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PubMed1993

A log-normal model for individual bioequivalence.

A log-normal model is developed for testing pi 1, the probability that a subject's response will fall within given bioequivalence limits. The model is a parametric analog of Anderson and Hauck's TIER rule. Confidence intervals and hypothesis tests are derived. Statistical power is compared with the that of the TIER rule. The probability of demonstrating mean bioequivalence is shown to greatly exceed that of showing individual bioequivalence.

Humans↗

PubMed2003

[Common biostatistical errors in clinical studies].

Roughly half to two third of all published biomedical studies that use statistical methods contain unacceptable errors. The present article points at common errors that may be avoided without requiring profound statistical knowledge. These errors mainly concern the minimal number of patients and sample size (statistical power), agreement between aim and conclusion, distribution of data as well as description of location and variability of data. An analysis of 150 papers in the New England Journal of Medicine and in Circulation demonstrates that these errors can also commonly be found in respected journals after statistical peer review. Editors of biomedical journals could reduce the problem by means of statistical guidelines.

Biometry↗

PubMed2002

Statistical properties of new neutrality tests against population growth.

A number of statistical tests for detecting population growth are described. We compared the statistical power of these tests with that of others available in the literature. The tests evaluated fall into three categories: those tests based on the distribution of the mutation frequencies, on the haplotype distribution, and on the mismatch distribution. We found that, for an extensive variety of cases, the most powerful tests for detecting population growth are Fu's F(S) test and the newly developed R(2) test. The behavior of the R(2) test is superior for small sample sizes, whereas F(S) is better for large sample sizes. We also show that some popular statistics based on the mismatch distribution are very conservative.

Computer Simulation↗

PubMed2002

New goals in the treatment of depression: moving toward recovery.

Depression is associated with marked suffering, morbidity, and high risk of recurrence and/or chronicity. As a result, the disorder represents a considerable public health problem and economic burden on society. Treatment of patients with depression to a state of remission is associated with a significantly improved long-term outcome, including a reduced risk of relapse and improved functioning. Thus, remission (which can be defined as attainment of a total score < or = 7 on the Hamilton Rating Scale for Depression) should be the goal of the acute phase of pharmacotherapy. Although it is widely assumed that available antidepressants are comparably effective, comparisons of the efficacy of antidepressants in clinical trials are flawed by a number of factors. Most notable problems are higher-than-expected placebo effects and low statistical power. Double-blind, comparative studies are also compromised by relatively high attrition rates and the often underestimated effects of patient nonadherence. Large study groups (in the order of 300 patients per treatment group) are needed to differentiate between a good and a potentially better antidepressant. The statistical technique of meta-analysis has been used to examine the results of studies of various antidepressants; these meta-analyses have shown that the selective serotonin reuptake inhibitors (SSRIs) have overall "within group" comparability and, overall, an efficacy profile comparable to the previous standard, tricyclic antidepressants (TCAs). However, in studies of hospitalized patients, TCAs affecting both serotonergic and noradrenergic systems (eg, amitriptyline or clomipramine) have been found to have greater efficacy when compared with SSRIs. Results of some individual studies, as well as a pooled analysis of the outcomes of more than 2,000 depressed patients, indicate that venlafaxine, a selective serotonin and norepinephrine reuptake inhibitor, may have a similar advantage relative to SSRIs across a broader range of patients. These findings suggest antidepressants that affect both serotonergic and noradrenergic neurotransmission may be more likely to accomplish the goal of remission. Compared with TCAs, the better safety profile of venlafaxine has established the drug as a more appropriate first-line treatment option.

Antidepressive Agents, Tricyclic↗

PubMed2007

Functional regression analysis using an F test for longitudinal data with large numbers of repeated measures.

Longitudinal data sets from certain fields of biomedical research often consist of several variables repeatedly measured on each subject yielding a large number of observations. This characteristic complicates the use of traditional longitudinal modelling strategies, which were primarily developed for studies with a relatively small number of repeated measures per subject. An innovative way to model such 'wide' data is to apply functional regression analysis, an emerging statistical approach in which observations of the same subject are viewed as a sample from a functional space. Shen and Faraway introduced an F test for linear models with functional responses. This paper illustrates how to apply this F test and functional regression analysis to the setting of longitudinal data. A smoking cessation study for methadone-maintained tobacco smokers is analysed for demonstration. In estimating the treatment effects, the functional regression analysis provides meaningful clinical interpretations, and the functional F test provides consistent results supported by a mixed-effects linear regression model. A simulation study is also conducted under the condition of the smoking data to investigate the statistical power for the F test, Wilks' likelihood ratio test, and the linear mixed-effects model using AIC.

Behavior Therapy↗

PubMed2000

Selection of an adaptive test statistic for use with multiple comparison analyses of neuroimaging data.

