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At least 199 records · Page 11Linked to original sources
PubMed2003

Sample size re-estimation in group-sequential response-adaptive clinical trials.

In clinical trials where the variances of the response variables are unknown, in accurate estimates of these can affect the type II error rate considerably. More accurate estimates of the variances may be obtained by taking a look at the data available part way through the trial and re-calculating the required sample size based on these new estimates. The main impetus for sample size re-estimation came from a two-stage procedure developed by Stein in 1945 and the literature is now replete with variations on this approach. In this paper, existing sample size re-estimation methods for both fixed sample and sequential clinical trial models will be reviewed. These will then be extended for use in group-sequential response-adaptive designs. In particular, a test for a recently developed group-sequential response-adaptive design, which compares two treatments with immediate normally distributed responses and unknown variances, is presented based on a modified version of Stein's test. The principal modifications involve updating the required sample size at each interim analysis and calculating the test statistic based on the current estimates of the variances. Hence, all the available information is used at each stage. Simulation is used to assess to what extent the updating of the required sample size at each interim analysis in the new test helps to attain the nominal error rates. The test is compared to modified versions of a simple test and a Stein-type group sequential t-test studied in the recent literature. These tests calculate the required sample sizes based on less accurate estimates of the variances. The type I error rate is close to the nominal value and the power is more accurately maintained in the new test.

Clinical Trials as Topic↗
PubMed1998

Sample size calculation in economic evaluations.

A simulation method is presented for sample size calculation in economic evaluations. As input the method requires: the expected difference and variance of costs and effects, their correlation, the significance level (alpha) and the power of the testing method and the maximum acceptable ratio of incremental effectiveness to incremental costs. The method is illustrated with data from two trials. The first compares primary coronary angioplasty with streptokinase in the treatment of acute myocardial infarction, in the second trial, lansoprazole is compared with omeprazole in the treatment of reflux oesophagitis. These case studies show how the various parameters influence the sample size. Given the large number of parameters that have to be specified in advance, the lack of knowledge about costs and their standard deviation, and the difficulty of specifying the maximum acceptable ratio of incremental effectiveness to incremental costs, the conclusion of the study is that from a technical point of view it is possible to perform a sample size calculation for an economic evaluation, but one should wonder how useful it is.

2-Pyridinylmethylsulfinylbenzimidazoles↗
PubMed1997

An examination of methods for sample size recalculation during an experiment.

In designing experiments, investigators frequently can specify an important effect that they wish to detect with high power, without the ability to provide an equally certain assessment of the variance of the response. If the experiment is designed based on a guess of the variance, an under-powered study may result. To remedy this problem, there have been several procedures proposed that obtain estimates of the variance from the data as they accrue and then recalculate the sample size accordingly. One class of procedures is fully sequential in that it assesses after each response whether the current sample size yields the desired power based on the current estimate of the variance. This approach is efficient, but it is not practical or advisable in many situations. Another class of procedures involves only two or three stages of sampling and recalculates the sample size based on the observed variance at designated times, perhaps coinciding with interim efficacy analyses. The two-stage approach can result in substantial oversampling, but it is feasible in many situations, whereas the three-stage approach corrects the problem of oversampling, but is less feasible. We propose a procedure that aims to combine the advantages of both the fully sequential and the two-stage approaches. This quasi-sequential procedure involves only two stages of sampling and it applies to the stopping rule from the fully sequential procedure to data beyond the initial sample which we obtain via multiple imputation. We show through simulations that when the initial sample size is substantially less than the correct sample size, the mean squared error of the final sample size calculated from the quasi-sequential procedure can be considerably less than that from the two-stage procedure. We compare the distributions of these recalculated sample sizes and discuss our findings for alternative procedures, as well.

Anti-HIV Agents↗
PubMed2007

Performance of adaptive sample size adjustment with respect to stopping criteria and time of interim analysis.

