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PubMed2003

A new powerful non-parametric two-stage approach for testing multiple phenotypes in family-based association studies.

We introduce a new powerful nonparametric testing strategy for family-based association studies in which multiple quantitative traits are recorded and the phenotype with the strongest genetic component is not known prior to the analysis. In the first stage, using a population-based test based on the generalized estimating equation approach, we test all recorded phenotypes for association with the marker locus without biasing the nominal significance level of the later family-based analysis. In the second stage the phenotype with the smallest p value is selected and tested by a family-based association test for association with the marker locus. This strategy is robust against population admixture and stratification and does not require any adjustment for multiple testing. We demonstrate the advantages of this testing strategy over standard methodology in a simulation study. The practical importance of our testing strategy is illustrated by applications to the Childhood Asthma Management Program asthma data sets.

Asthma↗
PubMed2004

Multiple testing of noninferiority hypotheses in active controlled trials.

For noninferiority testing with the maximum allowable noninferiority margin being prespecified, one can perform valid statistical testing at the same alpha level for multiple noninferiority hypotheses with margins being smaller than this maximum margin. This is easily comprehensible because only one confidence level is used to assess which margins within the interval bounded by the maximum margin can be ruled out. If different confidence intervals are used, e.g., the interval generated from the intent-to-treat population is used for testing superiority and the interval generated from the per-protocol population is used for testing noninferiority, the problem of multiplicity will surface and the adjustment of alpha for each testing may be needed. All these predicate on the condition that at least a certain element of the maximum allowable noninferiority margin, whether it is the entire margin or the fraction of the active control effect to be retained, must be fixed in advance. None of these elements can be allowed to be influenced directly or indirectly by any analysis of the noninferiority trial data. Otherwise, the noninferiority analysis may be invalid.

Controlled Clinical Trials as Topic↗
PubMed1997

Multiple testing to establish superiority/equivalence of a new treatment compared with kappa standard treatments.

In this paper we develop multiple hypotheses testing procedures to compare a new treatment with a set of standard treatments in a clinical trial. The aim is to classify the new treatment with respect to each of the standards, by specifying those to which the new treatment is superior, those to which the new treatment is equivalent and those to which one can establish neither superiority nor equivalence. We propose several stepwise procedures and compare them with respect to their familywise error rates and power. The step-down methods SD1 and SD2 test for superiority first, followed by tests for equivalence for those comparisons where we cannot establish superiority. The step-up methods SU1 and SU2 test for equivalence first, followed by tests for superiority for those comparisons where we can establish at least equivalence. The methods SD3 and SU3 apply the tests for superiority and equivalence in pairs. All the methods require that we specify a threshold value delta > 0 in advance for defining equivalence. In applications where it is not possible to specify a value delta, we can use the method SD1 by testing for superiority first, followed by one-sided confidence limits on the efficacy differences for those comparisons where we cannot establish superiority.

Clinical Trials as Topic↗
PubMed2004

Multiple testing to establish superiority/equivalence of a new treatment compared with k standard treatments for unbalanced designs.

In clinical studies, multiple superiority/equivalence testing procedures can be applied to classify a new treatment as superior, equivalent (same therapeutic effect), or inferior to each set of standard treatments. Previous stepwise approaches (Dunnett and Tamhane, 1997, Statistics in Medicine16, 2489-2506; Kwong, 2001, Journal of Statistical Planning and Inference 97, 359-366) are only appropriate for balanced designs. Unfortunately, the construction of similar tests for unbalanced designs is far more complex, with two major difficulties: (i) the ordering of test statistics for superiority may not be the same as the ordering of test statistics for equivalence; and (ii) the correlation structure of the test statistics is not equi-correlated but product-correlated. In this article, we seek to develop a two-stage testing procedure for unbalanced designs, which are very popular in clinical experiments. This procedure is a combination of step-up and single-step testing procedures, while the familywise error rate is proved to be controlled at a designated level. Furthermore, a simulation study is conducted to compare the average powers of the proposed procedure to those of the single-step procedure. In addition, a clinical example is provided to illustrate the application of the new procedure.

