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PubMed1998

Comparative evaluation of two models for estimating sample sizes for tests on trends across repeated measurements.

Two equations for calculating sample sizes that are required for power in testing differences in rates of change in repeated measurement designs have been presented by different authors. One equation provides support for the conclusion that increased frequency of measurements across a treatment period of fixed duration enhances power of the tests. The other equation supports the counterintuitive conclusion that increased frequency of measurements actually tends to decrease power in the presence of realistic serial dependencies in the data. Monte Carlo methods confirm that the equation providing support for the latter conclusion is accurate, whereas the alternative equation tends to underestimate sample sizes required for power in testing differences in slopes of regression lines fitted to changes in the repeated measurements across time when symmetry is absent from the covariance structure.

Clinical Trials as Topic↗
PubMed1995

Estimating sample sizes for a two-stage sampling survey of seroprevalence of pseudorabies virus (PRV)-infected swine at a regional level in The Netherlands.

In the European Union, vaccination campaigns against Pseudorabies virus (PRV) in swine have been started to eradicate PRV. Specific sampling designs are needed to monitor PRV seroprevalence at a regional level. This paper demonstrates how sampling theory can be applied to design a disease seroprevalence survey, using PRV as an example. In the spring of 1994, the four regions in the Netherlands covered by the regional Animal Health Services were monitored with respect to PRV seroprevalence. Per region, blood samples from approximately 1400 herds, with two animals per herd, were collected. The sampling design accounted for stratification by fattening pig and sow population within each region. The regional PRV seroprevalence of swine in the Southern region was the highest (24.9%), closely followed by the PRV seroprevalence of swine in the Eastern region (20.5%). These regions have the highest density of swine in the Netherlands. The PRV seroprevalence in the Western and Central region (11.7%) was about half of the seroprevalence in the Southern and Eastern regions; the lowest regional PRV seroprevalence was observed in the Northern region (3.5%). The Northern part also has the lowest pig density. The PRV seroprevalence was approximately two times higher in sows than in fattening pigs.

Animals↗
PubMed1999

Estimating sample sizes for continuous, binary, and ordinal outcomes in paired comparisons: practical hints.

Paired data occur in crossover trials and matched case-control studies, and it is rare to find studies reporting sample size calculations associated with these types of studies, despite recommendations from editors that sample size calculations should be justified. In this article we describe some simple formulas and strategies for calculating the number of patients that should be entered into a matched or paired study when the outcome measures are continuous, binary, or ordinal.

Aged↗
PubMed1995

Estimating sample sizes for binary, ordered categorical, and continuous outcomes in two group comparisons.

Sample size calculations are now mandatory for many research protocols, but the ones useful in common situations are not all easily accessible. This paper outlines the ways of calculating sample sizes in two group studies for binary, ordered categorical, and continuous outcomes. Formulas and worked examples are given. Maximum power is usually achieved by having equal numbers in the two groups. However, this is not always possible and calculations for unequal group sizes are given.

Data Interpretation, Statistical↗
PubMed1994

Malaria vaccine study site in Irian Jaya, Indonesia: Plasmodium falciparum incidence measurements and epidemiologic considerations in sample size estimation.

Malaria epidemiologic and entomologic studies were performed during both the high transmission and low transmission seasons to characterize the Plasmodium falciparum malaria transmission at a proposed malaria vaccine trial site in Irian Jaya, Indonesia. The study population consisted of two subsets: native Irianese men with lifelong exposure to malaria and transmigrants who arrived from a nonmalarious area 2.5 years before the start of the study. All subjects received a radical cure for malaria and were then monitored weekly by blood film. Both P. falciparum malaria attack rates and incidence densities were calculated; transmigrants had a significantly higher rate (P = 0.003) than the Irianese during the low transmission season study (20-weeks long) but not during the high transmission season study (12-weeks long). Lack of exposure-induced immunity left the transmigrants at a minimum 17-25% greater relative risk of becoming parasitemic compared with the Irianese during the low transmission season study. During the high transmission season study, 50% of the transmigrants were P. falciparum positive by week 6 and 50% of the Irianese by week 9. During the low transmission season, 50% of the transmigrants were positive by week 10 and 43% of the Irianese were positive by week 17. Entomologic studies showed that Anopheles koliensis was the predominant vector (> 98% of anopheline catch). Entomologic inoculation rates for P. falciparum were 0.018 and 0.39 infective bites/person/night for the low and high transmission seasons, respectively. New P. vivax cases represented between 16% and 42% of all initial malaria cases.(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed1994

Deviance estimates of sample size for equivalence tests in vaccine trials.

