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.

At least 991 records · Page 55Linked to original sources

PubMed2001

Sample size tables for exact single-stage phase II designs.

Tables for single-phase II trials based on the exact binomial distribution are presented. These are preferable to those generated using Fleming's design, which are based on the normal approximation and can give rise to anomalous results. For example, if the upper success rate is accepted, the lower success rate, which the trial is designed to reject, may be included in the final confidence interval for the proportion being estimated.

Antiemetics↗

PubMed1998

Sample size and optimal designs for reliability studies.

A method is developed to calculate the required number of subjects k in a reliability study, where reliability is measured using the intraclass correlation rho. The method is based on a functional approximation to earlier exact results. The approximation is shown to have excellent agreement with the exact results and one can use it easily without intensive numerical computation. Optimal design configurations are also discussed; for reliability values of about 40 per cent or higher, use of two or three observations per subject will minimize the total number of observations required.

Analysis of Variance↗

PubMedDate not supplied

Active-controlled, non-inferiority trials in oncology: arbitrary limits, infeasible sample sizes and uninformative data analysis. is there another way?

In oncology, it may not always be possible to evaluate the efficacy of new medicines in placebo-controlled trials. Furthermore, while some newer, biologically targeted anti-cancer treatments may be expected to deliver therapeutic benefit in terms of better tolerability or improved symptom control, they may not always be expected to provide increased efficacy relative to existing therapies. This naturally leads to the use of active-control, non-inferiority trials to evaluate such treatments. In recent evaluations of anti-cancer treatments, the non-inferiority margin has often been defined in terms of demonstrating that at least 50% of the active control effect has been retained by the new drug using methods such as those described by Rothmann et al., Statistics in Medicine 2003; 22:239-264 and Wang and Hung Controlled Clinical Trials 2003; 24:147-155. However, this approach can lead to prohibitively large clinical trials and results in a tendency to dichotomize trial outcome as either 'success' or 'failure' and thus oversimplifies interpretation. With relatively modest modification, these methods can be used to define a stepwise approach to design and analysis. In the first design step, the trial is sized to show indirectly that the new drug would have beaten placebo; in the second analysis step, the probability that the new drug is superior to placebo is assessed and, if sufficiently high in the third and final step, the relative efficacy of the new drug to control is assessed on a continuum of effect retention via an 'effect retention likelihood plot'. This stepwise approach is likely to provide a more complete assessment of relative efficacy so that the value of new treatments can be better judged.

Clinical Trials as Topic↗

PubMed1999

Influence of sample size on image cytometry of DNA ploidy measurements.

OBJECTIVE: To assess the number of nuclei required for significant image cytometry DNA ploidy measurements on one archival case of breast cancer. STUDY DESIGN: From one case of aneuploid DNA breast cancer, 18 subsets made up of 152-1,524 for the whole population of undamaged nuclei and made up of 74-735 epithelial nuclei had DNA measured. DNA ploidy type and five DNA ploidy indices, allowing DNA ploidy histogram interpretation were evaluated on each population. RESULTS: Three hundred nuclei were always sufficient for DNA typing, whereas reliable results for DNA ploidy indices required at least 750 nuclei. CONCLUSION: To DNA measure the above number of nuclei, fully automated image cytometry DNA ploidy measurements are required.

Aneuploidy↗

PubMed2004

A practical approach to calculate sample size for herd prevalence surveys.

When designing a herd-level prevalence study that will use an imperfect diagnostic test, it is necessary to consider the test sensitivity and specificity. A new approach was developed to take into account the imperfections of the test. We present an adapted formula that, when combined with an existing piece of software, allows improved planning. Bovine paratuberculosis is included as an example infection because it originally stimulated the work. Examples are provided of the trade-off between the benefit (low number of herds) and the disadvantage (large number of animals per herd and exclusion of small herds) that are associated with achieving high herd-level sensitivity and specificity. We demonstrate the bias in the estimate of prevalence and the underestimate of the confidence range that would arise if we did not account for test sensitivity and specificity.

Animals↗

PubMed1997

F-wave: sample size and normative values.

F-waves were recorded from the abductor digiti minimi in 60 healthy subjects aged between 22 and 65 years. F-waves following 80 supramaximal stimuli were recorded. The F with minimal latency (Fmin), the mean latency (Fmean) and chronodispersion (CD) were calculated for each set of ten stimuli. In each subject the values after 10, 20, 30 etc. stimuli were compared with the "true" values obtained examining all responses. After twenty stimuli (16-20 F-waves) a Fmean with latency within 95% of the "true" value was recorded in the 95% of subjects. After twenty stimuli a Fmin with latency within 95% of the "true" latency was recorded in the 88% of subjects. Usually the Fmin after twenty stimuli is little different from the "true" Fmin (mean 1.4 ms), but in one subject the difference was 4.1 ms. The CD increases according to the number of responses examined and a careful evaluation requires 64-80 F-waves.

