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PubMed1994

Malaria vaccine study site in Irian Jaya, Indonesia: Plasmodium falciparum incidence measurements and epidemiologic considerations in sample size estimation.

Malaria epidemiologic and entomologic studies were performed during both the high transmission and low transmission seasons to characterize the Plasmodium falciparum malaria transmission at a proposed malaria vaccine trial site in Irian Jaya, Indonesia. The study population consisted of two subsets: native Irianese men with lifelong exposure to malaria and transmigrants who arrived from a nonmalarious area 2.5 years before the start of the study. All subjects received a radical cure for malaria and were then monitored weekly by blood film. Both P. falciparum malaria attack rates and incidence densities were calculated; transmigrants had a significantly higher rate (P = 0.003) than the Irianese during the low transmission season study (20-weeks long) but not during the high transmission season study (12-weeks long). Lack of exposure-induced immunity left the transmigrants at a minimum 17-25% greater relative risk of becoming parasitemic compared with the Irianese during the low transmission season study. During the high transmission season study, 50% of the transmigrants were P. falciparum positive by week 6 and 50% of the Irianese by week 9. During the low transmission season, 50% of the transmigrants were positive by week 10 and 43% of the Irianese were positive by week 17. Entomologic studies showed that Anopheles koliensis was the predominant vector (> 98% of anopheline catch). Entomologic inoculation rates for P. falciparum were 0.018 and 0.39 infective bites/person/night for the low and high transmission seasons, respectively. New P. vivax cases represented between 16% and 42% of all initial malaria cases.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed1994

Deviance estimates of sample size for equivalence tests in vaccine trials.

This paper proposes a sample size procedure for both equivalence and conventional tests for the comparison of two binomial proportions, based on the signed square root of the deviance. When the comparison is based on the odds ratio, I describe an alternate 'close' conditional exact method that gives results that support those given by the deviance method. I summarize the advantages of the deviance-based method and also show that in general equivalence situations the sample size estimate depends upon the measure of comparison selected, odds ratio, risk ratio or risk difference.

Binomial Distribution↗
PubMed2005

MSurvPow: a FORTRAN program to calculate the sample size and power for cluster-randomized clinical trials with survival outcomes.

Manatunga and Chen [A.K. Manatunga, S. Chen, Sample size estimation for survival outcomes in cluster-randomized studies with small cluster sizes, Biometrics 56 (2000) 616-621] proposed a method to estimate sample size and power for cluster-randomized studies where the primary outcome variable was survival time. The sample size formula was constructed by considering a bivariate marginal distribution (Clayton-Oakes model) with univariate exponential marginal distributions. In this paper, a user-friendly FORTRAN 90 program was provided to implement this method and a simple example was used to illustrate the features of the program.

Cluster Analysis↗
PubMed2004

Sample size and power estimation for studies with health related quality of life outcomes: a comparison of four methods using the SF-36.

We describe and compare four different methods for estimating sample size and power, when the primary outcome of the study is a Health Related Quality of Life (HRQoL) measure. These methods are: 1. assuming a Normal distribution and comparing two means; 2. using a non-parametric method; 3. Whitehead's method based on the proportional odds model; 4. the bootstrap. We illustrate the various methods, using data from the SF-36. For simplicity this paper deals with studies designed to compare the effectiveness (or superiority) of a new treatment compared to a standard treatment at a single point in time. The results show that if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at the boundaries is high (scoring 0 or 100), then we would recommend using Whitehead's method (Method 3). Alternatively, if the HRQoL outcome has a large number of distinct values and the proportion at the boundaries is low, then we would recommend using Method 1. If a pilot or historical dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation (Method 4) based on this data will provide a more accurate and reliable sample size estimate than conventional methods (Methods 1, 2, or 3). In the absence of a reliable pilot set, bootstrapping is not appropriate and conventional methods of sample size estimation or simulation will need to be used. Fortunately, with the increasing use of HRQoL outcomes in research, historical datasets are becoming more readily available. Strictly speaking, our results and conclusions only apply to the SF-36 outcome measure. Further empirical work is required to see whether these results hold true for other HRQoL outcomes. However, the SF-36 has many features in common with other HRQoL outcomes: multi-dimensional, ordinal or discrete response categories with upper and lower bounds, and skewed distributions, so therefore, we believe these results and conclusions using the SF-36 will be appropriate for other HRQoL measures.

