• Communications for Statistical Applications and Methods
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• 제25권3호
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• pp.321-328
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• 2018

In clinical trials with repeated measurements, the time-averaged difference (TAD) may provide a more powerful evaluation of treatment efficacy than the rate of changes over time when the treatment effect has rapid onset and repeated measurements continue across an extended period after a maximum effect is achieved (Overall and Doyle, Controlled Clinical Trials, 15, 100-123, 1994). The sample size formula has been investigated by many researchers for the evaluation of TAD in two treatment groups. For the evaluation of TAD in multi-arm trials, Zhang and Ahn (Computational Statistics & Data Analysis, 58, 283-291, 2013) and Lou et al. (Communications in Statistics-Theory and Methods, 46, 11204-11213, 2017b) developed the sample size formulas for continuous outcomes and count outcomes, respectively. In this paper, we derive a sample size formula to evaluate the TAD of the repeated binary outcomes in multi-arm trials using the generalized estimating equation approach. This proposed sample size formula accounts for various correlation structures and missing patterns (including a mixture of independent missing and monotone missing patterns) that are frequently encountered by practitioners in clinical trials. We conduct simulation studies to assess the performance of the proposed sample size formula under a wide range of design parameters. The results show that the empirical powers and the empirical Type I errors are close to nominal levels. We illustrate our proposed method using a clinical trial example.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 1994년도 교수 연수회(역학)
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• pp.297-312
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• 1994

Over the last decade, considerable interest has focused on sample size estimation in the design of clinical trials. The resulting literature is scattered over many textbooks and journals. This paper presents these methods in a single review and comments on their application in practice.

• 여성건강간호학회지
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• 제4권3호
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• pp.375-387
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• 1998

In clinical trials of nursing research, the sample size determination is one of the most important factor. Although sample size must be considered at the design stage, it has been disregarded in most clinical trials. The power analysis is usually performed before study begins to compute sample size and the power can also be calculated at the end of study in order to justify study result. The power analysis is essential especially when the clinical trials can not show significant differences. In this paper, we review the statistical methods for power analysis and sample size formulae in nursing research. Sample size formulae and the corresponding examples are discussed according to the six types of studies ; mean for one sample, proportion for one sample, means in two samples, proportions in two samples, correlation coefficient and ANOVA.

• Communications for Statistical Applications and Methods
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• 제15권3호
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• pp.353-365
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• 2008

임상시험에서 피험자수는 검정가설, 변수값의 분산과 유효차이, 검정력과 유의수준 등에 의해 결정되어진다. 일반적으로 수학적으로 계산된 피험자수에 중도탈락 예상치를 고려한 피험자수를 추가하여 최종적인 실험참가자수를 결정하는데 본 논문에서는 이론적인 계산식에서부터 중도탈락을 고려하여 피험자수를 결정하는 방법을 제안한다. 임상시험에서 많은 자료는 경시적(longitudinal) 형태를 갖고, ITT(intention to treat) 실험의 경우 중도탈락이 생기면 결측값으로 처리하지 않고 탈락직전에 관측된 값을 최종값으로 대체하는 LOCF(last observation carried forward) 방법을 주로 사용한다. 이러한 LOCF 방법은 피험자수 계산에 사용했던 분산과 유효차이 값의 가정에 왜곡을 가져오기 때문에 우리가 원하는 검정력을 보장 받지 못할 수 있다. 본 연구에서는 중도탈락률에 관한 정보를 포함하는 피험자수의 결정식을 제안하고 평균의 동일성 검정 경우에 검정력을 비교하여 이러한 산출방식이 합리적임을 실증하였다.

• Restorative Dentistry and Endodontics
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• 제49권1호
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• pp.3.1-3.8
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• 2024

This article is a narrative review that discusses the recommended sample size requirements to design a pilot study to assess the reliability of a questionnaire. A list of various sample size tables that are based on the kappa agreement test, intra-class correlation test and Cronbach's alpha test has been compiled together. For all calculations, type I error (alpha) was set at a maximum value of 0.05, and power was set at a minimum value of 80.0%. For the kappa agreement test, intra-class correlation test, and Cronbach's alpha test, the recommended minimum sample size requirement based on the ideal effect sizes shall be at least 15, 22, and 24 subjects respectively. By making allowances for a non-response rate of 20.0%, a minimum sample size of 30 respondents will be sufficient to assess the reliability of the questionnaire. The clear guideline of minimum sample size requirement for the pilot study to assess the reliability of a questionnaire is discussed and this will ease researchers in preparation for the pilot study. This study provides justification for a minimum requirement of a sample size of 30 respondents specifically to test the reliability of a questionnaire.

• Journal of the Korean Data and Information Science Society
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• 제21권6호
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• pp.1343-1351
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• 2010

Sample size determination is very important part in clinical trials because it influences the time and the cost of the experimental studies. In this article, we consider the Bayesian methods for sample size determination based on hypothesis testing. Specifically we compare the usual Bayesian method using Bayes factor with the decision theoretic method using Bayesian reference criterion in mean difference problem for the normal case with known variances. We illustrate two procedures numerically as well as graphically.

• 응용통계연구
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• 제11권2호
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• pp.269-285
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• 1998

임상시험연구의 설계에서 적절한 표본수의 결정은 매우 중요한 문제 중의 하나이다. 본 논문에서는 생존분포를 비교하기 위한 여러 가지 방법들을 소개하고, 각 방법에서의 가정들을 고찰하였다. 또한 다양한 상황에서의 표본수와 검정력 등을 비교제시하고 모의실험을 통해 각 방법들의 이론상의 검정력과 실제 검정력을 알아보았다 그 결과로서 의학연구자들이 처한 여러 상황에 적합한 표본수의 결정방법을 제시하였다.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.411-418
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• 2007

Sample size calculation is very important in clinical trials. In this paper, we propose sample size calculation method for non-inferiority trials using sample size calculation method suggested by Wang et al.(2003) based on Wilcoxon's rank sum test. Also, sample size comparison between parametric method and proposed method are presented.

• 성인간호학회지
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• 제13권2호
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• pp.327-339
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• 2001

The purpose of this study is to explore perception types and to understand the nature of experience of nursing students' clinical education by using the Q methodology. A Q sample was developed through a review of the literature and descriptions about nursing students' experience in clinical practice. Thirty-six statements made up the finalized Q sample. The P sample consisted of 33 third grade nursing students in K university. Q statements were written on separate cards and were given to the 33 subjects to sort according to degree of agreement or disagreement. The Q-sorts by each subject were coded and analysed with the Quanl PC program. A a result, three major perception types, namely, 'alienation of ideal and reality', 'active participation', and 'perception of limitation of ability' were identified. By identifying the nature of the three types, this study suggests efficient strategies for developing clinical educational programs according to the perception types of nursing students. Clinical education would thereby be more valuable.

• 한국응급구조학회지
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• 제21권1호
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• pp.59-73
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• 2017

Purpose: This study aimed to enhance the efficiency of clinical training education by understanding paramedic students' perceptions of their hospital clinical training experiences. Methods: The subjects were 31 third paramedic students who participated in a population survey from June 25 to August 13, 2016. A Q card and Q sample distribution chart were created, and the P sample was selected by Q classification. The collected data were analyzed by factorial analysis using PC QUANL. Results: Four different perceptions were identified from the survey, which explained 44.1% of the variables. The four types were classified as Self-improvement-oriented (Type 1), Training-site avoidant (Type 2), Confidence acquiring (Type 3), and Over-willed (Type 4). Conclusion: Paramedic instructors and clinical training managers may want to consider these four perception types when planning clinical training and education programs to improve job performance.