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PubMed1991

Interoperator variability in quantitative electroencephalography.

The purpose of the study was to determine whether quantitative or discriminant analysis of the electroencephalograph (EEG) would vary significantly when the same EEG was analysed by 3 different operators. EEGs on 10 healthy volunteers were recorded on the Cadwell Spectrum AT 386, using the Electrocap (10-20 system). The EEGs were analysed independently, with each operator selecting the first 48 artifact-free epochs. The results were analysed using the non-parametric Friedman two-way analysis of variance (ANOVA) for the discrimination analysis and a one-way ANOVA for the monopolar and bipolar Absolute Power raw measures. Statistical analysis of the discriminant data showed no significant differences between operators, with 7 of 10 studies yielding the same results. The remaining 3 studies were classified either as borderline or normal when analysed by different operators. Although a series of "t" tests comparing 2 operators showed most variability occurring in Absolute Power as compared with Relative Power, Power Asymmetry and Coherence, ANOVA of the raw mono- and bipolar Absolute Power measures showed no significant differences between the operators at the P = 0.05 level. Thus the differences between the operators were non-significant when comparing quantitative EEG analyses with respect to both the raw measures and the discriminant analyses.

Analysis of Variance↗
PubMed2001

Understanding statistical power.

This article provides an introduction to power analysis so that readers have a basis for understanding the importance of statistical power when planning research and interpreting the results. A simple hypothetical study is used as the context for discussion. The concepts of false findings and missed findings are introduced as a way of thinking about type I and type II errors. The primary factors that affect power are described and examples are provided. Finally, examples are presented to demonstrate 2 uses of power analysis, 1 for prospectively estimating the sample size needed to insure finding effects of a known magnitude in a study and 1 for retrospectively estimating power to gauge the likelihood that an effect was missed.

Data Interpretation, Statistical↗
PubMed1995

Adherence of Candida albicans to epithelial cells: studies using fluorescently labelled yeasts and flow cytometry.

Candida albicans adherence to epithelial cells is the first step in the infectious process, but in spite of its importance, current methods for the quantitative measurement of adherence of C. albicans to epithelial cells in vitro have some serious limitations. They are based on filtration assays and either microscopic or radiometric analysis. The adherence reaction is usually carried out with a large excess of yeasts (100-fold) over epithelial cells in order to perform the microscopic analysis, which is slow, subjective and limited to 100-200 cells and thus lacks statistical power. The radiometric analysis fails to measure individual cells. A method for measuring yeast adherence that overcomes these problems has been developed. It is based on labelling the yeasts with the fluorogenic marker 2',7'-bis-(2-carboxyethyl)-5(6)-carboxyfluorescein acetoxymethyl ester (BCECF) prior to the adherence reaction, and analysing 10(4) epithelial cells by flow cytometry, while nonbound yeasts are excluded by gating. Two subpopulations of buccal epithelial cells (BECs) which differ in their mean fluorescence intensities per cell (MFIs) were observed: one with MFI which did not exceed nonspecific fluorescence, and the other with MFI as high or higher than the MFI of labelled yeasts. The two subpopulations represent yeast-free and yeast-binding epithelial cells, respectively, and the MFI increment of the BECs is a quantitative measure of the extent of yeast adherence. Control experiments confirming previously described basic features of adherence, such as enhanced adherence at increasing yeast excess, diminished adherence of trypsin-treated or heat-inactivated yeasts, and the differential adherence of various Candida species, supported the validity of the assay. The possibility of studying adherence reliably at low yeast:epithelial cell ratios, which better mimic adhesion as it occurs in vivo, is an important advantage of the assay. New findings, using this method, included the observation that exfoliated BECs from diabetic patients exhibited the same capacity for C. albicans adherence as cells from healthy controls, and that epithelial cells from early human ontogenic stages had a significantly lower adherence level than those from later stages.

Adult↗
PubMed1991

Occupational exposures to solvents and lead as risk factors for Alzheimer's disease: a collaborative re-analysis of case-control studies. EURODEM Risk Factors Research Group.

