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At least 937 records · Page 52Linked to original sources
PubMed2005

Effects of aggregation and sample size on composite faecal egg counts in sheep.

Composite faecal egg counts (FEC) are increasingly used to support strategic anthelmintic treatment decisions in grazing livestock. However, their accuracy as estimators of group mean FEC is affected by the number of individual samples included, how thoroughly they are mixed, and the underlying degree of parasite aggregation between individual hosts. This paper uses a Negative Binomial model for parasite aggregation, and a Poisson model for egg distribution within faecal suspensions, in order to optimise composite FEC protocol for commercial sheep flocks. Our results suggest that faecal egg density in a well-mixed composite sample from 10 sheep (3g of faeces from each), estimated by examination of four independently filled McMaster chambers, is likely to provide an adequate estimate of group mean FEC in the majority of situations. However, extra care is needed in groups of sheep for which high levels of FEC aggregation might be expected. The implications of statistical error in FEC estimates depend on how they are used. The simulation-based approach presented here is a powerful tool for investigating the risks of error in FEC-driven treatment decisions in different situations, as well as for the statistical analysis of parasitological data in general.

Animals↗
PubMed2005

Statistical power and sample size estimation for headache research: an overview and power calculation tools.

The present article reviews the concept of statistical power analysis for research designs in headache. First, we present a basic overview of the concepts of statistical hypothesis testing. Then we discuss the elements of power analysis and, where appropriate, we address conventions for power calculations. We offer, for public use, an applied power calculator for two applications that are often encountered in headache research. We intend to help headache researchers design trials with adequate statistical power by offering a conceptual overview and the power calculators. In closing, we briefly address the implications of the present trend toward reporting point estimates of effect sizes with confidence levels.

Headache Disorders↗
PubMed1986

Sample-size considerations and strategies for linkage analysis in autosomal recessive disorders.

The opportunity raised by recombinant DNA technology to develop a linkage marker panel that spans the human genome requires cost-efficient strategies for its optimal utilization. Questions arise as to whether it is more cost-effective to convert a dimorphic restriction enzyme marker system into a highly polymorphic system or, instead, to increase the number of families studied, simply using the available marker alleles. The choice is highly dependent on the population available for study, and, therefore, an examination of the informational content of the various family structures is important to obtain the most informative data. To guide such decisions, we have developed tables of the average sample number of families required to detect linkage for autosomal recessive disorders under single backcross and under "fully informative" matings. The latter cross consists of a marker locus with highly polymorphic codominant alleles such that the parental marker genotypes can be uniquely distinguished. The sampling scheme considers families with unaffected parents of known mating types ascertained via affected offspring, for sibship sizes ranging from two to four and various numbers of affected individuals. The sample-size tables, calculated for various values of the recombination fractions and lod scores, may serve as a guide to a more efficient application of the restriction fragment length polymorphism technology to sequential linkage analysis.

DNA Restriction Enzymes↗
PubMed2005

Sampling size in the verification of manufactured-supplied air kerma strengths.

Quality control mandate that the air kerma strengths (S(K)) of permanent seeds be verified, this is usually done by statistics inferred from 10% of the seeds. The goal of this paper is to proposed a new sampling method in which the number of seeds to be measured will be set beforehand according to an a priori statistical level of uncertainty. The results are based on the assumption that the S(K) has a normal distribution. To demonstrate this, the S(K) of each of the seeds measured was corrected to ensure that the average S(K) of its sample remained the same. In this process 2030 results were collected and analyzed using a normal plot. In our opinion, the number of seeds sampled should be determined beforehand according to an a priori level of statistical uncertainty.

Air↗