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At least 19 recordsLinked to original sources
PubMed1995

Hydrolytic degradation of benzylated poly(beta-malic acid): influence of sample size, sample shape, and polymer composition.

In order to investigate the effects of sample size, sample shape, and polymer composition on the hydrolytic degradation of water-insoluble benzylated poly(beta-malic acid) derivatives, three polymers were synthesized, namely PMLABe100, PMLABe90H10 and PMLABe80H20, containing 100, 90, and 80% of benzyl units respectively. For each polymer, 4-mm-thick compression molded pellets, 0.3-mm-thick casted films and 250-500 microns ground particles were made and allowed to age in 0.13 M, pH 7.4 sodium phosphate buffer at 37 degrees C. Degradation was monitored by measuring weight changes, variations of polymer composition, formation of benzyl alcohol, and molecular weight decreases. It was shown that degradation of particles and films depended initially on polymer composition due primarily to fast degradation of acid-rich segments present or formed by benzyl ester cleavages which led more or less rapidly to similar compositions for the three polymers. The higher the content in acid groups, the faster the degradation rate and the characteristic changes. It was also found that pellets made of PMLABe90H10 or PMLABe80H20 degraded heterogeneously and much faster than corresponding films and particles.

Benzene Derivatives↗
PubMed1996

A computer program for calculation of sample size.

Sample size must be determined while planning a study to ensure that valid conclusions can be drawn when the study is over. different formulae for calculating the required size of the sample are used for different study designs and situations. A computer program is described here to ease the complexity of calculation of sample size for studies designed to use students 't' test.

Sample Size↗
PubMed2005

Minimum sample size and sampling time requirements for assessment of rifampicin bioequivalence from FDC formulations.

SETTING: The WHO- and IUATLD-recommended protocol for rifampicin (RMP) bioequivalence utilises 20-22 volunteers and 8 h, whereas the requirement of other regulatory authorities is 12 volunteers with a 24 h sampling schedule. Differing sampling size and time requirements may change the outcome of RMP bioequivalence. OBJECTIVE: To determine the minimal sample size and time required to assess RMP bioequivalence from FDC formulations. DESIGN: Bioequivalence studies were conducted that fulfilled the criteria of the WHO and Indian regulatory protocols. From earlier studies, retrospective pharmacokinetic evaluation, power of the test and bioequivalence limits were also calculated using 8-22 volunteers and sampling points of 8-24 h. Pharmacokinetic and statistical evaluations from three representative studies showing low, moderate and high intra-subject variability are given to determine minimum requirements for RMP bioequivalence. RESULT: It was found that a sampling schedule up to 8 h was sufficient to compare the absorption process of RMP. There was no influence of reduced sample size on bioequivalence estimates of RMP that showed low or moderate variability. However, in a study showing higher variation, a sample size of 14-16 subjects was found to be optimal. CONCLUSION: It is possible to reduce the sample size requirement for determination of RMP bioequivalence using the WHO protocol.

Antibiotics, Antitubercular↗
PubMed1993

[Planning of sampling for linkage analysis. Sample size and sampling method].

There is a proposed method for evaluation of the minimal sample size needed for the predefined quality of the linkage decision. It is applied to samples of arbitrary structured pedigrees ascertained via proband in accordance with different ascertainment schemes. The quality of the linkage decision is determined by the I and II type errors of the likelihood ratio constructed with a simple alternative hypothesis about the recombination fraction value. Comparison of these minimal sample sizes for different ascertainment schemes leads to a possibility to choose the best of them when the model of genetic control of two traits is given. A possibility to evaluate each of these ascertainments by a cost of the sample collection and to point out the cheapest is considered.

Genes, Dominant↗
PubMedDate not supplied

A simple method of sample size calculation for unequal-sample-size designs that use the logrank or t-test.

