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PubMed1992

Power of likelihood ratio tests for heterogeneity of intraclass correlation and variance in balanced half-sib designs.

Statistical power of likelihood ratio tests was investigated for detection of heterogeneous variances and intraclass correlation in balanced half-sib designs. Powers of likelihood ratio tests were obtained from simulations. For half-sib designs of sires nested within herds, true intraclass correlations and phenotypic variances, and estimates thereof, were repeatedly sampled, and likelihood ratio tests were conducted. The power for detecting heterogeneity of intraclass correlations was low, but the power for detecting heterogeneous phenotypic variances was nearly always 100%. For balanced cross-classified designs, sires had progeny in all herds, and data were simulated by assuming that heterogeneity of between- and within-sire components was the result of a herd-dependent scale effect. Using this model, the power to detect heterogeneous between-sire components was substantially higher than the corresponding power to detect heterogeneous intraclass correlations in the nested design. It seems unlikely that reliable inference about heterogeneity of genetic variances or heritabilities between individual herds from daily cattle field data can be made.

Analysis of Variance↗

PubMed1979

Power functions for statistical control rules.

We have studied power functions for several control rules by use of a computer simulation program. These power functions show the relationship between the probability for rejection and the size of the analytical errors that are to be detected. They allow some assessment of the quality available from present statistical control systems and provide some guidance in the selection of control rules and numbers of control observations when new control systems are designed.

Chemistry, Clinical↗

PubMed2005

Increased power of microarray analysis by use of an algorithm based on a multivariate procedure.

MOTIVATION: The power of microarray analyses to detect differential gene expression strongly depends on the statistical and bioinformatical approaches used for data analysis. Moreover, the simultaneous testing of tens of thousands of genes for differential expression raises the 'multiple testing problem', increasing the probability of obtaining false positive test results. To achieve more reliable results, it is, therefore, necessary to apply adjustment procedures to restrict the family-wise type I error rate (FWE) or the false discovery rate. However, for the biologist the statistical power of such procedures often remains abstract, unless validated by an alternative experimental approach. RESULTS: In the present study, we discuss a multiplicity adjustment procedure applied to classical univariate as well as to recently proposed multivariate gene-expression scores. All procedures strictly control the FWE. We demonstrate that the use of multivariate scores leads to a more efficient identification of differentially expressed genes than the widely used MAS5 approach provided by the Affymetrix software tools (Affymetrix Microarray Suite 5 or GeneChip Operating Software). The practical importance of this finding is successfully validated using real time quantitative PCR and data from spike-in experiments. AVAILABILITY: The R-code of the statistical routines can be obtained from the corresponding author. CONTACT: Schuster@imise.uni-leipzig.de

Algorithms↗

PubMed2003

Sample size needed to detect gene-gene interactions using association designs.

It is likely that many complex diseases result from interactions among several genes, as well as environmental factors. The presence of such interactions poses challenges to investigators in identifying susceptibility genes, understanding biologic pathways, and predicting and controlling disease risks. Recently, Gauderman (Am J Epidemiol 2002;155:478-84) reported results from the first systematic analysis of the statistical power needed to detect gene-gene interactions in association studies. However, Gauderman used different statistical models to model disease risks for different study designs, and he assumed a very low disease prevalence to make different models more comparable. In this article, assuming a logistic model for disease risk for different study designs, the authors investigate the power of population-based and family-based association designs to detect gene-gene interactions for common diseases. The results indicate that population-based designs are more powerful than family-based designs for detecting gene-gene interactions when disease prevalence in the study population is moderate.

Genetic Predisposition to Disease↗

PubMed1998

Genetic linkage to the serotonin transporter protein and 5HT2A receptor genes excluded in generalized social phobia.

Social phobia, particularly the generalized form, is strongly familial and frequently comorbid with major depression, panic disorder, and obsessive-compulsive disorder. It has also recently been shown to be responsive to selective serotonin reuptake inhibitors. We conducted a study to determine if generalized social phobia is genetically linked to either of two candidate genes: the serotonin transporter protein (5HTT) gene, or the 5HT2A receptor (5HT2AR) gene. Rates of social phobia (using several phenotype definitions) were ascertained and blood samples obtained from consenting first-degree family members of generalized social phobic probands. 5HT2AR and 5HTT genotyping was performed using the polymerase chain reaction (PCR). Linkage was tested using LINKAGE and GENEHUNTER software. No evidence of linkage was found; power analysis indicated that failure to find linkage was unlikely due to inadequate statistical power. These findings reasonably exclude linkage between generalized social phobia and the 5HTT or 5HT2AR genes in these samples, although modifier effects cannot be ruled out. Other 5HT receptor subtypes or indirect modulatory effects of 5HT on other neurotransmitter systems may be involved.