Adult↗

PubMedDate not supplied

Sample size and the probability of a successful trial.

This paper describes the distinction between the concept of statistical power and the probability of getting a successful trial. While one can choose a very high statistical power to detect a certain treatment effect, the high statistical power does not necessarily translate to a high success probability if the treatment effect to detect is based on the perceived ability of the drug candidate. The crucial factor hinges on our knowledge of the drug's ability to deliver the effect used to power the study. The paper discusses a framework to calculate the 'average success probability' and demonstrates how uncertainty about the treatment effect could affect the average success probability for a confirmatory trial. It complements an earlier work by O'Hagan et al. (Pharmaceutical Statistics 2005; 4:187-201) published in this journal. Computer codes to calculate the average success probability are included.

Algorithms↗

PubMed1995

Sample size determination for a trial of breast cancer screening under age 50: population versus case mortality approach.

OBJECTIVE: To determine the efficacy of screening for breast cancer in women aged 40 to 49. METHODS: A large number of women will have to be enrolled to ensure adequate statistical power. Attention will be restricted to the deaths from breast cancer that occur among comparable groups of patients with breast cancer rather than to an analysis in which all women entering into the trial are considered at risk of dying from breast cancer (population mortality analysis). As the new trial is likely to be conducted in regions where screening has been established for all women from age 50 onward, comparability may be reached by including the breast cancers detected at the routine screening examination at age 50. CONCLUSIONS: When comparability of cases can be reached, such a case mortality analysis requires 15-30% fewer women to be enrolled than a population approach.

Adult↗

PubMed2004

Power and sample size determination when assessing the clinical relevance of trial results by 'responder analyses'.

A fundamental issue in regulatory decision making is the assessment of the benefit/risk profile of a compound. In order to do this, establishing the existence of a treatment effect by a significance test is not sufficient, but the clinical relevance of a potential benefit must also be taken into account. A number of regulatory guidelines propose that clinical relevance should be assessed by considering the rate of responders, i.e. the proportion of patients who are observed to achieve an apparently meaningful benefit. In this paper, we present methods for planning clinical trials that aim at demonstrating both statistical and clinical significance in superiority trials. Procedures based on analytical calculations are derived for normally distributed data and the case of a single endpoint as well as multiple primary outcomes. A bootstrap procedure is proposed that can be applied to non-normal data. Application is illustrated by a clinical trial in Alzheimer's disease.

Activities of Daily Living↗

PubMed2003

Sample size needed to detect gene-gene interactions using association designs.

It is likely that many complex diseases result from interactions among several genes, as well as environmental factors. The presence of such interactions poses challenges to investigators in identifying susceptibility genes, understanding biologic pathways, and predicting and controlling disease risks. Recently, Gauderman (Am J Epidemiol 2002;155:478-84) reported results from the first systematic analysis of the statistical power needed to detect gene-gene interactions in association studies. However, Gauderman used different statistical models to model disease risks for different study designs, and he assumed a very low disease prevalence to make different models more comparable. In this article, assuming a logistic model for disease risk for different study designs, the authors investigate the power of population-based and family-based association designs to detect gene-gene interactions for common diseases. The results indicate that population-based designs are more powerful than family-based designs for detecting gene-gene interactions when disease prevalence in the study population is moderate.

Genetic Predisposition to Disease↗

PubMed2001

Influence of body mass on maximal oxygen uptake: effect of sample size.

Basal metabolic rate is scaled to body mass to the power of 0.73, and we evaluated whether a similar scaling applies when the O2 transport capacity of the body is challenged during maximal exercise (i.e. at maximal O2 uptake, VO2max). The allometric relationship between VO2max and body mass (y = a.xb, where y is VO2max and x is body mass) was developed for 967 athletes representing 25 different sports, with up to 157 participants in each sport. With an increasing number of observations, the exponent approached 0.73, while for ventilation the exponent was only 0.55. By using the 0.73 exponent for VO2max, the highest value [mean (SD)] for the males was obtained for the runners and cyclists [234 (16) ml.kg-0.73.min-1], and for the females the highest value was found for the runners [189 (14) ml.kg-0.73.min-1]. For the females, aerobic power was about 80% of the value achieved by the males. Scaling may help both in understanding variation in aerobic power and in defining the physiological limitations of work capacity.