Statistical analysis of neuroimages is commonly approached with intergroup comparisons made by repeated application of univariate or multivariate tests performed on the set of the regions of interest sampled in the acquired images. The use of such large numbers of tests requires application of techniques for correction for multiple comparisons. Standard multiple comparison adjustments (such as the Bonferroni) may be overly conservative when data are correlated and/or not normally distributed. Resampling-based step-down procedures that successfully account for unknown correlation structures in the data have recently been introduced. We combined resampling step-down procedures with the Minimum Variance Adaptive method, which allows selection of an optimal test statistic from a predefined class of statistics for the data under analysis. As shown in simulation studies and analysis of autoradiographic data, the combined technique exhibits a significant increase in statistical power, even for small sample sizes (n = 8, 9, 10).

Algorithms↗

PubMed1981

On the detection of outlier clinics in medical and surgical trials: II. Theoretical considerations.

In the conduct of multicenter clinical trials, comparisons among the clinical centers are often made with respect to such measures as incidence of diagnosis of various events, operative mortality, and patients adherence to the treatment regimen. Slippage and outlier tests for statistically identifying clinics that are outliers - that is, truly different from the rest of the clinics - with respect to such measures are described. Critical values obtained for these tests by computer simulation are given for various numbers of clinics, numbers of patients enrolled per clinic, and probabilities (p) of a patient incurring a given event under the null hypothesis of no differences among the clinics. Previously published critical values based on normally distributed response variables are applicable only for p close to 0.5. The slippage and outlier tests are compared with respect to statistical power. Alternative forms of the slippage test based on the angular transformation and on hypergeometric probabilities are considered.

Clinical Trials as Topic↗

PubMed1999

The application of latent curve analysis to testing developmental theories in intervention research.

The effectiveness of a prevention or intervention program has traditionally been assessed using time-specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time-specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. It is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do the LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed-effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful but cost-efficient intervention programs for the future.

Case-Control Studies↗

PubMed2004

Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses.

The pathogenesis of incipient Alzheimer's disease (AD) has been resistant to analysis because of the complexity of AD and the overlap of its early-stage markers with normal aging. Gene microarrays provide new tools for addressing complexity because they allow overviews of the simultaneous activity of multiple cellular pathways. However, microarray data interpretation is often hindered by low statistical power, high false positives or false negatives, and by uncertain relevance to functional endpoints. Here, we analyzed hippocampal gene expression of nine control and 22 AD subjects of varying severity on 31 separate microarrays. We then tested the correlation of each gene's expression with MiniMental Status Examination (MMSE) and neurofibrillary tangle (NFT) scores across all 31 subjects regardless of diagnosis. These well powered tests revealed a major transcriptional response comprising thousands of genes significantly correlated with AD markers. Several hundred of these genes were also correlated with AD markers across only control and incipient AD subjects (MMSE > 20). Biological process categories associated with incipient AD-correlated genes were identified statistically (ease program) and revealed up-regulation of many transcription factor/signaling genes regulating proliferation and differentiation, including tumor suppressors, oligodendrocyte growth factors, and protein kinase A modulators. In addition, up-regulation of adhesion, apoptosis, lipid metabolism, and initial inflammation processes occurred, and down-regulation of protein folding/metabolism/transport and some energy metabolism and signaling pathways took place. These findings suggest a new model of AD pathogenesis in which a genomically orchestrated up-regulation of tumor suppressor-mediated differentiation and involution processes induces the spread of pathology along myelinated axons.

Algorithms↗

PubMed1997

Microsatellite genetic distances between oceanic populations of the humpback whale (Megaptera novaeangliae).

Mitochondrial DNA haplotypes of humpback whales show strong segregation between oceanic populations and between feeding grounds within oceans, but this highly structured pattern does not exclude the possibility of extensive nuclear gene flow. Here we present allele frequency data for four microsatellite loci typed across samples from four major oceanic regions: the North Atlantic (two mitochondrially distinct populations), the North Pacific, and two widely separated Antarctic regions, East Australia and the Antarctic Peninsula. Allelic diversity is a little greater in the two Antarctic samples, probably indicating historically greater population sizes. Population subdivision was examined using a wide range of measures, including Fst, various alternative forms of Slatkin's Rst, Goldstein and colleagues' delta mu, and a Monte Carlo approximation to Fisher's exact test. The exact test revealed significant heterogeneity in all but one of the pairwise comparisons between geographically adjacent populations, including the comparison between the two North Atlantic populations, suggesting that gene flow between oceans is minimal and that dispersal patterns may sometimes be restricted even in the absence of obvious barriers, such as land masses, warm water belts, and antitropical migration behavior. The only comparison where heterogeneity was not detected was the one between the two Antarctic population samples. It is unclear whether failure to find a difference here reflects gene flow between the regions or merely lack of statistical power arising from the small size of the Antarctic Peninsula sample. Our comparison between measures of population subdivision revealed major discrepancies between methods, with little agreement about which populations were most and least separated. We suggest that unbiased Rst (URst, see Goodman 1995) is currently the most reliable statistic, probably because, unlike the other methods, it allows for unequal sample sizes. However, in view of the fact that these alternative measures often contradict one another, we urge caution in the use of microsatellite data to quantify genetic distance.

