• Communications for Statistical Applications and Methods
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• v.25 no.3
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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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• 1994.02b
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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.

• Women's Health Nursing
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• v.4 no.3
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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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• v.15 no.3
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• pp.353-365
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• 2008

The sample size in a clinical trial is determined by the hypothesis, the variance of observations, the effect size, the power and the significance level. Dropouts in clinical trials are inevitable, so we need to consider dropouts on the determination of sample size. It is common that some proportion corresponding to the expected dropout rate would be added to the sample size calculated from a mathematical equation. This paper proposes new equations for calculating sample size dealing with dropouts. Since we observe data longitudinally in most clinical trials, we can use a last observation to impute for missing one in the intention to treat (ITT) trials, and this technique is called last observation carried forward(LOCF). But LOCF might make deviations on the assumed variance and effect size, so that we could not guarantee the power of test with the sample size obtained from the existing equation. This study suggests the formulas for sample size involving information about dropouts and shows the properties of the proposed method in testing equality of means.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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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.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.269-285
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• 1998

One of the most important issues in the area of clinical trial research is the determination of the sample size required to insure a specified power in detecting a real or clinically relevant difference of a stated magnitude. Increasingly, medical journals are requiring authors to provide information on the sample size needed to detect a given difference. We restrict our attention to the designs far comparirng two survival distributions. These are concerned with the survival time which is defined as the interval from a baseline(e.g. randomization) to failure (e.g. death, recurrence of disease). Survival times axe right censored when patients have not foiled by the time of analysis or have been loss to follow-up during the trial. For different types of clinical trials for comparing survival distributions, there have been marry research in sample size determination. We review the existing literature concerning commonly used sample size formulae in the design of randomized clinical trials, and compare the assumption, the power and the sample size calculation of these methods. We also compare by simulation the expected power and observed power of each method under various circumstances. As a result, guidelines in terms of practical usage are provided.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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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.

• Korean Journal of Adult Nursing
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• v.13 no.2
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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.

• The Korean Journal of Emergency Medical Services
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• v.21 no.1
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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.

• International Journal of Human Ecology
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• v.14 no.2
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• pp.15-24
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• 2013

This study investigated the differences between adolescents' own perceptions of their psychopathology and perceptions by clinically depressed parents of their adolescents' psychopathology. The study also examined parental characteristics that accounted for discrepancies between parents and adolescents. The clinical sample consisted of 61 adolescents and their parents who were diagnosed with a major depressive disorder. The adolescents and parents evaluated the adolescents' psychopathology in separate interviews with the Child Behavior Checklist (CBCL) and the Youth Self-Report (YSR). Parents reported on current depressive symptoms and parenting practices using questionnaires. The results revealed that parent-adolescent discrepancies were greater in regard to affective and anxiety problems compared to oppositional defiant and conduct problems. Parental rejection was associated with differences in scores for affective problems after controlling for parents' current depressive symptoms and adolescents' age and gender. The findings highlight the importance of considering adolescents' affective and anxiety problems when treating depressed parents. Furthermore, the findings suggest that parental rejection may play a pivotal role when interpreting the discrepancy concerning adolescents' affective problems.