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PubMed2001

Sample size requirement for detecting interference under the chi-square model.

The chi-square model (CHS) of recombination has been studied extensively, in recent years, for its ability of capturing the process and estimating the level of crossover interference. Exploration thus far shows that this model yields much better fits to human genetic data than Haldane's no-interference model, and the explicit level of interference can be easily estimated as well. This paper provides calculations of sample sizes required to detect interference under CHS for a variety of settings. Two data types, fully informative meioses and phase-unknown backcross families, are studied. Under each setting, we calculate the number of meioses/families needed to ensure that the expected log-likelihood difference between the chi(2) interference model and Haldane's no-interference model exceeds a prespecified threshold. It is found that joint consideration of multiple (more than three) markers dramatically reduces the number of meioses/families needed when compared to an analysis based on three-point data, a traditional setting for detecting interference using three-point tests. The results indicate that the numbers of meioses needed to detect interference under CHS are well within the reach of most genetic mapping studies.

Chi-Square Distribution↗
PubMedDate not supplied

Power and sample size considerations in clinical trials with competing risk endpoints.

In clinical trials with a time-to-event endpoint, subjects are often at risk for events other than the one of interest. When the occurrence of one type of event precludes observation of any later events or alters the probably of subsequent events, the situation is one of competing risks. During the planning stage of a clinical trial with competing risks, it is important to take all possible events into account. This paper gives expressions for the power and sample size for competing risks based on a flexible parametric Weibull model. Nonuniform accrual to the study is considered and an allocation ratio other than one may be used. Results are also provided for the case where two or more of the competing risks are of primary interest.

Clinical Trials as Topic↗
PubMed1997

Sample size determination for repeated measurements in bioequivalence test.

When the measurement of outcome is unreliable or the cost of obtaining an additional subject is relatively high compared to the cost of obtaining an additional measurement from the same subject, it may be desirable to consider taking more than one measurement per subject to increase power or to minimize the cost in a clinical trial. When each subject in two comparison groups has a fixed number of repeated measurements, this paper develops an asymptotic procedure to calculate the number of subjects per group required to achieve a given power for an a-level bioequivalence test. Furthermore, Monte Carlo simulation is used to evaluate the accuracy of the approximate sample size calculation procedure and a brief discussion on how to determine the optimal number of repeated measurements is included.

Humans↗
PubMed1999

Interaction, subgroup analysis and sample size.

The term "interaction" has both statistical and scientific connotations that do not always coincide. Multistage models are used as a bridge between these two viewpoints and as a way of illustrating the different types of qualitative interaction. The most important type of interaction for metabolic polymorphisms is between a genetic trait and an environmental or lifestyle exposure. In some cases these factors act multiplicatively on the risk ratios and so do not interact in the statistical sense. Nevertheless, the risk can be much higher when both are present. Interactions present severe problems of interpretation, as naive comparisons in subgroups can be very misleading and can produce false positive results because of the large number of comparisons. Methods for combating this are discussed. The main requirement, however, is for a substantially larger sample size than would be required for estimating the main effects.

Bayes Theorem↗
PubMed2002

Dealing with competing risks: testing covariates and calculating sample size.

It is universally agreed that Kaplan-Meier estimates overestimate the probability of the event of interest in the presence of competing risks. Kalbfleisch and Prentice recommend using the cumulative incidence as an estimate of the probability of an event of interest. However, there is no consensus on how to test the effect of a covariate in the presence of competing risks. Using simulations, this paper illustrates that the Cox proportional hazards model gives valid results when employed in testing the effect of a covariate on the hazard rate and when estimating the hazard ratio. A method to calculate the sample size for testing the effect of a covariate on outcome in the presence of competing risks is also provided.

Breast Neoplasms↗
PubMedDate not supplied

[Sample size for estimating attributable risk in cross-sectional studies].

The prevalence of a variety of risk factors and their strength of association with a disease can vary greatly among apparently similar communities. In small communities, risk estimates can also vary from year to year. An identification of important risk factors in each community is then needed, so that interventions can be specifically oriented towards the needs of each specific community. The attributable risk is the adequate measure of association for these purposes. The purpose of this paper is to determine the minimum sample size required to detect a given attributable risk in cross-sectional studies. A table was constructed, presenting the number of exposed subjects necessary to detect a given attributable risk for different combinations of prevalence of disease and prevalence of exposure to a given risk factor, with a power of 0.80 and alpha of 0.05.

Cross-Sectional Studies↗
PubMed2005

Translational clinical trials: an entropy-based approach to sample size.

Translational clinical trials are small studies of therapies emerging from the laboratory. These trials are essential for generating early evidence regarding the effects of treatment on specific targets in the disease pathway and for guiding the next studies to be done. The statistical properties of such studies have been neglected, in part, because they do not fit the well-known clinical trials developmental paradigm. This paper discusses the translational trial setting, and presents an information (entropy) based approach to understanding the properties and use of these trials. The combination of biological knowledge with a designed experiment (albeit a small one) is a powerful device for resolving much of the considerable uncertainty surrounding an emerging therapeutic concept. An approach to motivating the sample size for translational trials is presented.

