Female vs Males inmates: Authors' reply and sample size calculation

Bruno and colleagues highlighted the relatively low absolute-percentage of psychiatric morbidity that we found in our sample, as compared to their male sample. They invited to the use of diagnostic tools, specific for personality disorders and suicidal risk factors, to better investigate these critical issues. In summary, our results showed high percentages of psychiatric morbidity (44%), especially anxiety and depression. Female inmates declared to be most stressed by the distance from family and loved ones, and they did not present any antisocial personality diagnosis. They also showed a resilient reaction to their situation, because more than a half of women requested a supportive psychological therapy during their detention (14/25= 56%), and 10 of them were those with a psychiatric disorder (10/11= 90.9%), thus a higher proportion than those reported in male detainees at the Messina jail (56%), by Zoccali and colleagues (Zoccali et al., 2008). As we have acknowledged, we could further explore other personality disorders and not merely the antisocial personality pathological traits, as we did in our study. In conclusion, we agree with them about the need to repeat the screening for mental health and a deeper ascertainment of specific suicidal risk factors, with an adequate sample. Nonetheless, we believe that our data were not biased, and likely represented the psychological asset of the ladies’ detainee with a final sentence at the Pagliarelli jail of Palermo

Approaches to sample size calculation for clinical trials in rare diseases

We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively

Sample Size Calculation for Finding Unseen Species

Estimation of the number of species extant in a geographic region has been discussed in the statistical literature for more than sixty years. The focus of this work is on the use of pilot data to design future studies in this context. A Dirichlet-multinomial probability model for species frequency data is used to obtain a posterior distribution on the number of species and to learn about the distribution of species frequencies. A geometric distribution is proposed as the prior distribution for the number of species. Simulations demonstrate that this prior distribution can handle a wide range of species frequency distributions including the problematic case with many rare species and a few exceptionally abundant species. Monte Carlo methods are used along with the Dirichlet-multinomial model to perform sample size calculations from pilot data, e.g., to determine the number of additional samples required to collect a certain proportion of all the species with a pre-specified coverage probability. Simulations and real data applications are discussed

Sample size calculation : proportions

Um problema bastante comum, cuja definição é fundamental na etapa de criação de um projeto de pesquisa é o cálculo do tamanho da amostra. A partir deste cálculo, será definido o cronograma de coleta de dados, ou mesmo a viabilidade do projeto. O objetivo deste artigo é apresentar o cálculo de tamanho de amostra para a estimação de uma proporção (prevalência ou incidência) e para a comparação de duas proporções de grupos independentes, através de exemplos práticos. Verifica-se que o tamanho da amostra para estimação de uma proporção aumenta, quando aumentamos o nível de confiança do intervalo ou quando diminuímos a margem de erro. Quando o objetivo é comparar proporções, o tamanho da amostra aumenta, quando diminuímos o nível de significância ou quando aumentamos o poder do teste, ou quando diminuímos a diferença mínima clinicamente significativa que desejamos detectar entre as proporções.Sample size calculation is a fairly common problem that has to be faced when designing a research project. The following aspects of the project will be defined based on this calculation: budget, schedule of data collection, existence (or not) of research subjects, i.e., viability of the project. The objective of the present article was to present the sample size calculation for estimation of a proportion (prevalence or incidence) and for the comparison of two proportions of independent groups using practical examples. We demonstrated that the sample size for estimation of a proportion increases as the confidence interval increases or as the margin of error decreases. When the objective is to compare proportions, the sample size increases as the level of significance decreases or as the power of the test increases, or even as the minimum clinical difference between the proportions is reduced

Sample size calculation for a stepped wedge trial.