The benefit of adjusting the sample size in clinical trials on the basis of treatment effects observed in interim analysis has been the subject of several recent papers. Different conclusions were drawn about the usefulness of this approach for gaining power or saving sample size, because of differences in trial design and setting. We examined the benefit of sample size adjustment in relation to trial design parameters such as 'time of interim analysis' and 'choice of stopping criteria'. We compared the adaptive weighted inverse normal method with classical group sequential methods for the most common and for optimal stopping criteria in early, half-time and late interim analyses. We found that reacting to interim data might significantly reduce average sample size in some situations, while classical approaches can out-perform the adaptive designs under other circumstances. We characterized these situations with respect to time of interim analysis and choice of stopping criteria.

Clinical Trials as Topic↗
PubMed2004

Effect of a guarantee time on sample size determination for testing the ratio of means from two lifetime distributions.

This paper studies the problem of sample size determination for testing the ratio of means from two lifetime distributions when thresholds, or guarantee times, are present in the survival distributions. We consider several different approaches of sample size determination based on different distributional assumptions and discuss the role that guarantee times play in these sample size computations. We show that the guarantee times, if they exist, can significantly affect the sample size computations. The Aging Intervention Testing Program from the National Institute on Aging is used to demonstrate our results.

Clinical Trials as Topic↗
PubMed2004

Sample sizes for clinical trials with normal data.

This article gives an overview of sample size calculations for parallel group and cross-over studies with Normal data. Sample size derivation is given for trials where the objective is to demonstrate: superiority, equivalence, non-inferiority, bioequivalence and estimation to a given precision, for different types I and II errors. It is demonstrated how the different trial objectives influence the null and alternative hypotheses of the trials and how these hypotheses influence the calculations. Sample size tables for the different types of trials and worked examples are given.

Biometry↗
PubMed2002

Calculating sample size bounds for logistic regression.

The calculation of a study's required sample size is one of the most important aspects of the validity of an epidemiological study. Logistic regression often is used in modelling in epidemiology. A simplified method to calculate the sample size for the multiple logistic-regression model was proposed by Hsieh et al. [Stat. Med. 17 (1998) 1623]. The approach for estimating the sample size is described and then applied in the planning of an epidemiological cross-sectional study of the associations of different risk factors with Toxoplasma infection among pregnant women. Although the method demands some additional information which is often difficult to obtain, it is a very useful tool in veterinary epidemiology.

Animals↗
PubMed1995

Development of sample size models for national general practice surveys.

The most cost-effective method to measure the morbidity managed and treatments provided in general practice is from records of a cluster of consultations (encounters) from each general practitioner (GP) in a random sample. A cluster sampling method is proposed for future surveys for analysis of encounter-based general practice data. The sample sizes needed to measure the most common problems managed and drugs prescribed were estimated using ratio-estimator models for cluster sample surveys. Morbidity and treatment rates were estimated from the Australian Morbidity and Treatment Survey in General Practice 1990-1991 (AMTS). The 20 most common problems in the AMTS were managed at estimated rates of 1.5 to 9.5 per 100 encounters. The 20 most common drugs were prescribed at estimated rates of 0.7 to 3.6 per 100 problems. These rates were used to determine precision as a percentage of each true value for future surveys, that is, as relative precision. If we want to be 95 per cent confident that these rates will be within 5 per cent of each true rate, sample sizes of 552 to 5675 GPs are needed. If we fix the sample size at 1000 GPs, relative precision lies within 12 per cent of these rates. If the sample size is increased to 1500 GPs, relative precision improves only marginally. The differences in sample size for each of the most frequent morbidity and treatment data are largely due to their variable distributions and relatively infrequent occurrence in general practice. A sample size of 1000 GPs will enable measurement of the most common morbidity and treatments at 95 per cent confidence.

Australia↗
PubMed1993

Sample size calculation, power analysis and randomization: research project design in Windows.

