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PubMed1995

Sample size calculation for complex clinical trials with survival endpoints.

Sample size estimation is important in planning clinical trials. The purpose of this paper is to describe features and use of SIZE, a comprehensive computer program for calculating sample size, power, and duration of study in clinical trials with time-dependent rates of event, crossover, and loss to follow-up. SIZE covers a wide range of complexities commonly occurring in clinical trials, such as nonproportional hazards, lag in treatment effect, and uncertainties in treatment benefit. The use of SIZE is illustrated by several hypothetical examples as well as applications to real study designs, each featuring a statistical issue.

Algorithms↗
PubMed1998

Sample size justification in phase III/IV clinical trials.

A sample size justification is often a key component in securing funding to conduct a clinical trial. Sample size computations typically require supplying values of parameters that are unknown and subject to misspecification. As an alternative, we advocate a power analysis approach based on a range of reasonable values for such parameters. We illustrate this approach in the context of a secondary stroke prevention trial.

Clinical Trials, Phase III as Topic↗
PubMed1996

Formulae and tables for the determination of sample sizes and power in clinical trials for testing differences in proportions for the two-sample design: a review.

This paper is a compendium of exact and asymptotic formulae and tables for estimating the sample size in a clinical trial with two treatment groups and a dichotomous outcome. The paper provides separate formulae for equal and unequal treatment group sizes, formulae for the calculation of power given the sample size, and complete references for all formulae and tables cited.

Binomial Distribution↗
PubMed2002

A decision theoretic approach to sample size determination in clinical trials.

In this paper, we discuss a Behavioral Bayes approach to the determination of sample size in phase III clinical trials for which the data are assumed to come from a normal distribution for which the mean and variance are both unknown. Software is described which minimizes the expected net cost as a function of the sample size, thereby establishing the optimal sample size. This methodology extends previous work by the assumption of unknown variance. Numerical examples show that the more general model can have a large effect on the optimal sample size, compared with a procedure which uses the known variance model with an estimate of the variance.

Algorithms↗
PubMed2003

A new method for choosing sample size for confidence interval-based inferences.

Scientists often need to test hypotheses and construct corresponding confidence intervals. In designing a study to test a particular null hypothesis, traditional methods lead to a sample size large enough to provide sufficient statistical power. In contrast, traditional methods based on constructing a confidence interval lead to a sample size likely to control the width of the interval. With either approach, a sample size so large as to waste resources or introduce ethical concerns is undesirable. This work was motivated by the concern that existing sample size methods often make it difficult for scientists to achieve their actual goals. We focus on situations which involve a fixed, unknown scalar parameter representing the true state of nature. The width of the confidence interval is defined as the difference between the (random) upper and lower bounds. An event width is said to occur if the observed confidence interval width is less than a fixed constant chosen a priori. An event validity is said to occur if the parameter of interest is contained between the observed upper and lower confidence interval bounds. An event rejection is said to occur if the confidence interval excludes the null value of the parameter. In our opinion, scientists often implicitly seek to have all three occur: width, validity, and rejection. New results illustrate that neglecting rejection or width (and less so validity) often provides a sample size with a low probability of the simultaneous occurrence of all three events. We recommend considering all three events simultaneously when choosing a criterion for determining a sample size. We provide new theoretical results for any scalar (mean) parameter in a general linear model with Gaussian errors and fixed predictors. Convenient computational forms are included, as well as numerical examples to illustrate our methods.

Biometry↗
PubMed1997

Sample size requirements in case-only designs to detect gene-environment interaction.

With advances in molecular genetic technology, more studies will examine gene-environment interaction in disease etiology. If the primary purpose of the study is to estimate the effect of gene-environment interaction in disease etiology, one can do so without employing controls. The case-only design has been promoted as an efficient and valid method for screening for gene-environment interaction. The authors derive a method for estimating sample size requirements, present sample size estimates, and compare minimum sample size requirements to detect gene-environment interaction in case-only studies with case-control studies. Assuming independence between exposure and genotype in the population, the authors believe that the case-only design is more efficient than a case-control design in detecting gene-environment interaction. They also illustrate a method to estimate sample size when information on marginal effects (relative risk) of exposure and genotype is available from previous studies.

Case-Control Studies↗
PubMed1994

Estimation and sample size considerations for clustered binary responses.

Although there is much literature on sample size determination for clinical trials or experiments with independent responses, there is a lack of methodology to obtain sample sizes for dependent outcomes. This paper presents a simple way to calculate sample size for estimating treatment effects and diagnostic accuracy in the case of correlated binary outcomes. The proposed weighted procedure also has use in estimation, whose advantages we demonstrate through simulation. Recommendations are made for practical application.

