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PubMed1997

Overcoming feelings of powerlessness in "aging" researchers: a primer on statistical power in analysis of variance designs.

A general rationale and specific procedures for examining the statistical power characteristics of psychology-of-aging empirical studies are provided. First, 4 basic ingredients of statistical hypothesis testing are reviewed. Then, 2 measures of effect size are introduced (standardized mean differences and the proportion of variation accounted for by the effect of interest), and methods are given for estimating these measures from already-completed studies. Power and sample size formulas, examples, and discussion are provided for common comparison-of-means designs, including independent samples I-factor and factorial analysis of variance (ANOVA) design, analysis of covariance designs, repeated measures (correlated samples) ANOVA designs, and split-plot (combined between- and within-subjects) ANOVA designs. Because of past conceptual differences, special attention is given to the power associated with statistical interactions, and cautions about applying the various procedures are indicated. Illustrative power estimations also are applied to a published study from the literature. It is argued that psychology-of-aging researchers will be both better informed consumers of what they read and more "empowered" with respect to what they research by understanding the important roles played by power and sample size in statistical hypothesis testing.

Aged↗
PubMed1994

Achieving statistical power through research design sensitivity.

The challenge for nurse researchers is to design their intervention studies with sufficient sensitivity to detect the treatment effects they are investigating. In order to meet this challenge, researchers must understand the factors that influence statistical power. Underpowered studies can result in a majority of null results in a research area when, in fact, the interventions are effective. The sensitivity of a research design is not a function of just one element of the design but of the entire research design: its plan, implementation and statistical analysis. When discussing factors that can increase a research design's statistical power, attention is most often focused on increasing sample size. This paper addresses a variety of factors and techniques, other than increasing sample size, that nurse researchers can use to enhance the sensitivity of a research design so that it can attain adequate power.

Nursing Research↗
PubMed1991

Issue of statistical power in comparative evaluations of minimal and intensive controlled drinking interventions.

An analysis of recent studies of minimal and intensive cognitive-behavioural treatments for problem drinking was undertaken to decide to whether a lack of statistical power explains the failure of the majority of studies to find a difference in outcome between these two types of treatment. Although the sample sizes have typically been small (n = 12-21), the analysis suggests that low statistical power is unlikely to be the explanation for the majority of null findings. It seems more likely that the difference in outcome between one positive study and the majority of null results reflects some combination of differences in the type of clients who were treated, the therapists' experience, and the type of intensive therapy that was provided. The low power of these studies demonstrates the desirability of researchers calculating the sample size required to an effect before commencing an outcome study. If they continue to undertake studies with small sample sizes, then they should refrain from inferring that the failure to reject a null hypothesis means that there is no difference between treatments.

Alcohol Drinking↗
PubMed1999

Pilot trial for the assessment of relative bioavailability in generic drug product development: statistical power.

In developing generic drug products, pilot trials are used for identifying successful test formulations to enter pivotal trials. In this study, we derive the power function based on the log-normal distribution and evaluate the effects of potential influential factors-the true test-reference ratio, intrasubject variability, and sample sizes-on the statistical power of a pilot trial to identify successful test formulations, defined as the probability that the test-reference ratio estimate from a pilot trial falls within a predetermined acceptance range when the true ratio is acceptable. Of these influential factors, the test-reference ratio exhibits the largest impact on the statistical power of a pilot trial, followed by intrasubject variability, sample sizes of pivotal trials, and sample sizes of pilot trials. The sample sizes that are used in pilot trials (8-12 subjects) may be sufficient for test products with low intrasubject variability and true ratio close to 1 and may fall short otherwise.

Biological Availability↗
PubMedDate not supplied

Considerations of statistical power in the shared-haplotypes test.

This paper considers statistical power properties of a test of whether a disease is caused by a recessive gene, using data on HLA sharing properties of affected sibs. It is found that the test has very poor power characteristics, and in particular we are sometimes just as likely to accept the hypothesis that the gene is recessive when it is dominant as when it is truly recessive.

Child↗
PubMedDate not supplied

Statistical power: concepts, procedures, and applications.

This paper discusses the concept of statistical power and its application to psychological research. Power, the probability that a significance test will produce a significant result when the null hypothesis is false, often is neglected with potentially serious consequences. The concept of power should be considered as part of planning and interpreting research. This article provides explication of the concept of power and suggestions for researchers to increase the power of their investigations.

Behavior Therapy↗
PubMedDate not supplied

Familial and sporadic schizophrenia. A simulation study of statistical power.

