scholarly journals Using Pilot Data to Estimate Sample Size and Compare Question Forms for a Crossover Study *

1998 ◽  
Vol 40 (4) ◽  
pp. 307-312 ◽  
Author(s):  
Maxia Dong ◽  
Martin R. Petersen ◽  
Mark J. Mendell
Keyword(s):  
Crossover Study ◽  
Sample Size ◽  
Pilot Data ◽  
Estimate Sample Size

scholarly journals Tool for filtering PubMed search results by sample size

2018 ◽  
Vol 25 (7) ◽  
pp. 774-779
Author(s):  
Carlos Baladrón ◽  
Alejandro Santos-Lozano ◽  
Javier M Aguiar ◽  
Alejandro Lucia ◽  
Juan Martín-Hernández
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Search Engine ◽  
Sample Sizes ◽  
Search Results ◽  
Estimate Sample Size ◽  
Search Tool ◽  
Pubmed Search ◽  
Meta Analyses ◽  
Validation Tests

Abstract Objective The most used search engine for scientific literature, PubMed, provides tools to filter results by several fields. When searching for reports on clinical trials, sample size can be among the most important factors to consider. However, PubMed does not currently provide any means of filtering search results by sample size. Such a filtering tool would be useful in a variety of situations, including meta-analyses or state-of-the-art analyses to support experimental therapies. In this work, a tool was developed to filter articles identified by PubMed based on their reported sample sizes. Materials and Methods A search engine was designed to send queries to PubMed, retrieve results, and compute estimates of reported sample sizes using a combination of syntactical and machine learning methods. The sample size search tool is publicly available for download at http://ihealth.uemc.es. Its accuracy was assessed against a manually annotated database of 750 random clinical trials returned by PubMed. Results Validation tests show that the sample size search tool is able to accurately (1) estimate sample size for 70% of abstracts and (2) classify 85% of abstracts into sample size quartiles. Conclusions The proposed tool was validated as useful for advanced PubMed searches of clinical trials when the user is interested in identifying trials of a given sample size.

scholarly journals Processos de amostragem e cálculo para determinação do tamanho da amostra: critérios e métodos adotados em teses e dissertações em Ciências do Movimento Humano - um estudo descritivo

2019 ◽  
Vol 20 (5) ◽  
pp. 480-490
Author(s):  
Rochelle Rocha Costa ◽  
Othavio Porto Backes ◽  
Pedro Figueiredo ◽  
Flávio Antônio De Souza Castro
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Graduate Program ◽  
Sample Selection ◽  
Probabilistic Methods ◽  
Digital Repository ◽  
Estimate Sample Size ◽  
Quantitative Studies ◽  
Sampling Procedures ◽  
Theses And Dissertations

Quantitative monographic studies systematically use inferential statistical procedures to test hypotheses. For this purpose, sampling procedures and sample sizes need to be adequate for the proposed procedures. The aim of this study was to identify the sample selection methods, as well as the performance and types of calculation to determine the sample size adopted in theses and dissertations developed in a graduate program in the field of Physical Education. Theses and dissertations defended between 2003 and 2013 were obtained through digital repository. Only quantitative studies were included, in which the following issues were analyzed: (1) sample selection criteria; (2) presence of sample calculation; (3) calculation type to estimate sample size. A total of 199 studies were included. Of these, 6% (n=11) used probabilistic methods for sample selection and 3% (n=6) used animal models. As for the accomplishment of sample calculations, 36% (n=72) studies reported having adopted this procedure. Of studies that performed sample calculations, 25% (n=18) used predictive equations, 67% (n=48) considered methods with statistical power as their base, 3% (n=2) used confidence interval, 4% (n=3) did not mention the method and 1% (n=1) was based on the type of statistical test to be used later. Nonprobabilistic sampling methods predominate for the selection of subjects; most studies do not report adopting calculations to estimate sample size and, among those that reported the use, the models that consider statistical power as the main criterion are predominant. 

scholarly journals When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias

2017 ◽  
Author(s):  
Daniel Lakens ◽  
Casper J Albers
Keyword(s):  
Sample Size ◽  
Effect Size ◽  
A Priori ◽  
Main Study ◽  
Pilot Studies ◽  
Pilot Data ◽  
Size Estimate ◽  
Size Estimates ◽  
Power Analyses

