and to 258 other protein-coding sequence alignments from the Pandit database. We find the SDT model's inclusion of doublet and triplet mutations to be overwhelmingly successful in giving statistically significant improvements in fit of model to data, indicating that larger-scale mutation events do occur. Distributions of inferred parameter values over all alignments analyzed suggest that these events are far more prevalent than previously thought. Detailed consideration of our results and the absence of any known mechanism causing three adjacent nucleotides to be substituted simultaneously, however, leads us to suggest that the actual evolutionary events occurring may include still-larger-scale events, such as gene conversion, inversion, or recombination, or a series of rapid compensatory changes.

Animals↗

Statistical power of studies examining the cognitive effects of subthalamic nucleus deep brain stimulation in Parkinson's disease.

It has been argued that neuropsychological studies generally possess adequate statistical power to detect large effect sizes. However, low statistical power is problematic in neuropsychological research involving clinical populations and novel interventions for which available sample sizes are often limited. One notable example of this problem is evident in the literature regarding the cognitive sequelae of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in persons with Parkinson's disease (PD). In the current review, a post hoc estimate of the statistical power of 30 studies examining cognitive effects of STN DBS in PD revealed adequate power to detect substantial cognitive declines (i.e., very large effect sizes), but surprisingly low estimated power to detect cognitive changes associated with conventionally small, medium, and large effect sizes. Such wide spread Type II error risk in the STN DBS cognitive outcomes literature may affect the clinical decision-making process as concerns the possible risk of postsurgical cognitive morbidity, as well as conceptual inferences to be drawn regarding the role of the STN in higher-level cognitive functions. Statistical and methodological recommendations (e.g., meta-analysis) are offered to enhance the power of current and future studies examining the neuropsychological sequelae of STN DBS in PD.

Cognition Disorders↗

Causal criteria in nutritional epidemiology.

Making nutrition recommendations involves complex judgments about the balance between benefits and risks associated with a nutrient or food. Causal criteria are central features of such judgments but are not sufficient. Other scientific considerations include study designs, statistical tests, bias, confounding, and measurement issues. At a minimum, the set of criteria includes consistency, strength of association, dose response, plausibility, and temporality. The current practice, methods, and theory of causal inference permit flexibility in the choice of criteria, their relative priority, and the rules of inference assigned to them. Our approach is as follows. Consistency across study designs is compelling when the studies are of high quality and are not subject to biases. A statistically significant risk estimate with a > 20% increase or decrease in risk is considered a positive finding. A statistically significant linear or otherwise regularly increasing trend reinforces the judgment in favor of a recommendation. A plausible hypothesis likewise reinforces a recommendation, although the rules of inference for biological evidence are highly variable and depend on the situation. Temporality is, for nutrition recommendations, more a consideration of the extent to which a dietary factor affects disease onset or progression. Evidence supporting these criteria provides a strong basis for making a nutrition recommendation, given due consideration of the balance between presumed benefits and presumed harms. Recommendations should make clear their breadth of application; a narrow recommendation involves a single disease or condition whereas a broad recommendation involves all relevant diseases or conditions.

Causality↗

Haplotype association analysis of human disease traits using genotype data of unrelated individuals.

Haplotype inference has become an important part of human genetic data analysis due to its functional and statistical advantages over the single-locus approach in linkage disequilibrium mapping. Different statistical methods have been proposed for detecting haplotype - disease associations using unphased multi-locus genotype data, ranging from the early approach by the simple gene-counting method to the recent work using the generalized linear model. However, these methods are either confined to case - control design or unable to yield unbiased point and interval estimates of haplotype effects. Based on the popular logistic regression model, we present a new approach for haplotype association analysis of human disease traits. Using haplotype-based parameterization, our model infers the effects of specific haplotypes (point estimation) and constructs confidence interval for the risks of haplotypes (interval estimation). Based on the estimated parameters, the model calculates haplotype frequency conditional on the trait value for both discrete and continuous traits. Moreover, our model provides an overall significance level for the association between the disease trait and a group or all of the haplotypes. Featured by the direct maximization in haplotype estimation, our method also facilitates a computer simulation approach for correcting the significance level of individual haplotype to adjust for multiple testing. We show, by applying the model to an empirical data set, that our method based on the well-known logistic regression model is a useful tool for haplotype association analysis of human disease traits.

Computer Simulation↗