Confidence intervals and sample sizes: don't throw out all your old sample size tables.
Explore the source record for details and available documents.
Confidence Intervals↗
SEARCH · PubMed Health
Results for “Sample Size”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
A comparison of methods for adaptive sample size adjustment.
In fixed sample size designs, precise knowledge about the magnitude of the outcome variable's variance in the planning phase of a clinical trial is mandatory for an adequate sample size determination. Wittes and Brittain introduced the internal pilot study design that allows recalculation of the sample size during an ongoing trial using the estimated variance obtained from an interim analysis. However, this procedure requires the unblinding of the treatment code. Since unblinding of an ongoing trial should be avoided whenever possible, there should be some benefit of this design compared with blinded sample size recalculation procedures to justify the unveiling of the treatment code. In this paper, we compare several sample size recalculation procedures with and without unblinding. The simulation results indicate that the procedures behave similarly. In particular, breaking of the blind is not required for an efficient sample size adjustment. We also compare these pure sample size adaptation procedures with study designs which additionally allow for early stopping. Evaluation of the cumulative distribution function of the resulting sample sizes shows that the option for early stopping may lead to lower expectation but generally to a higher variability. The procedures are illustrated by an example of a trial in the treatment of depression.
Computer Simulation↗
The numbers game: sample-size determination.
Sample-size determination is a crucial component of study design. Estimates of sample size are influenced by the amount of change that must occur between study groups and the degree of risk that the investigator is willing to accept in evaluating the null hypothesis. A complete understanding of the impact of sample size on the interpretation of study data is therefore a prerequisite for quality, innovative, valid research.
Epidemiologic Factors↗
Test statistic and sample size for a two-sample McNemar test.
McNemar's (1947, Psychometrika 12, 153-157) test of marginal homogeneity is generalized to a two-sample situation where the hypothesis of interest is that the marginal changes in each of two independently sampled tables are equal. This situation is especially applicable to two cohorts (a control and an intervention cohort), each measured at baseline and after the intervention on a binary outcome variable. Some assumptions often realistic in this situation simplify the calculation of sample size. The calculation of sample size in a study designed to increase utilization of breast cancer screening is demonstrated.
Biometry↗
Wall-less sample preparation of microm-sized sample spots for femtomole detection limits of proteins from liquid based UV-MALDI matrices.
Previously, we introduced wall-less sample preparation (WaSP), technology that involves the use of an electrodynamic balance (EDB) to prepare microm-sized sample spots for analysis by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). In that work we demonstrated the detection of femtomole quantities of a low molecular weight peptide and a hydrophobic ester (both <600 Da). Here we use WaSP to test the hypothesis that the use of small sample spot sizes and an instrument equipped with delayed extraction would increase the analytical utility of liquid sample spots for peptide and protein (>2500 Da) analysis by UV-MALDI-TOF-MS (Sze et al.; J. Am. Soc. Mass Spectrom. 1998, 9, 166-174). To aid the optimization of preparing microm-sized sample spots by WaSP, optical microscopy and mass spectrometry were used to investigate nonvolatile solute concentration effects on droplet fission and sample spot size, modifications of the EDB electric field to control droplet ejection, and the use of multiple droplet deposition to increase sample loading. Also described is a rapid deposition mode of operation for WaSP that allows single droplets generated at 1 Hz to be levitated briefly ( approximately 500 ms) before being ejected autonomously and deposited as a concentrated sample spot with a spatial accuracy of +/-5 microm. To test the sensitivity of the method, one hundred glycerol droplets (270 pL each, 27 nL total) each containing 32 amol lysozyme were deposited on top of each other one-at-a-time to create a single sample spot. Using a mass spectrometer equipped with delayed extraction to analyze this sample spot, we verified the hypothesis of Sze et al. by achieving detection limits three orders of magnitude below that previously observed for the detection of a protein by UV-MALDI-TOF-MS with a chemical-doped liquid matrix sample preparation.