Adolescent↗

PubMed1999

Sample size and design considerations for phase II clinical trials with correlated observations.

Several methods are available for the design of phase II clinical trials with binary endpoints. A primary assumption for most methods is that observations on the endpoint of interest are uncorrelated; however, this assumption is violated if an individual patient provides more than one observation on the endpoint of interest. In such cases, one solution is to use a summary measure for each patient; an alternative solution is to perform an observation-specific analysis using a technique that properly accounts for the correlation. In this paper, we investigate the effect that ignoring correlated observations can have on the design properties of the typical phase II clinical trial. In cases in which an observation-specific analysis is desirable, we propose a simple method that adjusts a standard one- or two-stage phase II design to account for loss of information due to correlated observations. Simulations demonstrate that the method ensures that type I and type II error rate design requirements are met even in the presence of strong correlation. We develop the method in the context of phase II oncology trials, but the method applies readily to other clinical areas in which multiple responses per patient are of interest.

Algorithms↗

PubMed2004

Experimental infectious respiratory disease in groups of calves: lobar distribution, variance, and sample-size requirements for vaccine evaluation.

The distribution and variance of respiratory disease produced with aerosols of bovine herpesvirus 1 (BHV-1) and Mannheimia haemolytica in control (183 calves in 44 experiments) and vaccinated calves were studied in experiments conducted at the Animal Diseases Research Institute, Lethbridge, Alberta, from 1975 to 1989. All calves had been born and raised at this institute and exposed similarly for 5 min by means of a face mask to viral and bacterial aerosols produced by a Collison atomizer (particles < 3 microm in diameter). We summarized the macroscopic pathological responses of pneumonia (main end point), tonsillitis, tracheitis, and other microbiologic and experimental variables. We also summarized the lobar distribution of pneumonia in 202 control and 192 vaccinated calves with this disease model and in calves similarly exposed to parainfluenza 3 virus/M. haemolytica or BHV-1/Pasteurella multocida. Pneumonia in control calves began in ventral tissues of all lobes, with lobar preferences, and progressed dorsally, the dorsal parts of both large caudal lobes being least affected. A high variance of pneumonia was evident within and among experiments. From the magnitude of variance observed in the control groups, the number of calves per group required in vaccine-challenge studies using this BHV-1/M. haemolytica disease model was estimated. Such estimates are required for any disease model used in vaccine-challenge studies.

Aerosols↗

PubMed1997

Minimum sample sizes for identifying chromosomal fragile sites from individuals: Monte Carlo estimation.

A Monte Carlo simulation procedure was used to estimate the exact level of the standardized X2 test statistic (Xs2) for randomness in the FSM methodology for the identification of fragile sites from chromosomal breakage data for single individuals. A random-number generator was used to simulate 10,000 chromosomal breakage data sets, each corresponding to the null hypothesis of no fragile sites for numbers of chromosomal breaks (n) from 1 to 2000 and at three levels of chromosomal band resolution (k). The reliability of the test was assessed by comparisons of the empirical and nominal alpha levels for each of the corresponding values of n and k. These analyses indicate that the sparse and discrete nature of chromosomal breakage data results in large and unpredictable discrepancies between the empirical and nominal alpha levels when fragile site identifications are based on small numbers of breaks (n < 0.5 k). With n > or = 0.5 k, the distribution of Xs2 appears to be stable and non-significant differences in the empirical and nominal alpha levels are generally obtained. These results are inherent to the nature of the data and are, therefore, relevant to any statistical model for the identification of fragile sites from chromosomal breakage data. For FSM identification of fragile sites at alpha = 0.05, we suggest that n > or = 0.5 k is the minimum reliable number of mapped chromosomal breaks per individual.

Chromosome Fragile Sites↗

PubMed2002

Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses.

Estimation of statistical power in functional MRI (fMRI) requires knowledge of the expected percent signal change between two conditions as well as estimates of the variability in percent signal change. Variability can be divided into intra-subject variability, reflecting noise within the time series, and inter-subject variability, reflecting subject-to-subject differences in activation. The purpose of this study was to obtain estimates of percent signal change and the two sources of variability from fMRI data, and then use these parameter estimates in simulation experiments in order to generate power curves. Of interest from these simulations were conclusions concerning how many subjects are needed and how many time points within a scan are optimal in an fMRI study of cognitive function. Intra-subject variability was estimated from resting conditions, and inter-subject variability and percent signal change were estimated from verbal working memory data. Simulations derived from these parameters illustrate how percent signal change, intra- and inter-subject variability, and number of time points affect power. An empirical test experiment, using fMRI data acquired during somatosensory stimulation, showed good correspondence between the simulation-based power predictions and the power observed within somatosensory regions of interest. Our analyses suggested that for a liberal threshold of 0.05, about 12 subjects were required to achieve 80% power at the single voxel level for typical activations. At more realistic thresholds, that approach those used after correcting for multiple comparisons, the number of subjects doubled to maintain this level of power.

