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Results for “hypothesis testing”

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At least 163 records · Page 9Linked to original sources
PubMed2006

Multiple hypothesis testing strategies for genetic case-control association studies.

The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature.

Case-Control Studies↗
PubMedDate not supplied

Small sample behaviour of hypothesis tests related to indirect standardized rates: a Monte Carlo study.

Different distributions of confounding variables in populations complicate any comparison of the relative frequency of an event. To resolve this, methods for fitting statistical models to tables of rates have recently been developed. One such model is the multiplicative model. We performed a Monte Carlo study of the multiplicative model for a 4 X 3 table of rates. For small samples the likelihood ratio test statistic was conservative for small expected cell counts, liberal for moderate expected counts, and performed well for large expected counts. The weighted least squares test statistic was generally more conservative and less powerful than both the likelihood ratio statistic and the Pearson statistic.

Biometry↗
PubMed2001

Effects of covariance model assumptions on hypothesis tests for repeated measurements: analysis of ovarian hormone data and pituitary-pteryomaxillary distance data.

In the analysis of repeated measurements, multivariate methods which account for the correlations among the observations from the same experimental unit are widely used. Two commonly-used multivariate methods are the unstructured multivariate approach and the mixed model approach. The unstructured multivariate approach uses MANOVA types of models and does not require assumptions on the covariance structure. The mixed model approach uses multivariate linear models with random effects and requires covariance structure assumptions. In this paper, we describe the characteristics of tests based on these two methods of analysis and investigate the performance of these tests. We focus particularly on tests for group effects and parallelism of response profiles.

Adolescent↗
PubMed1997

A multivariate analysis of Pleistocene hominids: testing hypothesis of European origins.

Multivariate analysis of intra- and inter-group variability in Middle and Upper Pleistocene human remains, based on facial traits, show close affinities between Upper Palaeolithic and Mesolithic samples, which are clearly distinct from Lower Palaeolithic and Neanderthal samples. The between-group differences observed were significant, although no sexual differentiation was considered. This allowed the classification of the fossil remains by discriminant analysis. A modern metrical pattern can be recognized for the Upper Palaeolithic sample, falling within the variability of anatomically modern humans. The samples from Skhul and Qafzeh, although exhibiting some plesiomorphous traits, also show modern-like metrical traits. The analysis strongly support a monophyletic origin for modern humans.

Animals↗
PubMed1997

Hypothesis testing in evolutionary inference.

The comparative method, amongst other things, searches for correlations between evolutionary variables. These can be used to test null hypotheses. Here I consider, in the context of binary variables, the bases of such tests. I examine grounds upon which evolutionary traits and events can be regarded as statistically independent of each other. I argue that no description of observations as independent or non-independent makes sense except in the context of a population of possible observations from which they are regarded as having being sampled. Significant correlations between traits or changes in traits in comparative tests have been taken by some to imply causal links between traits. However, the statistical significance of an observed correlation between traits is neither necessary nor sufficient for the inference of a causal connection between them.

Animals↗
PubMed1986

Analysis of pharmacokinetic data using parametric models. III. Hypothesis tests and confidence intervals.

This is the third in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article the concern is how to test hypotheses about, and assign confidence intervals to, the values of the parameters of such models. The basic approach to both tasks involves determining the goodness of fit of the model to the data for alternative values of the parameters and using the change in goodness of fit to assess the plausibility of the alternative values. The goodness of fit is measured by the value of a (least-squares-type) objective function. An approximation to the dependence of the latter on the parameter values yields an estimate of the familiar asymptotic covariance matrix of the estimates. The latter can also be used to test hypotheses about, and assign confidence intervals to, functions of parameters.

Data Collection↗
PubMed1992

Evaluation of hypothesis testing for comparing two populations using NONMEM analysis.

In a simulation study of inference on population pharmacokinetic parameters, two methods of performing tests of hypotheses comparing two populations using NONMEM were evaluated. These two methods are the test based upon 95% confidence intervals and the likelihood ratio test. Data were simulated according to a monoexponential model and, in that context, power curves for each test were generated for (i) the ratio of mean clearance and (ii) the ratio of the population standard deviations of clearance. To generate the power curves, a range of these parameters was employed; other pharmacokinetic parameters were selected to reflect the variability typically present in a Phase II clinical trial. For tests comparing the means, the confidence interval tests had approximately the same power as the likelihood ratio tests and were consistently more faithful to the nominal level of significance. For comparison of the standard deviations, and when the volume of information available was relatively small, however, the likelihood ratio test was more able to detect differences between the two groups. These results were then compared to results on parameter estimation in order to gain insight into the question of power. As an example, the nonnormality of estimates of the ratio of standard deviations plays an important role in explaining the low power for the confidence interval tests. We conclude that, except for the situation of modeling standard deviations with only sparse information, NONMEM produces tests of significance that are effective at detecting clinically significant differences between two populations.

Computer Simulation↗
PubMed1989

Fitting genetic models with LISREL: hypothesis testing.

A brief introduction to the mathematical theory involved in model fitting is provided. The properties of maximum-likelihood estimates are described, and their advantages in fitting structural models are given. Identification of models is considered. Standard errors of parameter estimates are compared with the use of likelihood-ratio (L-R) statistics. For structural modeling, L-R tests are invariant to parameter transformation and give robust tests of significance. Some guidelines for fitting models to data collected from twins are given, with discussion of the relative merits of parsimony and data description.

Computer Simulation↗
PubMed1999

The evolution of codon preferences in Drosophila: a maximum-likelihood approach to parameter estimation and hypothesis testing.

Synonymous codon usage in related species may differ as a result of variation in mutation biases, differences in the overall strength and efficiency of selection, and shifts in codon preference-the selective hierarchy of codons within and between amino acids. We have developed a maximum-likelihood method to employ explicit population genetic models to analyze the evolution of parameters determining codon usage. The method is applied to twofold degenerate amino acids in 50 orthologous genes from D. melanogaster and D. virilis. We find that D. virilis has significantly reduced selection on codon usage for all amino acids, but the data are incompatible with a simple model in which there is a single difference in the long-term Ne, or overall strength of selection, between the two species, indicating shifts in codon preference. The strength of selection acting on codon usage in D. melanogaster is estimated to be |Nes| approximately 0.4 for most CT-ending twofold degenerate amino acids, but 1.7 times greater for cysteine and 1.4 times greater for AG-ending codons. In D. virilis, the strength of selection acting on codon usage for most amino acids is only half that acting in D. melanogaster but is considerably greater than half for cysteine, perhaps indicating the dual selection pressures of translational efficiency and accuracy. Selection coefficients in orthologues are highly correlated (rho = 0.46), but a number of genes deviate significantly from this relationship.

Amino Acids↗