What size sample do I need?
Explore the source record for details and available documents.
Confounding Factors, Epidemiologic↗
SEARCH · Search PubMed
Results for “Sample Size”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A sample size computation method for non-linear mixed effects models with applications to pharmacokinetics models.
We propose a simple method to compute sample size for an arbitrary test hypothesis in population pharmacokinetics (PK) studies analysed with non-linear mixed effects models. Sample size procedures exist for linear mixed effects model, and have been recently extended by Rochon using the generalized estimating equation of Liang and Zeger. Thus, full model based inference in sample size computation has been possible. The method we propose extends the approach using a first-order linearization of the non-linear mixed effects model and use of the Wald chi(2) test statistic. The proposed method is general. It allows an arbitrary non-linear model as well as arbitrary distribution of random effects characterizing both inter- and intra-individual variability of the mixed effects model. To illustrate possible uses of the method we present tables of minimum sample sizes, in particular, with an illustration of the effect of sampling design on sample size. We demonstrate how (D-)optimal or frequent sampling requires fewer subjects in comparison to a sparse sampling design. We also present results from Monte Carlo simulations showing that the computed sample size can produce the desired power. The proposed method greatly reduces computing times compared with simulation-based methods of estimating sample sizes for population PK studies.
Black People↗
Sample size determination based on rank tests in clinical trials.
The problem of sample size determination based on three commonly used non-parametric rank based tests, namely, one-sample Wilcoxon's rank sum test, two-sample's Wilcoxon's rank sum test, and the rank-based test for independence is studied. Explicit formulas for variabilities of the test statistics under the alternative hypotheses are derived. Consequently, close forms of power functions of these test statistics are obtained for sample size determination utilizing the concept of higher order polynominal equations. Simulation studies were performed to evaluate the finite samples performance of the derived sample size formulas. The results indicates that the derived methods work well with moderate sample size.
Clinical Trials as Topic↗
Sample size for regression analyses of theory of planned behaviour studies: case of prescribing in general practice.
OBJECTIVES: Interest has been growing in the use of the theory of planned behaviour (TBP) in health services research. The sample sizes range from less than 50 to more than 750 in published TPB studies without sample size calculations. We estimate the sample size for a multi-stage random survey of prescribing intention and actual prescribing for asthma in British general practice. To our knowledge, this is the first systematic attempt to determine sample size for a TPB survey. METHODS: We use two different approaches: reported values of regression models' goodness-of-fit (the lambda method) and zero-order correlations (the variance inflation factor or VIF method). Intra-cluster correlation coefficient (ICC) is estimated and a socioeconomic variable is used for stratification. We perform sensitivity analysis to estimate the effects of our decisions on final sample size. RESULTS: The VIF method is more sensitive to the requirements of a TPB study. Given a correlation of .25 between intention and behaviour, and of .4 between intention and perceived behavioural control, the proposed sample size is 148. We estimate the ICC for asthma prescribing to be around 0.07. If 10 general practitioners were sampled per cluster, the sample size would be 242. CONCLUSIONS: It is feasible to perform sophisticated sample size calculations for a TPB study. The VIF is the appropriate method. Our approach can be used with adjustments in other settings and for other regression models.
Asthma↗
The impact of ignoring measurement error when estimating sample size for epidemiologic studies.
The author presents two examples illustrating the bias in sample-size estimates that can result from ignoring measurement error among study variables. The first example examines the impact of ignoring misclassification of the study's outcome variable on the accuracy of sample-size estimates. In addition, the author outlines a simple yet effective means of adjusting sample-size estimates to account for outcome misclassification. In the second example, the author illustrates the potential for severe underestimation of required sample size in studies using linear regression to evaluate associations between the outcome of interest and an independent variable subject to classical measurement error. The author concludes with a discussion of pertinent literature that might be helpful to study planners interested in adjusting sample-size estimates to account for measurement errors in both outcome and predictor variables.
Epidemiologic Studies↗
Regression-based reference limits: determination of sufficient sample size.
Regression analysis is the method of choice for the production of covariate-dependent reference limits. There are currently no recommendations on what sample size should be used when regression-based reference limits and confidence intervals are calculated. In this study we used Monte Carlo simulation to study a reference sample group of 374 age-dependent hemoglobin values. From this sample, 5000 random subsamples, with replacement, were constructed with 10-220 observations per sample. Regression analysis was used to estimate age-dependent 95% reference intervals for hemoglobin concentrations and erythrocyte counts. The maximum difference between mean values of the root mean square error and original values for hemoglobin was 0.05 g/L when the sample size was > or = 60. The parameter estimators and width of reference intervals changed negligibly from the values calculated from the original sample regardless of what sample size was used. SDs and CVs for these factors changed rapidly up to a sample size of 30; after that changes were smaller. The largest and smallest absolute differences in root mean square error and width of reference interval between sample values and values calculated from the original sample were also evaluated. As expected, differences were largest in small sample sizes, and as sample size increased differences decreased. To obtain appropriate reference limits and confidence intervals, we propose the following scheme: (a) check whether the assumptions of regression analysis can be fulfilled with/without transformation of data; (b) check that the value of v, which describes how the covariate value is situated in relation to both the mean value and the spread of the covariate values, does not exceed 0.1 at minimum and maximum covariate positions; and (c) if steps 1 and 2 can be accepted, the reference limits with confidence intervals can be produced by regression analysis, and the minimum acceptable sample size will be approximately 70.
