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PubMed1991

Statistical power in single subject trials.

A controlled single subject trial compares the efficacy of a new treatment with a control treatment in an individual patient. The treatments are administered in a double-blind, randomized, multi-crossover sequence of periods. During the trial response measures are obtained from each treatment period and form the basis for the statistical evaluation. Similar to the situation in clinical trials using groups of patients the statistical power is dependent on sample size, variability of responses, magnitude of the differential treatment effect and the level of statistical significance. In addition, the randomization procedure is of importance and power estimations show that a pairwise random allocation of treatment periods is more powerful than an unrestricted randomization. Since a single subject trial is a time consuming approach, the total number of treatment periods, the sample size, is restricted in order to make such trials feasible. Accordingly, less rigorous statistical requirements and power must be accepted. The consequence is an increased risk of both Type I and II errors. However, in comparison with the trial and error approach frequently applied in clinical practice, the controlled single subject trial may improve the certainty of therapeutic decisions in the individual patient.

Clinical Trials as Topic↗
PubMed1990

The design of an experiment using statistical power with a startle chamber study as an example.

Using a startle chamber experiment as a case study, it is shown how the sensitivity of a study's design can be quantified by using the concept of statistical power and how the design can be planned to achieve the power desired. The purpose of the experiment was to compare background responses in three empty startle chambers. The study design nested groups of noise events into trials, entailing two sources of experimental error, variation within trials and between trials. For this nested design, the proper statistical analysis and calculation of power are described. It is shown how the power depends on the numbers of trials and events per trial (sampling effort), magnitudes of the sources of variability and background differences to be detected. A worked example shows the power associated with several sampling alternatives. Associated implications for cost and benefit are also discussed.

Animals↗
PubMed1992

Significance testing in mutagen screening: the dependence of statistical power on the control sample size.

The continuous accumulation of control data in multicellular mutagen screening systems prompted us to study the dependence of the statistical power on the size of the control sample (for fixed control values). Two widely used screening systems were chosen: dicentric chromosomes in human lymphocytes and recessive sex-linked lethals in Drosophila melanogaster. The power increases rapidly at first as the control sample size increases, then levels off at a few tens of thousands of control units tested and thereafter remains almost constant up to the historical control. The practical implications from our study are discussed.

Animals↗
PubMedDate not supplied

Statistical power of an exact test of Hardy-Weinberg proportions of genotypic data at a multiallelic locus.

A computer algorithm for numerical evaluation of the statistical power of an exact test of Hardy-Weinberg genotypic proportions (HWP), developed here, indicates that the power is dependent on the number of segregating alleles as well as allele frequencies. While low levels of departure from the null hypothesis are difficult to detect from single-locus data, should such deviation be due to population substructuring, multiple loci, at each of which the number of segregating alleles is large (as seen with hypervariable loci), may easily detect even low levels of departure from HWP. Undetected small levels of departure may still provide conservative estimates of genotype frequencies from allele frequency data, following the current practice in forensic genetics.

Algorithms↗
PubMed1990

Statistical power of psychological research: what have we gained in 20 years?

Power was calculated for 6,155 statistical tests in 221 journal articles published in the 1982 volumes of the Journal of Abnormal Psychology, Journal of Consulting and Clinical Psychology, and Journal of Personality and Social Psychology. Power to detect small, medium, and large effects was .17, .57, and .83, respectively. 20 years after Cohen (1962) conducted the first power survey, the power of psychological research is still low. The implications of these results concerning the proliferation of Type I errors in the published literature, the failure of replication studies, and the interpretation of null (negative) results are emphasized. An example is given of the use of power analysis to help interpret null results by setting probable upper bounds on the magnitudes of effects. Limitations of statistical power analyses, suggestions for future research, sources of computational information, and recommendations for improving power are discussed.

Humans↗
PubMed2002

Statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity.

