On the Power of the One-Sided Kolmogorov Test When the Sample Size is Small

1974 ◽  
Vol 19 (1) ◽  
pp. 207-212
Author(s):  
V. A. Epanecnikov
Keyword(s):  
Sample Size ◽  
Kolmogorov Test ◽  
The One

scholarly journals Importance of Inoculum Size and Sampling Effect in Rapid Antigen Detection for Diagnosis of Streptococcus pyogenes Pharyngitis

2000 ◽  
Vol 38 (1) ◽  
pp. 279-281 ◽  
Author(s):  
Bradley Kurtz ◽  
Michael Kurtz ◽  
Martha Roe ◽  
James Todd
Keyword(s):  
Sample Size ◽  
Streptococcus Pyogenes ◽  
Disease Status ◽  
Inoculum Size ◽  
Antigen Detection ◽  
Detection Rates ◽  
Sample Distribution ◽  
Antigen Tests ◽  
The One ◽  
Antigen Testing

ABSTRACT Current recommendations suggest that negative rapid Streptococcus pyogenes antigen tests be backed up with a culture, reflecting evidence that culture may have a higher sensitivity and also that testing of a second swab may yield a different (i.e., a positive) result because of variation in sample size or distribution. If the latter is common, the sensitivities of current antigen detection tests might be improved by simply increasing the amount of sample tested. The present study assessed the effect of antigen testing of two swabs extracted together compared to independent testing of each swab extracted separately for children with clinical pharyngitis. S. pyogenes grew from one or both swabs for 198 (37%) of 537 children. The combined culture was significantly ( P < 0.05) more sensitive than culture of either swab alone. Compared to combined culture, antigen testing of two swabs extracted and tested together was significantly more sensitive than two single swab extractions (94.1 versus 80%; P = 0.03); however, the specificity was decreased (81.5 versus 89.8 to 92.7%; P < 0.05). This study suggests that sample size and/or uneven sample distribution may have influenced the apparent sensitivities of prior studies that compared antigen tests to a single plate culture. A strategy, such as the one used in the present study, that increases the sample size available for antigen testing (i.e., extraction of samples from both swabs) may improve detection rates to a level that will better approximate true disease status and obviate the need for backup cultures if specificity can be improved.

How Large Should the Sample Be? Part II—The One-Sample Case for Survey Research

1985 ◽  
Vol 45 (2) ◽  
pp. 271-280 ◽  
Author(s):  
Dennis E. Hinkle ◽  
J. Dale Oliver ◽  
Charles A. Hinkle
Keyword(s):  
Sample Size ◽  
Survey Research ◽  
Effect Size ◽  
Previous Article ◽  
Type Ii ◽  
Type Ii Error ◽  
The One ◽  
Sample Case

In a previous article, the authors discuss the importance of the effect size and the Type II error as factors in determining the sample size (Hinkle and Oliver, 1983). Tables were developed and presented for one-factor designs with k levels (2 ≤ k ≤ 8). However, between the time the article was submitted and its publication, the authors presented these tables at several national and regional meetings. A recurring question from colleagues attending these meetings was how these tables could be used for the one-sample case ( k = 1). Since they could not be, we were encouraged to develop comparable tables for the one-sample case. Thus, the purpose of this paper is to readdress the sample size question and to present these tables.

Randomized Sample Size F Tests for the One-Way Layout

2010 ◽  
Author(s):  
João T. Mexia ◽  
Elsa E. Moreira ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras
Keyword(s):  
Sample Size ◽  
The One

scholarly journals Choosing the Target Difference (“effect size”) for a Randomised Controlled Trial - DELTA2 Guidance

