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PubMed2003

Clinical versus statistical significance as they relate to the efficacy of periodontal therapy.

BACKGROUND: The author discusses the shortcomings of using statistical significance testing as a method to infer that results of periodontal clinical trials are clinically meaningful. To compensate for these deficiencies, he also identifies criteria and periodontal parameters that can be used to reflect clinically significant outcomes. TYPES OF STUDIES REVIEWED: The author searched the medical and dental literature to identify commentaries that addressed the problems associated with interpreting statistical significance testing, or hypothesis testing, and defining clinical significance. RESULTS: The limitations of statistical significance testing related to identifying clinically significant changes include failure to indicate if the detected differences between variables in test and control groups are large or important. After reviewing various definitions of the term "clinical significance," the author reviews and proposes a comprehensive working definition of it. Regarding the efficacy of periodontal therapy, he delineates the advantages and limitations of specific criteria (such as absolute values, cut points) that can be used to define clinical significance. CLINICAL IMPLICATIONS: The author suggests that clinically significant results should be defined before initiating a study and statistical significance testing should be used to validate that findings did not occur by chance. This would help place the importance of clinical data into perspective, and it would enhance clinicians' ability to select the most appropriate therapies for particular sites in periodontal patients.

Clinical Trials as Topic↗
PubMed2003

Haplotype frequency estimation in the presence of genotyping errors.

Several statistical methods have been proposed to estimate haplotype frequencies, either based on unrelated individuals or based on families. These estimates may yield insights on population genetics as well as associations between candidate regions and disease of interest. One limitation of the existing methods is that all these methods make the implicit assumption that there are no genotyping errors. However, genotyping errors are unavoidable in practice. Numerous methods have been developed to incorporate genotyping errors in genetic studies, but none to date have addressed the issues of haplotype inference in the presence of genotyping errors. In this article, we develop statistical methods for haplotype inference incorporating genotyping errors. We describe how our methods can be applied to analyze unrelated individuals as well as nuclear families. Our simulation results show that the proposed methods perform well in the presence of genotyping errors.

Computer Simulation↗
PubMedDate not supplied

Computational tools for the modern andrologist.

With such a wide array of computational tools to solve inference problems, andrologists and their mathematical or statistical collaborators face perhaps bewildering choices. It is tempting to criticize a method with which one is unfamiliar for its apparent complexity. Yet, many methods are quite elegant; neural computation uses nature's own best biological classifier, for example, and genetic algorithms apply rules of natural selection. Computer scientists will likely find no one single best inference engine to solve all classification problems. Rather, the modeler should choose the most appropriate computational tool based on the specific nature of a problem. If the problem can be separated into obvious components, a Markov chain may be useful. If the andrologist would like to encode a well-known clinical algorithm into the computer, the programmer may use an expert system. Once a modeler builds an inference engine, that engine is not truly useful until other andrologists use it to make inferences with their own data. Because a wide variety of computer hardware and software exists, it is a significant endeavor to translate, or "port," software designed and built on one machine to many other different computers. Fortunately, the World Wide Web offers a means by which computational tools may be made directly available to multiple users on many different systems, or "platforms." The World Wide Web refers to a standardization of information traffic on the global computer network, the Internet. The Internet is simply the linkage of many computers worldwide by computer operators who have chosen to allow other users access to their systems. Because many different types of computers exist, until recently only communication in very rudimentary form, such as text, or between select compatible machines, was available. Within the last half-decade, computer scientists and operators began to use standard means of communication between computers. Interpreters of these standard languages, such as Mosaic and Netscape, are now widely available, and they allow the casual user to access the most sophisticated multimedia aspects of computer information on a variety of different systems. Andrologists may thus use the World Wide Web to make inference engines that they have programmed available to other clinicians and researchers. For example, we programmed a World Wide Web interface to the neural networks that we trained in order to solve a number of andrology classification problems. Interested users connect to our address (at this writing http:@godot.urol.uic.edu), and they may fill out electronic forms with their own patient data, press a "predict" button, and nearly immediately view the results of our neural networks' prediction on their own computers. With the explosion in computer hardware technology, mathematics and computer science that once seemed esoteric can now be investigated on computers available to nearly all andrologists. Rapid advances in computer network technology now render a tool developed by one andrologist immediately available to many. Clearly, andrologists may expect that computational investigations in their field will be a productive ground in the near and far future.

