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.
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- 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.
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Preliminary investigation into estimating eye disease incidence rate from age specific prevalence data
- Authors: Majeke, Lunga
- Date: 2011
- Subjects: Medicine -- Research -- Statistical methods , Eye -- Diseases -- Statistics , Incidence functions , Regression analysis -- Data processing , Regression analysis -- Mathematical models -- Evaluation , Medical statistics
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11776 , http://hdl.handle.net/10353/464 , Medicine -- Research -- Statistical methods , Eye -- Diseases -- Statistics , Incidence functions , Regression analysis -- Data processing , Regression analysis -- Mathematical models -- Evaluation , Medical statistics
- Description: This study presents the methodology for estimating the incidence rate from the age specific prevalence data of three different eye diseases. We consider both situations where the mortality may differ from one person to another, with and without the disease. The method used was developed by Marvin J. Podgor for estimating incidence rate from prevalence data. It delves into the application of logistic regression to obtain the smoothed prevalence rates that helps in obtaining incidence rate. The study concluded that the use of logistic regression can produce a meaningful model, and the incidence rates of these diseases were not affected by the assumption of differential mortality.
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- Authors: Majeke, Lunga
- Date: 2011
- Subjects: Medicine -- Research -- Statistical methods , Eye -- Diseases -- Statistics , Incidence functions , Regression analysis -- Data processing , Regression analysis -- Mathematical models -- Evaluation , Medical statistics
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11776 , http://hdl.handle.net/10353/464 , Medicine -- Research -- Statistical methods , Eye -- Diseases -- Statistics , Incidence functions , Regression analysis -- Data processing , Regression analysis -- Mathematical models -- Evaluation , Medical statistics
- Description: This study presents the methodology for estimating the incidence rate from the age specific prevalence data of three different eye diseases. We consider both situations where the mortality may differ from one person to another, with and without the disease. The method used was developed by Marvin J. Podgor for estimating incidence rate from prevalence data. It delves into the application of logistic regression to obtain the smoothed prevalence rates that helps in obtaining incidence rate. The study concluded that the use of logistic regression can produce a meaningful model, and the incidence rates of these diseases were not affected by the assumption of differential mortality.
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Randomization in a two armed clinical trial: an overview of different randomization techniques
- Authors: Batidzirai, Jesca Mercy
- Date: 2011
- Subjects: Clinical trials -- Statistical methods , Biometry , Sampling (Statistics)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11781 , http://hdl.handle.net/10353/395 , Clinical trials -- Statistical methods , Biometry , Sampling (Statistics)
- Description: Randomization is the key element of any sensible clinical trial. It is the only way we can be sure that the patients have been allocated into the treatment groups without bias and that the treatment groups are almost similar before the start of the trial. The randomization schemes used to allocate patients into the treatment groups play a role in achieving this goal. This study uses SAS simulations to do categorical data analysis and comparison of differences between two main randomization schemes namely unrestricted and restricted randomization in dental studies where there are small samples, i.e. simple randomization and the minimization method respectively. Results show that minimization produces almost equally sized treatment groups, but simple randomization is weak in balancing prognostic factors. Nevertheless, simple randomization can also produce balanced groups even in small samples, by chance. Statistical power is also improved when minimization is used than in simple randomization, but bigger samples might be needed to boost the power.
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- Authors: Batidzirai, Jesca Mercy
- Date: 2011
- Subjects: Clinical trials -- Statistical methods , Biometry , Sampling (Statistics)
- Language: English
- Type: Thesis , Masters , MSc (Biostatistics and Epidemiology)
- Identifier: vital:11781 , http://hdl.handle.net/10353/395 , Clinical trials -- Statistical methods , Biometry , Sampling (Statistics)
- Description: Randomization is the key element of any sensible clinical trial. It is the only way we can be sure that the patients have been allocated into the treatment groups without bias and that the treatment groups are almost similar before the start of the trial. The randomization schemes used to allocate patients into the treatment groups play a role in achieving this goal. This study uses SAS simulations to do categorical data analysis and comparison of differences between two main randomization schemes namely unrestricted and restricted randomization in dental studies where there are small samples, i.e. simple randomization and the minimization method respectively. Results show that minimization produces almost equally sized treatment groups, but simple randomization is weak in balancing prognostic factors. Nevertheless, simple randomization can also produce balanced groups even in small samples, by chance. Statistical power is also improved when minimization is used than in simple randomization, but bigger samples might be needed to boost the power.
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