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PubMed2002

A comprehensive, statistically powered analysis of GAD2 in type 1 diabetes.

GAD2 maps to chromosome 10p11.23 and encodes the 65-kDa isoform of GAD65, a major autoantigen in type 1 diabetes. The genetic variation that influences expression of preproinsulin mRNA, encoding another major autoantigen in type 1 diabetes, has already been shown to be genetically associated with disease. Previous reports that have assessed the association of GAD2 with type 1 diabetes have not used a dense map of markers surrounding the gene and have relied on very small clinical sample sizes. Consequently, no definite conclusions can be drawn from their negative results. We have therefore systematically searched all exons, the 3' untranslated region (UTR), the 5' UTR, and the 5' upstream region of GAD2, for polymorphisms in 32 white European individuals. We have genotyped these polymorphisms in a maximum of 472 U.K. type 1 diabetic affected sib pair families exhibiting linkage to type 1 diabetes on chromosome 10p and have tested both single variants and haplotypes in the GAD2 region for association with disease. We subsequently followed up our results by genotyping a subset of these single-nucleotide polymorphisms in a maximum of 873 Finnish families with at least one affected child. Our results suggest that GAD2 does not play a major role in type 1 diabetes in these two European populations.

3' Untranslated Regions↗
PubMed2000

Statistical power for the two-factor repeated measures ANOVA.

Determining a priori power for univariate repeated measures (RM) ANOVA designs with two or more within-subjects factors that have different correlational patterns between the factors is currently difficult due to the unavailability of accurate methods to estimate the error variances used in power calculations. The main objective of this study was to determine the effect of the correlation between the levels in one RM factor on the power of the other RM factor. Monte Carlo simulation procedures were used to estimate power for the A, B, and AB tests of a 2 x 3, a 2 x 6, a 2 x 9, a 3 x 3, a 3 x 6, and a 3 x 9 design under varying experimental conditions of effect size (small, medium, and large), average correlation (.4 and .8), alpha (.01 and .05), and sample size (n = 5, 10, 15, 20, 25, and 30). Results indicated that the greater the magnitude of the differences between the average correlation among the levels of Factor A and the average correlation in the AB matrix, the lower the power for Factor B (and vice versa). Equations for estimating the error variance of each test of the two-way model were constructed by examining power and mean square error trends across different correlation matrices. Support for the accuracy of these formulae is given, thus allowing for direct analytic power calculations in future studies.

Analysis of Variance↗
PubMed1990

Trending of impedance-monitored cardiac variables: method and statistical power analysis of 100 control studies in a pediatric intensive care unit.

The NCCOM3-R6 monitor continuously monitors cardiac output and five other cardiovascular variables from the thoracic electrical bioimpedance signal. We averaged data over 5-min intervals for 130 min in 100 control studies in 40 pediatric ICU patients, age 0.04 to 20.39 yr (median 1.39) and weighing 2.0 to 59.5 kg (median 8.8). For individual studies, 99% of the 5-min averages of cardiac output fell within +/- 44% of the baseline cardiac output for that study. Normal ranges were somewhat narrower for the other five variables. When we averaged data for 100 studies, 5-min interval observations for each variable did not deviate from baseline over a 2-h period (p greater than .70). With a sample size of 100 studies, we could detect a change in cardiac output of +/- 5% at the p less than .005 level with a power of 0.95. We conclude that with a sufficiently large sample size, studies employing the NCCOM3 can detect clinically significant cardiovascular changes due to pharmacologic or procedural stressors.

Cardiac Output↗
PubMed1995

Errors in exposure assessment, statistical power and the interpretation of residential radon studies.

To date, epidemiological studies of risk from residential radon have not convincingly demonstrated an association with lung cancer. These case-control studies, however, have inherent limitations due to errors in estimates of exposure to indoor radon. These errors take on special significance because the level of residential risk predicted from studies of underground miners is relatively low and possibly at the limit detectable by current epidemiological methods. To illustrate the problem caused by errors in exposure assessment, a series of case-control studies were simulated and resulting dose-response relationships evaluated. For each of four assumed error distributions for exposure to radon progeny, 10 indoor radon studies of 700 cases and 700 controls were generated randomly from a population with a risk of radon-induced lung cancer based on extrapolations from studies of underground miners. When exposures were assumed as known without error, 6 of 10 studies failed to find a significant dose response, in accord with the theoretical power of the study of 0.47. For simulations in which exposures were measured with error, the situation was worse, as the power of the study was reduced further and it was even less likely that a single study would result in a significant finding. For each error scenario, combining data from the 10 simulated studies did result in a significant dose response. However, the pooled results are somewhat misleading, because the effects of mobility, missing radon measurements, residential occupancy and potential confounding variables such as cigarette smoking were not taken into account. Empirical estimates of power were computed using 1,000 simulated case-control studies. When mobility and missing radon measurements in prior homes were incorporated into the design, the power of the study decreased, reducing the chance of detecting a significant effect of exposure. Enlarging study size to 2,000 cases and 2,000 controls increased the power of the study to 0.90 when exposure error was absent and subjects lived in one home only, but power was below 0.40 under realistic conditions for exposure error and mobility. When studies were generated under an assumption that exposure does not increase risk, up to 15% of simulated studies with 700 cases and 700 controls resulted in an estimated dose-response parameter in excess of the dose response from studies of miners. With increasing mobility and exposure error, it became virtually impossible to distinguish between the distributions of risk estimates from simulated studies based on an underlying excess relative risk of 0.015/working level month from estimates based on no risk from exposure.(ABSTRACT TRUNCATED AT 400 WORDS)

Case-Control Studies↗
PubMed2000

Strategies for genetic mapping of categorical traits.