This paper presents a simple method of calculating sample sizes for unequal-sample-size designs with use of published tables applicable to equal-sample-size design. The method applies to both the logrank test and the t-test. For the power of logrank test, this paper compares the proposed method with existing methods and with the Monte Carlo simulation.

Clinical Trials as Topic↗
PubMed1988

Detection of linkage between a quantitative trait and a marker locus by the lod score method: sample size and sampling considerations.

A simulation study is here conducted to measure the power of the lod score method to detect linkage between a quantitative trait and a marker locus in various situations. The number of families necessary to detect such linkage with 80% power is assessed for different sets of parameters at the trait locus and different values of the recombination fraction. The effects of varying the mode of sampling families and the sibship size are also evaluated.

Computer Simulation↗
PubMed2000

RSD requirement for different sample size for blend sampling.

A formula for calculating the equivalent relative standard deviation (RSD) for different sample sizes other than the Office of Generic Drugs (OGD) draft guidance was derived. These critical values vary with the population RSD and the sample size and guarantee the same confidence of passing a batch similar to the OGD blend uniformity test. Simulation results showed that the normal approximations give very close results to the simulation results. An assessment of the proposed OGD blend uniformity criteria against the first-stage USP content uniformity (CU) criteria for tablets was also made.

Drugs, Generic↗
PubMed1997

Bayesian and mixed Bayesian/likelihood criteria for sample size determination.

Sample size estimation is a major component of the design of virtually every experiment in medicine. Prudent use of the available prior information is a crucial element of experimental planning. Most sample size formulae in current use employ this information only in the form of point estimates, even though it is usually more accurately expressed as a distribution over a range of values. In this paper, we review several Bayesian and mixed Bayesian/likelihood approaches to sample size calculations based on lengths and coverages of posterior credible intervals. We apply these approaches to the design of an experiment to estimate the difference between two binomial proportions, and we compare results to those derived from standard formulae. Consideration of several criteria can contribute to selection of a final sample size.

Bayes Theorem↗
PubMed1967

Sample size and optimum sample design in tuberculosis surveys.

Tuberculosis surveys sponsored by the World Health Organization have been carried out in different communities during the last few years. Apart from the main epidemiological findings, these surveys have provided basic statistical data for use in the planning of future investigations. In this paper an attempt is made to determine the sample size desirable in future surveys that include one of the following examinations: tuberculin test, direct microscopy, and X-ray examination.The optimum cluster sizes are found to be 100-150 children under 5 years of age in the tuberculin test, at least 200 eligible persons in the examination for excretors of tubercle bacilli (direct microscopy) and at least 500 eligible persons in the examination for persons with radiological evidence of pulmonary tuberculosis (X-ray). Modifications of the optimum sample size in combined surveys are discussed.

Africa↗
PubMed2006

Developing optimum sample size and multistage sampling plans for Lobesia botrana (Lepidoptera: Tortricidae) larval infestation and injury in northern Greece.

The purpose of this research was to quantify the spatial pattern and develop a sampling program for larvae of Lobesia botrana Denis and Schiffermüller (Lepidoptera: Tortricidae), an important vineyard pest in northern Greece. Taylor's power law and Iwao's patchiness regression were used to model the relationship between the mean and the variance of larval counts. Analysis of covariance was carried out, separately for infestation and injury, with combined second and third generation data, for vine and half-vine sample units. Common regression coefficients were estimated to permit use of the sampling plan over a wide range of conditions. Optimum sample sizes for infestation and injury, at three levels of precision, were developed. An investigation of a multistage sampling plan with a nested analysis of variance showed that if the goal of sampling is focusing on larval infestation, three grape clusters should be sampled in a half-vine; if the goal of sampling is focusing on injury, then two grape clusters per half-vine are recommended.

Analysis of Variance↗
PubMed1996

Assessment of small health risks based on exact sample sizes.