Alleles↗

PubMed2003

Decline of North Atlantic eels: a fatal synergy?

Panmictic species pose particular problems for conservation because their welfare can be addressed effectively only on a global scale. We recently documented by means of microsatellite analysis that the European eel (Anguilla anguilla) is not panmictic but instead shows genetic isolation by distance. In this study, we extended the analysis to the American eel (A. rostrata) by applying identical analytical procedures and statistical power. Results obtained for the American eel were in sharp contrast with those obtained for the European eel: the null hypothesis of panmixia could not be rejected, and no isolation by distance was detected. This implies that the species must be managed as a single population. Using Bayesian statistics, we also found that the effective population sizes for both species were surprisingly low and that the populations had undergone severe contractions, most probably during the Wisconsinan glaciation. The apparent sensitivity of eels to climatic changes affecting the strength and position of the Gulf Stream 20,000 years ago is particularly worrying, given the effects of the ongoing global warming on the North Atlantic climate. Moreover, additional short-term stresses such as surging glass eel prizes, overfishing and lethal parasitic infections negatively affect eel population size. The fascinating transatlantic migration and life cycle of Atlantic eels is also their Achilles' heel as these negative short- and long-term effects will probably culminate in a fatal synergy if drastic conservation measures are not implemented to protect these international biological resources.

Anguilla↗

PubMed1993

Smoking, alcohol, and plasma levels of carotenes and vitamin E.

Taken together, the results from these studies clearly indicate that alcohol and cigarette smoking may both decrease serum or plasma beta-carotene levels after controlling for dietary intake and lipid profile. The variation among studies in the magnitude of the association and statistical significance may reflect, in part, the joint distribution of smoking and alcohol intake in the populations. For example, in the supplementation study reported by Costantino, 89% consumed alcohol, considerably higher than population averages. Further, one-third of the men in that study consumed more than 65 ml (52 gm) of pure alcohol per day. This contrasts with the data from the Health Professionals Follow-up Study, where the mean intake of alcohol was 12.3 gm/day with a standard deviation of 15.8. Even lower alcohol intakes are reported in women, further attenuating the relation and the statistical power to detect a relation between alcohol and beta-carotene levels. Measurement error in assessing dietary intake of beta-carotene could explain some of the association between smoking, alcohol and serum beta-carotene levels because of incomplete control (residual confounding) of dietary intake. Methods for statistically adjusting for error in measurement have not yet been implemented in analyses of this nature.

Adolescent↗

PubMed1998

[Case-control design in cardiovascular disease epidemiology. II--Analysis of data].

The wide spread distribution of statistical software recommended for multivariate analysis as well as the ease in handling it can lead the users into adopting wrong measures if they do not pay attention to the theoretical principles behind those methods. With a view to bringing out some of these principles some steps for the data analysis of a case-control study undertaken in the city of S. Paulo-Brazil from March, 1993 to February, 1994 in order to test the association between diabetes mellitus and ischaemic heart disease after adjusting for potential confounders and/or modifiers of effect are presented. Methodologic issues are emphasized in the development of four steps: a) the data bank structure: b) the calculation of statistical power; c) the definition of variables strata and codification and d) the choice of the logistic regression method.

Case-Control Studies↗

PubMed2003

Inter-test reliability for non-invasive measures of respiratory muscle function in healthy humans.

The aims of this study were to quantify the inter-test reliability of several voluntary, non-invasive measures of respiratory muscle function and to determine the implications of these data for studies using a repeated-measures design. Systematic measurement differences were found for 50% of the variables ( P</=0.05, t-tests). Nevertheless, 95% ratio limits of agreement for most measures proved acceptable and similar to those reported elsewhere. The random error component of the agreement ratios ranged from 1.047 to 1.149 for measures of pulmonary function in healthy subjects ( n=46), 1.045 to 1.056 for maximum static respiratory pressures ( n=24), 1.062 to 1.173 for measures relating to the maximum pressure-flow-power relationship ( n=16-22), and 1.036 to 1.071 for measures relating to maximum incremental inspiratory muscle performance ( n=12). The judgement that the limits of agreement were acceptable is supported by the sample-size calculations. Estimated sample sizes based upon an alpha level of 0.05 and a statistical power of 0.9 were mostly </=11 for a repeated-measures experimental design, particularly for the larger effect sizes (>/=5%). However, peak expiratory flow, the maximum rate of pressure development and the time constant of relaxation, require larger sample sizes to detect small within-group changes. In conclusion, the described protocols provide reliable measurements for most parameters of respiratory muscle function in healthy subjects. Furthermore, experiments utilising a within-subjects design lasting up to 3 weeks can be conducted with feasible sample sizes (</=11 per group) where substantial (>/=5%) changes are expected.

