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At least 721 records · Page 40Linked to original sources
PubMed2003

Epidemiological studies based on small sample sizes--a statistician's point of view.

We consider 3 basic steps in a study, which have relevance for the statistical analysis. They are: study design, data quality, and statistical analysis. While statistical analysis is often considered an important issue in the literature and the choice of statistical method receives much attention, less emphasis seems to be put on study design and necessary sample sizes. Finally, a very important step, namely assessment and validation of the quality of the data collected seems to be completely overlooked. Examples from veterinary epidemiological research and recommendations for each step are given together with relevant references to the literature.

Animals↗
PubMed1998

Effect of within-strain sample size on QTL detection and mapping using recombinant inbred mouse strains.

Increasing the number of mice used to calculate recombinant inbred (RI) strain means increases the accuracy of determining the phenotype associated with each genotype (strain), which in turn enhances quantitative trait locus (QTL) detection and mapping. The purpose of this paper is to examine quantitatively the effect of within-strain sample size (n) on additive QTL mapping efficiency and to make comparisons with F2 and backcross (BC) populations where each genotype is represented by only a single mouse. When 25 RI strains are used, the estimated equivalent number of F2 mice yielding the same power to detect WTLs varies inversely as a function of the heritability of the trait in the RI population (hRI2). For example, testing 25 strains with n = 10 per strain is approximately equivalent to 160 F2 mice when hRI2 = 0.2, but only 55 when hRI2 = 0.6. While increasing n is always beneficial, the gain in power as n increases is greatest when hRI2 is low and is much diminished at high hRI2 values. Thus, hRI2 is high, there is little advantage of large n, even when n approaches infinity. A cost analysis suggested that RI populations are more cost-effective than conventional selectively genotyped F2 populations at hRI2 values likely to be seen in behavioral studies. However, with DNA pooling, this advantage is greatly reduced and may be reversed depending on the values of hRI2 and n.

Animals↗
PubMed2006

Sample size planning for the standardized mean difference: accuracy in parameter estimation via narrow confidence intervals.

Methods for planning sample size (SS) for the standardized mean difference so that a narrow confidence interval (CI) can be obtained via the accuracy in parameter estimation (AIPE) approach are developed. One method plans SS so that the expected width of the CI is sufficiently narrow. A modification adjusts the SS so that the obtained CI is no wider than desired with some specified degree of certainty (e.g., 99% certain the 95% CI will be no wider than omega). The rationale of the AIPE approach to SS planning is given, as is a discussion of the analytic approach to CI formation for the population standardized mean difference. Tables with values of necessary SS are provided. The freely available Methods for the Behavioral, Educational, and Social Sciences (K. Kelley, 2006a) R (R Development Core Team, 2006) software package easily implements the methods discussed.

Confidence Intervals↗
PubMed1998

Sample size calculations for linkage analysis using extreme sib pairs based on segregation analysis with the quantitative phenotype body weight as an example.

One approach to establish linkage is based on allele-sharing methods for sib pairs. Recently, the use of extreme sib pairs (ESP) has been proposed to increase power for mapping quantitative traits in humans. Several approaches have been discussed. In this study, we calculate sample sizes for the various ESP approaches using segregation analyses of quantitative traits. We illustrate this approach by using previously published segregation analyses of body weight despite the fact that the assumptions imposed by these analyses do not hold up for this quantitative phenotype.

Body Weight↗
PubMed2002

How much for a star? Elements for a rational choice of sample size in preclinical trials.

In the preclinical field, preliminary estimation of the number of experimental units to be included in a study is an important step in the setting up of the study. This estimation must be based on a dialogue between the experimenter and the statistician. This article, intended for both experimenters and statisticians, is designed to facilitate their dialogue by describing certain elements that are involved in the calculation of the optimal sample size in addition to the factors that influence their quantification. The proposed approach will result in better analysis of each experiment and, therefore, more globally, in rationalization of the use of experimental units.

Drug Evaluation, Preclinical↗
PubMed2001

Cluster trials in implementation research: estimation of intracluster correlation coefficients and sample size.

The cluster randomized trial with a concurrent economic evaluation is considered the gold standard evaluative design for the conduct of implementation research evaluating different strategies to promote the transfer of research findings into clinical practice. This has implications for the planning of such studies, as information is needed on the effects of clustering on both effectiveness and efficiency outcomes. This paper describes the design considerations specific to implementation research studies, focusing particularly on the estimation of sample size requirements and on the need for reliable information on intracluster correlation coefficients for both effectiveness and efficiency outcomes.

