# Schedule

At the end of this session, participants will be able to:

- Identify current knowledge pertaining to basics of applied biostatistics

At the end of this session, participants will be able to:

- Demonstrate an understanding of IBM-SPSS software interface
- Create a data base in IBM-SPSS
- Produce data for different types of variables

At the end of this session, participants will be able to:

- Compute descriptive statistics
- Demonstrate how to stratify analysis
- Demonstrate how to select a certain group of patients from a data base

At the end of this session, participants will be able to:

- Generate data by creating new variables, recoding variables, and do data arithmetic
- Illustrate data using appropriate graphs.

At the end of this session, participants will be able to:

- Compute confidence interval for one mean and difference between two independent means
- Analyze data using one sample t-test, paired t-test and independent t-test

At the end of this session, participants will be ableto:

- Compute the confidence interval for a proportion and difference between two independent proportions
- Analyze data using binomial test, Chi-squared test, Fisher’s exact test, McNemar’s test

At the end of this session, participants will be able to:

- Create suitable demographic and clinical characteristic summary table for a clinical trial
- Produce the most appropriate analysis for the outcomes in a 2 parallel arm clinical trial

- Evaluate to which extent the learning objectives were met
- Summarize the key learning points

At the end of this session, participants will be able to:

- Identify current knowledge pertaining to basics of applied biostatistics

At the end of this session, participants will be able to:

- Demonstrate an understanding of IBM-SPSS software interface
- Create a data base in IBM-SPSS
- Produce data for different types of variables

At the end of this session, participants will be able to:

- Compute descriptive statistics
- Demonstrate how to stratify analysis
- Demonstrate how to select a certain group of patients from a data base

At the end of this session, participants will be able to:

- Generate data by creating new variables, recoding variables, and do data arithmetic
- Illustrate data using appropriate graphs.

At the end of this session, participants will be able to:

- Compute confidence interval for one mean and difference between two independent means
- Analyze data using one sample t-test, paired t-test and independent t-test

At the end of this session, participants will be ableto:

- Compute the confidence interval for a proportion and difference between two independent proportions
- Analyze data using binomial test, Chi-squared test, Fisher’s exact test, McNemar’s test

At the end of this session, participants will be able to:

- Create suitable demographic and clinical characteristic summary table for a clinical trial
- Produce the most appropriate analysis for the outcomes in a 2 parallel arm clinical trial

- Evaluate to which extent the learning objectives were met
- Summarize the key learning points

- Identify current knowledge pertaining tobiostatistical concepts that will be covered in the current training

**a. Descriptive statistics b. Analysis of numeric variables c. Analysis of categorical variables**

At the end of this session, participants will be able to:

- Compute descriptive statistics
- Demonstrate an understanding of analysis of numeric and categorical variables

**a. Simple linear regression b. Simple logistic regression**

At the end of this session, participants will be able to:

- Apply a simple linear regression and simple logistic regression to analyze their data

**a. Confounding b. Interaction c. Overfitting or underfitting in regression**

At the end of this session, participants will be able to:

- Demonstrate an understanding of how to account for confounding variables in regression
- Demonstrate an understanding of interaction and how to test for it in regression
- Demonstrate an understanding of overfitting and underfitting in regression

**a. Analysis of the full Model b. Confounding interaction and collinearity in linear regression**

At the end of this session, participants will be able to:

- Employ multiple linear regression to analyze a full model
- Demonstrate an understanding of confounding, interaction and collinearity in linear regression

**a. Analysis of the full Model b. Confounding interaction and collinearity in logistic regression**

At the end of this session, participants will be able to:

- Employ multiple logistic regression for the analysis of the full model
- Demonstrate an understanding of confounding, interaction and collinearity in logistic regression

- Evaluate to which extent the learning objectives were met
- Summarize the key learning points

- Identify current knowledge pertaining tobiostatistical concepts that will be covered in the current training

**a. Descriptive statistics b. Analysis of numeric variables c. Analysis of categorical variables**

At the end of this session, participants will be able to:

- Compute descriptive statistics
- Demonstrate an understanding of analysis of numeric and categorical variables
- Employ multiple linear regression to analyze a full model
- Employ multiple logistic regression to analyze a full model

**a. Computer based methods b. Other methods c. Application**

At the end of this session, participants will be able to:

- Employ forward, backward and stepwise methods of variables selection for linear and logistic regression models
- Employ other methods of variables selection for linear and logistic regression

**a. Understanding the ANOVA table b. Multiple testing model c. How does it work with categorical variables**

At the end of this session, participants will be able to:

- Employ one-way ANOVA and multiple testing procedures for numeric variables
- Employ Chi-squared test for multiple groups with pairwise comparison procedures

**a. For bivariate analysis b. For one way ANOVA**

At the end of this session, participants will be able to:

- Demonstrate an understanding of the difference between parametric and non-parametric tests
- Apply nonparametric tests such as Wilcoxon’s signed rank

**a. Kaplan Meier Method and Curve b. Log Rank test and Hazard Ratio**

At the end of this session, participants will be able to:

- Demonstrate an understanding of the concept of time to event and censoring
- Apply Kaplan Meier methodto obtain survival estimates and curves
- Employ the log rank test.4.Demonstrate an understanding of the concept of hazard ratio

- Evaluate to which extent thelearning objectives were met
- Summarize the key learning points