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Results for “Sample size estimation”

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At least 19 recordsLinked to original sources
PubMed1999

Sample size estimation for the sorcerer's apprentice. Guide for the uninitiated and intimidated.

OBJECTIVE: To review the importance of and practical application of sample size determination for clinical studies in the primary care setting. QUALITY OF EVIDENCE: A MEDLINE search was performed from January 1966 to January 1998 using the MeSH headings and text words "sample size," "sample estimation," and "study design." Article references, medical statistics texts, and university colleagues were also consulted for recommended resources. Citations that offered a clear and simple approach to sample size estimation were accepted, specifically those related to statistical analyses commonly applied in primary care research. MAIN MESSAGE: The chance of committing an alpha statistical error, or finding that there is a difference between two groups when there really is none, is usually set at 5%. The probability of finding no difference between two groups, when, in actuality, there is a difference, is commonly accepted at 20%, and is called the beta error. The power of a study, usually set at 80% (i.e., 1 minus beta), defines the probability that a true difference will be observed between two groups. Using these parameters, we provide examples for estimating the required sample size for comparing two means (t test), comparing event rates between two groups, calculating an odds ratio or a correlation coefficient, or performing a meta-analysis. Estimation of sample size needed before initiation of a study enables statistical power to be maximized and bias minimized, increasing the validity of the study. CONCLUSION: Sample size estimation can be done by any novice researcher who wishes to maximize the quality of his or her study.

Humans↗
PubMed1998

Estimating sample size for epidemiologic studies: the impact of ignoring exposure measurement uncertainty.

Sample size requirements for epidemiologic studies are usually determined on the basis of the desired level of statistical power. Suppose, however, that one is planning a study in which the participants' true exposure levels are unobservable. Instead, the analysis will be based on an imprecise surrogate measure that differs from true exposure by some non-negligible amount of measurement error. Sample size estimates for tests of association between the surrogate exposure measure and the outcome of interest may be misleading if they are based solely on the anticipated characteristics of the distribution of surrogate measures in the study population. We examine the accuracy of sample size estimates for cohort studies in which a continuous surrogate exposure measure is subject to either classical or Berkson measurement error. In particular, we evaluate the consequences of not adjusting the sample size estimation procedure for tests based on imprecise exposure measurements to account for anticipated differences between the distributions of the true exposure and the surrogate measure in the study population. As expected, failure to adjust for classical measurement error can lead to underestimation of the required sample size. Disregard of Berkson measurement error, however, can result in sample size estimates that exceed the actual number of participants required for tests of association between the outcome and the surrogate exposure measure. We illustrate this Berkson error effect by estimating sample size for a hypothetical cohort study that examines an association between childhood exposure to radioiodine and the development of thyroid neoplasms.

Adolescent↗
PubMed2003

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↗
PubMed1998

Statistical methods in epidemiology. II: A commonsense approach to sample size estimation.

PURPOSE: It has been argued, by many, that mathematical formulae for estimating sample size are unnecessarily complex, so much so that researchers may be reluctant to seek statistical advice. METHOD: This paper reviews methods of sample size estimation arguing that two formulae (one based on comparison of proportion of 'successes', the other based on comparison of means of normally distributed data) suffice for many situations. This paper argues the case by taking examples drawn mainly from clinical trials research. However, the methods outlined can also be used in epidemiology specifically in both case-control and cohort studies with no loss of information. RESULTS: For the situations outlined, worked examples are provided. CONCLUSIONS: Sample size estimation need not necessarily be a complex process. Simple techniques exist which enable the clinician and the statistician to work together. Continued dialogue between both parties is required so that good ideas do not go to waste.

Data Interpretation, Statistical↗
PubMed2004

How many do I need? Basic principles of sample size estimation.

BACKGROUND: In conducting randomized trials, formal estimations of sample size are required to ensure that the probability of missing an important difference is small, to reduce unnecessary cost and to reduce wastage. Nevertheless, this aspect of research design often causes confusion for the novice researcher. AIM: This paper attempts to demystify the process of sample size estimation by explaining some of the basic concepts and issues to consider in determining appropriate sample sizes. METHOD: Using a hypothetical two group, randomized trial as an example, we examine each of the basic issues that require consideration in estimating appropriate sample sizes. Issues discussed include: the ethics of randomized trials, the randomized trial, the null hypothesis, effect size, probability, significance level and type I error, and power and type II error. The paper concludes with examples of sample size estimations with varying effect size, power and alpha levels. CONCLUSION: Health care researchers should carefully consider each of the aspects inherent in sample size estimations. Such consideration is essential if care is to be based on sound evidence, which has been collected with due consideration of resource use, clinically important differences and the need to avoid, as far as possible, types I and II errors. If the techniques they employ are not appropriate, researchers run the risk of misinterpreting findings due to inappropriate, unrepresentative and biased samples.

