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PubMedDate not supplied

A method for normalizing drug responses to enhance reliability of parametric statistical tests.

Experimental constraints often require that pharmacologists study the effects of treatments upon responses elicited with a single concentration of agonist. The choice of statistical analysis, nonparametric or parametric, will depend upon whether or not the responses are normally distributed. This study uses both Monte Carlo and theoretical approaches to examine the normality assumption as it applies to drug responses. Responses measured in systems where drug sensitivities follow a lognormal distribution are generally not normally distributed. A simple transformation of responses, however, can restore normality. Researchers may find it beneficial to transform response data prior to performing t tests, analysis of variance or other parametric statistics in order to preserve the power and confidence level of the test.

Dose-Response Relationship, Drug↗

PubMed1997

Nonparametric tests for comparing two mean residual life functions.

We present nonparametric tests for the comparison of two mean residual life functions. We propose a new graphical approach for the simultaneous comparison of two mean residual life functions and their corresponding failure rate functions. We also consider the problem of testing against crossing mean residual life functions.

Animals↗

PubMed2001

Validation of the Turkish version of the Roland-Morris Disability Questionnaire for use in low back pain.

STUDY DESIGN: A reliability and validity study of a previously translated version of the Roland-Morris Disability Questionnaire (RMDQ). OBJECTIVES: To validate the Turkish version of the RMDQ for use in low back pain. SUMMARY OF BACKGROUND DATA: Clinical and epidemiologic research related to low back pain in the Turkish population would be facilitated by the availability of well-established outcome measures. METHODS: A total of 81 outpatients with low back pain, 64 of whom were followed up on a second occasion, were assessed by the RMDQ. Reliability was assessed using internal consistency and the intraclass correlation coefficient. Internal construct validity was assessed by Rasch analysis; external construct validity was assessed by association with pain and spinal movement. Responsiveness was tested by both the nonparametric and parametric effect sizes. RESULTS: Internal consistency of the RMDQ is found to be adequate (>0.85) at both times, with high intraclass correlation coefficient also at both time points. Internal construct validity of the scale is good, indicating a single underlying construct. Expected associations with pain confirm external construct validity. There is little evidence of differential item functioning. The scale is at the ordinal level. Responsiveness of the RMDQ is good and greater than observed change in spinal movement. CONCLUSIONS: The RMDQ is a robust unidimensional ordinal measure, largely free of differential item functioning, which works well in the Turkish population. Nonparametric effect sizes of ordinal scales are found to overestimate or underestimate the true effect size depending on the nature of the scale and the distribution of patients at baseline.

Adult↗

PubMed2004

Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer.

BACKGROUND: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups. RESULTS: We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation. CONCLUSIONS: This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups.

Algorithms↗

PubMed2005

Spectral analysis of heart rate variability: interchangeability between autoregressive analysis and fast Fourier transform.

Interchangeability between fast Fourier transform (FFT) and autoregressive (AR) analysis was assessed on series of 256 R-R intervals recorded in 56 seated subjects and in 15 men performing an orthostatic test. Low- (LF) and high-frequency (HF) components were calculated and expressed both in absolute (square milliseconds) and normalized units (NU). During orthostatic stress, the same upward trend for LF square milliseconds and LF/HF ratio and downward trend for HF square milliseconds and HF NU were observed with FFT and AR analysis. However, the values for HF square milliseconds were significantly greater with FFT, as compared with AR analysis in standing position (P < .05). Moreover, Bland & Altman method highlighted a large discrepancy between the results of FFT and AR analysis for all heart rate variability indices in the 3 conditions. Therefore, parametric and nonparametric spectral analyses could not be considered as interchangeable at rest in healthy subjects even if they give same qualitative results.

Adolescent↗

PubMed2006

A Thurstonian model for quantitative genetic analysis of ranks: a Bayesian approach.

A fully Bayesian method for quantitative genetic analysis of data consisting of ranks of, e.g., genotypes, scored at a series of events or experiments is presented. The model postulates a latent structure, with an underlying variable realized for each genotype or individual involved in the event. The rank observed is assumed to reflect the order of the values of the unobserved variables, i.e., the classical Thurstonian model of psychometrics. Parameters driving the Bayesian hierarchical model include effects of covariates, additive genetic effects, permanent environmental deviations, and components of variance. A Markov chain Monte Carlo implementation based on the Gibbs sampler is described, and procedures for inferring the probability of yet to be observed future rankings are outlined. Part of the model is rendered nonparametric by introducing a Dirichlet process prior for the distribution of permanent environmental effects. This can lead to potential identification of clusters of such effects, which, in some competitions such as horse races, may reflect forms of undeclared preferential treatment.