Adrenocorticotropic Hormone↗
Sample size computation for two-sample noninferiority log-rank test.
When an experimental therapy is less extensive, less toxic, or less expensive than a standard therapy, we may want to prove that the former is not worse than the latter through a noninferiority trial. In this article, we discuss a modification of the log-rank test for noninferiority trials with survival endpoint and propose a sample size formula that can be used in designing such trials. Performance of our sample size formula is investigated through simulations. Our formula is applied to design a real clinical trial.
Clinical Trials as Topic↗
Estimates, power and sample size calculations for two-sample ordinal outcomes under before-after study designs.
Sample size calculations are given for comparing two groups of subjects, typically referring to active and non-active intervention groups, on an ordinal outcome in experiments where the subjects are measured before and after intervention. These calculations apply to log-odds models with random intercepts, treatment, time and treatment-by-time interaction terms, the latter being the term of interest. The assumed forms of the odds ratios are flexible, allowing for proportional odds, adjacent categories, or other conditional models for ordinal responses. Simulations studies show that, for given sample sizes, the nominal and actual powers of the proposed test are similar.
Clinical Trials as Topic↗
Variance estimation, design effects, and sample size calculations for respondent-driven sampling.
Hidden populations, such as injection drug users and sex workers, are central to a number of public health problems. However, because of the nature of these groups, it is difficult to collect accurate information about them, and this difficulty complicates disease prevention efforts. A recently developed statistical approach called respondent-driven sampling improves our ability to study hidden populations by allowing researchers to make unbiased estimates of the prevalence of certain traits in these populations. Yet, not enough is known about the sample-to-sample variability of these prevalence estimates. In this paper, we present a bootstrap method for constructing confidence intervals around respondent-driven sampling estimates and demonstrate in simulations that it outperforms the naive method currently in use. We also use simulations and real data to estimate the design effects for respondent-driven sampling in a number of situations. We conclude with practical advice about the power calculations that are needed to determine the appropriate sample size for a study using respondent-driven sampling. In general, we recommend a sample size twice as large as would be needed under simple random sampling.
Analysis of Variance↗
Exact equivalence test for risk ratio and its sample size determination under inverse sampling.
When data are dichotomous, this paper notes the utility of inverse sampling in establishing equivalence with respect to the risk ratio. This paper develops an exact equivalence test that accounts for the risk ratio under inverse sampling and further discusses the relationship between the exact equivalence test and the exact conditional confidence limits. Also included are an exact and two asymptotic procedures for calculation of the minimum required number of index subjects for a desired power 1--beta at a given alpha-level. Finally, this paper provides a table that summarizes the minimum required number of index subjects for powers equal to 0.90 and 0.80 in application of the proposed exact equivalence test at 0.05-level in a variety of situations.
Confidence Intervals↗
The power of sample size and homogenous sampling: association between the 5-HTTLPR serotonin transporter polymorphism and major depressive disorder.
BACKGROUND: Several lines of evidence indicate that abnormalities in the functioning of the central serotonergic system are involved in the pathogenesis of affective illness. A 44-base-pair insertion/deletion polymorphism in the 5' regulatory region of the serotonin transporter gene (5-HTTLPR), which influences expression of the serotonin transporter, has been the focus of intensive research since an initial report on an association between 5-HTTLPR and depression-related personality traits. Consistently replicated evidence for an involvement of this polymorphism in the etiology of mood disorders, particularly in major depressive disorder (MDD), remains scant. METHODS: We assessed a potential association between 5-HTTLPR and MDD, using the largest reported sample to date (466 patients, 836 control subjects). Individuals were all of German descent. Patients were systematically recruited from consecutive inpatient admissions. Control subjects were drawn from random lists of the local Census Bureau and screened for psychiatric disorders. RESULTS: The short allele of 5-HTTLPR was significantly more frequent in patients than in control subjects (45.5% vs. 39.9%; p = .006; odds ratio = 1.26). CONCLUSIONS: These results support an involvement of 5-HTTLPR in the etiology of MDD. They also demonstrate that the detection of small genetic effects requires very large and homogenous samples.