Single estimates of sample size for a study may be easily obtained by use of a hand calculator or from published tables. In contrast, performing multiple calculations is a tedious and time-consuming task, which is greatly simplified by a computer program. The computer program presented here assists the investigator in calculating sample size estimates, determining statistical power and creating randomization tables for a study. The program is designed primarily for clinical trials and thus includes some features not found in other software packages performing similar tasks. Sample size calculation and power analysis are performed for dichotomous, continuous (parametric and non-parametric tests) and time-to-failure (exponential distribution and log-rank test) response variables, and for correlation coefficients. Sample size estimates and significance levels may be adjusted for multiple participating centers, non-compliance, interim analyses and.multiple testing. The randomization subroutine generates tables for studies with up to nine treatment arms and with any valid block size. As a Windows application, the program runs in a multitasking environment, allowing switching between programs and easy pasting of results into word-processing documents and other applications. It is very simple to use, with a completely menudriven interface and sufficient built-in help to obviate the use of a manual.

Algorithms↗
PubMed1986

Sample size for case-control studies using Cochran's statistic.

Sample size determination for case-control studies of chronic disease are often based on the simple 2 X 2 tabular cross-classification of exposure and disease, thereby ignoring stratification which may be considered in the analysis. One consequence of this approach is that the sample size may be inadequate to attain a specified power and size when performing a statistical analysis on J 2 X 2 tables using Cochran's (1954, Biometrics 10, 417-451) statistic or the Mantel-Haenszel (1959, Journal of the National Cancer Institute 22, 719-748) statistic. A sample size formula is derived from Cochran's statistic and it is compared with the corresponding one derived when the data are treated as unstratified, and also with two other formulas proposed for stratified data analysis. The formula developed yields values slightly higher than one recently proposed by Muñoz and Rosner (1984, Biometrics 40, 995-1004), which assumes that both margins of each 2 X 2 table are fixed, while the present study considers only the case-control margin to be fixed.

Biometry↗
PubMed1998

Exploratory treatment trials in multiple sclerosis using MRI: sample size calculations for relapsing-remitting and secondary progressive subgroups using placebo controlled parallel groups.

OBJECTIVES: Serial brain MRI is widely used in pilot studies of new agents to monitor treatment efficacy in relapsing-remitting (RR) and secondary progressive (SP) multiple sclerosis (MS). For pilot trials, sample size calculations for the RR subgroup are based on the data from small numbers of patients and separate calculations for the SP subgroup have not been performed. The present study considers these issues. METHODS: The sample size calculations were based on data from six months of monthly T2 weighted and gadolinium enhanced MRI in 31 RR and 28 SP untreated patients undergoing natural history studies or in the placebo arm of a therapeutic trial. The calculations were for a placebo controlled, parallel groups design lasting six months. The sample sizes were based on bootstrap analysis with an 80% likelihood of showing a given treatment effect. RESULTS: With a single pretreatment scan, demonstration of a 70% reduction in newly active lesions required 2x30 RR and 2x50 SP patients. With an extra run-in scan one month before treatment, the sample sizes were 2x20 for RR and 2x30 for SP patients. CONCLUSIONS: The sample sizes required for RR patients were comparable with previous smaller studies. Larger sample sizes were needed for the SP group, but the extra run in scan resulted in a reduction in both groups. The larger sample sizes in the SPMS group were probably due to the combination of a higher proportion of patients with low MRI activity (< or =2 active MRI lesions in 50% of SP and 32% RR patients), as well as a few patients who displayed extremely high activity, thus increasing interpatient variability. These data should be considered in planning pilot MRI outcome trials.

Adult↗
PubMedDate not supplied

Sample size calculations for clinical studies allowing for uncertainty about the variance.