Bayes Theorem↗

PubMed2006

A new method of evaluating antitumor activity from measured tumor volumes.

In studies of antitumor activity, measurement of tumor growth delay is commonly used to evaluate treatment effects. However, this measurement sometimes provides no information. We propose an approach for comparing tumor response by using nonparametric regression. In a xenograft model, we found that a significant difference between two treatment effects was undetectable by comparison of tumor growth delays. The results of our finding suggest that measurements used to evaluate the treatment effects should be carefully selected in analyses of antitumor activity.

Animals↗

PubMed1997

Bayesian nonparametric population models: formulation and comparison with likelihood approaches.

Population approaches to modeling pharmacokinetic and/or pharmacodynamic data attempt to separate the variability in observed data into within- and between-individual components. This is most naturally achieved via a multistage model. At the first stage of the model the data of a particular individual is modeled with each individual having his own set of parameters. At the second stage these individual parameters are assumed to have arisen from some unknown population distribution which we shall denote F. The importance of the choice of second stage distribution has led to a number of flexible approaches to the modeling of F. A nonparametric maximum likelihood estimate of F was suggested by Mallet whereas Davidian and Gallant proposed a semiparametric maximum likelihood approach where the maximum likelihood estimate is obtained over a smooth class of distributions. Previous Bayesian work has concentrated largely on F being assigned to a parametric family, typically the normal or Student's t. We describe a Bayesian nonparametric approach using the Dirichlet process. We use Markov chain Monte Carlo simulation to implement the procedure. We discuss each procedure and compare our approach with those of Mallet and Davidian and Gallant, using simulated data for a pharmacodynamic dose-response model.

Bayes Theorem↗

PubMed1997

Empirical genomewide significance levels established by whole genome simulations.

The advent of high-resolution genetic maps and semiautomated genotyping technology has opened the way for genome screening in genetically complex traits. Many such screens are now under way, or completed, most using multipoint nonparametric linkage analysis of affected sibling pairs. This type of linkage analysis uses all the available genotype information to calculate the maximum lod score (MLS) value at each point in the genome, and thereby generates MLS profiles along each chromosome. Any positive MLS values indicate potential linkage, but the peaks in these profiles, which may be referred to as "hits," identify the most likely locations of disease susceptibility genes. However, such analysis presents serious problems of multiple testing, and the assessment of the statistical significance of hits has become a contentious issue [Lander and Kruglyak (1995) Nat Genet 11:241-247; Curtis (1996) Nat Genet 12:356-357; Witte et al. (1996) Nat Genet 12:355-356]. Having recently completed a genome screen in multiple sclerosis, we decided to investigate the statistical properties of our study by simulation. We report here in detail the results of this simulation study. Our main conclusion is that, for the particular set of families and markers used in our screen, an MLS of 3.2 carries a genome-wide significance of 5% (that is, there is a 5% probability of observing at least one false hit, above this threshold in a complete genome screen). This value is closer to the familiar limit of 3.0, originally suggested by Morton [1955; Am J Hum Genet 7:277-318] than to the more stringent limit of 4.0 recently proposed by Lander and Kruglyak [1995; Nat Genet 11:241-247]. This is somewhat reassuring, in view of the very large sample sizes that would be necessary to achieve adequate power to detect linkage at the more stringent threshold.

Female↗

PubMed1996

Estimating relative risk functions in case-control studies using a nonparametric logistic regression.

The authors describe an approach to the analysis of case-control studies in which the exposure variables are continuous, i.e., quantitative variables, and one wishes neither to categorize levels of the exposure variable nor to assume a log-linear relation between level of exposure and disease risk. A dose-response association of an exposure variable with a disease outcome can be depicted by estimated relative risks at various exposure levels, and the functional relation between exposure dose and disease risk is here termed a relative risk function (RRF). A RRF takes values that are greater than zero: Values less than one imply lower risk; the value one implies no risk, and values greater than one imply increased risk, when compared with a reference value. The authors describe how a nonparametric logistic regression can be used to estimate and display these RRFs. Using data from a previously published case-control study of diet and colon cancer, RRFs for total energy, dietary fiber, and alcohol intakes are compared with the original results obtained from using categorized levels of exposure variables. For total energy and alcohol intakes, there were meaningful differences in study results based on the two analytic approaches. For energy, the nonparametric logistic regression detected a significant protective effect of low intakes, which was not found in the original analysis. For alcohol, the nonparametric logistic regression suggested that there were two underlying populations, non- or very light drinkers and moderate to heavy drinkers, with different relation of dose to disease risk. In contrast, the original analysis found a nonlinear increase in risk across intake categories and did not detect the complex, bimodal nature of the exposure distribution. These results demonstrate that nonparametric logistic regression can be a useful approach to displaying and interpreting results of case-control studies.