Cluster Analysis↗
PubMed2005

An analytical model for the dissolution of different particle size samples of Bioglass in TRIS-buffered solution.

We analyzed the early stages of reactivity of three different particle size samples of Bioglass 45S5 and a bulk sample in TRIS-buffered solution at pH 8. Ion release, measured with ion-coupled plasma emission spectroscopy, and pH variations are reported. It was demonstrated that differences in the initial surface area influence the increase in pH, the rate of elemental release, and the rate of calcium phosphate reprecipitation. In particular, a thicker Ca/P layer was obtained on larger particles. The equilibrium value of Si in solution was independent of sample form and amount of sample dissolved, and was always close to the value observed when bulk silica is dissolved at pH 8. An analytical model is proposed for cation release, based on a two-step mechanism. It was found that the early stage of dissolution was nearly diffusion controlled for larger particles and bulk samples. The second stage was similar to a first-order homogeneous dissolution. The influence of sample surface area/solution volume ratio seemed to be more complex than that proposed in the early works presented in the literature. It is suggested that variation of surface area has a significant impact on the course of the dissolution.

Biocompatible Materials↗
PubMed1984

Sample sizes for estimation of the odds ratio in unmatched case-control studies.

A method is presented to obtain sample sizes for cases and controls that are required to provide approximate confidence intervals on the log odds ratio of predetermined width 2d and probability of coverage as a function of assumed exposure rates in the control group, assumed odds ratio psi, required d, and ratio C:1 of controls to cases.

Humans↗
PubMed1995

Study design and sample sizes for a lacI transgenic mouse mutation assay.

Design features that adjust and account for excess variation in a transgenic mouse mutation assay based on a lacI target transgene from E. coli are considered. These features include proper identification of plate, packaging reaction, and animal identifier codes throughout the experimental and analysis phases of the study, "blocking" of exposed and unexposed animals when preparing and plating multiple packaging reactions from the same genomic DNA sample, separating sectored mutant plaques and complete mutant plaques before performing any quantitative analyses, and testing for sources of excess variation attributable to features of the experimental protocol--such as plate-to-plate (within packaging reactions), packaging reaction-to-packaging reaction (within animals), and animal-to-animal (within study). Control and ethylnitrosourea-treated animal data are presented from a fully designed study in the lacI assay. The study design incorporates many of these experimental principles. Statistical methods to identify excess variability are noted, and the designed study data are used to illustrate the types of variability encountered in practice. A standard statistical test for two-sample testing is highlighted, from which recommendations are made for sample size selection in future studies.

Analysis of Variance↗
PubMed2003

Carotid intima-media thickness measurements in intervention studies: design options, progression rates, and sample size considerations: a point of view.

BACKGROUND: Carotid intima-media thickness (CIMT) measurements are currently widely used in randomized controlled trials (RCTs) to study the efficacy of interventions. In designing a RCT with CIMT as a primary outcome, several ultrasound options may be considered. We discuss the various options and provide a pooled estimate of CIMT progression. In addition, we quantify the effect of these choices on the sample size for a RCT. SUMMARY OF COMMENT: To estimate the average CIMT progression rate, we performed a pooled analysis using CIMT progression rates of control groups from published RCTs. The pros and cons of the following ultrasound options are discussed: which arterial segments may be studied; whether near and far wall CIMT measurements should be performed; whether a single image (1 angle of interrogation) or multiple images (more angles of interrogation) should be used; whether a manual or an automated edge detection reading system should be used; and whether images should be read in a random fashion or in batches. The pooled analysis showed an annual rate of change in mean common CIMT of 0.0147 mm (95% CI, 0.0122 to 0.0173) and in mean maximum CIMT of 0.0176 mm (95% CI, 0.0149 to 0.0203). CONCLUSIONS: Given the current evidence together with our experience with recently developed ultrasound protocols, we favor the use of mean maximum CIMT rather than mean common CIMT as the primary outcome measure in RCTs designed to evaluate the efficacy of pharmacological and nonpharmacological interventions in carotid artery atherosclerosis.

Carotid Arteries↗
PubMed1988

Regression analysis in biological research: sample size and statistical power.

Regression analysis is often used to demonstrate associations among variables believed to be biologically related. Failure to demonstrate a "significant" relationship may be due to two factors: 1) the variables are truly unrelated, or 2) a relationship exists but goes undetected due to inadequate statistical power. Investigators must consider the second possibility since failure to detect a statistically significant relationship is often taken as evidence for no biological relationship. These issues are addressed in the context of the interrelationship between four features common to all statistical methods: the size of effect or relationship worth detecting, the Type I (alpha) error, the sample size, and the Type II (beta) error. An example derived from published data relating morphological characteristics of muscle fiber type and isokinetic strength performance illustrates the practical significance of this dilemma.