One of the most important steps in the design of a pharmaceutical clinical trial is the estimation of the sample size. For a superiority trial the sample size formula (to achieve a stated power) would be based on a given clinically meaningful difference and a value for the population variance. The formula is typically used as though this population variance is known whereas in reality it is unknown and is replaced by an estimate with its associated uncertainty. The variance estimate would be derived from an earlier similarly designed study (or an overall estimate from several previous studies) and its precision would depend on its degrees of freedom. This paper provides a solution for the calculation of sample sizes that allows for the imprecision in the estimate of the sample variance and shows how traditional formulae give sample sizes that are too small since they do not allow for this uncertainty with the deficiency being more acute with fewer degrees of freedom. It is recommended that the methodology described in this paper should be used when the sample variance has less than 200 degrees of freedom.

Clinical Trials as Topic↗
PubMed2006

Reproducibility of 5-HT2A receptor measurements and sample size estimations with [18F]altanserin PET using a bolus/infusion approach.

PURPOSE: To determine the reproducibility of measurements of brain 5-HT2A receptors with an [18F]altanserin PET bolus/infusion approach. Further, to estimate the sample size needed to detect regional differences between two groups and, finally, to evaluate how partial volume correction affects reproducibility and the required sample size. METHODS: For assessment of the variability, six subjects were investigated with [18F]altanserin PET twice, at an interval of less than 2 weeks. The sample size required to detect a 20% difference was estimated from [18F]altanserin PET studies in 84 healthy subjects. Regions of interest were automatically delineated on co-registered MR and PET images. RESULTS: In cortical brain regions with a high density of 5-HT2A receptors, the outcome parameter (binding potential, BP1) showed high reproducibility, with a median difference between the two group measurements of 6% (range 5-12%), whereas in regions with a low receptor density, BP1 reproducibility was lower, with a median difference of 17% (range 11-39%). Partial volume correction reduced the variability in the sample considerably. The sample size required to detect a 20% difference in brain regions with high receptor density is approximately 27, whereas for low receptor binding regions the required sample size is substantially higher. CONCLUSION: This study demonstrates that [18F]altanserin PET with a bolus/infusion design has very low variability, particularly in larger brain regions with high 5-HT2A receptor density. Moreover, partial volume correction considerably reduces the sample size required to detect regional changes between groups.

Adolescent↗
PubMed2006

Sample sizes for self-controlled case series studies.

We derive several formulae for the sample size required for a study designed using the self-controlled case series method without age effects. We investigate these formulae by simulation, and identify one based on the signed root likelihood ratio statistic which performs well. We extend this method to allow for age effects, which can have a big impact on the sample size needed. This more general sample size formula is also found to perform well in a broad range of situations.

Child, Preschool↗
PubMed2003

Sampling strategy in molecular microbial ecology: influence of soil sample size on DNA fingerprinting analysis of fungal and bacterial communities.

Assessing soil microbial community structure by the use of molecular techniques requires a satisfactory sampling strategy that takes into account the high microbial diversity and the heterogeneous distribution of microorganisms in the soil matrix. The influence of the sample size of three different soil types (sand, silt and clay soils) on the DNA yield and analysis of bacterial and fungal community structure were investigated. Six sample sizes from 0.125 g to 4 g were evaluated. The genetic community structure was assessed by automated ribosomal intergenic spacer analysis (A-RISA fingerprint). Variations between bacterial (B-ARISA) and fungal (F-ARISA) community structure were quantified by using principal component analysis (PCA). DNA yields were positively correlated with the sample size for the sandy and silty soils, suggesting an influence of the sample size on DNA recovery, whereas no correlation was observed in the clay soil. B-ARISA was shown to be consistent between the different sample sizes for each soil type indicating that the sampling procedure has no influence on the assessment of bacterial community structure. On the contrary for F-ARISA profiles, strong variations were observed between replicates of the smaller samples (<1 g). Principal component analysis analysis revealed that sampling aliquots of soil > or =1 g are required to obtain robust and reproducible fingerprinting analysis of the genetic structure of fungal communities. However, the smallest samples could be adequate for the detection of minor populations masked by dominant ones in larger samples. The sampling strategy should therefore be different according to the objectives: rather large soil samples (> or =1 g) for a global description of the genetic community structure, or a large number of small soil samples for a more complete inventory of microbial diversity.

