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At least 181 records · Page 10Linked to original sources
PubMed2005

Effects of reducing sample size on density estimates of citrus rust mite (Acari: Eriophyidae) on citrus fruit: simulated sampling.

The consequence of reducing sample size on the accuracy and precision of estimates of citrus rust mite, Phyllocoptruta oleivora (Ashmead), densities on oranges was investigated. The sample unit was a 1-cm2 surface area on fruit. Sampling plans consisting of 360, 300, 200, 160, 80, 48, 36, or 20 samples per 4 ha were evaluated through computer simulations by using real count data from 32 data sets of 600 sample units per 4 ha. The original and reduced sampling plans were hierarchical with different numbers of sample areas per 4 ha, trees per area, fruit per tree, and samples per fruit. Individual estimates (n=100 simulations per data set) using each plan were sometimes considerably below or above target densities. In an original set of count data with a mean of six mites per cm2, simulations of 36 samples per 4 ha produced individual estimates ranging from one to 16 mites per cm2, whereas 80 samples per 4 ha produced estimates ranging from two to 10 mites per cm2. The plans consisting of 36 or more samples were projected to provide precision levels of 0.25 (SEM/mean) or better at densities of five or more mites per cm2 based on log-data, a projection that needs to be verified under real-grove situations. Each plan consistently provided mite detection in these sampling simulations except those consisting of 20 or 36 samples, which sometimes failed to detect mites when the target density was less than five mites per cm2. The study provided insight into the probable precision, accuracy and detection thresholds for eight candidate sampling plans varying from relatively low to high resource input.

Acari↗
PubMed2006

Rule-of-thumb adjustment of sample sizes to accommodate dropouts in a two-stage analysis of repeated measurements.

Recent contributions to the statistical literature have provided elegant model-based solutions to the problem of estimating sample sizes for testing the significance of differences in mean rates of change across repeated measures in controlled longitudinal studies with differentially correlated error and missing data due to dropouts. However, the mathematical complexity and model specificity of these solutions make them generally inaccessible to most applied researchers who actually design and undertake treatment evaluation research in psychiatry. In contrast, this article relies on a simple two-stage analysis in which dropout-weighted slope coefficients fitted to the available repeated measurements for each subject separately serve as the dependent variable for a familiar ANCOVA test of significance for differences in mean rates of change. This article is about how a sample of size that is estimated or calculated to provide desired power for testing that hypothesis without considering dropouts can be adjusted appropriately to take dropouts into account. Empirical results support the conclusion that, whatever reasonable level of power would be provided by a given sample size in the absence of dropouts, essentially the same power can be realized in the presence of dropouts simply by adding to the original dropout-free sample size the number of subjects who would be expected to drop from a sample of that original size under conditions of the proposed study.

Analysis of Variance↗
PubMed2003

MRI as a biomarker of disease progression in a therapeutic trial of milameline for AD.

OBJECTIVE: To assess the feasibility of using MRI measurements as a surrogate endpoint for disease progression in a therapeutic trial for AD. METHODS: A total of 362 patients with probable AD from 38 different centers participated in the MRI portion of a 52-week randomized placebo-controlled trial of milameline, a muscarinic receptor agonist. The therapeutic trial itself was not completed due to projected lack of efficacy on interim analysis; however, the MRI arm of the study was continued. Of the 362 subjects who underwent a baseline MRI study, 192 subjects underwent a second MRI 1 year later. Hippocampal volume and temporal horn volume were measured from the MRI scans. RESULTS: The annualized percent changes in hippocampal volume (-4.9%) and temporal horn volume (16.1%) in the study patients were consistent with data from prior single-site studies. Correlations between the rate of MRI volumetric change and change in behavioral/cognitive measures were greater for the temporal horn than for the hippocampus. Decline over time was more consistently seen with imaging measures, 99% of the time for the hippocampus, than behavioral/cognitive measures (p < 0.001). Greater consistency in MRI than behavioral/clinical measures resulted in markedly lower estimated sample size requirements for clinical trials. The estimated number of subjects per arm required to detect a 50% reduction in the rate of decline over 1 year are: AD Assessment Scale-cognitive subscale 320; Mini-Mental Status Examination 241; hippocampal volume 21; temporal horn volume 54. CONCLUSION: The consistency of MRI measurements obtained across sites, and the consistency between the multisite milameline data and that obtained in prior single-site studies, demonstrate the technical feasibility of using structural MRI measures as a surrogate endpoint of disease progression in therapeutic trials. However, validation of imaging as a biomarker of therapeutic efficacy in AD awaits a positive trial.