Clinical Trials as Topic↗
PubMed2002

Comparison of ratio-synthetic, sample-size dependent and EBLUP estimators as estimators of food-animal productivity parameters.

A comparison was made of three small-area sampling methods [two traditional design-based methods (ratio-synthetic, sample-size dependent) and one model-based method (EBLUP)] in estimation of some cow and sow population productivity parameters. Performance was evaluated in estimating both farm-specific mean responses and mean animal response over all farms using sample sizes of 100 and 25. Differences in results obtained with the cow and sow data are discussed in terms of the impact of sample size and population size on sampling method. There was a tendency for the model-based method to be the best performer in situations most likely to be operational when the sampling is done as part of a food-animal monitoring scheme. The situations are identified where the sample-size-dependent method performed best.

Animals↗
PubMed2006

Sample size estimation for the van Elteren test--a stratified Wilcoxon-Mann-Whitney test.

The van Elteren test is a type of stratified Wilcoxon-Mann-Whitney test for comparing two treatments accounting for strata. In this paper, we study sample size estimation methods for the asymptotic version of the van Elteren test, assuming that the stratum fractions (ratios of each stratum size to the total sample size) and the treatment fractions (ratios of each treatment size to the stratum size) are known in the study design. In particular, we develop three large-sample sample size estimation methods and present a real data example to illustrate the necessary information in the study design phase in order to apply the methods. Simulation studies are conducted to compare the performance of the methods and recommendations are made for method choice. Finally, sample size estimation for the van Elteren test when the stratum fractions are unknown is also discussed.

Clinical Trials as Topic↗
PubMed2000

Cluster randomised trials in maternal and child health: implications for power and sample size.

BACKGROUND: Interventions based in the community can be evaluated by randomising clusters, such as general practices, rather than individuals, as in conventional randomised trials. This increases the sample size needed because of intracluster correlation. AIMS: To estimate sample size requirements for cluster randomised trials of interventions based in general practice directed at common health problems affecting mothers and infants. METHODS: Data were collected from a pilot trial of the effect of Citizen's Advice Bureau services involving six general practices. Outcome measures included the Edinburgh postnatal depression score, the Warwick child health and morbidity profile, number of visits to the general practitioner, and two questionnaires delivered at the beginning and end of the study. Intracluster correlation coefficients and inflation factors (the ratio of the sample size required for a cluster randomised trial to that required for an individually randomised trial) were calculated. RESULTS: Intracluster correlation coefficients ranged from 0 (sleeping problems, accidental injury, hospitalisation) to 0.09 (maternal smoking), with most being < 0.04 (for example, maternal depression, breast feeding, general health, minor illness, behavioural problems, and visits to the general practitioner). Assuming 50 cases/practice, cluster randomised trials require sample sizes up to 3 times greater than individually randomised trials for most health outcomes measured. CONCLUSIONS: These data enable sample sizes to be estimated for cluster randomised trials into a range of maternal and child health outcomes. Using such a design, approximately 40 practices would be sufficient to evaluate the effect of an intervention on maternal depression, sleeping, and behavioural problems, and non-routine visits to the general practitioner.

Analysis of Variance↗
PubMed1984

Sample-size calculations in segregation analysis.

Segregation analysis, employing nuclear families, is the most frequently used method to evaluate the mode of inheritance of a trait. To our knowledge, there exists no tabular information regarding the sample sizes required of individuals and families needed to perform a significance test of a specific segregation ratio for a predetermined power and significance level. To fill this gap, we have developed sample-size tables based on the asymptotic variance of the maximum likelihood estimate of the segregation ratio and on the normal approximation for two-sided hypothesis testing. Assuming homogeneous sibship size, minimum sample sizes were determined for testing the null hypothesis for the segregation ratio of 1/4 or 1/2 vs. alternative values of .05-.80, for the significance level of .05 and power of .8, for ascertainment probabilities of nearly 0 to 1.0, and sibship sizes 2-7. The results of these calculations indicate a complex interaction of the null and the alternate hypotheses, ascertainment probability, and sibship size in determining the sample size required for simple segregation analysis. The accompanying tables should aid in the appropriate design and cost assessment of future genetic epidemiologic studies.

Epidemiologic Methods↗
PubMed2006

Sample-size requirements for developing strategies, based on the pupal/demographic survey, for the targeted control of dengue.