Adolescent↗

PubMed2025

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

Journal Article↗

PubMed2003

Catching up on health outcomes: the Texas Medication Algorithm Project.

OBJECTIVE: To develop a statistic measuring the impact of algorithm-driven disease management programs on outcomes for patients with chronic mental illness that allowed for treatment-as-usual controls to "catch up" to early gains of treated patients. DATA SOURCES/STUDY SETTING: Statistical power was estimated from simulated samples representing effect sizes that grew, remained constant, or declined following an initial improvement. Estimates were based on the Texas Medication Algorithm Project on adult patients (age > or = 18) with bipolar disorder (n = 267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas. STUDY DESIGN: Study patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic. DATA COLLECTION/EXTRACTION METHODS: Hierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology-Clinician Version. PRINCIPAL FINDINGS: Declining-effect analyses had significantly greater power detecting program differences than traditional growth models in constant and declining-effects cases. Bipolar patients with severe depressive symptoms in an algorithm-driven, disease management program reported fewer symptoms after three months, with treatment-as-usual controls "catching up" within one year. CONCLUSIONS: In addition to psychometric properties, data collection design, and power, investigators should consider how outcomes unfold over time when selecting an appropriate statistic to evaluate service interventions. Declining-effect analyses may be applicable to a wide range of treatment and intervention trials.

Adult↗

PubMed2002

Treatment of intrabony periodontal defects with enamel matrix derivative: a literature review.

The enamel matrix derivative (EMD) has been recently introduced in the periodontal field to overcome short-comings associated with currently available regenerative techniques. Information accumulated over the past years with application of EMD guided regeneration (EGR) in intrabony periodontal defects allowed a thorough evidence-based retrospective analysis. Clinical data from EMD controlled studies were pooled for meta-analysis and weighted according to the number of treated defects. Clinical attachment gain amounted to 3.2 +/- 0.9 mm (33% of the original attachment level) and probing reduction averaged 4.0 +/- 0.9 mm (50% of the baseline probing depth) for a total of 317 lesions with a mean baseline depth of 5.4 +/- 0.8 mm. Improvements in clinical parameters achieved with EMD were statistically significant in reference to preoperative measurements. However, despite the overall efficacy of EGR therapy, a significant variation in clinical outcomes was observed. Similar therapeutic results were reported in studies where EGR was compared directly to guided tissue regeneration. However, the controlled clinical trials did not have adequate statistical power to firmly support superiority or equivalency between the 2 regenerative therapies. The statistical superiority of EGR over treatment with open flap debridement has been established. Preliminary histologic investigations with surgically created defects and experimental periodontal lesions demonstrated the ability of EGR to induce formation of acellular cementum and promote significant anaplasis of the supporting periodontal tissues. The potential of EMD to encourage periodontal regeneration was also confirmed in human intrabony defects. However, recent human histologic studies have questioned both the consistency of the histologic outcomes and the ability of EGR to predictably stimulate formation of acellular cementum. Identifying clinical modifying parameters and understanding cellular interactions are apparently essential for the development of methodologies to enhance predictability and extent of EGR clinical and histologic results.

Alveolar Bone Loss↗

PubMed1998

Human novelty-seeking personality traits and dopamine D4 receptor polymorphisms: a twin and genetic association study.

Although it is well-established that genetic variation is important in causing individual differences in many human personality traits or based on family, twin, and adoption studies, the first reports that specific genetic polymorphisms might influence a normal dimension of personality were only recently published. Specifically, two studies have described significant associations between a dopamine D4 receptor (D4DR) exon III 48-base pair (bp) insertion/deletion polymorphism and the personality traits of novelty-seeking and positive emotional experience [Benjamin et al. (1996): Nat Genet 12: 81-84; Ebstein et al. (1996): Nat Genet 12:78-80]. The present study was undertaken to attempt to replicate these important and heuristic initial findings. Personality questionnaires measuring novelty-seeking and positive emotional experience were administered to 306 male and female young adult twins (monozygotic 92 pairs, dizygotic 61 pairs) from the general population, 281 of whom were genotyped for D4DR exon I and III polymorphisms. No significant associations were observed between novelty-seeking or positive emotional experience and these D4DR polymorphisms. This failure to replicate the initial reports seems unlikely to represent measurement or genetic differences across studies, although environmental differences may be possible. Adequate statistical power in the present study suggests that these results are unlikely to be statistical "false negatives" and instead may reduce confidence in the generality of the initial positive findings.