Bacteria↗
PubMed1996

Standard error and sample size determination for estimation of probabilities based on a test variable.

A method of sample size determination for estimation of probabilities based on a test variable is presented. Applications to estimation of sensitivity and specificity of medical tests are the focus of this research, although the methods can be applied to other areas of study such as engineering reliability. Examples are given for determining sample sizes required for the classification of patients with cutaneous lupus erythematosus based on the incidence of several markers. In this example, the test variable is the number of markers present. The methodology employs a weighted average of model-based and non-model-based estimates of the probability with the weights determined by the closeness to or the confidence in the given model. Formulas and charts required for determining sample size are provided for test variables that can be modeled by the binomial, Poisson, or normal distributions, i.e., for the most commonly encountered distributions for counting events (binomial and Poisson) and for measurements (normal). However, the methods given can be applied to any distribution, including multivariate. Especially when relatively small probabilities (the rare events) are being estimated, the techniques provided assistance in safeguarding against undersampling brought on by unwarranted confidence in a test variable distribution and against oversampling required for high accuracy in non-model-based probability estimators.

Epidemiologic Methods↗
PubMed1981

Sample sizes for group sequential cohort and case-control study designs.

This paper proposes the use of group sequential methods of calculate sample sizes for cohort and case-control study designs. The methods are based upon the theory of repeated significance tests, one of several sequential methods currently available. Group sequential methods permit repeated significance testing of relative risks obtained from periodically accumulated data while maintaining the required overall level of significance. Tables are presented for cohort and case-control studies in which the average sample size required for a group sequential design is compared to that of the conventionl fixed sample size plan for the usual constant relative risk situation. The tables show that group sequential designs are in general more efficient than fixed sample size plans for cohort and case-control studies. Computer simulations showed that group sequential methods can be employed when stratified data analyses are to be used and when the sample sizes of the two study groups are unequal.

Biometry↗
PubMed2001

Sample size calculations for intervention trials in primary care randomizing by primary care group: an empirical illustration from one proposed intervention trial.

Because of the central role of the general practice in the delivery of British primary care, intervention trials in primary care often use the practice as the unit of randomization. The creation of primary care groups (PCGs) in April 1999 changed the organization of primary care and the commissioning of secondary care services. PCGs will directly affect the organization and delivery of primary, secondary and social care services. The PCG therefore becomes an appropriate target for organizational and educational interventions. Trials testing these interventions should involve randomization by PCG. This paper discusses the sample size required for a trial in primary care assessing the effect of a falls prevention programme among older people. In this trial PCGs will be randomized. The sample size calculations involve estimating intra-PCG correlation in primary outcome: fractured femur rate for those 65 years and over. No data on fractured femur rate were available at PCG level. PCGs are, however, similar in size and often coterminous with local authorities. Therefore, intra-PCG correlation in fractured femur rate was estimated from the intra-local authority correlation calculated from routine data. Three alternative trial designs are considered. In the first design, PCGs are selected for inclusion in the trial from the total population of England (eight regions). In the second design, PCGs are selected from two regions only. The third design is similar to the second except that PCGs are stratified by region and baseline value of fracture rate. Intracluster correlation is estimated for each of these designs using two methods: an approximation which assumes cluster sizes are equal and an alternative method which takes account of the fact that cluster sizes vary. Estimates of sample size required vary between 26 and 7 PCGs in each intervention group, depending on the trial design and the method used to calculate sample size. Not unexpectedly, stratification by baseline value of the outcome variable decreases the sample size required. In our analyses, geographic restriction of the population to be sampled reduces between-cluster variability in the primary outcome. This leads to an increase in precision. When allowance for variable cluster size is made, the increase in precision is not as great as would be expected with equal cluster sizes. This paper highlights the usefulness of routine data in work of this kind, and establishes one of the essential prerequisites for our proposed trial and other trials using primary outcomes with similar between-PCG variation: a feasible sample size.

Accidental Falls↗