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At least 685 records · Page 38Linked to original sources
PubMed1996

The importance of sample size for the estimation of F wave latency parameters in the peroneal nerve.

We studied the peroneal nerve F waves in 20 healthy subjects and 20 patients with neuropathy to assess the effect of sample size on the accuracy of measurements of the following F wave latency parameters: F wave minimum latency, mean latency, median latency and F chronodispersion. The values obtained from a large sample (65-110 F responses) were compared with the corresponding values from smaller samples of 10, 20 and 40 responses. The results indicated that equally accurate measurements for all parameters were provided by larger F wave samples in patients, compared with healthy subjects. Amongst the various parameters, FchR required the largest and FLmean the smallest sample, in order to achieve results of the same accuracy. A sample of 40 fulfilled the requirements for all F wave latency parameters of the peroneal nerve in almost all subjects, a finding which is in good agreement with that of a similar study for the ulnar nerve.

Adult↗
PubMed2000

On power and sample size calculations for likelihood ratio tests in generalized linear models.

A direct extension of the approach described in Self, Mauritsen, and Ohara (1992, Biometrics 48, 31-39) for power and sample size calculations in generalized linear models is presented. The major feature of the proposed approach is that the modification accommodates both a finite and an infinite number of covariate configurations. Furthermore, for the approximation of the noncentrality of the noncentral chi-square distribution for the likelihood ratio statistic, a simplification is provided that not only reduces substantial computation but also maintains the accuracy. Simulation studies are conducted to assess the accuracy for various model configurations and covariate distributions.

Biometry↗
PubMed1995

Transgenic lambda/lacI mutagenicity assay: statistical determination of sample size.

Statistical analysis of the lambda/lacI transgenic mutagenicity assay was used to determine optimal sample size and resource allocation in terms of number of animals and number of recovered target genes (recovered phage) required to demonstrate a statistically significant induction in mutant frequency. Statistical assumptions as applied to mutagenicity data are discussed for a number of frequently used statistical analyses. Log transformations are suggested as a means of meeting statistical assumptions and examples are given on interpreting results of analyses of log transformed data. The data analyzed in this study indicate that 300,000 lambda plaques from each of five animals should be analyzed per treatment group in order to detect a doubling of mutant frequencies. Additional sensitivity is gained primarily through increase of animal number and not the number of phage rescued, due to inherent animal-to-animal variability.

Animals↗
PubMed1987

Calculation of sample size in trials of screening for early diagnosis of disease.

The calculation of the sample sizes required for trials of screening for disease with the aim of reducing mortality involves the estimation of both the mortality in the control group at intervals after the start of the trial, and the potential reduction due to screening. Since at the start of a screening trial the population is selected to be free of the disease in question, the mortality rate in the control group will differ from that in the general population. A method of estimating this mortality rate using published incidence and survival data is described. The expected reduction in mortality due to screening depends both on the number of, and intervals between, screens and on parameters concerning the natural history of the disease; the means by which these parameters can be estimated are discussed. A trial of screening for colorectal cancer by a faecal occult blood test is used to illustrate these calculations.

Epidemiologic Methods↗
PubMed1992

Sample size for a dose-response study.

This paper deals with a method of sample size allocation for a dose-response study assuming a logistic model for the dose-response curve. The method is based on the precision with which one wishes to estimate the dose that would produce the efficacy resulting in a clinically important difference from a placebo. An example is given to illustrate the methodology. The main development of the paper is for a binary response and is suitably modified for a continuous variable.

Dose-Response Relationship, Drug↗
PubMed2003

Estimating sample size in clinical studies: basic methodological principles.

In order to be valid, clinical studies must be methodologically rigorous. The internal validity of a study is of crucial importance: a study is valid if its results are an unbiased estimation of the true result. In this case, the validity is internal because it refers to the group of patients under study and not necessarily different ones (external validity or applicability). Internal validity in clinical research is achieved through rigorous design, data collection and appropriate analysis, and is threatened by bias (systematic errors) or chance (random variation of the phenomena under study). Regardless of the type of study (analytic, descriptive, etc.), the characteristics of its sample are fundamental for the validity of the results. The sampling methods are crucial if the study patients are to be representative of the population to which one desires to extrapolate the results. One of the most fundamental characteristics of a sample is its size. Even the best executed study may fail to answer the research question if the sample size is too small. On the other hand, a study with too large a sample is harder to conduct and more costly. The goal of planning the sample size is to estimate the appropriate number of research subjects for the study. In this paper we will present and discuss the methodological principles underlying calculation of sample size: outcomes, type I and II error, alpha and beta, study power and variability.

Clinical Trials as Topic↗
PubMed1995

Imprecise exposure assessment and the sample size requirements of case-control studies of residential magnetic field exposure and cancer in adults.