Adolescent↗
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↗
PubMed2006

Evaluating the performance of species richness estimators: sensitivity to sample grain size.

1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations. 2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao 1, Jackknifel and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated. 4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).

Animals↗
PubMed1995

Misclassification of a prognostic dichotomous variable: sample size and parameter estimate adjustment.

Under general conditions, Lagakos showed that for an explanatory variable observed with error, the asymptotic relative efficiency (ARE) when using the observed rather than the true values in linear models, logistic models and proportional hazards models for survival is the square of the correlation between the true and observed variables. The result is useful for sample size adjustment when this correlation is estimable. Often, one cannot observe correct values of the explanatory variable under any circumstances. We show, however, that under the models considered by Lagakos for a dichotomous explanatory variable, the ARE equals the kappa statistic in a read-reread protocol. Consequently, one need not know 'truth' in this situation to estimate the ARE and to adjust sample size to maintain desired power; divide the estimated sample size obtained with the assumption of no measurement error by the consistent estimate of the kappa statistic (which is unlikely to be zero or negative). We then develop heuristically an adjusted estimate of the beta parameter in a proportional hazards survival model. The work was motivated by analyses of the Childhood Brain Tumour Consortium database. Examples from this database illustrate the method.

Brain Neoplasms↗
PubMed2004

Sample size for gene expression microarray experiments.

MOTIVATION: Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true discovery and accuracy rates. The sample size problem is formulated as: the number of arrays needed in order to achieve the desired fraction of the specified measure at the desired family-wise power at the given type I error and (standardized) effect size. RESULTS: We present a general approach for estimating sample size under independent and equally correlated models using binomial and beta-binomial models, respectively. The sample sizes needed for a two-sample z-test are computed; the computed theoretical numbers agree well with the Monte Carlo simulation results. But, under more general correlation structures, the beta-binomial model can underestimate the needed samples by about 1-5 arrays. CONTACT: jchen@nctr.fda.gov.

Algorithms↗
PubMed2003

Approximate estimation of minimal sample size required for marker-assisted backcross breeding.

Backcross breeding is a useful method to transfer favorable alleles from a donor parent into a recipient parent. Marker-assisted selection (MAS) can speed up the process. To make an appropriate plan before using MAS in a breeding program, breeders need to know the minimal sample size of the progeny generation required. A method to estimate the minimal sample size required for marker-assisted backcross breeding when both foreground selection and background selection are conducted is proposed. On the basis of a simplified assumption that the target loci are introgressed and the genetic background are independent, the probability of selecting individuals with desired genotypes in each generation is approximately estimated combining analytical approach (for foreground selection) and simulation according to the graphic genotypes of backcrossing parents (for background selection). The minimal sample size required to obtain at least one desired individual with a given probability is estimated. Application of the method is demonstrated with hypothesized examples. The method can be conveniently applied to practical backcross breeding programs.

Alleles↗
PubMed2006

Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.