Algorithms↗
PubMed1996

Prospective, longitudinal assessment of developmental neurotoxicity.

Methodological issues in the design of prospective, longitudinal studies of developmental neurotoxicity in humans are reviewed. A comprehensive assessment of potential confounding influences is important in these studies because inadequate assessment of confounders can threaten the validity of causal inferences drawn from the data. Potential confounders typically include demographic background variables, alcohol and smoking during pregnancy, the quality of parental stimulation, the child's age at test, and the examiner. Exposure to other substances is assessed where significant exposure is expected in the target population. In most studies, control variables even weakly related to outcome are included in all multivariate statistical analyses, and a toxic effect is inferred only if the effect of exposure is significant after controlling for the potential confounders. Once a neurotoxic effect has been identified, suspected mediating variables may be added to the analysis to examine underlying processes or mechanisms through which the exposure may impact on developmental outcome. Individual differences in vulnerability may be examined in terms of either an additive compensatory model or a synergistic "risk and resilience" approach. Failure to detect real effects (Type II error) is of particular concern in these studies because public policy considerations make it likely that negative findings will be interpreted to mean that the exposure is safe. Important sources of Type II error include inadequate representation of highly exposed individuals, overcontrol for confounders, and inappropriate correction for multiple comparisons. Given the high cost and complexity of prospective, longitudinal investigations, cross-sectional pilot studies focusing on highly exposed individuals can be valuable for the initial identification of salient domains of impairment.

Animals↗
PubMed2025

Polaris: Polarization of ancestral and derived polymorphic alleles for inferences of extended haplotype homozygosity in human populations.

SUMMARY: Statistical methods that measure the extent of haplotype homozygosity on chromosomes have been highly informative for identifying episodes of recent selection. For example, the integrated haplotype score (iHS) and the extended haplotype homozygosity (EHH) statistics detect long-range haplotype structure around derived and ancestral alleles indicative of classic and soft selective sweeps, respectively. However, to our knowledge, there are currently no publicly available methods that classify ancestral and derived alleles in genomic datasets for the purpose of quantifying the extent of haplotype homozygosity. Here, we introduce the Polaris package, which polarizes chromosomal variants into ancestral and derived alleles and creates corresponding genetic maps for analysis by selscan and HaploSweep, two versatile haplotype-based programs that perform scans for selection. With the input files generated by Polaris, selscan and/or HaploSweep can produce the appropriate sign (either positive or negative) for outlier iHS statistics, enabling users to distinguish between selection on derived or ancestral alleles. In addition, Polaris can convert the numerical output of these analyses into graphical representations of selective sweeps, increasing the functionality of our software. RESULTS: To demonstrate the utility of our approach, we applied the Polaris package to Chromosome 2 in the European Finnish, Middle Eastern Bedouin, and East African Maasai populations. More specifically, we examined the regulatory sequence in intron 13 of the MCM6 gene associated with lactase persistence (i.e. the ability to digest the lactose sugar present in fresh milk), a region of intense interest to human evolutionary geneticists. Our analyses showed that derived alleles (at known enhancers for lactase expression) sit on an extended haplotype background in the Finnish, Bedouin, and Maasai consistent with a classic selective sweep model as determined by iHS and EHH statistics. Importantly, we were able to immediately identify this target allele under selection based on the information generated by our software. We also explored outlier statistics across Chromosome 2 in two distinct datasets from these populations: (i) one containing polarized alleles generated with Polaris and (ii) the other containing unpolarized alleles in the original phased vcf file. Here, we found an excess of outlier statistics on Chromosome 2 in the unpolarized datasets, raising the possibility that a subset of these "hits" of selection may be unreliable. Overall, Polaris is a versatile package that enables users to efficiently explore, interpret, and report signals of recent selection in genomic datasets. AVAILABILITY AND IMPLEMENTATION: The Polaris package is free and open source on GitHub (https://github.com/alisi1989/Polaris) and DropBox (https://www.dropbox.com/scl/fo/mlxizft5267vem9u62qkn/AAnM0qX923zPzQBlPX8iteM?rlkey=uezrp4t2waffpj0nmo1evr320&e=1&st=jaodccws&dl=0).

Haplotypes↗
PubMed1995

A new method of inference of ancestral nucleotide and amino acid sequences.