Algorithms↗
PubMed2003

Inter-test reliability for non-invasive measures of respiratory muscle function in healthy humans.

The aims of this study were to quantify the inter-test reliability of several voluntary, non-invasive measures of respiratory muscle function and to determine the implications of these data for studies using a repeated-measures design. Systematic measurement differences were found for 50% of the variables ( P</=0.05, t-tests). Nevertheless, 95% ratio limits of agreement for most measures proved acceptable and similar to those reported elsewhere. The random error component of the agreement ratios ranged from 1.047 to 1.149 for measures of pulmonary function in healthy subjects ( n=46), 1.045 to 1.056 for maximum static respiratory pressures ( n=24), 1.062 to 1.173 for measures relating to the maximum pressure-flow-power relationship ( n=16-22), and 1.036 to 1.071 for measures relating to maximum incremental inspiratory muscle performance ( n=12). The judgement that the limits of agreement were acceptable is supported by the sample-size calculations. Estimated sample sizes based upon an alpha level of 0.05 and a statistical power of 0.9 were mostly </=11 for a repeated-measures experimental design, particularly for the larger effect sizes (>/=5%). However, peak expiratory flow, the maximum rate of pressure development and the time constant of relaxation, require larger sample sizes to detect small within-group changes. In conclusion, the described protocols provide reliable measurements for most parameters of respiratory muscle function in healthy subjects. Furthermore, experiments utilising a within-subjects design lasting up to 3 weeks can be conducted with feasible sample sizes (</=11 per group) where substantial (>/=5%) changes are expected.

Adult↗
PubMed2001

Use of sample size for estimating efficacy of a vaccine against an infectious disease.

OBJECTIVE: To determine the sample size necessary to evaluate the efficacy of a vaccine in a population. PROCEDURE: An equation was coded into a computer spreadsheet to compare the traditional sample size calculation with that needed when evaluating the efficacy of a vaccine applied in a population. RESULTS: The traditional approach used to conservatively estimate sample size necessary to detect a given difference in group proportions potentially greatly underestimates the number of animals needed for vaccine efficacy (VE) trials. In VE trials, it is necessary to estimate the effect of population-level vaccination prior to estimating sample size. In VE trials, as incidence proportion in the population or herd decreases or VE decreases, necessary sample size increases. CONCLUSIONS AND CLINICAL RELEVANCE: In designing a clinical or field trial, such as one to evaluate the efficacy of a vaccine against an infectious disease in a population, one needs to approach sample size calculations in a nontraditional manner. The proportion of the population vaccinated, disease transmission dynamics, and VE will affect the incidence in the nonvaccinated and vaccinated groups and, hence, sample size. Thus, estimation of the effect of the vaccination on the population must be made prior to calculating sample size. Otherwise, sample size and the power to identify VE will be insufficient.

Animal Diseases↗
PubMed2003

A model to estimate the optimal sample size for microbiological surveys.

Estimating optimal sample size for microbiological surveys is a challenge for laboratory managers. When insufficient sampling is conducted, biased inferences are likely; however, when excessive sampling is conducted valuable laboratory resources are wasted. This report presents a statistical model for the estimation of the sample size appropriate for the accurate identification of the bacterial subtypes of interest in a specimen. This applied model for microbiology laboratory use is based on a Bayesian mode of inference, which combines two inputs: (ii) a prespecified estimate, or prior distribution statement, based on available scientific knowledge and (ii) observed data. The specific inputs for the model are a prior distribution statement of the number of strains per specimen provided by an informed microbiologist and data from a microbiological survey indicating the number of strains per specimen. The model output is an updated probability distribution of strains per specimen, which can be used to estimate the probability of observing all strains present according to the number of colonies that are sampled. In this report two scenarios that illustrate the use of the model to estimate bacterial colony sample size requirements are presented. In the first scenario, bacterial colony sample size is estimated to correctly identify Campylobacter amplified restriction fragment length polymorphism types on broiler carcasses. The second scenario estimates bacterial colony sample size to correctly identify Salmonella enterica serotype Enteritidis phage types in fecal drag swabs from egg-laying poultry flocks. An advantage of the model is that as updated inputs from ongoing surveys are incorporated into the model, increasingly precise sample size estimates are likely to be made.

Animals↗
PubMed1988

Sample sizes based on the log-rank statistic in complex clinical trials.