Animals↗
PubMed2001

Analysis, sample size, and power for estimating incremental net health benefit from clinical trial data.

Stinnett and Mullahy recently introduced the concept of net health benefit as an alternative to cost-effectiveness ratios for the statistical analysis of patient-level data on the costs and health effects of competing interventions. Net health benefit addresses a number of problems associated with cost-effectiveness ratios by assuming a value for the willingness-to-pay for a unit of effectiveness. We extend the concept of net health benefit to demonstrate that standard statistical procedures can be used for the analysis, power, and sample size determinations of cost-effectiveness data. We also show that by varying the value of the willingness-to-pay, the point estimate and confidence interval for the incremental cost-effectiveness ratio can be determined. An example is provided.

Biometry↗
PubMed2007

Understanding sample size: what determines the required number of microarrays for an experiment?

DNA microarray experiments have become a widely used tool for studying gene expression. An important, but difficult, part of these experiments is deciding on the appropriate number of biological replicates to use. Often, researchers will want a number of replicates that give sufficient power to recognize regulated genes while controlling the false discovery rate (FDR) at an acceptable level. Recent advances in statistical methodology can now help to resolve this issue. Before using such methods it is helpful to understand the reasoning behind them. In this Research Focus article we explain, in an intuitive way, the effect sample size has on the FDR and power, and then briefly survey some recently proposed methods in this field of research and provide an example of use.

Data Interpretation, Statistical↗
PubMed1991

Parasite prevalence and host sample size.

Parasite prevalence is a summary statistic familiar to biologists. However, that there is an interspecific relationship between prevalence and sample size (the number of host individuals examined for parasites) is not widely appreciated. In this article, Richard Gregory and Tim Blackburn present some examples of this negative relationship, explain the mechanisms that underlie this pattern and discuss the potential problems this association might create for biological studies.

Journal Article↗
PubMed2004

Determining sample size for the morphological assessment of sperm.

Morphologic assessment of spermatozoa is an integral component in the analysis of semen. Whether a technician rapidly screening semen quality at a commercial stud, a veterinarian performing breeding soundness examinations, a clinician at a reference andrology laboratory providing auditing or diagnostic services, or a researcher evaluating morphology as a part of a fertility study, it is important to make an informed decision regarding the number of spermatozoa to include in the morphology assessment. Application of basic statistical principles such as the nature of proportions, level of confidence in an observed value, and the interaction of sample size with precision, can and should be used in the decision process. This paper outlines in detail the application of these statistical principles in relation to the morphologic assessment of spermatozoa. Guidelines on how these principles can be utilized in practical situations are discussed. Additionally, methodologies for comparison of results within and between laboratories (an area easily prone to misinterpretation) are reviewed. It is hoped that through the use of these fundamental statistical principles, this paper will bring clarity and delineation to the science of quantifying the morphology of spermatozoa.

Animals↗
PubMed2005

New adjustment factors and sample size calculation in a DNA-pooling experiment with preferential amplification.

In the post-genome era, disease gene mapping using dense genetic markers has become an important tool for dissecting complex inheritable diseases. Locating disease susceptibility genes using DNA-pooling experiments is a potentially economical alternative to those involving individual genotyping. The foundation of a successful DNA-pooling association test is a precise and accurate estimation of allele frequency. In this article, we propose two new adjustment methods that correct for preferential amplification of nucleotides when estimating the allele frequency of single-nucleotide polymorphisms. We also discuss the effect of sample size when calibrating unequal allelic amplification. We conducted simulation studies to assess the performance of different adjustment procedures and found that our proposed adjustments are more reliable with respect to the estimation bias and root mean square error compared with the current approach. The improved performance not only improves the accuracy and precision of allele frequency estimations but also leads to more powerful disease gene mapping.

Algorithms↗
PubMed2006

Patient acceptability of larval therapy for leg ulcer treatment: a randomised survey to inform the sample size calculation of a randomised trial.

BACKGROUND: A trial was commissioned to evaluate the effectiveness of larval therapy to debride and heal sloughy and necrotic venous leg ulcers. Larval therapy in the trial was to be delivered in either loose or bagged form. Researchers were concerned that resistance to larval therapy may threaten the feasibility of the trial. Additionally there was concern that the use of larval therapy may require a larger effect size in time to healing than originally proposed by the investigators. METHODS: To formally evaluate patient preferences a survey using two randomly allocated, nurse administered questionnaires was undertaken. Patients were randomised to receive one of the two following questionnaires (i) preferences between loose larvae and standard treatment (hydrogel) or (ii) patient preferences between bagged larvae and standard therapy (hydrogel). The study was undertaken in a Vascular Clinic, in an Outpatients Department of a large teaching hospital in the North of England. The sample consisted of 35 people aged 18 years and above with at least one leg ulcer of venous or mixed (venous and arterial) aetiology. RESULTS: Approximately 25% of participants would not consider the use of larval therapy as an acceptable treatment option for leg ulcers, regardless of the method of containment. For the patients that would consider the use of larval therapy, different preferences in healing times required to use the therapy were observed depending upon the method of containment. The median response of those participants questioned about bagged larvae found that they would be willing to use this therapy even if they were equally able to achieve healing with the use of hydrogel by 20 weeks. For those participants questioned about the use of loose larvae complete healing would have to have taken place over 17 weeks for them to choose larvae as their preferred option rather than hydrogel. This difference was not significant (p = 0.075). CONCLUSION: We found no evidence of widespread resistance to the utilisation of larval therapy from patients regardless of the method of larval therapy containment. These methods have the potential to inform sample size calculations where there are concerns of patient acceptability.