There has been considerable research conducted on the development of statistical models for predicting crashes on highway facilities. Despite numerous advancements made for improving the estimation tools of statistical models, the most common probabilistic structure used for modeling motor vehicle crashes remains the traditional Poisson and Poisson-gamma (or Negative Binomial) distribution; when crash data exhibit over-dispersion, the Poisson-gamma model is usually the model of choice most favored by transportation safety modelers. Crash data collected for safety studies often have the unusual attributes of being characterized by low sample mean values. Studies have shown that the goodness-of-fit of statistical models produced from such datasets can be significantly affected. This issue has been defined as the "low mean problem" (LMP). Despite recent developments on methods to circumvent the LMP and test the goodness-of-fit of models developed using such datasets, no work has so far examined how the LMP affects the fixed dispersion parameter of Poisson-gamma models used for modeling motor vehicle crashes. The dispersion parameter plays an important role in many types of safety studies and should, therefore, be reliably estimated. The primary objective of this research project was to verify whether the LMP affects the estimation of the dispersion parameter and, if it is, to determine the magnitude of the problem. The secondary objective consisted of determining the effects of an unreliably estimated dispersion parameter on common analyses performed in highway safety studies. To accomplish the objectives of the study, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size. Three estimators commonly used by transportation safety modelers for estimating the dispersion parameter of Poisson-gamma models were evaluated: the method of moments, the weighted regression, and the maximum likelihood method. In an attempt to complement the outcome of the simulation study, Poisson-gamma models were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size. The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process. The probability the dispersion parameter becomes unreliably estimated increases significantly as the sample mean and sample size decrease. Consequently, the results show that an unreliably estimated dispersion parameter can significantly undermine empirical Bayes (EB) estimates as well as the estimation of confidence intervals for the gamma mean and predicted response. The paper ends with recommendations about minimizing the likelihood of producing Poisson-gamma models with an unreliable dispersion parameter for modeling motor vehicle crashes.

Accidents, Traffic↗
PubMed1990

Sample size requirements for interval estimation of the odds ratio.

Sample sizes are calculated for unmatched case-control (or cohort) studies where the goal is interval estimation of the odds ratio. The procedure used gives the smallest sample size for which a 100(1-alpha)% confidence interval for the log odds ratio will not exceed a specified width with specified probability (1-gamma). Tables of sample sizes for various choices of parameter values are presented. Considerable disagreement is found with a published method which has as its basis expected cell counts.

Case-Control Studies↗
PubMed1994

Sample size for repeated measures studies with binary responses.

We consider the sample size required for repeated measures studies when the response variable is binary. We propose the use of weighted least squares (WLS) for calculating the minimum sample size required to detect some minimum clinically important treatment effect. We provide tabulated values of the estimated sample sizes for a simple example and we discuss some practical considerations in determination of sample size with repeated binary responses.

Bias↗
PubMed2005

Vitamin C and E supplementation in women at high risk for preeclampsia: a double-blind, placebo-controlled trial.

OBJECTIVE: We sought to determine the effect of supplemental antioxidant vitamins C and E on the rate of preeclampsia in high-risk pregnant women. STUDY DESIGN: Women at risk for preeclampsia (previous preeclampsia, chronic hypertension, pregestational diabetes, or multifetal gestation) were recruited at 14 to 20 weeks' gestation and randomly assigned to receive either 1000 mg of vitamin C and 400 IU of vitamin E or placebo daily in addition to their regular prenatal vitamins. The primary outcome was the occurrence of preeclampsia. An estimated sample size of 220 women in each arm was determined to be necessary to demonstrate a 50% reduction in the rate of preeclampsia. RESULTS: Funding was terminated after 109 women had been recruited; 9 were lost to follow-up or withdrew. We analyzed data from the remaining 100 women to look for differences in outcome and to estimate the required sample size for future studies. The rate of preeclampsia was not different: 17.3% in women who received supplemental vitamins C and E, versus 18.8% in the placebo group. Assuming a baseline rate of preeclampsia in the placebo group between 15% and 20%, we can estimate that 500 to 950 women in each arm will be required to show a clinically important reduction in the rate of preeclampsia. CONCLUSION: The potential benefit of vitamin C and E supplementation to prevent preeclampsia in women with clinical risk factors is smaller than we estimated. Future studies of antioxidant vitamin supplementation in this population will require more than 500 women in each arm.

Adult↗
PubMed1998

Equivalence and superiority testing in regeneration clinical trials.

The purpose of this report is to investigate sample size requirements for both equivalence and superiority studies investigating products used in regeneration. The goal of a superiority clinical trial is to determine if a new therapy is superior to an established therapy or placebo. In contrast to superiority trials, equivalence trials are used to determine if a new product has similar therapeutic properties to an established product. The sample sizes for the two different types of clinical trials were based on the following assumptions: an alpha of 0.05, a power of 0.80, a 2 group parallel arm study, and equal variances and sample sizes for both groups. Separate sample size calculations were done for both intrabony defects and Class II furcation defects. Sample sizes for the equivalence and superiority trials using the same criteria were the same. However, criteria for estimating sample sizes for equivalence clinical trials require much smaller differences between groups, resulting in much larger sample sizes. A criterion of a 20% difference between groups of the total therapeutic effect resulted in sample sizes which ranged from 64 to 127 in equivalence clinical trials. These samples sizes are much larger than have been generally used in clinical trials investigating periodontal regeneration.