Several methods to determine the sample size required for a reliable and practical assessment of the number of Aedes aegypti pupae in a community in Puerto Rico have been explored. Because the pupae were highly aggregated, the data were fitted to a negative binomial distribution. Classical statistical-inference methods for sample-size determination demanded the sampling of >3,000 premises for a reliable estimation of the mean number of pupae/person (with a 15% error). This number was reduced to 1,000-1,200 premises after applying a finite-population correction. Database sub-sampling simulations, with increasing sample sizes, showed that the variability in the mean relative abundance of container types and in the mean number of pupae/container substantially decreased after sampling 186 and 310 premises, respectively. Sequential sampling was applied to test the hypotheses that the number of female pupae/person was at least 0.19 (considered the dengue epidemic threshold) or no greater than 0.10 (arbitrarily set as the safe level). After sampling only 25 premises in the first survey and 125 in the second, it was determined that the densities of female pupae were above the epidemic threshold. Thus, sequential sampling provided substantial reductions in the sample size required to determine if vector control was needed. Validation of the Ae. aegypti thresholds required for dengue transmission could confer viability and efficiency to dengue-vector surveillance and control programmes.

Aedes↗
PubMed2004

[Sample size requirements for association studies on gene-gene interaction in case-control study].

OBJECTIVE: Sample size requirements for association studies on gene-gene interaction in case-control study. METHODS: Selecting different parameters (such as inheritance mode, susceptibility frequency, frequency of allele for disease, OR of gene main effect) and infilling them into QUANTO software based on conditional logistic regression mode. RESULTS: (1) The main parameters influencing the sample size requirements were the levels of interaction between genes and the susceptibility frequency. The numbers of sample were the same between recessive and dominant when susceptibility frequency were the same. (2) Sample size for testing of gene-gene interaction was different from that for testing of genetic effects. CONCLUSION: It was convenient to use the numbers of sample size from the results for gene-gene interaction in case-control study.

Case-Control Studies↗
PubMed2004

The use of item parcels in structural equation modelling: non-normal data and small sample sizes.

Maximum likelihood estimation in confirmatory factor analysis requires large sample sizes, normally distributed item responses, and reliable indicators of each latent construct, but these ideals are rarely met. We examine alternative strategies for dealing with non-normal data, particularly when the sample size is small. In two simulation studies, we systematically varied: the degree of non-normality; the sample size from 50 to 1000; the way of indicator formation, comparing items versus parcels; the parcelling strategy, evaluating uniformly positively skews and kurtosis parcels versus those with counterbalancing skews and kurtosis; and the estimation procedure, contrasting maximum likelihood and asymptotically distribution-free methods. We evaluated the convergence behaviour of solutions, as well as the systematic bias and variability of parameter estimates, and goodness of fit.

Factor Analysis, Statistical↗
PubMed1992

Sample size requirements for stratified cluster randomization designs.

Sample size requirements are provided for designs of studies in which clusters are randomized within each of several strata, where cluster size itself may be a stratifying factor. The approach generalizes a formula derived by Woolson et al., which provides sample size requirements for the Cochran-Mantel-Haenszel statistic. Issues of data analysis are also discussed.

Cluster Analysis↗
PubMed1996

Sample size for the exact conditional test under inverse sampling.

Inverse sampling is a sampling design in which one continues sampling subjects until one obtains a predetermined number of index subjects. This paper derives a procedure for calculation of the minimum required number of index subjects on the basis of the exact conditional test under inverse sampling. This paper studies quantitatively the effect on power calculations of the number of index subjects. To facilitate use of inverse sampling in study designs, this paper further provides a table that summarizes, in a variety of situations, the minimum required number of index subjects for powers equal to 0.90 and 0.80 at 0.05-level. It also includes a discussion on use of the approximation sample size formula derived on the basis of a variance-stabilizing transformation and large sample theory.

Analysis of Variance↗
PubMed1998

Sample size determination in complex clinical trials comparing more than two groups for survival endpoints.

This paper presents a sample size formula for testing the equality of kappa (> or = 2) survival distributions using the Tarone-Ware class of test statistics in the presence of non-proportional hazards, time dependent losses, non-compliance and drop-in. This method extends the derivation by Lakatos of a sample size formula for comparing two survival distributions. A sample size formula is also presented for the stratified logrank test. We describe how one can utilize these generalized formulae in calculating sample sizes and assessing power in complex multi-arm clinical trials.

Humans↗
PubMed1993

An alternative method for sample size determination in substance misuse prevention research.

There is considerable evidence that social science researchers often fail to adequately address statistical power and related sample size issues. This tendency has been particularly salient in substance misuse prevention research. Although failure to carefully address statistical power and sample size issues most frequently results in studies lacking statistical power, other problems can also occur. For example, sample size determination in controlled studies which focus on rates of substance initiation and similar measures require close consideration of baseline rates indicated by these measures. Otherwise, when conventional procedures for sample size determination are utilized, samples in these studies can exceed the size necessary for predesignated levels of power. This article presents a simple alternative to conventional procedures for sample size determination which can be applied to controlled study of substance initiation and similar outcomes.

Alcoholism↗