UNLABELLED: We performed a statistical power analysis to determine how many historical data are needed for optimal operating room (OR) management decision making. The work applies to hospitals that provide service for all of its surgeons' elective cases on whatever workday the surgeons and patients choose. The hospital and anesthesia group adjust OR staffing and patient scheduling to care for the patients while minimizing OR staffing costs and maximizing labor productivity. Two years of data were obtained from a seven-OR surgical suite. The data were repeatedly split into training and testing datasets. The optimal staffing solution was calculated for each training dataset to maximize the efficiency of OR time usage and was then applied to the corresponding testing dataset. Training datasets ranged in size from 30 to 270 consecutive workdays. With 30 workdays of data, the statistical method identified staffing solutions that had an average of 35% decreased costs and 27% increased productivity as compared to the existing staffing plan. There was no significant improvement in performance with more than 210 workdays (10 mo) of data. With 30 workdays of OR or anesthesia group data, the optimization method can significantly reduce staffing costs and increase productivity compared with existing staffing. When applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data should be used. IMPLICATIONS: With 30 workdays of operating room or anesthesia group data, the optimization method can propose staffing solutions that significantly decrease costs and increase productivity compared with existing staffing solutions. We recommend that, when the statistical method is applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data be used.

Efficiency↗
PubMed1988

Regression analysis in biological research: sample size and statistical power.

Regression analysis is often used to demonstrate associations among variables believed to be biologically related. Failure to demonstrate a "significant" relationship may be due to two factors: 1) the variables are truly unrelated, or 2) a relationship exists but goes undetected due to inadequate statistical power. Investigators must consider the second possibility since failure to detect a statistically significant relationship is often taken as evidence for no biological relationship. These issues are addressed in the context of the interrelationship between four features common to all statistical methods: the size of effect or relationship worth detecting, the Type I (alpha) error, the sample size, and the Type II (beta) error. An example derived from published data relating morphological characteristics of muscle fiber type and isokinetic strength performance illustrates the practical significance of this dilemma.

Regression Analysis↗
PubMed1995

The effect of trial size and variability on statistical power.

A computer model was developed, validated, and used in conjunction with Monte Carlo procedures to study the effects of sample size (subjects and trials), mean differences, and subject variability on statistical power. Also examined were the differences between single subject (SS) and group results. Mean differences were varied from 1/4 to 4 times the distribution SD resulting in improved power values. Mean group F results ranged from 63.6% to 100% while SS results were poorer, especially for the smaller mean differences (16.8%-100%). Subject variability was examined for a Simple model and two Complex (MOD1 and MOD4) models. MOD1 produced group results similar to the corresponding Simple model with an overall mean of 78.2% and a Complex/Simple (C/S) ratio of 0.99. The more variable model (MOD4) produced fewer significant results (52.9%) and a lesser C/S ratio (0.82). The SS results were more dramatic. The percentages of significant values were less (38.1% and 33.9%) and the C/S ratios favored the Complex models (1.48 and 3.17). Both sample size and trial size had a major impact on the results. In summary, these results provide additional insight into the interactive effects and importance of the factors investigated, especially in the area of SS experiments.

Humans↗
PubMed1999

Inferring the fitness effects of DNA mutations from polymorphism and divergence data: statistical power to detect directional selection under stationarity and free recombination.

The fitness effects of classes of DNA mutations can be inferred from patterns of nucleotide variation. A number of studies have attributed differences in levels of polymorphism and divergence between silent and replacement mutations to the action of natural selection. Here, I investigate the statistical power to detect directional selection through contrasts of DNA variation among functional categories of mutations. A variety of statistical approaches are applied to DNA data simulated under Sawyer and Hartl's Poisson random field model. Under assumptions of free recombination and stationarity, comparisons that include both the frequency distributions of mutations segregating within populations and the numbers of mutations fixed between populations have substantial power to detect even very weak selection. Frequency distribution and divergence tests are applied to silent and replacement mutations among five alleles of each of eight Drosophila simulans genes. Putatively "preferred" silent mutations segregate at higher frequencies and are more often fixed between species than "unpreferred" silent changes, suggesting fitness differences among synonymous codons. Amino acid changes tend to be either rare polymorphisms or fixed differences, consistent with a combination of deleterious and adaptive protein evolution. In these data, a substantial fraction of both silent and replacement DNA mutations appear to affect fitness.