Alveolar Bone Loss↗
PubMed2001

Clinical trials of multiple sclerosis monitored with enhanced MRI: new sample size calculations based on large data sets.

OBJECTIVE: A new parametric simulation procedure based on the negative binomial (NB) model was used to evaluate the sample sizes needed to achieve optimal statistical powers for parallel groups (with (PGB) and without (PG) a baseline correction scan). It was also used for baseline versus treatment (BVT) design clinical trials in relapsing-remitting (RR) and secondary progressive (SP) multiple sclerosis (MS), when using the number of new enhancing lesions seen on monthly MRI of the brain as the measure of outcome. METHODS: MRI data obtained from 120 untreated patients with RRMS selected for the presence of MRI activity at baseline, 66 untreated and unselected patients with RRMS, and 81 untreated and unselected patients with SPMS were fitted using an NB distribution. All these patients were scanned monthly for at least 6 months and were all from the placebo arms of three large scale clinical trials and one natural history study. The statistical powers were calculated for durations of follow up of 3 and 6 months. RESULTS: The frequency of new enhancing lesions in patients with SPMS was lower, but not significantly different, from that seen in unselected patients with RRMS. As expected, enhancement was more frequent in patients with RRMS selected for MRI activity at baseline than in the other two patient groups. As a consequence, the estimated sample sizes needed to detect treatment efficacy in selected patients with RRMS were smaller than those of unselected patients with RRMS and those with SPMS. Baseline correction was also seen to reduce the sample sizes of PG design trials. An increased number of scans reduced the sample sizes needed to perform BVT trials, whereas the gain in power was less evident in PG and PGB trials. CONCLUSION: This study provides reliable estimates of the sample sizes needed to perform MRI monitored clinical trials in the major MS clinical phenotypes, which should be useful for planning future studies.

Adult↗
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↗
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↗
PubMed1999

Laboratory assay reproducibility of serum estrogens in umbilical cord blood samples.

We evaluated the reproducibility of laboratory assays for umbilical cord blood estrogen levels and its implications on sample size estimation. Specifically, we examined correlation between duplicate measurements of the same blood samples and estimated the relative contribution of variability due to study subject and assay batch to the overall variation in measured hormone levels. Cord blood was collected from a total of 25 female babies (15 Caucasian and 10 Chinese-American) from full-term deliveries at two study sites between March and December 1997. Two serum aliquots per blood sample were assayed, either at the same time or 4 months apart, for estrone, total estradiol, weakly bound estradiol, and sex hormone-binding globulin (SHBG). Correlation coefficients (Pearson's r) between duplicate measurements were calculated. We also estimated the components of variance for each hormone or protein associated with variation among subjects and variation between assay batches. Pearson's correlation coefficients were >0.90 for all of the compounds except for total estradiol when all of the subjects were included. The intraclass correlation coefficient, defined as a proportion of the total variance due to between-subject variation, for estrone, total estradiol, weakly bound estradiol, and SHBG were 92, 80, 85, and 97%, respectively. The magnitude of measurement error found in this study would increase the sample size required for detecting a difference between two populations for total estradiol and SHBG by 25 and 3%, respectively.

Asian People↗
PubMed2005

Sample size determination for estimation of the accuracy of two conditionally independent tests in the absence of a gold standard.

We developed an Excel spreadsheet template (available at http://www.epi.ucdavis.edu/diagnostictests/) to calculate sample sizes to estimate sensitivity and specificity with desired precision in the absence of a gold standard. Calculations are predicated on the use of two conditionally independent tests for screening animals from two populations and are based on the methods of Hui and Walter(1980). Sample size calculations rely on asymptotic normality of maximum likelihood (ML) estimates of parameters. Spreadsheets for calculating standard errors for the parameter estimates and for providing ML estimates using cross-tabulated data also are included. An example of application of the methods to bovine paratuberculosis is presented.

Animals↗