Exact sample sizes and critical numbers of cases for the rejection of a known event probability (10(-2) to 10(-6)) in favour of an increased probability (1.5- to 50-fold) at levels -alpha;beta- = -0.05; 0.10- and -alpha;beta- = -0.10;0.05- are presented. The numbers are thoroughly validated using the characteristics of the confidence interval for the unknown true event probability. Equivalence is shown to be obtainable for the tolerated maximal value of the relative risk and the upper limit of the confidence interval for the true event probability. Also demonstrated is the use of the tables for planned actions to reduce given empirical risks. In addition, use of the tables is shown for judging results from given data sets.

Bias↗
PubMed1994

An expert system for the calculation of sample size.

Calculation of sample size is a useful technique for researchers who are designing a study, and for clinicians who wish to interpret research findings. The elements that must be specified to calculate the sample size include alpha, beta, Type I and Type II errors, 1- and 2-tail tests, confidence intervals, and confidence levels. A computer software program written by one of the authors (MHE), Sample Size Expert, facilitates sample size calculations. The program uses an expert system to help inexperienced users calculate sample sizes for analytic and descriptive studies. The software is available at no cost from the author or electronically via several on-line information services.

Data Interpretation, Statistical↗
PubMed2002

Comparing several robust tests of stochastic equality with ordinally scaled variables and small to moderate sized samples.

In a comparison of 2 treatments, if outcome scores are denoted by X in 1 condition and by Y in the other, stochastic equality is defined as P(X < Y) = P(X > Y). Tests of stochastic equality can be affected by characteristics of the distributions being compared, such as heterogeneity of variance. Thus, various robust tests of stochastic equality have been proposed and are evaluated here using a Monte Carlo study with sample sizes ranging from 10 to 30. Three robust tests are identified that perform well in Type I error rates and power except when extremely skewed data co-occur with very small n. When tests of stochastic equality might be preferred to tests of means is also considered.

Humans↗
PubMed2003

Obtaining power or obtaining precision. Delineating methods of sample-size planning.

Sample-size planning historically has been approached from a power analytic perspective in order to have some reasonable probability of correctly rejecting the null hypothesis. Another approach that is not as well-known is one that emphasizes accuracy in parameter estimation (AIPE). From the AIPE perspective, sample size is chosen such that the expected width of a confidence interval will be sufficiently narrow. The rationales of both approaches are delineated and two procedures are given for estimating the sample size from the AIPE perspective for a two-group mean comparison. One method yields the required sample size, such that the expected width of the computed confidence interval will be the value specified. A modification allows for a defined degree of probabilistic assurance that the width of the computed confidence interval will be no larger than specified. The authors emphasize that the correct conceptualization of sample-size planning depends on the research questions and particular goals of the study.

Analysis of Variance↗
PubMed1989

Multivariate outcome research in arthritis. Implications for sample sizes.

Planning appropriate sample sizes prior to data collection is critical to the overall success of a research project. However, the investigator often encounters practical limitations regarding the number of subjects available. While the literature discusses the various options of analysis of clinical trial data, there is little discussion as to the sample size implications of the statistical method chosen. Since the analysis technique employed affects the required sample size, the investigator should select the analysis that is most appropriate for the data and has the most efficient sample size requirement. This paper addresses the sample size implications of three commonly used methods for comparing two populations when there are two or more outcome measures (multivariate outcome). The concepts and methods are illustrated using a Pain Management Study that was conducted using people with rheumatoid arthritis as subjects.

Arthritis↗
PubMed1996

On planning of samples for linkage analysis: two ways of a sample size reduction.

The sample size required for a given type I and type II errors of linkage decision is determined by using the earlier proposed method of the linkage test power estimation [Ginsburg et al., 1996]. Dependence of the sample size on the heterozygotic genotype penetrance is investigated. Two approaches to sample size reduction are considered: the choice of marker gene and the special extension of analyzed pedigree structures. Their effects are quantitatively evaluated in some illustrative situations. It is shown that a practical reduction in sample size can be achieved only when the added part of a pedigree has a special set of phenotypic characteristics in its members.

Genetic Linkage↗