Adaptation, Biological↗
PubMed1993

Statistical power in the detection of matrix effects.

Matrix-induced bias can adversely affect the performance evaluation a clinical laboratory receives on proficiency testing results. Therefore, it is vital that matrix effects from a matrix-biased system are detected and that laboratories using these systems are not falsely penalized on their proficiency testing results. The College of American Pathologists has developed an experimental protocol to test whether an observed bias is, in fact, due to the proficiency testing sample matrix rather than true performance or calibration problems. The probability of detecting a matrix effect using this protocol given a matrix-biased system is defined as statistical power. Five parameters are known to affect the probability of detection: (1) size of the bias in the proficiency testing material, (2) lack of fit coefficient of variation-natural variation in patient samples used to define the relationship between the test and reference methods, (3) pure error coefficient of variation-random variation in the test method, (4) the number of fresh patient samples, and (5) the number of replicates for each sample. The level of significance of the statistical test will also affect the probability of detection. Power curves are generated to show the effect these five parameters have on the determination of power. With the exception of bias, power is most influenced by the components of variance, lack of fit (nonlinear), and pure (random) error. Of these two components, the lack of fit error, which represents an uncontrollable source of error, is usually more influential than pure error, which can be reduced by a larger number of replicates. A large increase in power will result from an increase of fresh patient samples from 10 to 20, and a moderate increase in power will result from an increase of fresh patient samples from 20 to 40; no noticeable increase in power is seen with greater than 40 fresh patient samples. Large increases in power were observed for increases in the number of replicates per sample from one to two, two to three, and three to five. To markedly increase power further, 10 replicates would have to be assayed.

Bias↗
PubMed2001

Sample size and statistical power of randomised, controlled trials in orthopaedics.

We reviewed all 717 manuscripts published in the 1997 issues of the British and American volumes of the Journal of Bone and Joint Surgery and in Clinical Orthopaedics and Related Research, from which 33 randomised, controlled trials were identified. The results and sample sizes were used to calculate the statistical power of the study to distinguish small (0.2 of standard deviation), medium (0.5 of standard deviation), and large (0.8 of standard deviation) effect sizes. Of the 33 manuscripts analysed, only three studies (9%) described calculations of sample size. To perform post-hoc power assessments and estimations of deficiencies of sample size, the standard effect sizes of Cohen (small, medium and large) were calculated. Of the 25 studies which reported negative results, none had adequate power (beta < 0.2) to detect a small effect size and 12 (48%) lacked the power necessary to detect a large effect size. Of the 25 studies which did not have an adequate size of sample to detect small differences, the average used was only 10% of the required number Our findings suggest that randomised, controlled trials in clinical orthopaedic research utilise sample sizes which are too small to ensure statistical significance for what may be clinically important results.

Humans↗
PubMed2003

Improved statistical power of the multilinear reference tissue approach to the quantification of neuroreceptor ligand binding by regularization.

A multilinear reference tissue approach has been widely used recently for the assessment of neuroreceptor-ligand interactions with positron emission tomography. The authors analyzed this "multilinear method" with respect to its sensitivity to statistical noise, and propose regularization procedures that reduce the effects of statistical noise. Computer simulations and singular value decomposition of its operational equation were used to investigate the sensitivity of the multilinear method to statistical noise. Regularization was performed by truncated singular value decomposition, Tikhonov-Phillips regularization, and by imposing boundary constraints on the rate constants. There was a significant underestimation of distribution volume ratios. Singular value decomposition showed that the bias was caused by statistical noise. The regularization procedures significantly increased the test-retest stability. The bias could be reduced by applying linear constraints on the rate constants based on their normal range. Underestimation of distribution volume ratios by the multilinear method is caused by its sensitivity to statistical noise. Statistical power in the discrimination of different groups of subjects can be significantly improved by regularization procedures without introducing additional bias. Correct distribution volume ratios can be obtained by imposing physiologic constraints on the rate constants.

Brain Chemistry↗
PubMed1994

Statistical power to detect occupationally related respiratory cancer risk in a cohort of female employees in the US man-made vitreous fiber industry.

