Power analysis for comparative controlled clinical trial with non-continuous cutcome variables

M. Kwak, Y. J. Lee, Byung Joo Park

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background : Calculating appropriate sample size is indispensable process in planning clinical trials. If the result of a comparative clinical trial cannot reject null hypothesis, four possibilities might be considered. Three of those can be the truly no difference of therapeutic effects between the two drugs, poor compliance of the patients to the prescribed drugs, and the effect of bias during the study. The last possibility can be the falsely accepting the null hypothesis due to the lack of statistical power. This paper reported power analysis with non-continuous outcome variables in comparative controlled clinical trials to point out latter problem. Methods : A clinical trial of new anti-cancer agent using two groups was simulated with binary outcome variable. Another clinical trial was simulated to evaluate two treatment for lung cancer patients. After double-blind randomization to radiotherapy alone group and chemotherapy parallel group, follow-up observations were conducted. Outcome variable is survival time. By using PASS program, the statistical power and least significant number of sample size were calculated in the two different situation. Results : The statistical power of anti-cancer agent data was 29%, which meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 200 persons. The statistical power of lung-cancer data was 39%, which also meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 84 persons. Conclusion : Inadequate sample size in clinical trials can cause wrong decision due to decrease in statistical power, so proper power analysis is needed. The program introduced in this paper can be used in design, interim analysis, and final analysis of clinical trials to calculate statistical power very usefully.

Original languageEnglish
Pages (from-to)168-177
Number of pages10
JournalJournal of Korean Society for Clinical Pharmacology and Therapeutics
Volume8
Issue number2
StatePublished - 1 Dec 2000

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Controlled Clinical Trials
Sample Size
Clinical Trials
Lung Neoplasms
Therapeutic Uses
Patient Compliance
Random Allocation
Power (Psychology)
Pharmaceutical Preparations
Neoplasms
Radiotherapy
Therapeutics
Drug Therapy
Survival

Keywords

  • Clinical trial
  • Non-continuous outcome variable
  • Sample size
  • Statistical power

Cite this

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title = "Power analysis for comparative controlled clinical trial with non-continuous cutcome variables",
abstract = "Background : Calculating appropriate sample size is indispensable process in planning clinical trials. If the result of a comparative clinical trial cannot reject null hypothesis, four possibilities might be considered. Three of those can be the truly no difference of therapeutic effects between the two drugs, poor compliance of the patients to the prescribed drugs, and the effect of bias during the study. The last possibility can be the falsely accepting the null hypothesis due to the lack of statistical power. This paper reported power analysis with non-continuous outcome variables in comparative controlled clinical trials to point out latter problem. Methods : A clinical trial of new anti-cancer agent using two groups was simulated with binary outcome variable. Another clinical trial was simulated to evaluate two treatment for lung cancer patients. After double-blind randomization to radiotherapy alone group and chemotherapy parallel group, follow-up observations were conducted. Outcome variable is survival time. By using PASS program, the statistical power and least significant number of sample size were calculated in the two different situation. Results : The statistical power of anti-cancer agent data was 29{\%}, which meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 200 persons. The statistical power of lung-cancer data was 39{\%}, which also meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 84 persons. Conclusion : Inadequate sample size in clinical trials can cause wrong decision due to decrease in statistical power, so proper power analysis is needed. The program introduced in this paper can be used in design, interim analysis, and final analysis of clinical trials to calculate statistical power very usefully.",
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Power analysis for comparative controlled clinical trial with non-continuous cutcome variables. / Kwak, M.; Lee, Y. J.; Park, Byung Joo.

In: Journal of Korean Society for Clinical Pharmacology and Therapeutics, Vol. 8, No. 2, 01.12.2000, p. 168-177.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Lee, Y. J.

AU - Park, Byung Joo

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N2 - Background : Calculating appropriate sample size is indispensable process in planning clinical trials. If the result of a comparative clinical trial cannot reject null hypothesis, four possibilities might be considered. Three of those can be the truly no difference of therapeutic effects between the two drugs, poor compliance of the patients to the prescribed drugs, and the effect of bias during the study. The last possibility can be the falsely accepting the null hypothesis due to the lack of statistical power. This paper reported power analysis with non-continuous outcome variables in comparative controlled clinical trials to point out latter problem. Methods : A clinical trial of new anti-cancer agent using two groups was simulated with binary outcome variable. Another clinical trial was simulated to evaluate two treatment for lung cancer patients. After double-blind randomization to radiotherapy alone group and chemotherapy parallel group, follow-up observations were conducted. Outcome variable is survival time. By using PASS program, the statistical power and least significant number of sample size were calculated in the two different situation. Results : The statistical power of anti-cancer agent data was 29%, which meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 200 persons. The statistical power of lung-cancer data was 39%, which also meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 84 persons. Conclusion : Inadequate sample size in clinical trials can cause wrong decision due to decrease in statistical power, so proper power analysis is needed. The program introduced in this paper can be used in design, interim analysis, and final analysis of clinical trials to calculate statistical power very usefully.

AB - Background : Calculating appropriate sample size is indispensable process in planning clinical trials. If the result of a comparative clinical trial cannot reject null hypothesis, four possibilities might be considered. Three of those can be the truly no difference of therapeutic effects between the two drugs, poor compliance of the patients to the prescribed drugs, and the effect of bias during the study. The last possibility can be the falsely accepting the null hypothesis due to the lack of statistical power. This paper reported power analysis with non-continuous outcome variables in comparative controlled clinical trials to point out latter problem. Methods : A clinical trial of new anti-cancer agent using two groups was simulated with binary outcome variable. Another clinical trial was simulated to evaluate two treatment for lung cancer patients. After double-blind randomization to radiotherapy alone group and chemotherapy parallel group, follow-up observations were conducted. Outcome variable is survival time. By using PASS program, the statistical power and least significant number of sample size were calculated in the two different situation. Results : The statistical power of anti-cancer agent data was 29%, which meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 200 persons. The statistical power of lung-cancer data was 39%, which also meant that sample size need to be increased. The least significant sample size for detecting the therapeutic difference between groups as statistically significant requires 84 persons. Conclusion : Inadequate sample size in clinical trials can cause wrong decision due to decrease in statistical power, so proper power analysis is needed. The program introduced in this paper can be used in design, interim analysis, and final analysis of clinical trials to calculate statistical power very usefully.

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