Analgesics↗
PubMed2005

Sample size calculation for multiple testing in microarray data analysis.

Microarray technology is rapidly emerging for genome-wide screening of differentially expressed genes between clinical subtypes or different conditions of human diseases. Traditional statistical testing approaches, such as the two-sample t-test or Wilcoxon test, are frequently used for evaluating statistical significance of informative expressions but require adjustment for large-scale multiplicity. Due to its simplicity, Bonferroni adjustment has been widely used to circumvent this problem. It is well known, however, that the standard Bonferroni test is often very conservative. In the present paper, we compare three multiple testing procedures in the microarray context: the original Bonferroni method, a Bonferroni-type improved single-step method and a step-down method. The latter two methods are based on nonparametric resampling, by which the null distribution can be derived with the dependency structure among gene expressions preserved and the family-wise error rate accurately controlled at the desired level. We also present a sample size calculation method for designing microarray studies. Through simulations and data analyses, we find that the proposed methods for testing and sample size calculation are computationally fast and control error and power precisely.

Computer Simulation↗
PubMed2002

Correction of the p-value after multiple tests in a Cox proportional hazard model.

We consider a situation which is common in epidemiology, in which several transformations of an explanatory variable are tried in a Cox model and the most significant test is retained. The p-value should then be corrected to take account of the multiplicity of tests. Bonferroni method is often too conservative because the tests may be highly positively correlated. We propose an asymptotically exact correction of the p-value. The method uses the fact that the tests are asymptotically normal to compute numerically the distribution of the maximum of several tests. Counting processes theory is used to derive estimators of the correlations between tests. The method is illustrated by a simulation and an analysis of the relation between concentration of aluminum in drinking water and risk of dementia.

Aluminum↗
PubMed2004

Handling multiple testing while interpreting microarrays with the Gene Ontology Database.

BACKGROUND: The development of software tools that analyze microarray data in the context of genetic knowledgebases is being pursued by multiple research groups using different methods. A common problem for many of these tools is how to correct for multiple statistical testing since simple corrections are overly conservative and more sophisticated corrections are currently impractical. A careful study of the nature of the distribution one would expect by chance, such as by a simulation study, may be able to guide the development of an appropriate correction that is not overly time consuming computationally. RESULTS: We present the results from a preliminary study of the distribution one would expect for analyzing sets of genes extracted from Drosophila, S. cerevisiae, Wormbase, and Gramene databases using the Gene Ontology Database. CONCLUSIONS: We found that the estimated distribution is not regular and is not predictable outside of a particular set of genes. Permutation-based simulations may be necessary to determine the confidence in results of such analyses.

Animals↗
PubMed1991

Step-down multiple tests for comparing treatments with a control in unbalanced one-way layouts.

We show how a well-known multiple step-down significance testing procedure for comparing treatments with a control in balanced one-way layouts can be applied in unbalanced layouts (unequal sample sizes for the treatments). The method we describe has the advantage that it provides p-values, for each treatment versus control comparison, that take account of the multiple step-down testing nature of the procedure. These joint p-values can be used with any value of alpha, the fixed type I family wise error rate bound, that may be specified by the investigator. To determine the p-values, it is necessary to compute a multivariate Student t integral, for which a computer program is available. This procedure is more powerful than the step-down Bonferroni procedure of Holm and the single-step procedure of Dunnett. An example from the pharmaceutical literature is used to illustrate the procedure.

Animals↗
PubMed1986

Radiologic diagnosis of an intra-abdominal abscess. Do multiple tests help?