Adult↗

PubMedDate not supplied

Biostatistical evaluation of focal hepatic preneoplasia.

Qualitative analyses of focal hepatic preneoplasia are relatively easy and fast but hypothesis tests based on these analyses often lack statistical power. Evaluating focal hepatic preneoplasia quantitatively, on the other hand, requires more effort but is rewarded by an increased ability to detect differences between treatment groups and by the possibility to investigate the mechanism of a treatment under study. Due to the stereological problems inherent in the data a statistical analysis that concentrates on the evaluation of area fraction will provide clear results whereas the analysis of focal transection density and size distribution can produce misleading results. In addition, the area fraction is a valid variable even in the presence of confluent foci. The number and size distribution of focal transections in liver sections cannot be directly translated to the number and sizes of foci in the liver. As no general statements about the relationship between focal transection density and foci density as well as between focal transection size and foci size distribution can be made, there is need for a parametric mechanistic model to link the number and size distribution of focal transections to those of the underlying foci. The stereological problem therefore can be avoided by introducing a model for foci appearance and change of volume that then can be used to conclude whether the treatment induces foci and whether it changes their volume.

Animals↗

PubMed2002

Comparison of clinical, radiographic, and histometric measurements following treatment with guided tissue regeneration or enamel matrix proteins in human periodontal defects.

BACKGROUND: The purpose of this study was to compare the clinical and radiographic parameters with the histometric findings following 2 different regenerative procedures in humans. METHODS: Fourteen advanced intrabony defects at teeth scheduled for extraction were randomly treated as follows: 8 with guided tissue regeneration (GTR) using bioabsorbable barriers and 6 with an enamel matrix protein derivative (EMD). Standardized radiographs, probing depths (PD), and attachment levels (CAL) at baseline and 6 months after therapy were evaluated and compared to the histometric measurements made following the removal of teeth and surrounding tissues 6 months after the surgery. RESULTS: Significant PD reductions (GTR: -5.62 mm; EMD: -5.00 mm) and CAL gains (GTR: 3.87 mm; EMD: 2.67 mm) were observed in both groups. Six months after surgery, minor resorptions of the alveolar crest (AC) (GTR: 0.40 mm; EMD: 0.33 mm) and bony gain at the bottom of the defects (GTR: 0.47 mm; EMD: 1.05 mm) were observed radiographically. No statistically significant differences in the change of clinical and radiographic parameters between the GTR and EMD groups were found. Histometrically, significant amounts of new connective tissue attachment (i.e., cementum with inserting collagen fibers) were observed in both groups (GTR: 2.29 mm; EMD: 1.81 mm). Bone regeneration was found to be significant only in the GTR group (GTR: 1.93 mm; EMD: 0.78 mm). However, the study lacked statistical power for determining equivalence between the groups. CONCLUSIONS: Within the limitations of the present study, it may be concluded that at 6 months after GTR or enamel matrix protein derivative therapy, formation of new cementum and bone may be histometrically demonstrated. Except for the formation of new bone, no statistically significant differences between both therapies could be seen for clinical, radiographic, and histometric results 6 months after surgery.

Absorbable Implants↗

PubMed2026

Systematic common and rare variant association testing in 392,030 whole genomes in All of Us.

Large-scale genome-wide association studies (GWAS) and rare variant association studies (RVAS) from population biobanks provide valuable resources for gene discovery in complex human traits. We present an analysis of the All of Us Research Program v8 release, which includes whole genome sequencing data and harmonized phenotypic information of 392,030 participants after quality control, enabling a unified investigation of rare and common variants across a spectrum of human traits and diseases. We build an extensive phenome- and genome-wide ("All by All") computational framework to perform GWAS and RVAS on 3,602 phenotypes and identify 49,863 approximately independent, high-quality single-variant and gene-level associations. Meta-analyses of All of Us and UK Biobank, with sample sizes as large as 786,871 participants, further enhance statistical power and find 193 pLoF gene-phenotype associations that are not significant in either cohort alone, including 22 associations not highlighted by previous studies. We also present a public interactive browser that integrates association results for common and rare variants to facilitate interpretation and rapid querying of summary statistics, along with supporting documentation, and a Featured Workspace in the All of Us Researcher Workbench. Our framework will apply to iterative data releases as All of Us grows, empowering researchers worldwide to uncover insights into the functional effects of genetic components on complex traits and diseases.

Journal Article↗

PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