The current update of the US man-made vitreous fiber production worker cohort includes women for the first time. Preliminary comparisons of 3,820 female and 27,767 male workers from 11 participating fibrous glass plants show different hiring patterns during World War II. The gender-specific person-year distributions are similar with respect to duration of employment and time since first employment. The current follow-up of 118,559 person-years for women provides an estimated 80% power to detect a threefold relative risk of respiratory cancer for women who worked more than 14 years in these plants, based on a Poisson regression analysis of the cohort rates. When women comprise a small fraction of the cohort, the statistical power may be inadequate to detect risks of the magnitude typically of interest in studies of men.

Aged↗
PubMed1989

Sample size and statistical power in the hierarchical analysis of variance: applications in morphometry of the nervous system.

Analysis of variance is commonly used in morphometry in order to ascertain differences in parameters between several populations. Failure to detect significant differences between populations (type II error) may be due to suboptimal sampling and lead to erroneous conclusions; the concept of statistical power allows one to avoid such failures by means of an adequate sampling. Several examples are given in the morphometry of the nervous system, showing the use of the power of a hierarchical analysis of variance test for the choice of appropriate sample and subsample sizes. In the first case chosen, neuronal densities in the human visual cortex, we find the number of observations to be of little effect. For dendritic spine densities in the visual cortex of mice and humans, the effect is somewhat larger. A substantial effect is shown in our last example, dendritic segmental lengths in monkey lateral geniculate nucleus. It is in the nature of the hierarchical model that sample size is always more important than subsample size. The relative weight to be attributed to subsample size thus depends on the relative magnitude of the between observations variance compared to the between individuals variance.

Aging↗
PubMed1995

Age and handedness: patterns of change in the population and sex differences become visible with increased statistical power.

In a sample of 12,030 subjects, ranging in age from 8 to 99 years, significant decreases in both mixed and consistent left-handedness were found as age increased. There were also significant sex differences, with males more likely to be left- or mixed-handed. These age and sex differences were reported as non-significant in Porac's (1993) smaller sample of 654. Methodological issues associated with asserting the null hypothesis in handedness studies when statistical power is low are also discussed.

Adolescent↗
PubMed2025

PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

Gene Expression Profiling↗
PubMed1992

Linkage studies of schizophrenia: a stimulation study of statistical power.

In planning for a linkage study, it is important to determine the number of pedigrees needed to show linkage. Our study overcomes some of the limitations of previous power studies by simulating multigeneration pedigrees to be compatible with the demographic and genetic epidemiological features of schizophrenia; these are variable age at onset, reduced fertility, and increased mortality after onset. We evaluate the power of these pedigrees by first simulating an ascertainment rule requiring at least three ill family members per pedigree and then simulating the trait and marker genotypes according to a single gene model known to fit epidemiological family study data. Our analysis allows for incomplete and age-dependent penetrance, phenocopies, and interpedigree heterogeneity. We present the power to detect linkage at several lod score thresholds since the multiple tests and phenotypic models required for complex diseases may necessitate using a lod score significance level greater than three. The sample size needed to achieve sufficient power is feasible if 50% of the pedigrees are linked to the marker under test. It may not be feasible to detect linkage if only 25% of the pedigrees are linked, even if a very closely linked marker is used. Our results indicate that to be certain of adequate statistical power, linkage analyses of schizophrenia will require very large samples that do not have a marked degree of genetic heterogeneity.

Adult↗
PubMed1992

Analytical method comparison based upon statistical power calculations.

Testing for the equivalence of results generated by different analytical methodology is a common practice in the pharmaceutical sciences. Methodology changes are implemented for both scientific and economic reasons during a scientific study. Thus, the need to demonstrate the appropriateness of considering data generated by distinct methods as part of a single information population arises. This paper describes a rapid and simple approach to the statistical design and interpretation of method comparison experiments. The approach presented is based upon a statistical power calculation technique, a knowledge of the variability associated with the methods to be compared and the criteria for equivalence (the limits within which differences become immeasurable or, for practical purposes, insignificant). Reference tables are included which show necessary sample sizes for comparison experiments for common combinations of these three variables.

Chemistry Techniques, Analytical↗