BACKGROUND: Stepped wedge trials (SWTs) can be considered as a variant of a clustered randomised trial, although in many ways they embed additional complications from the point of view of statistical design and analysis. While the literature is rich for standard parallel or clustered randomised clinical trials (CRTs), it is much less so for SWTs. The specific features of SWTs need to be addressed properly in the sample size calculations to ensure valid estimates of the intervention effect. METHODS: We critically review the available literature on analytical methods to perform sample size and power calculations in a SWT. In particular, we highlight the specific assumptions underlying currently used methods and comment on their validity and potential for extensions. Finally, we propose the use of simulation-based methods to overcome some of the limitations of analytical formulae. We performed a simulation exercise in which we compared simulation-based sample size computations with analytical methods and assessed the impact of varying the basic parameters to the resulting sample size/power, in the case of continuous and binary outcomes and assuming both cross-sectional data and the closed cohort design. RESULTS: We compared the sample size requirements for a SWT in comparison to CRTs based on comparable number of measurements in each cluster. In line with the existing literature, we found that when the level of correlation within the clusters is relatively high (for example, greater than 0.1), the SWT requires a smaller number of clusters. For low values of the intracluster correlation, the two designs produce more similar requirements in terms of total number of clusters. We validated our simulation-based approach and compared the results of sample size calculations to analytical methods; the simulation-based procedures perform well, producing results that are extremely similar to the analytical methods. We found that usually the SWT is relatively insensitive to variations in the intracluster correlation, and that failure to account for a potential time effect will artificially and grossly overestimate the power of a study. CONCLUSIONS: We provide a framework for handling the sample size and power calculations of a SWT and suggest that simulation-based procedures may be more effective, especially in dealing with the specific features of the study at hand. In selected situations and depending on the level of intracluster correlation and the cluster size, SWTs may be more efficient than comparable CRTs. However, the decision about the design to be implemented will be based on a wide range of considerations, including the cost associated with the number of clusters, number of measurements and the trial duration

Methods of sample size calculation for clinical trials

Sample size calculations should be an important part of the design of a trial, but are researchers choosing sensible trial sizes? This thesis looks at ways of determining appropriate sample sizes for Normal, binary and ordinal data. The inadequacies of existing sample size and power calculation software and methods are considered, and new software is offered that will be of more use to researchers planning randomised clinical trials. The software includes the capacity to assess the power and required sample size for incomplete block crossover trial designs for Normal data. Following on from these the difference between calculated power for published trials and the actual results are investigated. As a result, the appropriateness of the standard equations to determine a sample size is questioned- in particular the effect of using a variance estimate based on a sample variance from a pilot study is considered. Taking into account the distribution of this statistic alternative approaches beyond power are considered that take into account the uncertainty in sample variance. Software is also presented that will allow these new types of sample size and Expected Power calculations to be carried out

artcat: Sample-size calculation for an ordered categorical outcome

We describe a new command, artcat, that calculates sample size or power for a randomized controlled trial or similar experiment with an ordered categorical outcome, where analysis is by the proportional-odds model. artcat implements the method of Whitehead (1993, Statistics in Medicine 12: 2257–2271). We also propose and implement a new method that 1) allows the user to specify a treatment effect that does not obey the proportional-odds assumption, 2) offers greater accuracy for large treatment effects, and 3) allows for noninferiority trials. We illustrate the command and explore the value of an ordered categorical outcome over a binary outcome in various settings. We show by simulation that the methods perform well and that the new method is more accurate than Whitehead’s method

Sample Size Calculation For Complex Sampling Designs (Version 1.0)

Before conducting a survey, researchers frequently ask themselves how large the resulting sample of respondents needs to be to answer their research questions. In this guideline, we discuss how sample size calculation is affected by the sampling design. We give practical advice on how to conduct sample size calculation for complex samples.Bevor eine Umfrage durchgeführt wird, stellen sich Forscher häufig die Frage, wie groß die Stichprobe der Befragten sein muss, um ihre Forschungsfragen zu beantworten. In diesem Leitfaden wird erörtert, wie die Berechnung des Stichprobenumfangs durch das Stichprobendesign beeinflusst wird. Wir geben praktische Ratschläge, wie der Stichprobenumfang für komplexe Stichproben berechnet werden kann