A simulation study of the behaviour of the logrank test under different levels of stratification and sample sizes
- Authors: Jubane, Ido
- Date: 2013
- Subjects: Statistics , Survival analysis (Biometry)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11784 , http://hdl.handle.net/10353/d1018558 , http://hdl.handle.net/10353/d1018559 , Statistics , Survival analysis (Biometry)
- Description: In clinical trials, patients are enrolled into two treatment arms. A researcher may be interested in studying the effectiveness of a new drug or the comparison of two drugs for the treatment of a disease. This survival data is later analysed using the logrank test or the Cox regression model to detect differences in survivor functions. However, the power function of the logrank test depends solely on the number of patients enrolled into the study. Because statisticians will always minimise type I and type II errors, a researcher carrying out a clinical trial must define beforehand, the number of patients to be enrolled into the clinical study. Without proper sample size and power estimation a clinical trial may fail to detect a false hypothesis of the equality of survivor functions. This study presents through simulation, a way of power and sample size estimation for clinical trials that use the logrank test for their data analysis and suggests an easy method to estimate power and sample size in such clinical studies. Findings on power analysis and sample size estimation on logrank test are applied to two real examples: one is the Veterans' Administration Lung Cancer study; and the other is the data from a placebo controlled trial of gamma interferon in chronic granulotomous disease.
- Full Text:
- Date Issued: 2013
- Authors: Jubane, Ido
- Date: 2013
- Subjects: Statistics , Survival analysis (Biometry)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11784 , http://hdl.handle.net/10353/d1018558 , http://hdl.handle.net/10353/d1018559 , Statistics , Survival analysis (Biometry)
- Description: In clinical trials, patients are enrolled into two treatment arms. A researcher may be interested in studying the effectiveness of a new drug or the comparison of two drugs for the treatment of a disease. This survival data is later analysed using the logrank test or the Cox regression model to detect differences in survivor functions. However, the power function of the logrank test depends solely on the number of patients enrolled into the study. Because statisticians will always minimise type I and type II errors, a researcher carrying out a clinical trial must define beforehand, the number of patients to be enrolled into the clinical study. Without proper sample size and power estimation a clinical trial may fail to detect a false hypothesis of the equality of survivor functions. This study presents through simulation, a way of power and sample size estimation for clinical trials that use the logrank test for their data analysis and suggests an easy method to estimate power and sample size in such clinical studies. Findings on power analysis and sample size estimation on logrank test are applied to two real examples: one is the Veterans' Administration Lung Cancer study; and the other is the data from a placebo controlled trial of gamma interferon in chronic granulotomous disease.
- Full Text:
- Date Issued: 2013
A cox proportional hazard model for mid-point imputed interval censored data
- Authors: Gwaze, Arnold Rumosa
- Date: 2011
- Subjects: Statistics -- Econometric models , Survival analysis (Biometry) , Mathematical statistics -- Data processing , Nonparametric statistics , Sampling (Statistics) , Multiple imputation (Statistics)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11780 , http://hdl.handle.net/10353/385 , http://hdl.handle.net/10353/d1001135 , Statistics -- Econometric models , Survival analysis (Biometry) , Mathematical statistics -- Data processing , Nonparametric statistics , Sampling (Statistics) , Multiple imputation (Statistics)
- Description: There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.
- Full Text:
- Date Issued: 2011
- Authors: Gwaze, Arnold Rumosa
- Date: 2011
- Subjects: Statistics -- Econometric models , Survival analysis (Biometry) , Mathematical statistics -- Data processing , Nonparametric statistics , Sampling (Statistics) , Multiple imputation (Statistics)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11780 , http://hdl.handle.net/10353/385 , http://hdl.handle.net/10353/d1001135 , Statistics -- Econometric models , Survival analysis (Biometry) , Mathematical statistics -- Data processing , Nonparametric statistics , Sampling (Statistics) , Multiple imputation (Statistics)
- Description: There has been an increasing interest in survival analysis with interval-censored data, where the event of interest (such as infection with a disease) is not observed exactly but only known to happen between two examination times. However, because so much research has been focused on right-censored data, so many statistical tests and techniques are available for right-censoring methods, hence interval-censoring methods are not as abundant as those for right-censored data. In this study, right-censoring methods are used to fit a proportional hazards model to some interval-censored data. Transformation of the interval-censored observations was done using a method called mid-point imputation, a method which assumes that an event occurs at some midpoint of its recorded interval. Results obtained gave conservative regression estimates but a comparison with the conventional methods showed that the estimates were not significantly different. However, the censoring mechanism and interval lengths should be given serious consideration before deciding on using mid-point imputation on interval-censored data.
- Full Text:
- Date Issued: 2011
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