When designing a study, the planned sample size is often based on power analyses. One way to choose an effect size for power analyses is by relying on pilot data. A-priori power analyses are only accurate when the effect size estimate is accurate. In this paper we highlight two sources of bias when performing a-priori power analyses for between-subject designs based on pilot data. First, we examine how the choice of the effect size index (η2, ω2 and ε2) affects the sample size and power of the main study. Based on our observations, we recommend against the use of η2 in a-priori power analyses. Second, we examine how the maximum sample size researchers are willing to collect in a main study (e.g. due to time or financial constraints) leads to overestimated effect size estimates in the studies that are performed. Determining the required sample size exclusively based on the effect size estimates from pilot data, and following up on pilot studies only when the sample size estimate for the main study is considered feasible, creates what we term follow-up bias. We explain how follow-up bias leads to underpowered main studies.Our simulations show that designing main studies based on effect sizes estimated from small pilot studies does not yield desired levels of power due to accuracy bias and follow-up bias, even when publication bias is not an issue. We urge researchers to consider alternative approaches to determining the sample size of their studies, and discuss several options.

scholarly journals Estimation of sensitivity and specificity and calculation of sample size for a validation study with stratified sampling

2020 ◽  
Author(s):  
Kiyoshi Kubota ◽  
Masao Iwagami ◽  
Takuhiro Yamaguchi
Keyword(s):  
Sample Size ◽  
Sensitivity And Specificity ◽  
Validation Study ◽  
Predictive Value ◽  
Validation Studies ◽  
Bootstrap Method ◽  
Stratified Sampling ◽  
Delta Method ◽  
Estimate Sample Size ◽  
The Bootstrap Method

Abstract Background:We propose and evaluate the approximation formulae for the 95% confidence intervals (CIs) of the sensitivity and specificity and a formula to estimate sample size in a validation study with stratified sampling where positive samples satisfying the outcome definition and negative samples that do not are selected with different extraction fractions. Methods:We used the delta method to derive the approximation formulae for estimating the sensitivity and specificity and their CIs. From those formulae, we derived the formula to estimate the size of negative samples required to achieve the intended precision and the formula to estimate the precision for a negative sample size arbitrarily selected by the investigator. We conducted simulation studies in a population where 4% were outcome definition positive, the positive predictive value (PPV)=0.8, and the negative predictive value (NPV)=0.96, 0.98 and 0.99. The size of negative samples, n0, was either selected to make the 95% CI fall within ± 0.1, 0.15 and 0.2 or set arbitrarily as 150, 300 and 600. We assumed a binomial distribution for the positive and negative samples. The coverage of the 95% CIs of the sensitivity and specificity was calculated as the proportion of CIs including the sensitivity and specificity in the population, respectively. For selected studies, the coverage was also estimated by the bootstrap method. The sample size was evaluated by examining whether the observed precision was within the pre-specified value.Results:For the sensitivity, the coverage of the approximated 95% CIs was larger than 0.95 in most studies but in 9 of 18 selected studies derived by the bootstrap method. For the specificity, the coverage of the approximated 95% CIs was approximately 0.93 in most studies, but the coverage was more than 0.95 in all 18 studies derived by the bootstrap method. The calculated size of negative samples yielded precisions within the pre-specified values in most of the studies.Conclusion:The approximation formulae for the 95% CIs of the sensitivity and specificity for stratified validation studies are presented. These formulae will help in conducting and analysing validation studies with stratified sampling.

Sample size determinations using logistic regression with pilot data

1993 ◽  
Vol 12 (11) ◽  
pp. 1079-1084 ◽  
Author(s):  
Virginia F. Flack ◽  
T. Lynn Eudey
Keyword(s):  
Logistic Regression ◽  
Sample Size ◽  
Pilot Data

Effects of radiotelemetry error on sample size and bias when testing for habitat selection

1989 ◽  
Vol 67 (7) ◽  
pp. 1631-1636 ◽  
Author(s):  
Vilis O. Nams
Keyword(s):  
Habitat Selection ◽  
Sample Size ◽  
Habitat Size ◽  
Estimate Sample Size

Radiolocations of animals by triangulation have certain errors. I show how the ratio of telemetry error to habitat size affects efficiency of testing for habitat selection and how to estimate sample size when telemetry error is large. When telemetry error is more than 1.5 times average habitat size, the required sample size increases immensely. When telemetry error is large, measurements of habitat selection are biased. I present a technique to remove the bias and estimate the habitat selection one would observe if telemetry error were zero.

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