Adult↗

PubMed2004

More powerful tests of predictor subsets in regression analysis under nonnormality.

It is well-known that for normally distributed errors parametric tests are optimal statistically, but perhaps less well-known is that when normality does not hold, nonparametric tests frequently possess greater statistical power than parametric tests, while controlling Type I error rate. However, the use of nonparametric procedures has been limited by the absence of easily performed tests for complex experimental designs and analyses and by limited information about their statistical behavior for realistic conditions. A Monte Carlo study of tests of predictor subsets in multiple regression analysis indicates that various nonparametric tests show greater power than the F test for skewed and heavy-tailed data. These nonparametric tests can be computed with available software.

Humans↗

PubMed2001

A loss-function based approach for dose selection in two-stage dose-response trials.

BACKGROUND: Adaptive two-stage designs are a flexible tool in drug development and have the potential for an improvement of power over one-stage designs. In an adaptive two-stage dose-response trial, the dose-response relationship is examined in a preplanned interim analysis. If efficacy has not yet been proved, a subset of the most favourable doses may be selected for the second stage. This design offers the opportunity to combine dose selection and proof of efficacy within a single trial. METHODS: We consider a change-point regression model describing the dose-response relationship. In this framework, selection of the most favourable dose can be achieved by estimating the change point in the regression model. We introduce a change-point estimator that can be optimised by a loss-function based approach, taking into account both efficacy and safety aspects. We investigate the power characteristics of our approach. RESULTS: The proposed procedure performs well with regard to statistical power. The proposal demonstrates the feasibility of simultaneous modelling of efficacy and safety aspects by a loss-function based approach. CONCLUSION: Adaptive two-stage designs, in conjunction with an elaborated dose-selection rule, can support the decision about the suitable dose to use, leading to a considerable gain in power (or saving in sample size) and possibly speeding up the time-to-market in drug development.

Clinical Trials as Topic↗

PubMed2005

Practical threshold for micronucleated reticulocyte induction observed for low doses of mitomycin C, Ara-C and colchicine.

Micronucleus induction was studied for the DNA target clastogens mitomycin C (MMC) and 1-beta-D-arabinofuranosylcytosine (Ara-C), and also the non-DNA target aneugen colchicine (COL) in order to evaluate the dose-response relationship at very low dose levels. The acridine orange (AO) supravital staining method was used for microscopy and the anti-CD71-FITC based method was used for flow cytometric analysis. In the AO method, 2000 reticulocytes were analysed as commonly advised, but in the flow cytometric method, 2000, 20,000, 200,000 and 1,000,000 reticulocytes were analysed for each sample to increase the detecting power (i.e. sensitivity) of the assay. The present data show that increasing the number of cells scored increases the statistical power of the assay when the cell was considered as a statistical unit. Even so, statistically significant differences from respective vehicle controls were not observed at the lowest dose level for MMC and Ara-C, or the lower four dose levels for COL, even after one million cells were analysed. When the animal was considered as a statistical unit, only the top dose group for each chemical showed significant increase of micronucleated reticulocytes frequency. As non-linear dose-response curves were obtained for each of the three chemicals studied, these observations provide evidence for the existence of a practical threshold for the DNA target clastogens as well as the non-DNA target aneugen studied.

Acridine Orange↗

PubMed2003

Considerations on sample size and power calculations in randomized clinical trials.

Many studies in orthopaedics and sports medicine have not considered sample size or statistical power as important issues in study design. This article addresses the importance of a sample size calculation in randomized clinical trials and the components of the calculations that researchers must consider in their preliminary planning of an investigation. The types of data being collected, level of significance, types I and II errors, and power are also addressed.

Humans↗

PubMed1996

Criticism of cardiovascular studies with negative results due to a negative correlation.