Child, Preschool↗
Sample size estimation in phase III cancer clinical trials.
This paper deals with the basic principles involved in sample size calculation of phase III cancer clinical trials. It illustrates the concepts and factors determining the sample size. Various examples of phase III cancer clinical trials are provided and the sample size is calculated taking into account the assumptions made. The examples provided include sample sizes for comparing proportions and sample sizes for comparing survival times. Several special topics are also discussed including choice of endpoint, number of treatment groups, factorial designs and equivalence trials.
Clinical Trials, Phase III as Topic↗
Sample sizes for experiments with multivariate repeated measures.
Procedures for determining sample size in multivariate repeated measures experiments are discussed. The focus is on designs with either one group or two independent groups of subjects, the point of departure being multivariate repeated measures. Determination of the minimum sample size required is based on power considerations associated with Hotelling's T2 and the F-test. We first consider procedures for determining sample size assuming an arbitrary covariance matrix. The special case where we assume that the transformed data have a multivariate spherical covariance matrix is also considered. Tables of the minimum sample sizes required for several hypotheses from multivariate analysis are presented.
Mathematical Computing↗
Minimizing sample size when using exploratory factor analysis for measurement.
Traditional protocol for the determination of an adequate sample size is power analysis. Such a protocol is not useful when the primary hypothesis focuses on psychometric measurement properties. Traditional psychometrics advises that there should be 10 respondents per item. Both hypothetical and real research examples illustrate the usefulness of subsample analysis in determining that a sample size of at least 50 and not more than 100 subjects is adequate to represent and evaluate the psychometric properties of measures of social constructs. The "10 respondents per item" advice builds a sample size disincentive into the research design; it also represents "sample size overkill." Sample-size overkill occurs when the research design specifies a number of cases needed, which is in excess of the number actually needed for a desired inference.
Computer Simulation↗
Variation of estimates of SNP and haplotype diversity and linkage disequilibrium in samples from the same population due to experimental and evolutionary sample size.
Studies of genetic polymorphisms and diversity between and within human populations are increasingly characterised by a very large number of genetic markers but using a relatively small number of individuals from which DNA samples were taken. In this report we examine the limitations of a small experimental sample size relative to a large genomic sample size, and quantify the sampling variance of a number of measures of diversity and linkage disequilibrium. The relationship between sample size and observed levels of polymorphism and haplotype diversity at the level of a gene is investigated under a neutral model of sequence evolution, using coalescent simulations. It is shown that the effect of evolutionary sampling, as manifested by differences between samples (genes) in measures of diversity estimated using very large sample sizes, is substantial, with a coefficient of variation of the number of detected polymorphic SNPs or haplotypes in the order of 15%. The effect of experimental design (sample size) is also very large, and a number of 'significant' results reported in the literature can be explained by sampling alone. The expected correlation coefficient of measures of linkage disequilibrium across samples from the same population has been quantified and found to be consistent with empirical estimates from the literature.
Chromosomes, Human↗
Estimating sample size in critical care clinical trials.
Estimating the required sample size for a study is necessary during the design phase to ensure that it will have maximal efficiency to answer the primary question of interest. Clinicians require a basic understanding of the principles underlying sample size calculation to interpret and apply research findings. This article reviews the critical components of sample size calculation, including the selection of a primary outcome, specification of the acceptable types I and II error rates, identification of the minimal clinically important difference, and estimation of the error associated with measuring the primary outcome. The relationship among confidence intervals, precision, and study power is also discussed.
Bias↗
Sample size calculations in randomised trials: mandatory and mystical.
Investigators should properly calculate sample sizes before the start of their randomised trials and adequately describe the details in their published report. In these a-priori calculations, determining the effect size to detect--eg, event rates in treatment and control groups--reflects inherently subjective clinical judgments. Furthermore, these judgments greatly affect sample size calculations. We question the branding of trials as unethical on the basis of an imprecise sample size calculation process. So-called underpowered trials might be acceptable if investigators use methodological rigor to eliminate bias, properly report to avoid misinterpretation, and always publish results to avert publication bias. Some shift of emphasis from a fixation on sample size to a focus on methodological quality would yield more trials with less bias. Unbiased trials with imprecise results trump no results at all. Clinicians and patients deserve guidance now.
Randomized Controlled Trials as Topic↗
Methodological issues concerning the sensitive query in AIDS/alcohol research: sample size estimates for randomized response procedures.
Quantification of sample size requirements for two common models of the RRT as compared to conventional survey techniques demonstrates that Campbell is fundamentally correct. However, the absolute increase in sample size necessitated by either model of the RRT is not of such a magnitude as to make use of the method always impractical. Where the appropriate sample size exists, it may well be the method of choice for selected issues pertaining to AIDS and alcohol research.