A statistical method was developed for reconstructing the nucleotide or amino acid sequences of extinct ancestors, given the phylogeny and sequences of the extant species. A model of nucleotide or amino acid substitution was employed to analyze data of the present-day sequences, and maximum likelihood estimates of parameters such as branch lengths were used to compare the posterior probabilities of assignments of character states (nucleotides or amino acids) to interior nodes of the tree; the assignment having the highest probability was the best reconstruction at the site. The lysozyme c sequences of six mammals were analyzed by using the likelihood and parsimony methods. The new likelihood-based method was found to be superior to the parsimony method. The probability that the amino acids for all interior nodes at a site reconstructed by the new method are correct was calculated to be 0.91, 0.86, and 0.73 for all, variable, and parsimony-informative sites, respectively, whereas the corresponding probabilities for the parsimony method were 0.84, 0.76, and 0.51, respectively. The probability that an amino acid in an ancestral sequence is correctly reconstructed by the likelihood analysis ranged from 91.3 to 98.7% for the four ancestral sequences.

Amino Acid Sequence↗
PubMed2000

Binary formal inference-based recursive modeling using multiple atom and physicochemical property class pair and torsion descriptors as decision criteria

Analysis of a large amount of information, typically generated by high-throughput screening, is a very difficult task. To address this problem, we have developed binary formal inference-based recursive modeling using atom and physicochemical property class pair and torsion descriptors. Recursive partitioning is an exploratory technique for identifying structure in data. The implemented algorithm utilizes a statistical hypothesis testing, similar to Hawkins' formal inference-based recursive modeling program, to separate a data set into two homogeneous subsets at each splitting node. This process is repeated recursively until no further separation can occur. Our implementation of recursive partitioning differs from previously reported approaches by employing a method to extract multiple features at each splitting node. The method was examined for its ability to distinguish random and real data sets. The effect of including a single descriptor and multiple descriptors in the splitting descriptor set was also studied. The method was tested using 27 401 National Cancer Institute (NCI) compounds and their pGI50 (-log(GI50)) against the NCI-H23 cell line. The analyses show that partitioning using multiple descriptors is advantageous in analyzing the structure-activity relationship information.

Journal Article↗
PubMed2004

Stress-induced DNA duplex destabilization (SIDD) in the E. coli genome: SIDD sites are closely associated with promoters.

We present the first analysis of stress-induced DNA duplex destabilization (SIDD) in a complete chromosome, the Escherichia coli K12 genome. We used a newly developed method to calculate the locations and extents of stress-induced destabilization to single-base resolution at superhelix density sigma = -0.06. We find that SIDD sites in this genome show a statistically highly significant tendency to avoid coding regions. And among intergenic regions, those that either contain documented promoters or occur between divergently transcribing coding regions, and hence may be inferred to contain promoters, are associated with strong SIDD sites in a statistically highly significant manner. Intergenic regions located between convergently transcribing genes, which are inferred not to contain promoters, are not significantly enriched for destabilized sites. Statistical analysis shows that a strongly destabilized intergenic region has an 80% chance of containing a promoter, whereas an intergenic region that does not contain a strong SIDD site has only a 24% chance. We describe how these observations may illuminate specific mechanisms of regulation, and assist in the computational identification of promoter locations in prokaryotes.

Chromosomes, Bacterial↗
PubMed2005

The acquisition of allophonic rules: statistical learning with linguistic constraints.

Phonological rules relate surface phonetic word forms to abstract underlying forms that are stored in the lexicon. Infants must thus acquire these rules in order to infer the abstract representation of words. We implement a statistical learning algorithm for the acquisition of one type of rule, namely allophony, which introduces context-sensitive phonetic variants of phonemes. This algorithm is based on the observation that different realizations of a single phoneme typically do not appear in the same contexts (ideally, they have complementary distributions). In particular, it measures the discrepancies in context probabilities for each pair of phonetic segments. In Experiment 1, we test the algorithm's performances on a pseudo-language and show that it is robust to statistical noise due to sampling and coding errors, and to non-systematic rule application. In Experiment 2, we show that a natural corpus of semiphonetically transcribed child-directed speech in French presents a very large number of near-complementary distributions that do not correspond to existing allophonic rules. These spurious allophonic rules can be eliminated by a linguistically motivated filtering mechanism based on a phonetic representation of segments. We discuss the role of a priori linguistic knowledge in the statistical learning of phonology.

Humans↗
PubMed2005

Statistical consideration of the strategy for demonstrating clinical evidence of effectiveness--one larger vs two smaller pivotal studies.