This paper proposes a sample size procedure for both equivalence and conventional tests for the comparison of two binomial proportions, based on the signed square root of the deviance. When the comparison is based on the odds ratio, I describe an alternate 'close' conditional exact method that gives results that support those given by the deviance method. I summarize the advantages of the deviance-based method and also show that in general equivalence situations the sample size estimate depends upon the measure of comparison selected, odds ratio, risk ratio or risk difference.

Binomial Distribution↗
PubMed2004

Sample size and power estimation for studies with health related quality of life outcomes: a comparison of four methods using the SF-36.

We describe and compare four different methods for estimating sample size and power, when the primary outcome of the study is a Health Related Quality of Life (HRQoL) measure. These methods are: 1. assuming a Normal distribution and comparing two means; 2. using a non-parametric method; 3. Whitehead's method based on the proportional odds model; 4. the bootstrap. We illustrate the various methods, using data from the SF-36. For simplicity this paper deals with studies designed to compare the effectiveness (or superiority) of a new treatment compared to a standard treatment at a single point in time. The results show that if the HRQoL outcome has a limited number of discrete values (< 7) and/or the expected proportion of cases at the boundaries is high (scoring 0 or 100), then we would recommend using Whitehead's method (Method 3). Alternatively, if the HRQoL outcome has a large number of distinct values and the proportion at the boundaries is low, then we would recommend using Method 1. If a pilot or historical dataset is readily available (to estimate the shape of the distribution) then bootstrap simulation (Method 4) based on this data will provide a more accurate and reliable sample size estimate than conventional methods (Methods 1, 2, or 3). In the absence of a reliable pilot set, bootstrapping is not appropriate and conventional methods of sample size estimation or simulation will need to be used. Fortunately, with the increasing use of HRQoL outcomes in research, historical datasets are becoming more readily available. Strictly speaking, our results and conclusions only apply to the SF-36 outcome measure. Further empirical work is required to see whether these results hold true for other HRQoL outcomes. However, the SF-36 has many features in common with other HRQoL outcomes: multi-dimensional, ordinal or discrete response categories with upper and lower bounds, and skewed distributions, so therefore, we believe these results and conclusions using the SF-36 will be appropriate for other HRQoL measures.

Algorithms↗
PubMed2003

Inter-test reliability for non-invasive measures of respiratory muscle function in healthy humans.

The aims of this study were to quantify the inter-test reliability of several voluntary, non-invasive measures of respiratory muscle function and to determine the implications of these data for studies using a repeated-measures design. Systematic measurement differences were found for 50% of the variables ( P</=0.05, t-tests). Nevertheless, 95% ratio limits of agreement for most measures proved acceptable and similar to those reported elsewhere. The random error component of the agreement ratios ranged from 1.047 to 1.149 for measures of pulmonary function in healthy subjects ( n=46), 1.045 to 1.056 for maximum static respiratory pressures ( n=24), 1.062 to 1.173 for measures relating to the maximum pressure-flow-power relationship ( n=16-22), and 1.036 to 1.071 for measures relating to maximum incremental inspiratory muscle performance ( n=12). The judgement that the limits of agreement were acceptable is supported by the sample-size calculations. Estimated sample sizes based upon an alpha level of 0.05 and a statistical power of 0.9 were mostly </=11 for a repeated-measures experimental design, particularly for the larger effect sizes (>/=5%). However, peak expiratory flow, the maximum rate of pressure development and the time constant of relaxation, require larger sample sizes to detect small within-group changes. In conclusion, the described protocols provide reliable measurements for most parameters of respiratory muscle function in healthy subjects. Furthermore, experiments utilising a within-subjects design lasting up to 3 weeks can be conducted with feasible sample sizes (</=11 per group) where substantial (>/=5%) changes are expected.

Adult↗
PubMed2001

Use of sample size for estimating efficacy of a vaccine against an infectious disease.

OBJECTIVE: To determine the sample size necessary to evaluate the efficacy of a vaccine in a population. PROCEDURE: An equation was coded into a computer spreadsheet to compare the traditional sample size calculation with that needed when evaluating the efficacy of a vaccine applied in a population. RESULTS: The traditional approach used to conservatively estimate sample size necessary to detect a given difference in group proportions potentially greatly underestimates the number of animals needed for vaccine efficacy (VE) trials. In VE trials, it is necessary to estimate the effect of population-level vaccination prior to estimating sample size. In VE trials, as incidence proportion in the population or herd decreases or VE decreases, necessary sample size increases. CONCLUSIONS AND CLINICAL RELEVANCE: In designing a clinical or field trial, such as one to evaluate the efficacy of a vaccine against an infectious disease in a population, one needs to approach sample size calculations in a nontraditional manner. The proportion of the population vaccinated, disease transmission dynamics, and VE will affect the incidence in the nonvaccinated and vaccinated groups and, hence, sample size. Thus, estimation of the effect of the vaccination on the population must be made prior to calculating sample size. Otherwise, sample size and the power to identify VE will be insufficient.