The search for efficient and powerful statistical methods and optimal mapping strategies for categorical traits under various experimental designs continues to be one of the main tasks in genetic mapping studies. Methodologies for genetic mapping of categorical traits can generally be classified into two groups, linear and non-linear models. We develop a method based on a threshold model, termed mixture threshold model to handle ordinal (or binary) data from multiple families. Monte Carlo simulations are done to compare its statistical efficiencies and properties of the proposed non-linear model with a linear model for genetic mapping of categorical traits using multiple families. The mixture threshold model has notably higher statistical power than linear models. There may be an optimal sampling strategy (family size vs number of families) in which genetic mapping reaches its maximal power and minimal estimation errors. A single large-sibship family does not necessarily produce the maximal power for detection of quantitative trait loci (QTL) due to genetic sampling of QTL alleles. The QTL allelic model has a marked impact on efficiency of genetic mapping of categorical traits in terms of statistical power and QTL parameter estimation. Compared with a fixed number of QTL alleles (two or four), the model with an infinite number of QTL alleles and normally distributed allelic effects results in loss of statistical power. The results imply that inbred designs (e.g. F2 or four-way crosses) with a few QTL alleles segregating or reducing number of QTL alleles (e.g. by selection) in outbred populations are desirable in genetic mapping of categorical traits using data from multiple families.

Alleles↗
PubMed2000

Methodologic issues in measuring physical activity and physical fitness when evaluating the role of dietary supplements for physically active people.

Physical activity and physical fitness are complex entities comprising numerous diverse components that present a challenge in terms of accurate, reliable measurement. Physical activity can be classified by its mechanical (static or dynamic) or metabolic (aerobic or anaerobic) characteristics and its intensity (absolute or relative to the person's capacity). Habitual physical activity can be assessed by using a variety of questionnaires, diaries, or logs and by monitoring body movement or physiologic responses. Selection of a measurement method depends on the purpose of the evaluation, the nature of the study population, and the resources available. The various components of physical fitness can be assessed accurately in the laboratory and, in many cases, in the field by using a composite of performance tests. Most coaches and high-level athletes would accept as very beneficial a dietary supplement that would increase performance in a competitive event by even 3%; for example, lowering a runner's time of 3 min, 43 s in the 1500 m by 6.7 s. To establish that such small changes are caused by the dietary supplement requires carefully conducted research that involves randomized, placebo-controlled, double-blind studies designed to maximize statistical power. Statistical power can be increased by enlarging sample size, selecting tests with high reliability, selecting a potent but safe supplement, and maximizing adherence. Failure to design studies with adequate statistical power will produce results that are unreliable and will increase the likelihood that a true effect will be missed.

Data Collection↗
PubMed1988

The power of analysis: statistical perspectives. Part 2.

A discussion of the importance of statistical power in research is presented accompanied by nomograms for determining sample size and statistical power for the Student's paired and unpaired t tests with a Type I error of 5%. A brief review of statistical inference is presented. Some findings from Part I are reviewed.

Clinical Trials as Topic↗
PubMed2003

Evaluating dissolution profiles of an anti-HIV agent using ANOVA and non-linear regression models in JMP software.

A powerful statistical method was designed using JMP software to detect factors contributing to differences in the dissolution process of an antiviral drug delivered in an oral dosage form. Due to the large number of dissolution media available for solid dosage forms, a statistical method to choose the appropriate medium is critical for testing solid dosage forms. We have developed an analysis of variance model to analyze the overall dissolution profile obtained from the various media. In vitro tests were performed using a standard USP basket apparatus (Vankel Inc., Cary, NC), and the analysis used the restricted/residual maximum likelihood method (JMP software) to partition the variance due to media (pH 1.2 and 6.8, +SDS, water alone and at pH 1.2 with pepsin), time (repeated measure) and capsule (random effect). This allowed correct standard error estimates to be used to compare dissolution in different media using planned linear contrasts. The model provided us with statistically powerful criteria to identify significant differences in capsule dissolution across time and to quantify capsule-to-capsule population variance estimate. The time specific linear contrasts showed the largest sum of square values (SS) occurred at 180 min (SS=0.268) for the simulated SIF (pH 6.8) versus SGF (pH 1.2) comparison (DF=166, MSE=3.92 x 10(-3)). The dissolution processes were further characterized using a non-linear regression fit of a power law function to the data for each capsule. This resulted in a method to statistically differentiate between the dissolution processes of the capsules in different media.

Analysis of Variance↗
PubMed2000

[Progress in the studies on the molecular genetics of schizophrenia].

Although population genetic studies have long confirmed the genetic vulnerability of schizophrenia,ongoing advances in molecular genetic technology and biostatistic analysis are only now making it possible to search for the susceptibility gene of the disease. This article reviewed some of the recent findings in this area: (1) The heritability of schizophrenia is estimated around 60%-80%. The phenotype differentiation is based on standard diagnostic scales and symptom rating scales. (2) The two main approaches to finding the genes that influence the disorder are now genomic scan and candidate gene detection. Affected sib-pair (ASP) method and transmission disequilibrium test(TDT) are considered promising analyses. (3) The positive candidate regions with some independent replicable reports concentrated on 6p, 22q and 8p. Positive findings of candidate gene research involved 5-HT2A receptor, DRD3, NT-3, etc. Further directions to identify the susceptibility genes include: Applying more precise instruments to define clinical phenotype of the disease. Application of proper biological markers such as electrophysiologic parameters and brain imaging will be a prospective approach. Using larger sample to increase statistic power and developing more powerful statistic analysis, and performing advanced molecular genetic technique such as DNA pooling, DNA chips, genomic mismatch scanning (GMS), representational difference analysis(RDA), comparative genomic hybridization(CGH) and two-dimensional DNA typing methods will also facilitate this research area to greater perspective.

Chromosome Aberrations↗