BACKGROUND: Trials that do not allow rejection of the null hypothesis of no treatment effect may have had an inappropriate design. Self- controlled trials, although routinely used for the study of cardiovascular diseases, are virtually never assessed for correlation between treatment modalities. METHODS: Using three models and a series of published studies as examples, the author studies the influence of correlation levels on the statistical power of self-controlled studies. RESULTS: Between-subject variation as estimated by SD is largely dependent on the level of correlation, and so is the power of testing. The assessment of paired data as though they are unpaired can be used as a simple test to estimate correlation levels. CONCLUSION: It is extremely relevant to assess correlation levels in a paired comparison a priori. With a presumably negative correlation, a self- controlled design is likely to lack power.

Cardiovascular Diseases↗

PubMed2003

Intersection tests for single marker QTL analysis can be more powerful than two marker QTL analysis.

BACKGROUND: It has been reported in the quantitative trait locus (QTL) literature that when testing for QTL location and effect, the statistical power supporting methodologies based on two markers and their estimated genetic map is higher than for the genetic map independent methodologies known as single marker analyses. Close examination of these reports reveals that the two marker approaches are more powerful than single marker analyses only in certain cases. Simulation studies are a commonly used tool to determine the behavior of test statistics under known conditions. We conducted a simulation study to assess the general behavior of an intersection test and a two marker test under a variety of conditions. The study was designed to reveal whether two marker tests are always more powerful than intersection tests, or whether there are cases when an intersection test may outperform the two marker approach.We present a reanalysis of a data set from a QTL study of ovariole number in Drosophila melanogaster. RESULTS: Our simulation study results show that there are situations where the single marker intersection test equals or outperforms the two marker test. The intersection test and the two marker test identify overlapping regions in the reanalysis of the Drosophila melanogaster data. The region identified is consistent with a regression based interval mapping analysis. CONCLUSION: We find that the intersection test is appropriate for analysis of QTL data. This approach has the advantage of simplicity and for certain situations supplies equivalent or more powerful results than a comparable two marker test.

Animals↗

PubMed1989

Screening for possible human carcinogens and mutagens. False positives, false negatives: statistical implications.

A screening method aimed at identifying potential human carcinogens using either animal cancer bioassays or short-term genotoxic assays has 4 possible results: true positive, true negative, false positive and false negative. Such a categorisation is superficially similar to the results of hypothesis testing in a statistical analysis. In this latter case the false positive rate is determined by the significance level of the test and the false negative rate by the statistical power of the test. Although the two types of categorisation appear somewhat similar, different statistical issues are involved in their interpretation. Statistical methods appropriate for the analysis of the results of a series of assays include the use of Bayes' theorem and multivariate methods such as clustering techniques for the selection of batteries of short-term test capable of a better prediction of potential carcinogens. The conclusions drawn from such studies are dependent upon the estimates of values of sensitivity and specificity used, the choice of statistical method and the nature of the data set. The statistical issues resulting from the analysis of specific genotoxicity experiments involve the choice of suitable experimental designs and appropriate analyses together with the relationship of statistical significance to biological importance. The purpose of statistical analysis should increasingly be to estimate and explore effects rather than for formal hypothesis testing.

Bayes Theorem↗

PubMed2001

A simple computerized program for the calculation of the required sample size necessary to ensure statistical accuracy in medical experiments.

We developed a sample size estimation program (SSEP) with which medical researchers can easily estimate the appropriate sample size for a specific significance level and statistical power using their favorite WWW browsers. SSEP can estimate the sample sizes for six statistical methods by Monte-Carlo simulation: Student's t-test, Welch's t-test, Analysis of variance, Wilcoxon's rank sum test, Kruskal-Wallis test, and the Cochran-Armitage test for linear trends. The SSEP simulation programs were created using the SAS software macro language. Medical researchers can interactively use this program and determine reliable sample sizes when planning new prospective clinical studies and animal experiments.

Computer Simulation↗

PubMed2003

Responsivity: the value of providing intensive services to high-risk offenders.

Tests of the importance of service matching have had varying results, yet little attention has been given to testing the hypotheses about outcomes for clients based on differing risks to recidivate. We set out to test the risk and responsivity principles using a sample of clients from one site of a four-site randomized block experimental design study examining the effectiveness of a seamless system approach vs. traditional criminal justice supervision. Findings from our preliminary examination of official and self-report data from this site suggest the importance of the risk and responsivity concepts in providing substance abuse treatment, particularly for high-risk clients. Because of the relatively low statistical power of the tests employed in this exploratory analysis, many observed relationships were not statistically significant. Nonetheless, findings offer important implications for the delivery of substance abuse treatment. Future analyses are also recommended to further explore the impact of different service delivery systems.

Adult↗