Animal Diseases↗
PubMed2003

A model to estimate the optimal sample size for microbiological surveys.

Estimating optimal sample size for microbiological surveys is a challenge for laboratory managers. When insufficient sampling is conducted, biased inferences are likely; however, when excessive sampling is conducted valuable laboratory resources are wasted. This report presents a statistical model for the estimation of the sample size appropriate for the accurate identification of the bacterial subtypes of interest in a specimen. This applied model for microbiology laboratory use is based on a Bayesian mode of inference, which combines two inputs: (ii) a prespecified estimate, or prior distribution statement, based on available scientific knowledge and (ii) observed data. The specific inputs for the model are a prior distribution statement of the number of strains per specimen provided by an informed microbiologist and data from a microbiological survey indicating the number of strains per specimen. The model output is an updated probability distribution of strains per specimen, which can be used to estimate the probability of observing all strains present according to the number of colonies that are sampled. In this report two scenarios that illustrate the use of the model to estimate bacterial colony sample size requirements are presented. In the first scenario, bacterial colony sample size is estimated to correctly identify Campylobacter amplified restriction fragment length polymorphism types on broiler carcasses. The second scenario estimates bacterial colony sample size to correctly identify Salmonella enterica serotype Enteritidis phage types in fecal drag swabs from egg-laying poultry flocks. An advantage of the model is that as updated inputs from ongoing surveys are incorporated into the model, increasingly precise sample size estimates are likely to be made.

Animals↗
PubMed1988

Sample sizes based on the log-rank statistic in complex clinical trials.

The log-rank test is frequently used to compare survival curves. While sample size estimation for comparison of binomial proportions has been adapted to typical clinical trial conditions such as noncompliance, lag time, and staggered entry, the estimation of sample size when the log-rank statistic is to be used has not been generalized to these types of clinical trial conditions. This paper presents a method of estimating sample sizes for the comparison of survival curves by the log-rank statistic in the presence of unrestricted rates of noncompliance, lag time, and so forth. The method applies to stratified trials in which the above conditions may vary across the different strata, and does not assume proportional hazards. Power and duration, as well as sample sizes, can be estimated. The method also produces estimates for binomial proportions and the Tarone-Ware class of statistics.

Clinical Trials as Topic↗
PubMed1994

Estimation and sample size considerations for clustered binary responses.

Although there is much literature on sample size determination for clinical trials or experiments with independent responses, there is a lack of methodology to obtain sample sizes for dependent outcomes. This paper presents a simple way to calculate sample size for estimating treatment effects and diagnostic accuracy in the case of correlated binary outcomes. The proposed weighted procedure also has use in estimation, whose advantages we demonstrate through simulation. Recommendations are made for practical application.

Clinical Trials as Topic↗
PubMed1997

Sample size requirements in case-only designs to detect gene-environment interaction.

With advances in molecular genetic technology, more studies will examine gene-environment interaction in disease etiology. If the primary purpose of the study is to estimate the effect of gene-environment interaction in disease etiology, one can do so without employing controls. The case-only design has been promoted as an efficient and valid method for screening for gene-environment interaction. The authors derive a method for estimating sample size requirements, present sample size estimates, and compare minimum sample size requirements to detect gene-environment interaction in case-only studies with case-control studies. Assuming independence between exposure and genotype in the population, the authors believe that the case-only design is more efficient than a case-control design in detecting gene-environment interaction. They also illustrate a method to estimate sample size when information on marginal effects (relative risk) of exposure and genotype is available from previous studies.

Case-Control Studies↗
PubMed2004

The effect of sample size for estimating Rasch/IRT parameters with dichotomous items.

Thirteen samples were randomly drawn from the normative database for the latest edition of Knox's Cube Test-Revised (KCT-R). Parameter estimates for the Rasch model and two and three parameter logistic models were derived and compared. Sample size influenced these estimates as might be expected. Rasch parameter estimates consistently showed the smallest values by sample size using a goodness of fit index.

Data Interpretation, Statistical↗