The log-rank test is frequently used to compare survival curves. While sample size estimation for comparison of binomial proportions has been adapted to typical clinical trial conditions such as noncompliance, lag time, and staggered entry, the estimation of sample size when the log-rank statistic is to be used has not been generalized to these types of clinical trial conditions. This paper presents a method of estimating sample sizes for the comparison of survival curves by the log-rank statistic in the presence of unrestricted rates of noncompliance, lag time, and so forth. The method applies to stratified trials in which the above conditions may vary across the different strata, and does not assume proportional hazards. Power and duration, as well as sample sizes, can be estimated. The method also produces estimates for binomial proportions and the Tarone-Ware class of statistics.

Clinical Trials as Topic↗
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↗
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↗
PubMed2006

Independence estimating equations for controlled clinical trials with small sample sizes--interval estimation.

OBJECTIVES: The application of independence estimating equations (IEE) for controlled clinical trials (CCTs) has recently been discussed, and recommendations for its use have been derived for testing hypotheses. The robust estimator of variance has been shown to be liberal for small sample sizes. Therefore a series of modifications has been proposed. In this paper we systematically compare confidence intervals (CIs) proposed in the literature for situations that are common in CCTs. METHODS: Using Monte-Carlo simulation studies, we compared the coverage probabilities of CIs and non-convergence probabilities for the parameters of the mean structure for small samples using modifications of the variance estimator proposed by Mancl and de Rouen [7], Morel et al. [8] and Pan [3]. RESULTS: None of the proposed modifications behave well in each investigated situation. For parallel group designs with repeated measurements and binary response the method proposed by Pan maintains the nominal level. We observed non-convergence of the IEE algorithm in up to 10% of the replicates depending on response probabilities in the treatment groups. For comparing slopes with continuous responses, the approach of Morel et al. can be recommended. CONCLUSIONS: Results of non-convergence probabilities show that IEE should not be used in parallel group designs with binary endpoints and response probabilities close to 0 or 1. Modifications of the robust variance estimator should be used for sample sizes up to 100 clusters for CI estimation.

Algorithms↗
PubMed2004

The effect of sample size for estimating Rasch/IRT parameters with dichotomous items.

Thirteen samples were randomly drawn from the normative database for the latest edition of Knox's Cube Test-Revised (KCT-R). Parameter estimates for the Rasch model and two and three parameter logistic models were derived and compared. Sample size influenced these estimates as might be expected. Rasch parameter estimates consistently showed the smallest values by sample size using a goodness of fit index.

Data Interpretation, Statistical↗
PubMed2000

Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects.

OBJECTIVE: To evaluate the rate of brain atrophy calculated from serial magnetic resonance imaging (MRI) registration as a surrogate marker of disease progression for use in clinical trials in Alzheimer disease (AD). METHODS: Eighteen patients with mild to moderate AD and 18 age-matched normal controls underwent 2 MRI brain scans separated by a 12-month interval. Each individual's later scan was registered to their first scan, and the volume of cerebral tissue loss calculated directly from the registered and subtracted MRI scan pairs. The mean and SD of the rate of brain volume changes were used to estimate the sample sizes that would be needed in a clinical trial with a drug anticipated to modify disease progression by varying degrees. Comparable sample size estimates were performed with data for other methods of monitoring rates of brain atrophy, extracted from published papers. RESULTS: The mean (SD) rate of brain atrophy for the patients with AD was 2.37% (1.11%) per year, while in the control group it was 0.41% (0.47%) per year. Based on these figures, to have 90% power to detect a drug effect equivalent to a 20% reduction in the rate of atrophy, 207 patients would be needed in each treatment arm. This assumes a 1-year placebo-controlled trial with a 10% patient dropout rate, and that 10% of scan pairs are unusable. CONCLUSION: Registration of serial MRI volume images provides a powerful method of quantification of brain atrophy that can be used to monitor progression of AD in clinical trials.

Aged↗
PubMed2005

Variance estimation in clinical studies with interim sample size re-estimation.

We consider clinical studies with a sample size re-estimation based on the unblinded variance estimation at some interim point of the study. Because the sample size is determined in such a flexible way, the usual variance estimator at the end of the trial is biased. We derive sharp bounds for this bias. These bounds have a quite simple form and can help for the decision if this bias is negligible for the actual study or if a correction should be done. An exact formula for the bias is also provided. We discuss possibilities to get rid of this bias or at least to reduce the bias substantially. For this purpose, we propose a certain additive correction of the bias. We see in an example that the significance level of the test can be controlled when this additive correction is used.