A meta-analysis, involving the secondary analysis of original data from 11 case-control studies of Alzheimer's disease, is presented for occupational exposures to solvents and lead. Three studies had data on occupational exposure to solvents. Among cases, 21.3% were reported to have been exposed; among controls, this figure was comparable (20.9%). This yielded a pooled matched relative risk of 0.76 (95% CI: 0.47-1.23). Four studies had data on exposure to lead. Exposure frequencies were 6.1% in cases and 8.3% in controls. This resulted in a pooled matched relative risk of 0.71 (95% CI: 0.36-1.41). The meta-analysis was particularly useful in validating negative results from individual studies and in increasing the statistical power for the analysis of lead exposure, where stratum-specific cell sizes were frequently smaller than five in individual studies. However, since exposure in the various studies was ascertained in a rather broad manner, prospective studies are recommended which focus on high-risk occupational populations and which determine the incidence of Alzheimer's disease in these and comparable unexposed populations.

Alzheimer Disease↗
PubMedDate not supplied

Increasing the power of clinical trials through judgment analysis.

A method for increasing the statistical power of clinical trials to detect clinically important differences is described. Inconsistency in physicians' overall judgments of treatment effectiveness adds "noise" to a trial that may mask either the superiority or the inferiority of particular treatments. The method described here uses "judgment analysis" to reduce errors in an individual's overall judgments of treatment effectiveness. The method can also be used to reduce error variance due to differences in judgment between physicians and may thus be particularly useful in multicenter trials. The method is illustrated with results from a recent trial.

Analgesics↗
PubMed1997

Systematic reviews of medical evidence: the use of meta-analysis in obstetrics and gynecology.

OBJECTIVE: To review the technique of meta-analysis and its uses and limitations in obstetrics and gynecology. DATA SOURCES: We reviewed four major journals in obstetrics and gynecology (American Journal of Obstetrics and Gynecology, Fertility and Sterility, Journal of Reproductive Medicine, and Obstetrics & Gynecology). METHODS OF STUDY SELECTION: Journals were reviewed to determine frequency of meta-analysis as a method of systematic review in obstetrics and gynecology. We also summarized objectives and scientific guidelines for performing a meta-analysis. TABULATION, INTEGRATION, AND RESULTS: Meta-analysis is used with increased frequency in obstetrics and gynecology as a way of systematically reviewing medical evidence. This technique is an attempt to improve on traditional methods of narrative review by an expert and as a framework for evidence-based medicine and developing practice guidelines. By combining data from replicate studies, a meta-analysis can increase statistical power, more precisely estimate the typical effect size of treatment or risk factor, and attempt to resolve controversies in the medical literature. Meta-analysis is a retrospective look at data already collected and is therefore subject to the biases of all retrospective studies. CONCLUSIONS: The technique of meta-analysis requires all the scientific rigor of a randomized clinical trial with careful attention to study design, including a formal protocol for literature search strategies, quality assessment of candidate studies, specific inclusion and exclusion criteria, issues of sampling and publication bias, statistical tests of homogeneity, and sensitivity analysis.

Evidence-Based Medicine↗
PubMed2002

Segmented regression analysis of interrupted time series studies in medication use research.

Interrupted time series design is the strongest, quasi-experimental approach for evaluating longitudinal effects of interventions. Segmented regression analysis is a powerful statistical method for estimating intervention effects in interrupted time series studies. In this paper, we show how segmented regression analysis can be used to evaluate policy and educational interventions intended to improve the quality of medication use and/or contain costs.

Cost Control↗
PubMed2001

Combined quality improvement ratio: a method for a more robust evaluation of changes in screening rates.