Data Interpretation, Statistical↗
PubMed1994

Estimating sample sizes for repeated measurement designs.

Formulas for estimating sample sizes that are required to provide specified power for analysis of variance (ANOVA) tests of significance in a two-group repeated measurements design are presented and evaluated. Power and sample size requirements depend on the pattern of treatment effects and the pattern of correlations among the repeated measurements, as well as on parameters common to sample size estimation for cross-sectional comparisons of treatment effects in simple randomized designs. Simplifying assumptions permit generation of these numerous parameter estimates from predictions of the magnitude of the standardized "effect size" at end of trial and the single correlation between the baseline and endpoint measurements. Monte Carlo methods are used to verify the actual power of different tests of significance for treatment effects in repeated measurement designs using sample sizes estimated by the formulas. The sample size implications of different patterns of treatment effects, levels of correlation, and numbers of repeated measurements are evaluated.

Analysis of Variance↗
PubMed1997

Using aspects of study design in sample size estimation.

The basis of sample size calculations is usually needed in protocols for clinical trials and when publishing results in respected journals. Although a large amount of research has been undertaken on sample size estimation for different trial designs, in practice the methods are rarely used. This paper describes some useful theory that has practical relevance.

Clinical Trials as Topic↗
PubMed2004

Sample size estimation in research with dependent measures and dichotomous outcomes.

I reviewed sample estimation methods for research designs involving nonindependent data and a dichotomous response variable to examine the importance of proper sample size estimation and the need to align methods of sample size estimation with planned methods of statistical analysis. Examples and references to published literature are provided in this article. When the method of sample size estimation is not in concert with the method of planned analysis, poor estimates may result. The effects of multiple measures over time also need to be considered. Proper sample size estimation is often overlooked. Alignment of the sample size estimation method with the planned analysis method, especially in studies involving nonindependent data, will produce appropriate estimates.

Computer Simulation↗
PubMed2005

Effect of dropouts on sample size estimates for test on trends across repeated measurements.

Sample size calculation is an important component at the design stage of clinical trials. We investigate the implications of dropouts for the sample size estimates in testing differences in the rates of changes produced by two treatments in a randomized parallel-groups repeated measurement design. Statistical models for calculating sample sizes for repeated measurement designs often fail to take into account the impact of dropouts correctly. In this article, we examine the impact of dropouts on sample size estimate and compare the power with the approach of Jung and Ahn [Jung, S. H., Ahn, C. (2003). Sample size estimation for GEE method for comparing slopes in repeated measurements data. Stat. Med. 22: 1305-1315] with that suggested by Patel and Rowe [Patel, H., Rowe, E. (1999). Sample size for comparing linear growth curves. J. Biopharm. Stat. 9:339-350] through a simulation study.

Clinical Trials as Topic↗
PubMed2005

The use of bootstrap methods for estimating sample size and analysing health-related quality of life outcomes.

Health-related quality of life (HRQoL) measures are increasingly used in trials as primary outcome measures. Investigators are now asking statisticians for advice on how to plan and analyse studies using such outcomes. HRQoL outcomes, like the SF-36, are usual measured on an ordinal scale, although most investigators assume that there exists an underlying continuous latent variable and that the actual measured outcomes (the ordered categories) reflect contiguous intervals along this continuum. The ordinal scaling of HRQoL measures means they tend to generate data that have discrete, bounded and skewed distributions. Thus, standard methods of analysis that assume Normality and constant variance may not be appropriate. For this reason, conventional statistical advice would suggest non-parametric methods be used to analyse HRQoL data. The bootstrap is one such computer intensive non-parametric method for estimating sample sizes and analysing data. We describe three methods of estimating sample sizes for two-group cross-sectional comparisons of HRQoL outcomes. We then compared the power of the three methods for a two-group cross-sectional study design using bootstrap simulation. The results showed that under the location shift alternative hypothesis, conventional methods of sample size estimation performed well, particularly Whitehead's method. Whitehead's method is recommended if the HRQoL outcome has a limited number of discrete values (<7) and/or the expected proportion of cases at either of the bounds is high. If a pilot data set is readily available then bootstrap simulation will provide a more accurate and reliable estimate, than conventional methods.Finally, we used the bootstrap for hypothesis testing and the estimation of standard errors and confidence intervals for parameters, in an example data set. We then compared and contrasted the bootstrap with standard methods of analysing HRQoL outcomes. In the data set studied, with the SF-36 outcome, the use of the bootstrap for estimating sample sizes and analysing HRQoL data produces results similar to conventional statistical methods. These results suggest that bootstrap methods are not more appropriate for analysing HRQoL outcome data than standard methods.