A review was made of the charts of 94 patients who underwent ultrasonography (US), computed tomography (CT), and gallium citrate Ga 67 (Gall) scan to rule out intra-abdominal abscesses. Of all the clinical and laboratory data, only the presence of pain and tenderness differentiated patients with and without abscesses. A review of radiologic data showed that CT was superior to US, and that US was superior to Gall scan with regard to sensitivity, specificity, accuracy, and positive and negative predictive values. When multiple radiologic tests were performed, results agreed in 72% of cases; therefore, the additional tests were essentially redundant. When radiologic test results disagreed, accuracy rates were CT, 0.86; US, 0.00; and Gall scan, 0.44. These findings suggest that, except to rule out pelvic abscesses in the presence of pelvic inflammatory disease, CT is usually the only special radiologic test that should be performed to localize a suspected intra-abdominal abscess.

Abdomen↗
PubMedDate not supplied

Multiple hypothesis tests in multiple investigations.

Inferential statistical methods have traditionally been based on the assumption that one experiment is performed and that interest centres on one or more predetermined hypothesis tests. Exploratory research, on the other hand, often involves multiple hypotheses or repeated investigations under similar or different conditions or both. Several techniques have been proposed to deal with multiple or simultaneous hypothesis testing in single investigations, and procedures to combine observed significance levels for an individual hypothesis test from two or more investigations have been suggested. In this paper we propose a method for identifying important results from multiple statistical tests in multiple investigations. The method is illustrated by using high performance liquid chromatography to identify potential aetiologic contaminants in L-tryptophan samples.

Chromatography, High Pressure Liquid↗
PubMed1998

Metronidazole- and clarithromycin-resistant Helicobacter pylori in dyspeptic patients in western Sydney as determined by testing multiple isolates from different gastric sites.

It is unknown whether antibiotic susceptibility testing of antral isolates alone is representative of Helicobacter pylori susceptibility. We aimed to determine: (i) the prevalence of metronidazole- and clarithromycin-resistant strains in infected dyspeptic patients; and (ii) whether there is consistency in the susceptibility to metronidazole and clarithromycin among isolates cultured from different gastric sites. Antral, body and fundus biopsies were taken from 242 consecutive patients and cultured on blood agar under micro-aerophilic conditions for 5-7 days. Isolates from 66 patients (13 had one, 15 had two and 38 had three isolates) were tested for susceptibility to metronidazole and clarithromycin using previously validated disc diffusion tests. Of the 66 patients, 42 (64%) had strains resistant to metronidazole while four (6.1%) had clarithromycin-resistant strains. The prevalence of metronidazole resistance was not significantly different between men and women (65% vs 60%) or across different age groups. In five (9.4%) of the 53 patients with multiple isolates, discrepant results for metronidazole susceptibility were observed: susceptible antral and body isolates but resistant fundus isolates in two cases and susceptible antral isolates but resistant body and fundus isolates in the others. Clarithromycin susceptibilities were consistent among the isolates cultured from different gastric sites in all patients. It is concluded that metronidazole-resistant strains of H. pylori are common while clarithromycin-resistant strains are rare. Metronidazole susceptibility testing of antral isolates does not appear to be representative of isolates from the body and fundus in a subset of patients.

Adult↗
PubMed2005

A multiple testing procedure to associate gene expression levels with survival.

In many microarray studies the primary objective is to identify, from a large panel of genes, those which are prognostic markers of a censored survival endpoint such as time to disease recurrence or death. Often, these genes are considered prognostic in that their respective expressions are associated with the survival endpoint of interest. To assess this association requires specifying an appropriate measure of association, a suitable test statistic and, as the number of genes is large, proper handling of multiplicity issues. In this paper, we will address these issues by utilizing a general correlation measure, a non-parametric test statistic, and control of the family-wise error rate by employing permutation resampling. Comprehensive simulation studies are conducted to investigate the statistical properties of the proposed procedure. The proposed method is applied to a recently published data set on patients with lung cancer.

Adenocarcinoma↗
PubMed2005

Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms.

