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

1.  Identify current knowledge pertaining to basics of applied biostatistics

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

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

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

1. Compute descriptive statistics
2. Demonstrate how to stratify analysis
3. Demonstrate how to select a certain group of patients from a data base

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

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

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

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

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

1. Compute the confidence interval for a proportion and difference between two independent proportions
2. 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:

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

1. Evaluate to which extent the learning objectives were met
2. Summarize the key learning points

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

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

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

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

1. Compute descriptive statistics
2. Demonstrate how to stratify analysis
3. Demonstrate how to select a certain group of patients from a data base

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

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

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

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

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

1. Compute the confidence interval for a proportion and difference between two independent proportions
2. 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:

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

1. Evaluate to which extent the learning objectives were met
2. Summarize the key learning points

1. 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:

1. Compute descriptive statistics
2. 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:

1. 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:

1. Demonstrate an understanding of how to account for confounding variables in regression
2. Demonstrate an understanding of interaction and how to test for it in regression
3. 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:

1. Employ multiple linear regression to analyze a full model
2. 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:

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

1. Evaluate to which extent the learning objectives were met
2. Summarize the key learning points

1. 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:

1. Compute descriptive statistics
2. Demonstrate an understanding of analysis of numeric and categorical variables
3. Employ multiple linear regression to analyze a full model
4. 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:

1. Employ forward, backward and stepwise methods of variables selection for linear and logistic regression models
2. 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:

1. Employ one-way ANOVA and multiple testing procedures for numeric variables
2. 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:

1. Demonstrate an understanding of the difference between parametric and non-parametric tests
2. 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:

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

1. Evaluate to which extent thelearning objectives were met
2. Summarize the key learning points