Aged↗
PubMed2005

The kappa statistic in reliability studies: use, interpretation, and sample size requirements.

PURPOSE: This article examines and illustrates the use and interpretation of the kappa statistic in musculoskeletal research. SUMMARY OF KEY POINTS: The reliability of clinicians' ratings is an important consideration in areas such as diagnosis and the interpretation of examination findings. Often, these ratings lie on a nominal or an ordinal scale. For such data, the kappa coefficient is an appropriate measure of reliability. Kappa is defined, in both weighted and unweighted forms, and its use is illustrated with examples from musculoskeletal research. Factors that can influence the magnitude of kappa (prevalence, bias, and non-independent ratings) are discussed, and ways of evaluating the magnitude of an obtained kappa are considered. The issue of statistical testing of kappa is considered, including the use of confidence intervals, and appropriate sample sizes for reliability studies using kappa are tabulated. CONCLUSIONS: The article concludes with recommendations for the use and interpretation of kappa.

Data Interpretation, Statistical↗
PubMed1998

Sample size determination for controlling the upper confidence limit of incidence rate of a binomial endpoint.

Assume that in a comparative clinical study the primary endpoint is a binary event, such as life or death. A new treatment or therapy is tested for a significant reduction of the incidence of the binary event compared with a control group. Another objective is to ensure that the incidence in the new treatment group is below some clinically acceptable value. This is done by calculating the exact upper 95% confidence limit for the probability of the event. The study is considered successful if the upper confidence limit is lower than a historical threshold, as well as if there is a significant reduction in the incidence of the event by the new treatment. In this article, we provide an exact method for calculating the sample size so that there will be adequate power to ensure that the exact upper confidence limit is below the threshold. Based on this we can design a study to achieve both objectives.

Binomial Distribution↗
PubMedDate not supplied

Choosing sample sizes to maximize expected health benefits subject to a constraint on total trial costs.

The authors present a method for choosing sample sizes for randomized controlled trials that maximizes expected health benefits (measured in expected discounted life years gained) subject to the decision maker's budget constraint. In comparison with similar approaches, the method introduces richer and more realistic models for the following quantities: costs and benefits during and after the trial, rates of adopting interventions after a positive recommendation, based on the results of the trial. Although the methodology is applicable to any type of trial, the emphasis in the paper is on prevention trials. Calculations involve Monte Carlo methods. An example is provided.

Bayes Theorem↗
PubMed1995

Confidence intervals, hypothesis tests, and sample sizes for the prevented fraction in cross-sectional studies.

The prevented fraction (PF) is the proportion of disease occurrence in a population averted due to a protective risk factor or public health intervention. The PF is not equivalent to the population attributable risk (AR). The AR is appropriate for epidemiologic studies of disease etiology, and for estimating the potential impact of modifying risk factor prevalence. The PF more directly measures the impact of public health interventions, however, and thus is an important evaluation tool. We derived the variance of the estimated PF by using maximum likelihood theory for cross-sectional studies. We used simulations to compare the performance of confidence intervals based on various transformations of the estimated PF. The logit transformation was the best choice when PF > or = 0.3, whereas the untransformed estimate was best when PF < 0.3. We present formulae for hypothesis testing and sample size calculations, discuss the issues of interaction and confounding and give two estimators adjusted for confounding.

Analysis of Variance↗
PubMed2003

Recommendation for confidence interval and sample size calculation for the Pearl Index.

A new guideline on the clinical investigation of steroid contraceptives in women, which has been released by the European Agency for the Evaluation of Medicinal Products (EMEA), calls for the calculation of a confidence interval for the Pearl Index, a widely used measure to describe the effectiveness of a contraceptive method. However, the interpretation of the Pearl Index as a statistical parameter, for which a confidence interval can be calculated, needs further clarification. The guideline does not provide the necessary definitions. In this paper, two statistical models, the Bernoulli model and the Poisson model, are compared; both can be used for the calculation of the Pearl Index and its upper confidence limit. The Poisson model proved to be more suitable, because it can accommodate incomplete treatment cycles. Unambiguous definitions and statistical formulae for the calculation of overall Pearl Index and the Method Failure Pearl Index are given. Finally, the sample sizes required to fulfill the EMEA's guideline are given.

Confidence Intervals↗