**Sesi 1. Introduction: Basic concepts of Biostatistics in Public Health**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

- Identify the contents of course syllabus,
- Identify the laboratory exercise activities,
- Record on how course evaluates students’ activities
- List the use of biostatistics
- Assess the difference between nominal, ordinal, and metric discrete and metric continuous variables.
- Distinguish the type of a variable.
- Identify the non-numeric nature of ordinal data.
- Illustrate examples of dependent, independent and confounding variables

**Reading:**

- Rosner B (2016). General Overview. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 1, pp: 1-4. California: Chengage Learning.

**Sesi 2. Descriptive Data Analysis Using Table, Graph and Numbers**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

*Table:*

- Assessing the use of table for each type of data,
- Differentiate a frequency distribution,
- Create a frequency table from raw data,
- Constructs relative frequency, cumulative frequency and relative cumulative frequency tables.
- Construct grouped frequency tables.
- Construct a cross-tabulation table.
- Illustrate the use of a contingency table is.
- Create table with rank data.

*Graph:*

- Assessing the most appropriate chart for a given data type.
- Construct pie charts and simple, clustered and stacked, bar charts.
- Create histograms.
- Create step charts and ogives.
- Construct time series charts, including statistics process control (SPC).
- Interpret and assess a chart reveals.
- Assess the meaning by looking at the ‘shape’ of a frequency distribution.
- Appraise negatively skewed, symmetric and positively skewed distributions.
- Describe a bimodal distribution.
- Describe the approximate shape of a frequency distribution from a frequency table or chart.
- Assess whether data is considered a normal distribution.

*Numeric Summary:*

- Describe a summary measure of location is, and understand the meaning of, and the difference between, the mode, the median and the mean.
- Compute the mode, median and mean for a set of values.
- Formulate the role of data type and distributional shape in choosing the most appropriate measure of location.
- Describe what a percentile is, and calculate any given percentile value.
- Describe what a summary measure of spread is
- Differentiate the difference between, and can calculate, the range, the interquartile range and the standard deviation.
- Interpret estimate percentile values
- Formulate the role of data type and distributional shape in choosing the most appropriate measure of spread.

**Reading:**

- Rosner B (2016). Descriptive Statistics. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 2, pp: 5-35. California: Chengage Learning.

**Sesi 3. Population, Parameter, Statistics and the Probability Application on Statistics Distribution**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Review definition of probability
- Recall some useful probabilistic notation
- Give example the multiplication law of probability
- Give example the addition law of probability
- Describe the proportional frequency approach to calculating probability.
- Define and describe the idea of risk and its relationship with probability.
- Calculate the risk of some outcome from a contingency table and interpret the result.
- Define and describe the idea of odds.
- Calculate odds from a case-control 2 × 2 table and interpret the result.
- Illustrate the equation linking probability and odds and be able to calculate one given the other.
- Apply conditional probability
- Apply Bayes’ rule and screening tests
- Apply Bayesian inference
- Demonstrate making ROC curves
- Prevalence and incidence
- Describe what the risk ratio of some outcome is, calculate a risk ratio and interpret the result

**Reading:**

- Rosner B (2016). Probability. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 3, pp: 42-65. California: Chengage Learning.

**Sesi 4. Discrete and Continues Probability Distributions**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Review concept of random variables
- Describe the probability-mass function for a discrete random variable
- Compute the expected value of a discrete random variable
- Describe the variance of a discrete random variable
- Describe the cumulative-distribution function of a discrete random variable
- Illustrate permutations and combinations
- Describe the binomial distribution
- Compute expected value and variance of the binomial distribution
- Describe the Poisson distribution
- Describe computation of Poisson probabilities
- Describe expected value and variance of the Poisson distribution
- Compute Poisson approximation to the binomial distribution
- Describe general concepts of continues distribution
- Illustrate the normal distribution
- Describe properties of the standard normal distribution
- Illustrate conversion from an N(μ,σ2) distribution to an N(0,1) distribution
- Describe linear combinations of random variables
- Describe normal approximation to the binomial distribution
- Describe normal approximation to the poisson distribution

**Reading:**

- Rosner B (2016). Discrete Probability Distributions. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 4, pp: 77-107. California: Chengage Learning. - Rosner B (2016). Continuous Probability Distributions. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 5, pp: 115-142. California: Chengage Learning.

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Describe the relationship between population and sample
- Illustrate random-number tables
- Discussed estimation from difference research design (i.e.: survey vs randomized clinical trials)
- Formulate estimation of the mean of a distribution
- Formulate estimation of the variance of a distribution
- Formulate estimation for the binomial distribution
- Formulate estimation for the Poisson distribution
- Describe confidence intervals (Cis)

**Reading:**

- Rosner B (2016). Estimation. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 6, pp: 154-203. California: Chengage Learning.