2018 ◽  
Author(s):  
Jonathan A. Cook ◽  
Steven A. Julious ◽  
William Sones ◽  
Lisa V. Hampson ◽  
Catherine Hewitt ◽  
...  
Keyword(s):  
Randomised Controlled Trial ◽  
Sample Size ◽  
Controlled Trial ◽  
Sample Size Calculation ◽  
Main Body ◽  
Target Difference ◽  
Superiority Trial ◽  
The Difference ◽  
The One ◽  
Randomised Controlled

The aim of this document is to provide practical guidance on the choice of target difference used in the sample size calculation of a randomised controlled trial (RCT). Guidance is provided with a definitive trial, one that seeks to provide a useful answer, in mind and not those of a more exploratory nature. The term &ldquo;target difference&rdquo; is taken throughout to refer to the difference that is used in the sample size calculation (the one that the study formally &ldquo;targets&rdquo;). Please see the glossary for definitions and clarification with regards other relevant concepts. In order to address the specification of the target difference, it is appropriate, and to some degree necessary, to touch on related statistical aspects of conducting a sample size calculation. Generally the discussion of other aspects and more technical details is kept to a minimum, with more technical aspects covered in the appendices and referencing of relevant sources provided for further reading.The main body of this guidance assumes a standard RCT design is used; formally, this can be described as a two-arm parallel-group superiority trial. Most RCTs test for superiority of the interventions, that is, whether or not one of the interventions is superior to the other (See Box 1 for a formal definition of superiority, and of the two most common alternative approaches). Some common alternative trial designs are considered in Appendix 3. Additionally, it is assumed in the main body of the text that the conventional (Neyman-Pearson) approach to the sample size calculation of an RCT is being used. Other approaches (Bayesian, precision and value of information) are briefly considered in Appendix 2 with reference to the specification of the target difference.

scholarly journals Is Pool Testing Method of COVID-19 Employed in Germany and India Effective?

2020 ◽  
Author(s):  
Jia Liu ◽  
Yi Chen ◽  
Kefan Xie ◽  
Xiaohong Chen
Keyword(s):  
Nucleic Acid ◽  
Sample Size ◽  
Infection Rate ◽  
Large Population ◽  
Nucleic Acid Detection ◽  
Testing Method ◽  
Optimal Sample ◽  
The One ◽  
Novel Coronavirus ◽  
Test Result

Abstract At present, several countries, such as Germany and India, have employed a pool testing method on the nucleic acid testing of COVID-19 for the shortage of detection kits. In this method, the testing is performed on several samples of the cases together as a bunch. If the test result of the bunch is negative, then it is shown that none of the cases in the bunch has been infected with the novel coronavirus. On the contrary, if the test result of the bunch is positive, then the samples are tested one by one to confirm which cases are infected. We verified that the pool testing method of COVID-19 is effective in the situation of the shortage of nucleic acid detection kits based on probabilistic modeling. Moreover, the following interesting results are also obtained. (1) If the infection rate is extremely low, while the same number of detection kits are used, the expected number of cases that can be tested by the pool testing method is far more than that by the one-by-one testing method. (2) The pool testing method is effective only when the infection rate is less than 0.3078. While the infection rate decreases from 0.3078 to 0.0018, the optimal sample sizes in one bunch increases from 3 to 25. In general, the higher the infection rate, the smaller the optimal sample size in one bunch. (3) If N samples are tested by the pool testing method, while the sample size in one bunch is G, the number of detection kits required is in the interval (N/G, N). Additionally, the lower the infection rate, the fewer detection kits are needed. Therefore, the pool testing method is not only suitable for the situation of the shortage of detection kits, but also the situation of the overall or sampling detection for a large population.

scholarly journals Sample size calculation and re-estimation based on the prevalence in a single-arm confirmatory diagnostic accuracy study

2020 ◽  
Vol 29 (10) ◽  
pp. 2958-2971 ◽  
Author(s):  
Maria Stark ◽  
Antonia Zapf
Keyword(s):  
Diagnostic Accuracy ◽  
Sample Size ◽  
Sensitivity And Specificity ◽  
Sample Size Calculation ◽  
Type I ◽  
Diagnostic Accuracy Study ◽  
Optimal Sample Size ◽  
Optimal Sample ◽  
Accuracy Study ◽  
The One