As a regulatory strategy, it is nowadays not uncommon to conduct one confirmatory pivotal clinical trial, instead of two, to demonstrate efficacy and safety in drug development. This paper is intended to investigate the statistical foundation of such an approach. The one-study approach is compared with the conventional two-study approach in terms of power, type-I error, and fundamental statistical assumptions. Necessary requirements for a single-study model is provided in order to maintain equivalent evidence as that from a two-study model. In general, one-study model is valid only under a 'one population' assumption. In addition, higher data quality and more convincing and robust results need to be demonstrated in such cases. However, when 'one-population' assumption is valid and appropriate methods are selected, a one-study model can have a better power using the same sample size. The paper also investigates statistical assumptions and methods for making an overall inference when a two-study model has been used. The methods for integrated analysis are evaluated. It is important for statisticians to select correct pooling strategy based on the project objective and statistical hypothesis.

Clinical Trials, Phase III as Topic↗
PubMed2004

Simple solution to a common statistical problem: interpreting multiple tests.

BACKGROUND: The misinterpretation of the results of multiple statistical tests is an error commonly made in scientific literature. When testing several outcome variables simultaneously, many researchers declare a statistically significant result for each test having a P value of <0.05, for example. This approach ignores the fact that, based on a probability result called the Bonferroni inequality, the risk of incorrectly declaring as significant > or =1 test result increases with the number of tests conducted. The implication of this practice is that many scientific results are presented as statistically significant when the underlying data do not adequately support such a claim (sometimes referred to as false-positive results). Although the sequentially rejective Bonferroni test is well known among statisticians, it is not used routinely in scientific literature. OBJECTIVE: The intent of this article was to increase the awareness and understanding of the sequentially rejective Bonferroni test, thereby expanding its use. METHODS: This article describes the statistical problem and demonstrates how the use of the sequentially rejective Bonferroni test ensures that incorrect declarations of statistical significance for > or =1 test result are bounded by 0.05, for example. CONCLUSION: The sequentially rejective Bonferroni test is an easily applied, versatile statistical tool that enables researchers to make simultaneous inferences from their data without risking an unacceptably high overall type I error rate.

Confidence Intervals↗
PubMed1993

The relative importance of prognostic factors in studies of survival.

The relative importance of prognostic factors in regression can be measured either by standardized regression coefficients or by percentages of explained variation in a dependent variable. One advantage of using explained variation is the direct comparability of qualitative prognostic factors with others, or of groups of prognostic factors. The description of relative importance can be accomplished within marginal or partial effects analyses. It is demonstrated that it is possible not only to provide a descriptive ranking of prognostic factors according to their statistically determined importance, but also to make inferences concerning their relative importance, employing bootstrap techniques and procedures for multiple comparisons. The methods presented, which are new in the context of Cox regression, are exemplified by analyses of studies of lung cancer and breast cancer.

Adult↗
PubMed1997

On the interpretation of microbial clusters in periodontal disease.

Cluster analysis is sometimes used in periodontal research to describe patterns of microbial identifications observed among cases (subjects or sites). These patterns are assumed to define subsets of cases that are meaningful, in the sense of defining periodontal subgroups that have reliable differences in some aspect of their disease. However, cluster analysis is an exploratory, first-stage technique that does not incorporate the machinery that allows one to evaluate the statistical significance and reliability of the patterns observed. Problems inherent in drawing inferences from cluster analysis are discussed and, as one methodological example of how the statistical significance of clustering might be determined, a randomization-based test for binary scaled microbial identifications is described. The introduction of cluster analysis to the study of periodontal microbiology was important and innovative, but future work needs to incorporate validation by either statistical means or independent replication. The present rarity of such confirmatory methods in the literature suggests caution in accepting specific cluster-based hypotheses about patient-microbial patterns.

Analysis of Variance↗
PubMed2005

The power of single-nucleotide polymorphisms for large-scale parentage inference.