The importance of genetic factors in schizophrenia is clear but the mechanism involved remains obscure. Etiological heterogeneity may be responsible. Recently there has been interest in a putative distinction between genetic and environmental forms of the illness based on a positive or negative family history of the disorder. Those with a positive family history are classified as 'familial' and are considered to be more likely to have the genetic form of the illness. Those with a negative family history are classified as 'sporadic' and considered more likely to have an environmental form of the illness. This paper reports the results of a Monte Carlo simulation study with varying rates of misclassification to determine the statistical power of comparisons between familial and sporadic groups. For a large sample (n = 175) statistical power was moderate to good for effect sizes greater than or equal to 1.0 standard deviation unit and positive predictive value of 0.3 or greater.

Humans↗
PubMed1995

Exposure measurement errors, risk estimate and statistical power in case-control studies using dichotomous analysis of a continuous exposure variable.

BACKGROUND: Non-differential errors in exposure measurements have been shown to lead to differential misclassification of exposure. As a consequence, the common tenet that, in absence of bias, imprecise exposure assessment can only bias the risk estimates conservatively does not necessarily hold. We investigate the effects of exposure measurement errors on the risk estimate and on statistical power. METHODS: We used a computer model that simulates a case-control study. We used both hypothetical data and data modelled on empirical measurements of environmental magnetic fields exposure. RESULTS: Measurement errors are found to have a lesser impact on risk estimates and statistical power than would have been the case had misclassification been truly non-differential. However, for a given cutpoint, a bias away from the null cannot be excluded. The predominant direction of the errors is found to have important consequences on both the study power and the risk estimates. CONCLUSION: When sufficient empirical data are available, computer modelling may give a more accurate estimate of the effects of measurement errors than algebraic corrections.

Bias↗
PubMed1985

Prognosis in valvular heart disease. I. Description of purpose, organization, data collection techniques, estimates of statistical power, and criteria for termination of patient entry. VA Cooperative Study Group on Valvular Heart Disease.

This report describes the design of a multicenter study with two major goals: the identification of valvular heart disease patients at risk for death or a serious complication, and the comparison of hemodynamic function and late outcome of a mechanical prosthetic valve (Björk-Shiley) with a bioprosthesis (Hancock porcine heterograft valve). Strengths of the study design are quantitative assessment of valvular and left ventricular function before and 6 months after valve replacement, measurement in a central laboratory of critical data items such as valve orifice area and left ventricular volumes, and assignment of cause of death and valve-related complications by a committee blinded to valve type. Statistical power calculated by a simulation technique shows only modest loss of power with frequent examination of outcome compared to infrequent examination. Guidelines for premature termination of the study because of superiority of one valve type are described; this includes a critical region with a sloping boundary, which allows for greater chance variation early in the study when the number of patients and events is small, gives the greatest statistical power, and yet appears to provide adequate protection for the subjects in the study.

Bioprosthesis↗
PubMed1990

Semen analysis and fertility assessment in rabbits: statistical power and design considerations for toxicology studies.

Semen analysis is commonly used in evaluating human response to reproductive toxicants. Serial semen samples can be collected from rabbits and fertility assessed by artificial insemination, hence this species is potentially well suited for male reproductive toxicity studies that might be extrapolated to humans. However, the size and cost of rabbits often restricts the number of animals used, reducing the sensitivity of such studies. Therefore, it was of interest to optimize study design for semen analysis and fertility assessment in rabbits. Semen samples were collected weekly from sexually mature New Zealand white rabbits and a range of parameters was analyzed (Semen--pH, volume, osmolality; Sperm--number and concentration, morphology, viability, percentage motility, motion characteristics; Seminal plasma--fructose, citric acid, carnitine and protein concentrations, acid phosphatase activity). Male fertility was assessed by inseminating female rabbits with the minimum number of motile sperm required for normal fertility, determined to be one million. The within- and between-buck variabilities were determined for all parameters and used to calculate the statistical power of different study designs. The variability of sperm number and concentration was decreased when measured in four ejaculates collected within a short period of time rather than in a single ejaculate; this was not true of other endpoints measured. In addition, use of preexposure observations further increased the statistical power for all of the parameters. These data can be used to determine the optimum design for studies of male reproductive toxicity using rabbits, with particular regard to cost and the number of animals used.

Acid Phosphatase↗
PubMed1990

Detecting disease clusters: the importance of statistical power.