INTRODUCTION: It has been proposed that a ratio of the discordant cells from a McNemar's Chi-square table be used as a measure of quality improvement, and that this measure be called the Quality Improvement Ratio (QuIR). As proposed, patients enrolled in only one year of a two-year study are excluded from the McNemar's table of the QuIR. Since the original proposal of the McNemar's Chi-square in 1947 included application to matched pair data, a more comprehensive analysis would be possible if the single-year enrollees were matched into pairs. METHODS: Patients enrolled in only the first study year are matched and paired with patients enrolled in only the second study year. The pairs are matched on variables important to the disease or process being evaluated. The matched pairs are combined with the repeatedly measured subjects to increase the statistical power of the analysis. The Combined Quality Improvement Ratio (CQuIR) is demonstrated with parameters from the original articles, in a--Markov chain Monte-Carlo simulation, so a direct comparison can be made. RESULTS: CQuIR improved statistical power, especially in simulations of small populations. In some simulations the statistical power was double that of the QuIR alone. DISCUSSION: Although the QuIR provides important information, the CQuIR allows more of the data to be used to evaluate the effect of interventions in policy, delivery, and practice. The increase in statistical power of the CQuIR over the QuIR can facilitate successful evaluation of health care services.

Breast Neoplasms↗
PubMed1988

Meta-analysis. A quantitative approach to research integration.

Meta-analysis is being used with increasing frequency in clinical medicine as an attempt to improve on traditional methods of narrative review by systematically aggregating information and quantifying its impact. Combining data from several studies using meta-analysis can increase statistical power, provide insight into the nature of relationships among variables, and increase generalizability of results more rigorously than less quantitative review methods. Like all review methods, meta-analysis can be limited by sampling bias, inadequate data, and biased outcome interpretation. Still, the advantages noted above make meta-analysis a methodology that warrants testing and empirical evaluation.

Data Collection↗
PubMed2000

The HLA class II locus DPB1 can influence susceptibility to type 1 diabetes.

HLA-DPB1 genotypes were determined for samples from 269 multiplex Caucasian families from the Human Biological Data Interchange. DRB1 and DQB1 loci were also characterized, allowing assignment of DPB1 alleles to haplotypes and calculation of linkage disequilibrium values. Frequencies for several DPB1 alleles differed significantly between patients and affected family-based control subjects. Some differences were attributable to linkage disequilibrium with DR and DQ alleles, whereas others were not. DPB1*0301 and DPB1*0202 alleles are predisposing for type 1 diabetes in these data, not only in analyses of individual alleles, but also in genotype analyses. DPB1*0402 appears protective; however, stratification analysis indicates that its protective effect is specific for DR3 haplotypes. A protective role for DPB1*0401 is suggested by genotype analysis. For increased statistical power, DPB1 alleles were pooled into three categories: susceptible, neutral, and protective after removal of effects due to linkage disequilibrium with DR-DQ. Analysis of these pools suggests that DPB1 primarily affects susceptibility to, rather than protection from, type 1 diabetes in a dominant fashion. This effect is more apparent in patients with genotypes other than the highest risk DR3/DR4-DQB1*0302 genotype. These data support a role for the DPB1 locus in conferring susceptibility to type 1 diabetes.

Alleles↗
PubMedDate not supplied

Statistical power in nursing research.

A power analysis was performed on 62 articles that were published in Nursing Research and Research in Nursing and Health during 1989. The analysis revealed that when effects were small, the mean power of the statistical tests being performed to test research hypotheses was .26, indicating a very high risk of committing a Type II error. When effects were moderate, the mean power increased to .71, which is still below the conventionally acceptable power of .80. Only when a study involved large effects was the power adequate (mean of .95). Of the 583 power estimates calculated, 53% were for small effects. These analyses indicate that a substantial number of published nursing studies, and presumably even more of unpublished studies, have insufficient power to detect real effects, primarily because the samples used are too small.

Nursing↗
PubMed2000

A simulator for evaluating methods for the detection of lesion-deficit associations.