Regression Analysis↗
PubMed2001

On determination of sample size in hierarchical binomial models.

We consider a two- and a three-stage hierarchical design containing the effects of k clusters with n units per cluster. In the two-stage model, the conditional distribution of the discrete response Y(i) is assumed to be independent binomial with mean n(straight theta)i (I=1,....k). The success probabilities, straight theta(i)'s, are assumed exchangeable across the k clusters, each arising from a beta distribution. In the three-stage model, the parameters in the beta distribution are assumed to have independent gamma distributions. The size of each cluster, n, is determined for functions of straight theta(i). Lengths of central posterior intervals are computed for various functions of the straight theta(i)'s using Markov chain Monte Carlo and Monte Carlo simulations. Several prior distributions are characterized and tables are provided for n with given k. Methods for sample size calculations under the two- and three-stage models are illustrated and compared for the design of a multi-institutional study to evaluate the appropriateness of discharge planning rates for a cohort of patients with congestive heart failure.

Clinical Trials as Topic↗
PubMed2000

Sample-size calculations for the Cox proportional hazards regression model with nonbinary covariates.

This paper derives a formula to calculate the number of deaths required for a proportional hazards regression model with a nonbinary covariate. The method does not require assumptions about the distributions of survival time and predictor variables other than proportional hazards. Simulations show that the censored observations do not contribute to the power of the test in the proportional hazards model, a fact that is well known for a binary covariate. This paper also provides a variance inflation factor together with simulations for adjustment of sample size when additional covariates are included in the model. Control Clin Trials 2000;21:552-560

Analysis of Variance↗
PubMed1998

An application of methods for clustered binary responses to a cardiovascular study with small sample size.

This paper discusses statistical methods for a cardiovascular study in which each of eight animals had a dichotomous outcome observed for each of several treatments. There were five treatments in all: shunt, control, two doses of a test drug for potentially causing an unfavorable cardiovascular event, and a combination of the test drug and a counteracting agent. Exact conditional methods were used through LogXact, a statistical software for exact logistic regression and an alternative framework for performing a large class of nonparametric tests performed by StatXact. The results agreed reasonably with asymptotic methods even though the sample size was small.

Animals↗
PubMed1976

Nomograms for obtaining a necessary, minimum sample size. II. When distribution of proportions from binomial data is approximately normal.

Two different nomograms corresponding to finite and infinite populations were devised to obtain a necessary, minimum sample size at a 95% confidence rate when the distribution of sample proportions calculated from binomial data was approximately normal. They could also be used for obtaining the 95% confidence limits of the population proportion from a sample proportion after having carried out a work.

Ecology↗
PubMed2001

Sample size and power calculations with correlated binary data.

Correlated binary data are common in biomedical studies. Such data can be analyzed using Liang and Zeger's generalized estimating equations (GEE) approach. An attractive point of the GEE approach is that one can use a misspecified working correlation matrix, such as the working independence model (i.e., the identity matrix), and draw (asymptotically) valid statistical inference by using the so-called robust or sandwich variance estimator. In this article we derive some explicit formulas for sample size and power calculations under various common situations. The given formulas are based on using the robust variance estimator in GEE. We believe that these formulas will facilitate the practice in planning two-arm clinical trials with correlated binary outcome data.

Clinical Trials as Topic↗
PubMedDate not supplied

Approaches to sample size estimation in the design of clinical trials--a review.

Over the last decade, considerable interest has focused on sample size estimation in the design of clinical trials. The resulting literature is scattered over many textbooks and journals. This paper presents these methods in a single review and comments on their application in practice.

Clinical Trials as Topic↗
PubMed2002

Power and sample size for DNA microarray studies.

A microarray study aims at having a high probability of declaring genes to be differentially expressed if they are truly expressed, while keeping the probability of making false declarations of expression acceptably low. Thus, in formal terms, well-designed microarray studies will have high power while controlling type I error risk. Achieving this objective is the purpose of this paper. Here, we discuss conceptual issues and present computational methods for statistical power and sample size in microarray studies, taking account of the multiple testing that is generic to these studies. The discussion encompasses choices of experimental design and replication for a study. Practical examples are used to demonstrate the methods. The examples show forcefully that replication of a microarray experiment can yield large increases in statistical power. The paper refers to cDNA arrays in the discussion and illustrations but the proposed methodology is equally applicable to expression data from oligonucleotide arrays.

Analysis of Variance↗