A computer program simulating case-control studies is described. It is used to estimate the minimum sample size required and to assess how this is affected by imprecise exposure assessment. In particular, the consequences of neglecting measurements of nonresidential exposure in case-control studies of residentially exposed adults are investigated. According to this model, while the consequent loss of power is not as large as was predicted by algebraic methods, it would be unwise to neglect it when planning a study.

Adult↗
PubMedDate not supplied

[Unconditioned logistic regression and sample size: a bibliographic review].

Unconditioned logistic regression is a highly useful risk prediction method in epidemiology. This article reviews the different solutions provided by different authors concerning the interface between the calculation of the sample size and the use of logistics regression. Based on the knowledge of the information initially provided, a review is made of the customized regression and predictive constriction phenomenon, the design of an ordinal exposition with a binary output, the event of interest per variable concept, the indicator variables, the classic Freeman equation, etc. Some skeptical ideas regarding this subject are also included.

Logistic Models↗
PubMedDate not supplied

Determination of sample size for validation of allergen-screening methods.

For various levels of confidence (i.e., 80 and 90%) and ratios (K = sigmap2/sigmaN2, where sigmap2 and sigmaN2 are the analyte variances for the positive and negative distributions, respectively), sample sizes sufficient to test the requirements that a given method detects > or = 90% of the positives (> or = 5 ppm of a given analyte) while misclassifying < or = 10% of the negatives (implying a specificity rate, true negatives that will be correctly classified, of 90%) were estimated by using a rationale that minimizes the cost of sampling.

Allergens↗
PubMed2003

Design and sample size estimation in clinical trials with clustered survival times as the primary endpoint.

Many clinical trials involve the collection of data on the time to occurrence of the same type of multiple events within sample units, in which ordering of events is arbitrary and times are usually correlated. To design a clinical trial with this type of clustered survival times as the primary endpoint, estimating the number of subjects (sampling units) required for a given power to detect a specified treatment difference is an important issue. In this paper we derive a sample size formula for clustered survival data via Lee, Wei and Amato's marginal model. It can be easily used to plan a clinical trial in which clustered survival times are of primary interest. Simulation studies demonstrate that the formula works very well. We also discuss and compare cluster survival time design and single survival time design (for example, time to the first event) in different scenarios.

Cluster Analysis↗
PubMed2005

Procedures for the analysis of differential item functioning (DIF) for small sample sizes.

An item with differential item functioning (DIF) displays different statistical properties, conditional on a matching variable. The presence of DIF in measures can invalidate the conclusions of medical outcome studies. Numerous approaches have been developed to examine DIF in many areas, including education and health-related quality of life. There is little consensus in the research community regarding selection of one best method, and most methods require large sample sizes. This article describes some approaches to examine DIF with small samples (e.g., less than 200).

Health Status Indicators↗
PubMed1994

Sample size requirements for evaluating heart valves with constant risk events.

A method is described for computing sample size requirements for a clinical study of a new heart valve, according to the guidance document recently revised by the FDA. The FDA requirements specify a one-sample hypothesis test in which the complication rates for the study valve are compared to fixed values determined from previous experience with approved devices. These values, which can differ for different complications and for mechanical or tissue valve types, are called OPC's (Objective Performance Criteria). The method described, using the Poisson distribution, provides a simple formula for computing the valve-years required corresponding to any OPC. At the OPC level required by the FDA guidelines (1.2%/year), the minimum follow-up necessary is about 800 valve-years.

Guidelines as Topic↗
PubMed2006

Thrombospondin-4 1186G>C (A387P) is a sex-dependent risk factor for myocardial infarction: a large replication study with increased sample size from the same population.

BACKGROUND: Case-control studies have successfully identified many genetic associations for complex diseases but suffer from lack of reproducibility in the same population. Demonstrating weak genetic effect requires large sample sizes to minimize statistical bias. Based on a study examining 500 myocardial infarction (MI) patients and 500 controls from the genetically isolated Newfoundland population, we previously reported that thrombospondin-4 (THBS-4) 1186G>C variant associates with MI in women. To validate this sex-dependent association with the THBS-4 variant, we analyzed an additional 532 patients and 514 controls from the same population and the combined cohort consisting of 1032 patients and 1014 controls. METHODS: Genotyping of THBS-4 1186G>C was conducted using Taq Man 1186G>C (A3879P) (rs 1866389) genotyping technology on real-time polymerase chain reaction. RESULTS: The genotype distributions of THBS-4 1186G>C in the validation and combined cohorts were similar with those in our initial study, which supports genetic homogeneity in the studied population. The association of the CC genotype with MI in women (odds ratio [OR], 2.96; P = .008) reported in our initial cohort failed to achieve statistical significance in our validation cohort (OR, 1.53; P = .307) but was confirmed in the combined cohort (OR, 2.14; P = .009). In contrast to the results from the initial cohort was a significant association of the CC genotype with later onset MI in the validation (OR, 2.37; P = .029) and combined cohorts (OR, 2.22; P = .011). Moreover, the larger studied population gave statistical power to associate the CC genotype with risk of MI in the total patient population (OR, 1.58; P = .023). CONCLUSION: Homozygosity for the THBS-4 1186C variant is a weak risk factor for MI especially in older women.