A variety of methods and models have been proposed for the statistical analysis of disease excesses, yet rarely are these methods compared with respect to their ability to detect possible clusters. Evaluation of statistical power is one approach for comparing different methods. In this paper, the authors study the probability that a test will reject the null hypothesis, given that the null hypothesis is indeed false. They present a discussion of some considerations involved in power studies of cluster methods and review two methods for detecting space-time clusters of disease, one based on cell occupancy models and the other based on interevent distance comparisons. The authors compare these approaches with respect to: 1) the sensitivity to detect disease excesses (false negatives); 2) the likelihood of detecting clusters that do not exist (false positives); and 3) the structure of a cluster in a given investigation (the alternative hypothesis). The methods chosen, which are two of the most commonly used, are specific to different hypotheses. They both show low power for the small number of cases which are typical of citizen reports to health departments.

Clinical Protocols↗
PubMed1991

The internal validity of efficacy studies: design and statistical power in studies of language therapy for aphasics.

In this study the internal validity of efficacy studies of language therapy for aphasic patients is discussed. The lack of sufficient internal validity is demonstrated with respect to research designs used in these studies and the statistical power of their statistical significance tests. The internal validity problems are viewed as the major cause of the conflicting conclusions in the efficacy studies in the past three decades.

Aphasia↗
PubMed1994

Updates of bioequivalence programs (including statistical power approximated by Student's t)

Updates are given of the earlier published MS-DOS programs BIOEQV37, BIOPAR37 and BIOEQNEW for the statistical analysis of bioequivalence studies. The updates are named BIOEQV52, BIOPAR40 and BIOEQNEW, respectively. Modifications and improvements in the updated programs are discussed. Calculations of statistical power for two-period crossover studies are now based on Schuirmann's two one-sided tests procedure. It is demonstrated that good approximations of power can be obtained by Student's t-statistic. This not only applies to the equations published elsewhere for the 'additive model', but also to hitherto unpublished equations for the 'multiplicative model'.

Software↗
PubMed1998

Statistical power analysis for PET studies in humans.

UNLABELLED: Although simple techniques have been established to determine statistical power when comparing, for example, the means of two groups of sampled data, the analysis is more complicated when establishing a trend in the data, such as with a linear regression. We present an approach to calculate the sample size necessary to reject the hypothesis that there is no trend in the data (slope is not different from zero) at a given level of statistical significance, given the intra- and inter-subject variability of the measurement. METHODS: We have derived analytically the distribution of the t statistic, for a given non-zero slope, and integrated this distribution to determine in what fraction of trials a real trend in the population would be missed. We illustrate our approach by re-examining the issue of an age-related impairment in presynaptic dopamine metabolism as measured by PET. RESULTS: We showed that the sample size necessary to determine whether 6-18F-fluoro-L-dopa retention decreases with age depends critically on both the variability of the quantitative method used and on the magnitude of the expected change. CONCLUSION: The method we have illustrated is a simple statistical test that allows investigators to be certain that an experimental design has a sufficient sample size to demonstrate the effect under study.

Age Factors↗
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↗
PubMed1991

Simulation program for estimating statistical power of Cox's proportional hazards model assuming no specific distribution for the survival time.

Small sample properties of the maximum partial likelihood estimates for Cox's proportional hazards model depend on the sample size, the true values of regression coefficients, covariate structure, censoring pattern and possibly baseline hazard functions. Therefore, it would be difficult to construct a formula or table to calculate the exact power of a statistical test for the treatment effect in any specific clinical trial. The simulation program, written in SAS/IML, described in this paper uses Monte-Carlo methods to provide estimates of the exact power for Cox's proportional hazards model. For illustrative purposes, the program was applied to real data obtained from a clinical trial performed in Japan. Since the program does not assume any specific function for the baseline hazard, it is, in principle, applicable to any censored survival data as long as they follow Cox's proportional hazards model.

Clinical Trials as Topic↗
PubMed2024

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 of current ST data generation techniques restricts its application in large-scale population studies. Consequently, there is a pressing need to maximize the use of available resources to achieve robust statistical power. One fundamental question in ST analysis is to detect differentially expressed genes (DEGs) among different conditions using ST data. Such DEG analysis is often performed but the associated power calculation is rarely discussed in the literature. To address this gap, we introduce, PoweREST (https://github.com/lanshui98/PoweREST), a power estimation tool designed to support power calculation of DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments or 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 (https://lanshui.shinyapps.io/PoweREST/), allowing users to interactively calculate and visualize the study power along with relevant the parameters.

Differentially expressed genes↗
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↗