Adult↗

PubMed2006

The effect of German chamomile (Marticaria recutita L.) extract and tea tree (Melaleuca alternifolia L.) oil used as irrigants on removal of smear layer: a scanning electron microscopy study.

AIM: To compare the cleaning effectiveness of chamomile hydroalcoholic extract and tea tree oil to 2.5% sodium hypochlorite (NaOCl) solution as an intracanal irrigant for the removal of the smear layer. METHODOLOGY: Forty extracted, single-rooted, mature, permanent, human teeth were allocated at random into one of three experimental groups of ten teeth and two control groups of five teeth. For each tooth, the pulp chamber was accessed and the canal prepared using K-type files and Gates-Glidden burs, using a step-back technique; the apical stop was prepared to a size 30. Each canal was subsequently irrigated with one of the following solutions: distilled water (as a negative control), 2.5% NaOCl + 17% ethylenediamine tetraacetic acid (EDTA) (as a positive control), chamomile or tea tree oil or 2.5% NaOCl. Each tooth was split longitudinally and prepared for examination by scanning electron microscopy (SEM). The quantity of smear layer remaining on the three levels of each canal (coronal, middle and apical) was examined using magnifications of 2000 and 5000x. The data were analysed using nonparametric Kruskal-Wallis and Mann-Whitney U-tests. RESULTS: The most effective removal of smear layer occurred with the use of NaOCl with a final rinse of 17% EDTA (negative control) followed by the use of a chamomile extract. Chamomile extract was found to be significantly more effective than distilled water and tea tree oil (P < 0.008). The use of a 2.5% NaOCl solution alone, without EDTA and that of tea tree oil, was found to have only minor effects. There was no statistical difference between distilled water, 2.5% NaOCl and tea tree oil. CONCLUSIONS: The efficacy of chamomile to remove smear layer was superior to NaOCl alone but less than NaOCl combined with EDTA.

Dentin↗

PubMed1999

A genome-wide scan for a simulated data set using two newly developed methods.

A genome-wide scan of a simulated data set for fictitious disease genes was conducted using both semiparametric and nonparametric methods. The semiparametric model-based method, which tests for linkage/linkage disequilibrium separately and together, correctly identified all three underlying disease loci along with two false positives through the linkage analysis. However, the nonparametric model-free method which tests combined linkage/linkage disequilibrium, failed to yield any results due to the lack of linkage disequilibrium information in the data.

Chromosome Mapping↗

PubMed2001

Using data mining to address heterogeneity in the Southampton data.

When analyzing complex traits such as asthma, heterogeneity needs to be assumed. With this in mind, to identify a more homogeneous group of asthmatic patients, we analyzed the Southampton data using the data mining technique known as the regression tree method and the two most inheritable quantitative phenotypes (LnIgE and RAST) as the target variables. Two-point and multipoint nonparametric linkage analyses were carried out using one of the subgroups as affected. In addition, we performed quantitative trait loci nonparametric linkage analysis using each phenotype as the outcome. The results from the affected-sibpairs method and quantitative linkage analysis were compared.

Adult↗

PubMed2001

Should we consider gene x environment interaction in the hunt for quantitative trait loci?

We address the question of whether one can obtain increased power for finding a quantitative trait locus (QTL) if a gene x environment (G x E) interaction is incorporated directly into the linkage analysis. We consider both parametric and nonparametric analysis approaches to including G x E interaction. For the former, we utilize joint segregation and linkage analysis to estimate simultaneously the recombination fraction and a G x E interaction effect, as well as the remaining model parameters. The nonparametric approach is based on an extension of the Haseman-Elston method applied to sib pairs to include a regression of the squared trait difference on marker-identity-by-descent (IBD) probability (pi), the sibling covariate sum (z), and pi x z. We utilize 50 replicates of the simulated data and compare empirical power of the various approaches to detect MG4, a locus that is involved in a strong interaction with age for Q4 and in a weaker interaction with environmental factor E2 for Q3. Using the parametric approach, including a G x age effect does increase power for detecting linkage between MG4 and Q4 compared with ignoring the interaction (powers 58% and 38%, respectively, to exceed a lod score of 3.0). On the other hand, including a G x E2 interaction has little effect on the power to detect linkage between MG4 and Q3. The nonparametric approach leads to qualitatively similar findings. We conclude that it is beneficial to incorporate G x E interaction into a linkage analysis, provided the interaction effect is of sufficiently strong magnitude.

