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Results for “Sample size estimation”

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PubMed1999

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↗
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↗
PubMed1987

Estimating sample size for longitudinal studies of age-related cognitive decline.

Cross-sectional age-group norms for cognitive tests reveal a progressive age-related decline. There is current interest in the possibility of developing interventions that forestall the normal cognitive decline in elderly adults. This raises new research design issues, one of which is the estimation of appropriate sample sizes for longitudinal studies when only cross-sectional data are presently available. Formulae for estimating change parameters from cross-sectional data are presented in this article. Based on age-related changes in cross-sectional norms for the Wechsler Adult Intelligence Scale (WAIS), the sample sizes required for controlled intervention studies of 1 to 3 years duration are very large. If cognitive decline within individuals is not as great as the decline evident in cross-sectional norms, the required sample sizes will be even larger.

Aged↗
PubMed1997

Planning significant and meaningful research in exercise science: estimating sample size.

Exercise science researchers are familiar with the use of parametric tests to detect significant differences among treatment groups. However, in planning research a question asked with increasing frequency is, "How many participants are needed to detect real and meaningful differences among groups?" In this paper, we provide an overview of the use of alpha, power, and effect size in planning sample sizes that allow tests of real and meaningful differences among groups. Because effect size is the parameter most often missing, we have located meta-analyses in sport and exercise psychology (n = 26), and motor behavior (n = 6). We provide examples and a discussion of how researchers can use these effect sizes along with common estimates of alpha and power to plan for the sample size needed to detect real and meaningful group differences.

Exercise↗
PubMed1994

Dispersion statistics and sample size estimates for house fly (Diptera: Muscidae) larvae and Macrocheles muscaedomesticae (Acari: Macrochelidae) in poultry manure.

The precision of the density estimate for a given population depends upon the population's density and degree of clumping and upon sampling characteristics, such as the number and surface area of the quadrats. The parameters k of the negative binomial distribution and b of Taylor's power law (s2 = a mean b) were determined for third instars of the muscaedomesticae (Scopoli), which were sampled by random quadrats at two shallow-pit, caged-layer poultry houses. Most calculated values of k were < 1, but agreement with the negative binomial distribution was found only in 4, 18, and 14 of the 27 weekly samples for house fly larvae, female mites, and male mites, respectively. Taylor's power law provided the best fit for the distribution (P < 0.01 for all regression coefficients) with s2 = 9.08 mean 1.83 (r2 = 0.97) for third-instar house flies and s2 = 4.65 mean 1.76 (r2 = 0.97) for both sexes of M. muscaedomesticae. Two ecological processes, social clustering and environmental heterogeneity, were hypothesized as the mechanisms determining aggregation of house fly larvae and the macrochelid mites. Regardless of the biological model influencing their distributions, Taylor's power law provided a reliable index for measuring their degree of aggregation. Reliability defined by the coefficient of variability and Taylor's regression coefficients was used to calculate optimum sample size-density estimates for each species with coefficients of variability of 10, 15, 20, and 25%.

Animals↗
PubMed1994

Sample size estimation using repeated measurements on biomarkers as outcomes.

The objectives of this paper are to (1) examine methods of using longitudinal data in designing comparative trials and calculating sample sizes or power and (2) show the effect of autocorrelation of repeated measures on the assessment of sample sizes. A statistical model with a simple regression structure for the mean trajectory of the longitudinal data and a two-parameter model for the correlations of within-individual observations given by corr(yt,yt+s) = gamma s theta is used. The methods are illustrated by considering a two-group trial and investigating the effect of different values of the correlation parameters, gamma and theta on the sample size. The results show that taking account of the autocorrelation structure of longitudinal data may lead to more efficient designs. Specifically, the stronger the autocorrelation is, the smaller the sample size that is required.

AIDS Vaccines↗
PubMedDate not supplied

Internal pilot studies for estimating sample size.

We develop the idea of using data from the first 'few' patients entered in a clinical trial to estimate the final trial size needed to have specified power for rejecting H0 in favour of H1 if a real difference exists. When comparing means derived from Normally distributed data, there is no important effect on test size, power or expected trial size, provided that a minimum of about 20 degrees of freedom are used to estimate residual variance. Relative advantages and disadvantages of using larger internal pilot studies are presented. These revolve around crude expectations of the final study size, recruitment rate, duration of follow-up and practical constraints on the ability to prevent the circulation of unblinded randomization codes to investigators and those involved in editing and checking data.

Clinical Trials as Topic↗
PubMed1992

A goodness-of-fit approach to inference procedures for the kappa statistic: confidence interval construction, significance-testing and sample size estimation.

We propose a new procedure for constructing a confidence interval about the kappa statistic in the case of two raters and a dichotomous outcome. The procedure is based on a chi-square goodness-of-fit test as applied to a model frequently used for clustered binary data. The procedure provides coverage levels that are accurate in samples of smaller size than those required for other procedures. The procedure also has use for significance-testing and the planning of corresponding sample size requirements.

Confidence Intervals↗
PubMed1997

Estimating sample size for continuous outcomes, comparing more than two parallel groups with unequal sizes.

This paper contains a short generalization of a known method for sample size determination in the case of more than two parallel groups. The term 'set of allocation ratios' corresponding to the allocation ratio from the two-group design is defined. A formula using these ratios to determine the non-centrality parameter of the F distribution is deduced. It is shown that in case of more than two groups, equal group numbers does not constitute an optimal design. Two worked examples are presented.

Analysis of Variance↗
PubMed1998

Comparative evaluation of two models for estimating sample sizes for tests on trends across repeated measurements.

Two equations for calculating sample sizes that are required for power in testing differences in rates of change in repeated measurement designs have been presented by different authors. One equation provides support for the conclusion that increased frequency of measurements across a treatment period of fixed duration enhances power of the tests. The other equation supports the counterintuitive conclusion that increased frequency of measurements actually tends to decrease power in the presence of realistic serial dependencies in the data. Monte Carlo methods confirm that the equation providing support for the latter conclusion is accurate, whereas the alternative equation tends to underestimate sample sizes required for power in testing differences in slopes of regression lines fitted to changes in the repeated measurements across time when symmetry is absent from the covariance structure.

Clinical Trials as Topic↗