Although much has been learned about the functional organization of the human brain through lesion-deficit analysis, the variety of statistical and image-processing methods developed for this purpose precludes a closed-form analysis of the statistical power of these systems. Therefore, we developed a lesion-deficit simulator (LDS), which generates artificial subjects, each of which consists of a set of functional deficits, and a brain image with lesions; the deficits and lesions conform to predefined distributions. We used probability distributions to model the number, sizes, and spatial distribution of lesions, to model the structure-function associations, and to model registration error. We used the LDS to evaluate, as examples, the effects of the complexities and strengths of lesion-deficit associations, and of registration error, on the power of lesion-deficit analysis. We measured the numbers of recovered associations from these simulated data, as a function of the number of subjects analyzed, the strengths and number of associations in the statistical model, the number of structures associated with a particular function, and the prior probabilities of structures being abnormal. The number of subjects required to recover the simulated lesion-deficit associations was found to have an inverse relationship to the strength of associations, and to the smallest probability in the structure-function model. The number of structures associated with a particular function (i.e., the complexity of associations) had a much greater effect on the performance of the analysis method than did the total number of associations. We also found that registration error of 5 mm or less reduces the number of associations discovered by approximately 13% compared to perfect registration. The LDS provides a flexible framework for evaluating many aspects of lesion-deficit analysis.

Brain↗
PubMed1985

Regional changes in EEG power and coherence during cognition: intensive study of two individuals.

Two men underwent weekly electroencephalogram (EEG) recordings while living for several months in a controlled laboratory environment. Data collected from an eight-channel EEG during a resting period and during performance of two cognitive tasks (word fluency and mental imagery) were subjected to spectral analysis. Statistical analyses on power and coherence were conducted for each subject separately, to determine whether that individual showed a characteristic pattern of EEG activity for a given cognitive task which was stable over time. Although substantial individual differences were observed, particularly for the theta band, both subjects showed changes in the spectral information over the anterior left hemisphere during the word fluency task.

Cerebral Cortex↗
PubMed1996

Developmental characteristics of topographic EEG in school-age children using an autoregressive model.

Developmental characteristics of the resting EEG were investigated in 47 school-age children using statistical analysis. Total and component EEG power were statistically analyzed between the subject groups from 7 to 14 year-old using our autoregressive pattern discrimination system. In early school-age children, significant topographic differences were seen in theta waves, while in late school-age children, the differences were found in alpha waves in the frontal and occipital regions, and in beta waves in the frontal region.

Adolescent↗
PubMed1998

Using factor analysis to identify neuromuscular synergies during treadmill walking.

Neuroscientists are often interested in grouping variables to facilitate understanding of a particular phenomenon. Factor analysis is a powerful statistical technique that groups variables into conceptually meaningful clusters, but remains underutilized by neuroscience researchers presumably due to its complicated concepts and procedures. This paper illustrates an application of factor analysis to identify coordinated patterns of whole-body muscle activation during treadmill walking. Ten male subjects walked on a treadmill (6.4 km/h) for 20 s during which surface electromyographic (EMG) activity was obtained from the left side sternocleidomastoid, neck extensors, erector spinae, and right side biceps femoris, rectus femoris, tibialis anterior, and medial gastrocnemius. Factor analysis revealed 65% of the variance of seven muscles sampled aligned with two orthogonal factors, labeled 'transition control' and 'loading'. These two factors describe coordinated patterns of muscular activity across body segments that would not be evident by evaluating individual muscle patterns. The results show that factor analysis can be effectively used to explore relationships among muscle patterns across all body segments to increase understanding of the complex coordination necessary for smooth and efficient locomotion. We encourage neuroscientists to consider using factor analysis to identify coordinated patterns of neuromuscular activation that would be obscured using more traditional EMG analyses.

Adult↗
PubMed1996

Study design in clinical research: sample size estimation and power analysis.

The purpose of this review is to describe the statistical methods available to determine sample size and power analysis in clinical trials. The information was obtained from standard textbooks and personal experience. Equations are provided for the calculations and suggestions are made for the use of power tables. It is concluded that sample size calculations and power analysis can be performed with the information provided and that the validity of clinical investigation would be improved by greater use of such analyses.

Clinical Trials as Topic↗