Introduction In a confirmatory diagnostic accuracy study, sensitivity and specificity are considered as co-primary endpoints. For the sample size calculation, the prevalence of the target population must be taken into account to obtain a representative sample. In this context, a general problem arises. With a low or high prevalence, the study may be overpowered in one subpopulation. One further issue is the correct pre-specification of the true prevalence. With an incorrect assumption about the prevalence, an over- or underestimated sample size will result. Methods To obtain the desired power independent of the prevalence, a method for an optimal sample size calculation for the comparison of a diagnostic experimental test with a prespecified minimum sensitivity and specificity is proposed. To face the problem of an incorrectly pre-specified prevalence, a blinded one-time re-estimation design of the sample size based on the prevalence and a blinded repeated re-estimation design of the sample size based on the prevalence are evaluated by a simulation study. Both designs are compared to a fixed design and additionally among each other. Results The type I error rates of both blinded re-estimation designs are not inflated. Their empirical overall power equals the desired theoretical power and both designs offer unbiased estimates of the prevalence. The repeated re-estimation design reveals no advantages concerning the mean squared error of the re-estimated prevalence or sample size compared to the one-time re-estimation design. The appropriate size of the internal pilot study in the one-time re-estimation design is 50% of the initially calculated sample size. Conclusions A one-time re-estimation design of the prevalence based on the optimal sample size calculation is recommended in single-arm diagnostic accuracy studies.

Sample size calculation for agreement between two raters with binary endpoints using exact tests

2016 ◽  
Vol 27 (7) ◽  
pp. 2132-2141 ◽  
Author(s):  
Guogen Shan
Keyword(s):  
Sample Size ◽  
Error Control ◽  
Type I Error ◽  
Sample Size Calculation ◽  
Sample Size Determination ◽  
Type I ◽  
Test Statistic ◽  
Testing Procedures ◽  
Alternative Hypotheses ◽  
The One

In an agreement test between two raters with binary endpoints, existing methods for sample size calculation are always based on asymptotic approaches that use limiting distributions of a test statistic under null and alternative hypotheses. These calculated sample sizes may be not reliable due to the unsatisfactory type I error control of asymptotic approaches. We propose a new sample size calculation based on exact approaches which control for the type I error rate. The two exact approaches are considered: one approach based on maximization and the other based on estimation and maximization. We found that the latter approach is generally more powerful than the one based on maximization. Therefore, we present the sample size calculation based on estimation and maximization. A real example from a clinical trial to diagnose low back pain of patients is used to illustrate the two exact testing procedures and sample size determination.

scholarly journals THE THEORY OF THE INTERNALLY STUDENTIZED RANGE DISTRIBUTION REVISITED

2018 ◽  
Vol 36 (4) ◽  
pp. 802
Author(s):  
Daniel Furtado FERREIRA ◽  
Lucas Monteiro CHAVES ◽  
Devanil Jaques de SOUZA
Keyword(s):  
Quality Control ◽  
Standard Deviation ◽  
Sample Size ◽  
Sample Standard Deviation ◽  
Normal Case ◽  
Detailed Proof ◽  
Range Distribution ◽  
Studentized Range ◽  
Uniform Case ◽  
The One

The present paper intends to revisit the distribution of the ratio of the range to the sample standard deviation, known as the distribution of the internally studentized range, in the normal case. This distribution has its importance recognized in several areas, as quality control and inference, for testing the lack of homogeneity of the data or kurtosis. An alternative distribution to the one presented by David et al. (1954), based on the distribution of the maximum, is proposed. We exhibit a detailed proof for the distribution of the internally studentized range in the normal case and sample size 3. We also provide a new result: the distribution for the uniform case with sample of size 3.

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