Chromosome Mapping↗

PubMed2005

The use of a sum of inverse Gaussian functions to describe the absorption profile of drugs exhibiting complex absorption.

PURPOSE: The aim of this study was to evaluate the utility of a parametric deconvolution method using a sum of inverse Gaussian functions (IG) to characterize the absorption and concentrations vs. time profile of drugs exhibiting complex absorption. METHODS: For a linear time-invariant system the response, Y(t), following an arbitrary input function I(t), is the convolution of I(t) with the disposition function, H(t), of the system: [Formula: see text]. The method proposed uses a sum of n inverse Gaussian functions to characterize I(t). The approach was compared with a standard nonparametric method using linear splines. Data were provided from previously published studies on two drugs (hydromorphone and veralipride) showing complex absorption and analyzed with NONMEM. RESULTS: A satisfactory fit for hydromorphone and veralipride data following oral administration was achieved by fitting a sum of two or three IG functions. The predictions of the input functions were very similar to those using linear splines. CONCLUSIONS: The use of a sum of IG as opposed to nonparametric functions, such as splines, offers a simpler implementation, a more intuitive interpretation of the results, a built-in extrapolation, and an easier implementation in a population context. Disadvantages are an apparent greater sensitivity to initial value estimates (when used with NONMEM).

Algorithms↗

PubMed2002

Acylglycerol and fatty acid components of pulp, seed, and whole olive fruit oils. Their use to characterize fruit variety by chemometrics.

The contents of triacylglycerols and diacylglycerols in three kinds of olive fruit oils (pulp, seed, and whole fruit) were determined. The fatty acid composition and the quality ratios 1,2-diacylglycerols/1,3-diacylglycerols and 1,2-diacylglycerols/total diacylglycerols were also assessed. Seven major Italian olive varieties were considered. Results of univariate statistical analyses indicated that the above analytical parameters (glyceridic ratios excepted) were effective in discriminating between pulp and seed oils. The seed oil fraction did not determine any change in the glyceridic indices and the acylglycerol or fatty acid composition concerning the whole fruit oil (mixture of pulp and seed oil fractions), the weight (%) of seed ( approximately 2%) being by far lower than the weight (%) of pulp ( approximately 85%) (fruit weight basis). Based on the data of triacylglycerol or fatty acid composition, and using appropriate parametric or nonparametric multivariate statistics, the genetic origins (olive variety) of the three fruit oil kinds were characterized.

Diglycerides↗

PubMed2000

Nonparametric and parametric estimation for a linear germination-growth model.

Seeds are planted on the interval [0, L] at various locations. Each seed has a location x and a potential germination time t epsilon [0, infinity), and it is assumed that the collection of such (x, t) pairs forms a Poisson process in [0, L] x [0, infinity) with intensity measure dxd lambda(t). From each seed that germinates, an inhibiting region grows bidirectionally at rate 2v. These regions inhibit germination of any seed in the region with a later potential germination time. Thus, seeds only germinate in the uninhibited part of [0, L]. We want to estimate lambda on the basis of one or more realizations of the process, the data being the locations and germination times of the germinated seeds. We derive the maximum likelihood estimator of v and a nonparametric estimator of lambda and describe methods of obtaining parametric estimates from it, illustrating these with reference to gamma densities. Simulation results are described and the methods applied to some neurobiological data. An Appendix outlines the S-PLUS code used.

Animals↗

PubMed2000

Power comparison of parametric and nonparametric linkage tests in small pedigrees.

When the mode of inheritance of a disease is unknown, the LOD-score method of linkage analysis must take into account uncertainties in model parameters. We have previously proposed a parametric linkage test called "MFLOD," which does not require specification of disease model parameters. In the present study, we introduce two new model-free parametric linkage tests, known as "MLOD" and "MALOD." These tests are defined, respectively, as the LOD score and the admixture LOD score, maximized (subject to the same constraints as MFLOD) over disease-model parameters. We compared the power of these three parametric linkage tests and that of two nonparametric linkage tests, NPLall and NPLpairs, which are implemented in GENEHUNTER. With the use of small pedigrees and a fully informative marker, we found the powers of MLOD, NPLall, and NPLpairs to be almost equivalent to each other and not far below that of a LOD-score analysis performed under the assumption the correct genetic parameters. Thus, linkage analysis is not much hindered by uncertain mode of inheritance. The results also suggest that both parametric and nonparametric methods are suitable for linkage analysis of complex disorders in small pedigrees. However, whether these results apply to large pedigrees remains to be answered.

Chromosome Mapping↗