**Sesi 6. Hypothesis Testing: One-Sample Inference**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Describe general concepts of hypothesis testing
- Describe one-sample test for the mean of a normal distribution: one-sided alternatives
- Describe one-sample test for the mean of a normal distribution: two-sided alternatives
- Apply sample test for the mean of a normal distribution: one and two sided alternatives
- Formulate the use of test for the mean of a normal distribution: one and two sided alternatives
- Interpret the results of test for the mean of a normal distribution: one and two sided alternatives
- Describe basic concept of one-sample inference for the binomial and Poisson distributions
- Apply one-sample inference for the binomial and Poisson distributions
- Formulate the use of one-sample inference for the binomial and Poisson distributions
- Calculate the power of a test
- Apply sample-size determination for public health research
- Describe the relationship between hypothesis testing and confidence intervals
- Describe Bayesian inference
- Describe basic concept of one-sample χ2 test for the variance of a normal distribution
- Apply one-sample χ2 test for the variance of a normal distribution
- Formulate the use of one-sample χ2 test for the variance of a normal distribution
- Interpret the results of one-sample χ2 test for the variance of a normal distribution
- Interpret the results of one-sample inference for the binomial and Poisson distributions

**Reading:**

- Rosner B (2016). Hypothesis Testing: One-Sample Inference. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 7, pp: 211-269. California: Chengage Learning.

**Sesi 7. Hypothesis Testing: Two-Sample Inference**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

*The paired t test and interval estimation for the comparison of means from two paired samples*

- Describe basic concept of the paired t test and interval estimation for the comparison of means from two paired samples
- Apply the paired t test and interval estimation for the comparison of means from two paired samples
- Formulate the use of the paired t test and interval estimation for the comparison of means from two paired samples
- Interpret the results of the paired t test and interval estimation for the comparison of means from two paired samples

*Apply two-sample t test for independent samples with equal variances and interval estimation for the comparison of means*

- Describe basic concept of two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
- Apply two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
- Formulate the use of two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
- Interpret the results of two-sample t test for independent samples with equal and unequal variances and interval estimation for the comparison of means from two independent samples
- Assess the treatment of outliers
- Estimate sample size and power for comparing two means
- Calculate sample-size estimation for longitudinal studies

**Reading:**

- Rosner B (2016). Hypothesis Testing: Two-Sample Inference. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 8, pp: 279-320. California: Chengage Learning.

**Sesi 8. Nonparametric Methods**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

*The Sign test*

Describe basic concept of the Sign test

- Apply the Sign test for public health data
- Formulate the use of the Sign test
- Interpret the results of the Sign test

*The Wilcoxon signed-rank test*

- Describe basic concept of the Wilcoxon signed-rank test
- Apply the Wilcoxon signed-rank test for public health data
- Formulate the use of the Wilcoxon signed-rank test
- Interpret the results of the Wilcoxon signed-rank test

*The Wilcoxon rank-sum test*

- Describe basic concept of the Wilcoxon rank-sum test
- Apply the Wilcoxon rank-sum test
- Formulate the use of the Wilcoxon rank-sum test
- Interpret the results of the Wilcoxon rank-sum test

**Reading:**

- Rosner B (2016). Nonparametric Methods. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 9, pp: 338-365. California: Chengage Learning.

**Sesi 9. Hypothesis Testing: Categorical Data**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

*Binomial proportions and Fisher’s exact test*

- Describe basic concept of two-sample test for binomial proportions and Fisher’s exact test
- Apply two-sample test for binomial proportions and Fisher’s exact test for public health data
- Formulate the use of two-sample test for binomial proportions and Fisher’s exact test
- Interpret the results of two-sample test for binomial proportions and Fisher’s exact test

*T**wo-sample test for binomial proportions for matched-pair data (McNemar’s test)*

- Describe basic concept of two-sample test for binomial proportions for matched-pair data (McNemar’s test)
- Apply two-sample test for binomial proportions for matched-pair data (McNemar’s test)
- Formulate the use of two-sample test for binomial proportions for matched-pair data (McNemar’s test)
- Interpret the results of two-sample test for binomial proportions for matched-pair data (McNemar’s test)

*R × c contingency tables and Chi-Square goodness-of-fit test*

- Describe basic concept of R × c contingency tables and Chi-Square goodness-of-fit test
- Apply R × c contingency tables and Chi-Square goodness-of-fit test
- Formulate the use of R × c contingency tables and Chi-Square goodness-of-fit test
- Interpret the results of R × c contingency tables and Chi-Square goodness-of-fit test

*The Kappa statistic*

- Describe basic concept of the Kappa statistic
- Apply the Kappa statistic for public health data
- Formulate the use of the Kappa statistic
- Interpret the results of the Kappa statistic
- Estimate sample size and power for comparing two binomial proportions

**Reading:**

- Rosner B (2016). Hypothesis Testing: Categorical Data. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 10 , pp: 372-439. California: Chengage Learning.