Although permutation testing has been the gold standard for assessing significance levels in studies using multiple markers, it is time-consuming. A Bonferroni correction to the nominal p-value that uses the underlying pair-wise linkage disequilibrium (LD) structure among the markers to determine the number of effectively independent tests has recently been proposed. We propose using the number of independent LD blocks plus the number of independent single-nucleotide polymorphisms for correction. Using the Collaborative Study on the Genetics of Alcoholism LD data for chromosome 21, we simulated 1,000 replicates of parent-child trio data under the null hypothesis with two levels of LD: moderate and high. Assuming haplotype blocks were independent, we calculated the number of independent statistical tests using 3 haplotype blocking algorithms. We then compared the type I error rates using a principal components-based method, the three blocking methods, a traditional Bonferroni correction, and the unadjusted p-values obtained from FBAT. Under high LD conditions, the PC method and one of the blocking methods were slightly conservative, whereas the 2 other blocking methods exceeded the target type I error rate. Under conditions of moderate LD, we show that the blocking algorithm corrections are closest to the desired type I error, although still slightly conservative, with the principal components-based method being almost as conservative as the traditional Bonferroni correction.

Algorithms↗
PubMed1994

A step-down procedure for multiple tests of treatment versus control in each of several groups.

Cheung and Holland extended Dunnett's treatment versus control multiple comparison procedure to the case of several groups. Dunnett and Tamhane provided a step-down procedure more powerful than that of Dunnett for situations where the researcher's interest is in testing and not in simultaneous confidence interval estimation. In this paper we show how one can use the approach of Dunnett and Tamhane to devise a testing procedure for the multi-group problem with power greater than that of Cheung and Holland. We illustrate the new procedure with reanalyses of published data from a clinical experiment.

Confidence Intervals↗
PubMedDate not supplied

Gastrointestinal bleeding of obscure origin undetected by multiple tests for fecal occult blood and diagnosed only by capsule endoscopy: a case report.

The term gastrointestinal bleeding of obscure origin is used to describe bleeding of unknown origin that persists or recurs after a negative initial esophagogastroduodenoscopy and colonoscopy. We report the case of a middle-aged woman with gastrointestinal bleeding of obscure origin who had 9 stool specimens that tested negative for occult blood but was found to have adenocarcinoma of the distal duodenum on capsule endoscopy. This case illustrates that, in the presence of unexplained iron-deficiency anemia, multiple negative fecal occult blood tests do not exclude the presence of GI blood loss, and that capsule endoscopy is a valuable diagnostic study in this context.

Capsule Endoscopy↗
PubMed2001

Achievement goals and optimal motivation: testing multiple goal models.

Currently, there is a debate about which types of achievement goals promote optimal motivation. A number of theorists argue for a mastery goal perspective focusing on the adaptive consequences of mastery goals and the maladaptive consequences of performance goals. Others endorse a multiple goal perspective in which both mastery and performance goals can be beneficial. The purpose of the present investigation was to review why this debate has emerged and to offer a critical test of the mastery versus multiple goal perspectives. In Study 1, a correlational approach was used to identify the optimal goals for college participants to adopt for a learning activity. In Study 2, an experimental approach was used to identify the optimal goals to assign for the same activity. Each study revealed benefits of both mastery and performance goals, providing support for the multiple goal perspective.

Achievement↗
PubMed2001

Grid-immunoblotting: a fast and simple technique to test multiple allergens with small amounts of antibody for cross-reactivity.

Grid-immunoblotting is a procedure that allows the simultaneous testing of up to 20 different antibodies such as monoclonal antibody-containing hybridoma supernatants or human sera for specific antibodies to up to 20 different antigens or allergens on a single sheet of nitrocellulose membrane. Since only 150 to 200 microl of antibody-containing solution are required this technique is uniquely suited to test growing hybridomas and small amounts of sera (e.g. mouse and children's sera). Compared to a standard ELISA, approximately ten times less antibody is needed to obtain the same information.

Allergens↗