Adolescent↗
PubMed1991

Sample size estimation for comparing two or more treatment groups in clinical trials.

Methods for estimating required sample size for comparing two population means have been published. Most involve the use of complicated formulae and tables. These methods are limited to comparing two groups. Although techniques exist to determine sample sizes for comparing more than two groups, they are intrinsically far more complicated. A simple linear nomogram is proposed as a solution to these problems, and its use is illustrated with examples of parallel group, ordered parallel group and factorial designs.

Algorithms↗
PubMed2006

Reproducibility of 5-HT2A receptor measurements and sample size estimations with [18F]altanserin PET using a bolus/infusion approach.

PURPOSE: To determine the reproducibility of measurements of brain 5-HT2A receptors with an [18F]altanserin PET bolus/infusion approach. Further, to estimate the sample size needed to detect regional differences between two groups and, finally, to evaluate how partial volume correction affects reproducibility and the required sample size. METHODS: For assessment of the variability, six subjects were investigated with [18F]altanserin PET twice, at an interval of less than 2 weeks. The sample size required to detect a 20% difference was estimated from [18F]altanserin PET studies in 84 healthy subjects. Regions of interest were automatically delineated on co-registered MR and PET images. RESULTS: In cortical brain regions with a high density of 5-HT2A receptors, the outcome parameter (binding potential, BP1) showed high reproducibility, with a median difference between the two group measurements of 6% (range 5-12%), whereas in regions with a low receptor density, BP1 reproducibility was lower, with a median difference of 17% (range 11-39%). Partial volume correction reduced the variability in the sample considerably. The sample size required to detect a 20% difference in brain regions with high receptor density is approximately 27, whereas for low receptor binding regions the required sample size is substantially higher. CONCLUSION: This study demonstrates that [18F]altanserin PET with a bolus/infusion design has very low variability, particularly in larger brain regions with high 5-HT2A receptor density. Moreover, partial volume correction considerably reduces the sample size required to detect regional changes between groups.

Adolescent↗
PubMed2001

Sample size estimations for MRI-monitored trials of MS comparing new vs standard treatments.

The authors estimated the sample sizes needed for exploratory trials of MS assessing the efficacy of new treatments in reducing the number of new enhancing lesions vs those of interferon-beta or glatiramer acetate. The sample sizes per arm ranged from 868 (effect: 20%) to 94 (effect: 50%) for patients with relapsing-remitting MS and from 2,484 (effect: 20%) to 361 (effect: 50%) for patients with secondary progressive MS. In MS, exploratory trials of new vs available therapies require large numbers of patients, even when MR end-points are used.

Adjuvants, Immunologic↗
PubMed1979

Sample-size estimation: a sensitivity analysis in the context of a clinical trial for treatment of mild hypertension.

The effectiveness of treatment for mild hypertension (diastolic pressures of 85 to 105 mm Hg) has not been conclusively demonstrated. Both the costs of a carefully designed clinical trial and the likelihood that it will produce definitive answers will depend importantly on the sample size. This paper presents sample-size estimates under a variety of assumptions regarding the characteristics of the population to be studied, the degree of blood pressure control to be achieved, and the health benefits to be expected. Under a central set of assumptions, the estimated sample size per group is 22,700 with death as an endpoint and 14,000 with morbid events (CHD and stroke) as endpoints. As individual assumptions are varied one at a time, required sample sizes range from 10,900 to 101,100 and from 6,800 to 63,100 for the respective endpoints. Results are most sensitive to the degree of blood pressure control actually achieved to the expected health benefits from blood pressure control. They are also highly sensitive to the sex composition of the population and to expected dropout rates. The choice of sample size will depend on the decision maker's assessment of the likelihood that each assumption will be fulfilled and on the degree of willingness to risk an inconclusive study result. By making explicit the effect of variation in each assumption, decision making is rendered more susceptible to critical examination by outside reviewers.

Adult↗
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↗
PubMed2005

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↗
PubMed2004

Sample size estimation: a glimpse beyond simple formulas.

Small increments in the complexity of clinical studies can readily take sample size estimation and statistical power analysis beyond the capabilities of simple mathematic formulas. In this article, the method of simulation is presented as a general technique with which sample size may be calculated for complex study designs. Applications of simulation for determining sample size requirements in studies involving correlated data and comparisons of receiver operating characteristic curves are discussed.

Clinical Trials as Topic↗