Maximization of power in randomized clinical trials using the minimization treatment allocation technique
- Authors: Marange, Chioneso Show
- Date: 2010
- Subjects: Clinical trials -- Statistical methods , Statistical hypothesis testing , Regression analysis , Logistic distribution , Estimation theory
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11777 , http://hdl.handle.net/10353/399 , Clinical trials -- Statistical methods , Statistical hypothesis testing , Regression analysis , Logistic distribution , Estimation theory
- Description: Generally the primary goal of randomized clinical trials (RCT) is to make comparisons among two or more treatments hence clinical investigators require the most appropriate treatment allocation procedure to yield reliable results regardless of whether the ultimate data suggest a clinically important difference between the treatments being studied. Although recommended by many researchers, the utilization of minimization has been seldom reported in randomized trials mainly because of the controversy surrounding the statistical efficiency in detecting treatment effect and its complexity in implementation. Methods: A SAS simulation code was designed for allocating patients into two different treatment groups. Categorical prognostic factors were used together with multi-level response variables and demonstration of how simulation of data can help to determine the power of the minimization technique was carried out using ordinal logistic regression models. Results: Several scenarios were simulated in this study. Within the selected scenarios, increasing the sample size significantly increased the power of detecting the treatment effect. This was contrary to the case when the probability of allocation was decreased. Power did not change when the probability of allocation given that the treatment groups are balanced was increased. The probability of allocation { } k P was seen to be the only one with a significant effect on treatment balance. Conclusion: Maximum power can be achieved with a sample of size 300 although a small sample of size 200 can be adequate to attain at least 80% power. In order to have maximum power, the probability of allocation should be fixed at 0.75 and set to 0.5 if the treatment groups are equally balanced.
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- Authors: Marange, Chioneso Show
- Date: 2010
- Subjects: Clinical trials -- Statistical methods , Statistical hypothesis testing , Regression analysis , Logistic distribution , Estimation theory
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11777 , http://hdl.handle.net/10353/399 , Clinical trials -- Statistical methods , Statistical hypothesis testing , Regression analysis , Logistic distribution , Estimation theory
- Description: Generally the primary goal of randomized clinical trials (RCT) is to make comparisons among two or more treatments hence clinical investigators require the most appropriate treatment allocation procedure to yield reliable results regardless of whether the ultimate data suggest a clinically important difference between the treatments being studied. Although recommended by many researchers, the utilization of minimization has been seldom reported in randomized trials mainly because of the controversy surrounding the statistical efficiency in detecting treatment effect and its complexity in implementation. Methods: A SAS simulation code was designed for allocating patients into two different treatment groups. Categorical prognostic factors were used together with multi-level response variables and demonstration of how simulation of data can help to determine the power of the minimization technique was carried out using ordinal logistic regression models. Results: Several scenarios were simulated in this study. Within the selected scenarios, increasing the sample size significantly increased the power of detecting the treatment effect. This was contrary to the case when the probability of allocation was decreased. Power did not change when the probability of allocation given that the treatment groups are balanced was increased. The probability of allocation { } k P was seen to be the only one with a significant effect on treatment balance. Conclusion: Maximum power can be achieved with a sample of size 300 although a small sample of size 200 can be adequate to attain at least 80% power. In order to have maximum power, the probability of allocation should be fixed at 0.75 and set to 0.5 if the treatment groups are equally balanced.
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Statistical methods to model the influence of age and gender on the behavioral risk factors of HIV/AIDS
- Authors: Tlou, Boikhutso
- Date: 2010
- Subjects: AIDS (Disease) -- Statistics , HIV infections -- Statistics , AIDS (Disease) -- South Africa , Health risk assessment , HIV infections -- South Africa , AIDS (Disease) -- Social aspects
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11779 , http://hdl.handle.net/10353/400 , AIDS (Disease) -- Statistics , HIV infections -- Statistics , AIDS (Disease) -- South Africa , Health risk assessment , HIV infections -- South Africa , AIDS (Disease) -- Social aspects
- Description: The effects of gender and age on the behavioral risk of HIV/AIDS are not clearly understood as previous distinct studies which have been carried out, have given disputable and contradictory outcomes. This study therefore, discusses the statistical methods which can be used to model the influence of age and gender on the behavioral risk factors of HIV/AIDS. In general, generalized linear models are the main methods which can be applied to depict the impact of age and gender on the behavioral risk of becoming infected with HIV/AIDS virus. In this study, the main methods used were logistic regression, log-linear regression and multiple regressions. Behavioral risk was taken as the dependent variable while age, gender, number of sexual partners, religious beliefs and alcohol and drug abuse were fitted as predictor variables. The three statistical methods gave significant results for gender and insignificant results for age. Furthermore, comparisons were made on the three regression methods and the logistic regression gave the best results. It was therefore concluded that gender plays a significant role on the behavioral risk of HIV/AIDS. The results of the study showed that gender of the student and number of sexual partners had a significant effect on the risk behavior of the university students. In future, it may be very important to find out why age is not a significant factor on risk behavior of HIV/AIDS among university students.
- Full Text:
- Authors: Tlou, Boikhutso
- Date: 2010
- Subjects: AIDS (Disease) -- Statistics , HIV infections -- Statistics , AIDS (Disease) -- South Africa , Health risk assessment , HIV infections -- South Africa , AIDS (Disease) -- Social aspects
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11779 , http://hdl.handle.net/10353/400 , AIDS (Disease) -- Statistics , HIV infections -- Statistics , AIDS (Disease) -- South Africa , Health risk assessment , HIV infections -- South Africa , AIDS (Disease) -- Social aspects
- Description: The effects of gender and age on the behavioral risk of HIV/AIDS are not clearly understood as previous distinct studies which have been carried out, have given disputable and contradictory outcomes. This study therefore, discusses the statistical methods which can be used to model the influence of age and gender on the behavioral risk factors of HIV/AIDS. In general, generalized linear models are the main methods which can be applied to depict the impact of age and gender on the behavioral risk of becoming infected with HIV/AIDS virus. In this study, the main methods used were logistic regression, log-linear regression and multiple regressions. Behavioral risk was taken as the dependent variable while age, gender, number of sexual partners, religious beliefs and alcohol and drug abuse were fitted as predictor variables. The three statistical methods gave significant results for gender and insignificant results for age. Furthermore, comparisons were made on the three regression methods and the logistic regression gave the best results. It was therefore concluded that gender plays a significant role on the behavioral risk of HIV/AIDS. The results of the study showed that gender of the student and number of sexual partners had a significant effect on the risk behavior of the university students. In future, it may be very important to find out why age is not a significant factor on risk behavior of HIV/AIDS among university students.
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