Age Factors↗
PubMed1983

Phonological process analysis from spontaneous speech: the influence of sample size.

Phonological process analysis is becoming a popular technique for the evaluation of unintelligible children and adults. Spontaneous speech sampling procedures have been advocated as a representative sampling base for phonological process analysis; however, little research has been reported detailing the parameters of spontaneous samples in reference to this assessment technique. The purpose of the present study was to evaluate the influence of increasing sample size on phonological process analyses from spontaneous speech. Results clearly indicated that samples of 50 words provided descriptive information similar to samples of 100 words. Additional studies are called for to investigate other variables that might influence the results of spontaneous speech analysis.

Child↗
PubMed1998

A note on the sample size determination in two-period repeated measurements crossover design with application to clinical trials.

The two-period repeated measurements crossover design is often used in clinical trials. In this article we give a formula for sample size determination for testing treatment effect in two-period repeated measurements crossover design by taking an analysis of variance approach to the repeated measurements analysis. A balanced situation is considered: two treatment sequences that have the same number of patients, and the time points of measurements on each subject within each treatment period equal in number. The formula reveals the relationship between the required number of patients for each treatment sequence, the number of repeated measurements within each treatment period, the detectable treatment difference with respect to a primary response variable, and the power for testing the treatment difference. A clinical trial example is given to illustrate the use of the formula.

Clinical Trials as Topic↗
PubMed1992

Estimation of leprosy prevalence in Bago and Kawa townships using two-stage probability proportionate to size sampling technique.

Two surveys to estimate leprosy prevalence using two-stage probability proportionate to size sampling technique were conducted in Bago and Kawa townships. A total of 3519 and 3739 individuals were examined in each township. The two surveys were finished within 25 (Bago) and 30 (Kawa) working days at a cost of Kyats 10,000 (US $1500) for each survey. The estimated leprosy prevalence obtained in Bago was 9.95 per 1000 population (95% confidence interval (CI): 7.11-12.78) and in Kawa it was 12.04 per 1000 population (95% CI: 8.85-15.22). A total of 30 (Bago) and 34 (Kawa) new leprosy cases were detected in the two surveys. Grade I disability was seen to be 20% in Bago and 18.78% in Kawa, whereas grade II disability was 17.14% in Bago and 15.56% in Kawa.

Adolescent↗
PubMed2002

Power and sample size calculations for case-control genetic association tests when errors are present: application to single nucleotide polymorphisms.

The purpose of this work is to quantify the effects that errors in genotyping have on power and the sample size necessary to maintain constant asymptotic Type I and Type II error rates (SSN) for case-control genetic association studies between a disease phenotype and a di-allelic marker locus, for example a single nucleotide polymorphism (SNP) locus. We consider the effects of three published models of genotyping errors on the chi-square test for independence in the 2 x 3 table. After specifying genotype frequencies for the marker locus conditional on disease status and error model in both a genetic model-based and a genetic model-free framework, we compute the asymptotic power to detect association through specification of the test's non-centrality parameter. This parameter determines the functional dependence of SSN on the genotyping error rates. Additionally, we study the dependence of SSN on linkage disequilibrium (LD), marker allele frequencies, and genotyping error rates for a dominant disease model. Increased genotyping error rate requires a larger SSN. Every 1% increase in sum of genotyping error rates requires that both case and control SSN be increased by 2-8%, with the extent of increase dependent upon the error model. For the dominant disease model, SSN is a nonlinear function of LD and genotyping error rate, with greater SSN for lower LD and higher genotyping error rate. The combination of lower LD and higher genotyping error rates requires a larger SSN than the sum of the SSN for the lower LD and for the higher genotyping error rate.

Case-Control Studies↗
PubMed2003

Sample size considerations for establishing clinical bioequivalence of allergen formulations.

Bioequivalence of formulations must be established by proving that the differences between the formulations are within a specified interval according to Equation 1, the Interval Hypothesis. Explicit estimates of sample size determined from Equation 8 and listed in Table 1 are qualitatively larger than those that would be determined from Equation 2, the Hypothesis of No Difference. Equation 8 was derived from the TOST procedure; other valid methods should yield comparable results. In any context, this discussion has illustrated that the failure to demonstrate a difference is not sufficient to demonstrate equivalence, and that a properly powered equivalence study of allergen formulations will generally demand many more than four study subjects.

Allergens↗