Adult↗

PubMed1997

From genotypes to genes: doubling the sample size.

This paper considers the analysis of genetic case-control data. One approach considers the allele frequency in cases and controls. Because each individual has two alleles at any autosomal locus, there will be twice as many alleles as people. Another approach considers the risk of the disease in those who do not have the allele of interest (A), those who have a single copy (heterozygous), and those who are homozygous for A. A third approach does not differentiate between individuals with one or two copies of A. This was common when alleles were determined serologically and one could not distinguish between homozygotes and those with one copy of A and one of an unknown allele. All three approaches have been used in the literature, but this is the first systematic comparison of them. The different interpretations of the odds ratios from such analyses are explored and conditions are given under which the first two approaches are asymptotically equivalent. The chi-squared statistics from the three approaches are discussed. Both the odds ratio and the chi-squared statistic from the analysis that treats alleles rather than genotypes as individual entities are appropriate only when the Hardy-Weinberg equilibrium holds. When the equilibrium holds, the allele-based test statistic is asymptotically equivalent to the test for trend using the genotype data. Thus, analyses that treat alleles rather than people as observations should not be used. Instead, we recommend that such data should be analyzed by genotype.

Alleles↗

PubMed2002

Weakly modulated spike trains: significance, precision, and correction for sample size.

Many single-unit electrophysiological studies of visual cortex have investigated strong evoked responses to simple stimuli such as oriented gratings. Experiments involving other types of stimuli, such as natural scenes, higher-order features, and surface brightness, produce single-unit responses that are more difficult to interpret. Experiments with brightness, in particular, evoke single-unit responses that are typically weakly modulated. When the brightness is generated by a visual illusion such as the Cornsweet illusion, statistical tests are often necessary to distinguish true responses from baseline fluctuations. Here, using data collected from cat Areas 17 and 18 in response to real and illusory brightness stimuli, we provide a method for detecting and quantifying weak but significant periodic responses. By randomizing spike trains (via bootstrap methods), we provide confidence levels for response significance, permitting the evaluation of both weak and strong responses. We show that because of a strong dependence on total spike number, response significance can only be appropriately determined with randomized spike trains of similar spike number. Such randomizations can be performed for both stimulus-elicited and spontaneously occurring spike trains. By developing a method for generating randomized modulated spike trains (phase-restricted randomization) from actual recordings, we calculate upper and lower confidence limits of modulated spike trains and describe how measurement precision varies as a function of total spike count. Finally, using this randomization method, we describe how a correction function can be determined to correct for measurement bias introduced at low spike counts. These methods may also be useful in the study of small but potentially significant responses in other systems.

Action Potentials↗

PubMed2004

Tradeoff between no-call reduction in genotyping error rate and loss of sample size for genetic case/control association studies.

Single nucleotide polymorphisms (SNP) may be genotyped for use in case-control designs to test for association between a SNP marker and a disease using a 2 x 3 chi-squared test of independence. Genotyping is often based on underlying continuous measurements, which are classified into genotypes. A "no-call" procedure is sometimes used in which borderline observations are not classified. This procedure has the simultaneous effect of reducing the genotype error rate and the expected number of genotypes observed. Both quantities affect the power of the statistic. We develop methods for calculating the genotype error rate, the expected number of genotypes observed, and the expected power of the resulting test as a function of the no-call procedure. We examine the statistical properties of the chi-squared test using a no-call procedure when the underlying continuous measure of genotype classification is a three-component mixture of univariate normal distributions under a range of parameter specifications. The genotype error rate decreases as the no-call region is increased. The expected number of observations genotyped also decreases. Our key finding is that the expected power of the chi-squared test is not sensitive to the no-call procedure. That is, the benefits of reduced genotype error rate are almost exactly balanced by the losses due to reduced genotype observations. For an underlying univariate normal mixture of genotype classification to be analyzed with a 2 x 3 chi-squared test, there is little, if any, increase in power using a no-call procedure.

Case-Control Studies↗