**Sesi 10. Simple Regression and Correlation Methods**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

*A simple regression*

- Describe basic concept of simple regression (i.e.: the method of least squares, inferences about parameters from regression lines, interval estimation for linear regression, and assessing the goodness of fit of regression lines)
- Apply simple regression for public health data
- Formulate the use of simple regression
- Interpret the results of simple regression

*A simple correlation*

- Describe basic concept of a simple correlation method
- Apply correlation method to public health data
- Formulate the use of correlation method
- Interpret the results of coefficient correlation, including interval estimation and hypothesis testing.

**Reading:**

- Rosner B (2016). Regression and Correlation Methods. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 11, pp: 457-540. California: Chengage Learning.

**Sesi 11. Multiple Regression and Correlation Methods**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

*A Multiple regression*

- Describe basic concept of multiple regression
- Apply multiple regression equation for public health data
- Formulate multiple regression modelling
- Interpret multiple regression model, including the use of dummy variables

*Partial and multiple correlations*

- Describe basic concept of partial and multiple correlations
- Apply partial and multiple correlation equation for public health data
- Formulate partial and multiple correlations
- Interpret partial and multiple correlations

*A rank** correlation*

- Describe basic concept of rank correlation
- Apply partial and rank correlation for public health data
- Formulate rank correlation
- Interpret rank correlation

Reading:

- Rosner B (2016). Regression and Correlation Methods. In Rosner:
*Fundamentals of Biostatistics, 8th ed*. Chapter 11, pp: 457-540. California: Chengage Learning.

**Sesi 12. Multi-sample Inference**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Describe basic concept of one-way analysis of variance (ANOVA) and A Multiway ANOVA (i.e.: Two or three ways ANOVA)
- Apply one-way ANOVA and A Multiway ANOVA
- Formulate the use of one-way ANOVA and A Multiway ANOVA
- Interpret the result of the use of one-way ANOVA and A Multiway ANOVA, including the hypothesis testing multiple and comparisons of specific groups in one-way ANOVA
- Illustrate statistical model for fixed and random effects of ANOVA
- Compute the Intra-class Correlation Coefficient (ICC)
- Illustrate the use of mixed models in public health research

**Reading:**

- Rosner B (2016). Multisample Inference. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 12, pp: 551-621. California: Chengage Learning.

**Sesi 13. Design and Analysis Techniques for Epidemiologic Studies**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Describe statistical methods for study design in epidemiology research
- Formulate measures of effect for categorical data
- Compute and interpret Odds Ratios (OR), Relative Risk (RR), and population attributable risk (PAR)
- Asses confounding and justify the use of standardization approach
- Apply methods of inference for stratified categorical data—the Mantel-Haenszel test
- Compute power and sample-size estimation for stratified categorical data
- Describe basic concept of multiple logistic regression and its extension
- Apply multiple logistic regression and its extension for public health research
- Formulate the use multiple logistic regression and its extension
- Interpret models of multiple logistic regression and its extension
- Illustrate the use of meta-analysis in epidemiology study
- Describe basic statistical analysis for equivalence studies, the cross-over design, clustered binary data and longitudinal data analysis, including to deal with measurement-error methods and missing data

**Reading:**

- Rosner B (2016). Design and Analysis Techniques for Epidemiologic Studies in B Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 13, pp: 633-762. California: Chengage Learning.

**Sesi 14. Hypothesis Testing: Person-Time Data using Life Table and Survival Analysis**

**Dosen Pengampu: Siswanto Agus Wilopo**

**Learning Objectives:**

By end of class, students will be able to:

- Formulate measure of effect for person-time data
- Illustrate one-sample inference for incidence-rate data
- Illustrate two-sample inference for incidence-rate data
- Compute power and sample-size estimation for person-time data
- Inference for stratified person-time data
- Compute power and sample-size estimation for stratified person-time data
- Illustrate testing for trend: incidence-rate data
- Describe basic concept of survival analysis
- Describe basic concept, apply, formulate and interpret result of survival curves: The Kaplan-Meier estimator
- Compute log-rank test
- Describe basic concept of the proportional-hazards model
- Apply the proportional-hazards model for public health data
- Construct best model for Cox Proportional Hazard Model

**Reading:**

- Rosner B (2016). Hypothesis Testing: Person-Time Data. In Rosner:
*Fundamentals of Biostatistics,**8**th ed*. Chapter 14, pp: 777-856. California: Chengage Learning.