Analysis of Variance↗
PubMed1987

Sample sizes for estimation of exposure-specific disease rates in population-based case-control studies.

This paper discusses sample sizes for estimation of exposure-specific disease rates for population-based case-control studies. Neutra and Drolette's confidence limits, which are based on the approximate normality of the logarithm of the ratio of independent binomial exposure rates, are used to determine the sample sizes required for precise estimation of exposure-specific disease rates. It is shown that, for large sample sizes, the disease rate in the exposed population is more precisely estimated than the disease rate in the unexposed population when more than 50% of the cases are exposed, and that the converse is true when fewer than 50% of the cases are exposed. Expressions are derived for the optimal case and control sample sizes that ensure the required level of precision and minimize the total study size. The optimum control-to-case ratio is found to be equal to the square root of the exposure odds ratio. The optimum number of cases and the total study size are found to be smaller for precise estimation of the disease rate in the exposed population than for precise estimation of the exposure odds ratio when the disease is rare.

Humans↗
PubMed2001

A simple computerized program for the calculation of the required sample size necessary to ensure statistical accuracy in medical experiments.

We developed a sample size estimation program (SSEP) with which medical researchers can easily estimate the appropriate sample size for a specific significance level and statistical power using their favorite WWW browsers. SSEP can estimate the sample sizes for six statistical methods by Monte-Carlo simulation: Student's t-test, Welch's t-test, Analysis of variance, Wilcoxon's rank sum test, Kruskal-Wallis test, and the Cochran-Armitage test for linear trends. The SSEP simulation programs were created using the SAS software macro language. Medical researchers can interactively use this program and determine reliable sample sizes when planning new prospective clinical studies and animal experiments.

Computer Simulation↗
PubMed2006

Operating characteristics of sample size re-estimation with futility stopping based on conditional power.

Various methods have been described for re-estimating the final sample size in a clinical trial based on an interim assessment of the treatment effect. Many re-weight the observations after re-sizing so as to control the pursuant inflation in the type I error probability alpha. Lan and Trost (Estimation of parameters and sample size re-estimation. Proceedings of the American Statistical Association Biopharmaceutical Section 1997; 48-51) proposed a simple procedure based on conditional power calculated under the current trend in the data (CPT). The study is terminated for futility if CPT < or = CL, continued unchanged if CPT > or = CU, or re-sized by a factor m to yield CPT = CU if CL < CPT < CU, where CL and CU are pre-specified probability levels. The overall level alpha can be preserved since the reduction due to stopping for futility can balance the inflation due to sample size re-estimation, thus permitting any form of final analysis with no re-weighting. Herein the statistical properties of this approach are described including an evaluation of the probabilities of stopping for futility or re-sizing, the distribution of the re-sizing factor m, and the unconditional type I and II error probabilities alpha and beta. Since futility stopping does not allow a type I error but commits a type II error, then as the probability of stopping for futility increases, alpha decreases and beta increases. An iterative procedure is described for choice of the critical test value and the futility stopping boundary so as to ensure that specified alpha and beta are obtained. However, inflation in beta is controlled by reducing the probability of futility stopping, that in turn dramatically increases the possible re-sizing factor m. The procedure is also generalized to limit the maximum sample size inflation factor, such as at m max = 4. However, doing so then allows for a non-trivial fraction of studies to be re-sized at this level that still have low conditional power. These properties also apply to other methods for sample size re-estimation with a provision for stopping for futility. Sample size re-estimation procedures should be used with caution and the impact on the overall type II error probability should be assessed.

Clinical Trials as Topic↗
PubMed1983

Sample size for estimating the quantiles of endothelial cell-area distribution.

The estimation of corneal endothelium mean cell area (and, hence, mean cell density) is an important problem in clinical ophthalmology. Mitotic division of these cells is not known to occur, and cell deaths are followed by the enlargement of adjacent cells. As a consequence, cell-area distributions change drastically as functions of age and disease. Changes in cell-area distributions, in particular multimodality and skewness due to aging, are observed, and give rise to some difficult sampling problems. In this paper, sample quantiles are investigated as an alternative to the use of the sample mean. Asymptotic approximations are provided for the sample sizes required to estimate population quantiles with a desired precision. Asymptotic sample sizes are then compared with those obtained from tolerance limits. Empirical sample quantiles that can be used as benchmarks to compare corneas of normal individuals against corneas with unknown cell-area distributions are also presented. Aspects that merit further investigation are noted.

Adolescent↗