Acquired Immunodeficiency Syndrome↗
Sample size calculations in scleroderma: a rational approach to choosing outcome measurements in scleroderma trials.
Subjects with both diffuse and limited scleroderma were studied to calculate the baseline characteristics of several commonly used outcome measurements in order to provide parameters for sample size calculations for scleroderma clinical trials. From these estimates, outcome measurements were chosen as potentially responsive to change in clinical trials if their sample sizes were not prohibitively large. Forty-five patients with scleroderma were systematically assessed to determine the means and standard deviations whereby sample size calculations can be performed using this information. Examples of sample sizes were determined for the entire group, and for 2 subsets: those with diffuse scleroderma and those with diffuse disease of recent onset. Many baseline characteristics were significantly different in patients with diffuse compared to limited systemic sclerosis. The baseline values were different for the Health Assessment Questionnaire (HAQ) disability score, Functional Index, grip strength, oral aperture, finger-to-palm distance, skin score, and physician global assessment. Sample sizes can vary widely depending upon the outcome measurement chosen and the range of deltas used within the different scleroderma subsets. Sample size requirements for many outcome measures are extremely large due to marked variability in the baseline measures. Primary outcome measures in scleroderma trials should be chosen which have adequate power to detect a minimal clinically relevant change in the primary outcome measurements at the sample sizes employed. All other outcome measures should be ranked as secondary. Skin scores, global assessments, and grip strength measurements require smaller sample sizes than the other outcome measurements which were studied. Sample sizes in future trials will vary depending upon the proportion of patients with diffuse and limited scleroderma who are included.
Female↗
The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed.
BACKGROUND AND OBJECTIVE: Publication bias and other sample size effects are issues for meta-analyses of test accuracy, as for randomized trials. We investigate limitations of standard funnel plots and tests when applied to meta-analyses of test accuracy and look for improved methods. METHODS: Type I and type II error rates for existing and alternative tests of sample size effects were estimated and compared in simulated meta-analyses of test accuracy. RESULTS: Type I error rates for the Begg, Egger, and Macaskill tests are inflated for typical diagnostic odds ratios (DOR), when disease prevalence differs from 50% and when thresholds favor sensitivity over specificity or vice versa. Regression and correlation tests based on functions of effective sample size are valid, if occasionally conservative, tests for sample size effects. Empirical evidence suggests that they have adequate power to be useful tests. When DORs are heterogeneous, however, all tests of funnel plot asymmetry have low power. CONCLUSION: Existing tests that use standard errors of odds ratios are likely to be seriously misleading if applied to meta-analyses of test accuracy. The effective sample size funnel plot and associated regression test of asymmetry should be used to detect publication bias and other sample size related effects.
Diagnostic Errors↗
Calculation of sample size in survival trials: the impact of informative noncompliance.
Sample size calculations for survival trials typically include an adjustment to account for the expected rate of noncompliance, or discontinuation from study medication. Existing sample size methods assume that when patients discontinue, they do so independently of their risk of an endpoint; that is, that noncompliance is noninformative. However, this assumption is not always true, as we illustrate using results from a published clinical trial database. In this article, we introduce a modified version of the method proposed by Lakatos (1988, Biometrics 44, 229-241) that can be used to calculate sample size under informative noncompliance. This method is based on the concept of two subpopulations: one with high rates of endpoint and discontinuation and another with low rates. Using this new method, we show that failure to consider the impact of informative noncompliance can lead to a considerably underpowered study.
Biometry↗
Sample size estimation for clinicians.
The purpose of this paper is to describe the importance of an adequate sample size in clinical research and to enable clinicians to estimate their own sample size requirements for the more common types of studies (comparison of two means or two proportions). Four pieces of information are required to determine sample size: the desired level of statistical power, the level of statistical significance, the variability of the data, and the smallest difference between the study groups that is considered to be of clinical significance. Worked examples from the literature are used to illustrate how clinicians may easily do their own sample size calculations using published tables or available computer software, or both. The consideration of sample size and power during the planning stages of clinical research is crucial to the subsequent interpretation of study results, especially if the study is negative, and yet this point is often neglected in the medical literature. Attention to these simple guidelines will help ensure that research results lead to valid conclusions.
Biometry↗
Calculation of power and sample size with bounded outcome scores.
The two-sample Wilcoxon rank sum test is the most popular non-parametric test for the comparison of two samples when the underlying distributions are not normal. Although the underlying distributions need not be known in detail to calculate the null distribution of the test statistic, parametric assumptions are often made to determine the power of the test or the sample size. We encountered difficulties with this approach in the planning of a recent clinical trial in stroke patients. It is shown that, for power and sample size estimation, it can be dangerous to apply the classical formulae routinely, especially with outcome scores having a U-shaped or a J-shaped distribution. As an example we have taken the Barthel index, a quality-of-life outcome measure in stroke patients. Further, we have investigated alternative methods by means of Monte Carlo simulation. The distributional characteristics of the estimated powers were compared. Our findings suggest more appropriate computer software is necessary for the calculation of power and sample size when efficacy is measured by a non-parametric method.
Cerebrovascular Disorders↗