Adult↗
["And now, how many individuals do I take?" Some basic ideas on sample size. I. The size of a sample in a problem of estimation].
Explore the source record for details and available documents.
Humans↗
Sample size and dimensionality in multivariate classification: implications for body surface potential mapping.
This paper presents empirically determined guidelines for specifying the number of features appropriate for multivariate classification studies for given sample sizes. Sample size was considered adequate if the mean distance between two sample sets, taken from the same continuous multivariate distribution and projected onto the best separating direction, remained below a prescribed level. To quantitate the sample size requirement, homogeneity of sample set pairs of equal size. N, taken from the same continuous multivariate distribution was studied as a function of dimensionality. M. Homogeneity was characterized by the maximum absolute distances (Dmax) between the corresponding pairs of empirical cumulative probability distributions on the best separating projection. Computer generated data sets were used to estimate the cumulative probability distribution, P(D)M.N, for sample sizes, N, ranging from 5 to 100 and the dimensionality, M, ranging from 1 to 4. An empirical relationship between the estimated step-polygons and the Kolmogorov type one dimensional limiting distribution L(z) has been established. Based on the sample size data of 34 key papers on clinical body surface potential mapping (BSPM) it is noted that in 30% of the cases only one, and in 6% of the cases only two parameters could be used for statistical group representation to ensure a reasonable reliability (Dmax less than 0.2). In 56% of the published cases the sample sizes could not guarantee this reliability even for one feature or parameter.
Computer Simulation↗
Research Q and Q. Is there an optimal sample size for research involving human subjects? Is there a rule of thumb that might be used in determining sample size?
Explore the source record for details and available documents.
Research↗
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↗
Sample size determination in epidemiologic studies.
Sample size determination is an important issue in epidemiologic studies. Standard methods for determining sample size in cohort and case-control studies have generally been restricted to dichotomous disease and exposure variables and discrete confounding variables, and are based on simplifying assumptions that could often be unrealistic. Methods for sample size determination that make less restrictive and more realistic assumptions regarding the distribution of disease, exposure and confounding variables and which more closely parallel the analyses that are performed on the data, after the study has been conducted, have been developed in recent years. In this article some recent developments in the methodology for sample size determination in epidemiologic studies are reviewed.
Case-Control Studies↗
Biostatistics in clinical trials: Part 2. Determining sample sizes for clinical trials.
The appropriate sample size for a clinical trial depends on the objectives of the trial. For Phase II trials, the recommended sample size is generally in the neighborhood of 25-40 patients, regardless of the choice of design. Sample sizes for Phase III trials are much more variable, ranging from less than a hundred to thousands of patients. The methodology of sample size determination for Phase III trials depends on the type of endpoint that is to be the primary focus of analysis. Tables of sample sizes for a variety of situations are presented. Use of sequential designs may reduce the required sample size by permitting early termination of trials whose results become definitive prior to completion of patient entry.
Clinical Trials as Topic↗
An overview of variance inflation factors for sample-size calculation.
For power and sample-size calculations, most practicing researchers rely on power and sample-size software programs to design their studies. There are many factors that affect the statistical power that, in many situations, go beyond the coverage of commercial software programs. Factors commonly known as design effects influence statistical power by inflating the variance of the test statistics. The authors quantify how these factors affect the variances so that researchers can adjust the statistical power or sample size accordingly. The authors review design effects for factorial design, crossover design, cluster randomization, unequal sample-size design, multiarm design, logistic regression, Cox regression, and the linear mixed model, as well as missing data in various designs. To design a study, researchers can apply these design effects, also known as variance inflation factors to adjust the power or sample size calculated from a two-group parallel design using standard formulas and software.
Analysis of Variance↗