Likelihood-based parentage inference depends on the distribution of a likelihood-ratio statistic, which, in most cases of interest, cannot be exactly determined, but only approximated by Monte Carlo simulation. We provide importance-sampling algorithms for efficiently approximating very small tail probabilities in the distribution of the likelihood-ratio statistic. These importance-sampling methods allow the estimation of small false-positive rates and hence permit likelihood-based inference of parentage in large studies involving a great number of potential parents and many potential offspring. We investigate the performance of these importance-sampling algorithms in the context of parentage inference using single-nucleotide polymorphism (SNP) data and find that they may accelerate the computation of tail probabilities >1 millionfold. We subsequently use the importance-sampling algorithms to calculate the power available with SNPs for large-scale parentage studies, paying particular attention to the effect of genotyping errors and the occurrence of related individuals among the members of the putative mother-father-offspring trios. These simulations show that 60-100 SNPs may allow accurate pedigree reconstruction, even in situations involving thousands of potential mothers, fathers, and offspring. In addition, we compare the power of exclusion-based parentage inference to that of the likelihood-based method. Likelihood-based inference is much more powerful under many conditions; exclusion-based inference would require 40% more SNP loci to achieve the same accuracy as the likelihood-based approach in one common scenario. Our results demonstrate that SNPs are a powerful tool for parentage inference in large managed and/or natural populations.

Algorithms↗
PubMed2002

Simulation study of the reliability and robustness of the statistical methods for detecting positive selection at single amino acid sites.

Inferring positive selection at single amino acid sites is of biological and medical importance. Parsimony-based and likelihood-based methods have been developed for this purpose, but the reliabilities of these methods are not well understood. Because the evolutionary models assumed in these methods are only rough approximations to reality, it is desirable that the methods are not very sensitive to violation of the assumptions made. In this study we show by computer simulation that the likelihood-based method is sensitive to violation of the assumptions and produces many false-positive results under certain conditions, whereas the parsimony-based method tends to be conservative. These observations, together with those from previous studies, suggest that the positively selected sites inferred by the parsimony-based method are more reliable than those inferred by the likelihood-based method.

Amino Acid Sequence↗
PubMed1999

Nonparametric estimation of bounded survival functions with censored observations.

Stochastic ordering of survival functions is a useful concept in many areas of statistics, especially in nonparametric and order restricted inferences. In this paper we introduce an algorithm to compute maximum likelihood estimates of survival functions where both upper and lower bounds are given. The algorithm allows censored survival data. In a simulation study, we found that the proposed estimates are more efficient than the unrestricted Kaplan-Meier product limit estimates both with and without censored observations.

Algorithms↗
PubMed2005

Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach.

BACKGROUND: The study of the network between transcription factors and their targets is important for understanding the complex regulatory mechanisms in a cell. Unfortunately, with standard microarray experiments it is not possible to measure the transcription factor activities (TFAs) directly, as their own transcription levels are subject to post-translational modifications. RESULTS: Here we propose a statistical approach based on partial least squares (PLS) regression to infer the true TFAs from a combination of mRNA expression and DNA-protein binding measurements. This method is also statistically sound for small samples and allows the detection of functional interactions among the transcription factors via the notion of "meta"-transcription factors. In addition, it enables false positives to be identified in ChIP data and activation and suppression activities to be distinguished. CONCLUSION: The proposed method performs very well both for simulated data and for real expression and ChIP data from yeast and E. Coli experiments. It overcomes the limitations of previously used approaches to estimating TFAs. The estimated profiles may also serve as input for further studies, such as tests of periodicity or differential regulation. An R package "plsgenomics" implementing the proposed methods is available for download from the CRAN archive.

Algorithms↗
PubMed2002

Is there reliable experimental evidence for a low-dose RBE of about 4 for mammography X rays relative to 200 kV X rays?

recently reported, on the basis of observations of neoplastic transformation in human hybrid CGL1 cells, a low-dose relative biological effectiveness (RBE(M)) of 4.3 for mammography X rays (29 kV) relative to 200 kV X rays. With reference to data in the literature, they inferred a factor of about 8 relative to 60Co gamma rays and concluded that this result is relevant to risk estimation. However, the conclusions do not appear to be valid. The data from the transformation study exhibit uncertainties in the statistical analysis that preclude any generalization of the inferred RBE(M). The data selected or inferred from the literature are likewise insufficient to support the stated RBEs. Our own uniform data set for the yields of dicentrics was obtained for widely varying photon energies with blood samples from the same donor, and it avoids interindividual variations in sensitivity as well as the differences in methodology that are associated with interlaboratory comparisons. Our data provide RBE(M) values for 29 kV X rays of 1.64 +/- 0.27 relative to 220 kV X rays and 4.75 +/- 1.67 and 6.12 +/- 2.51 relative to 60Co gamma rays.

Cell Transformation, Neoplastic↗