Introduction to Biostatistics Course Number Biostatistics 100A. Term Fall 2017

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1 Course Title Introduction to Biostatistics Course Number Biostatistics 100A Credit 4 units Term Fall 2017 Instructor Meeting Days/ Locations Course Description and Policies David W. Gjertson, PhD Department of Biostatistics, UCLA Fielding School of Public Health Phone: (310) ; gjertson@ucla.edu Office hour: W 12p/3p, or by appointment in CHS A Lecture: MWF 1 2:50p CHS Lab: 1A R 2 2:50p CHS A B R 1 1:50p CHS A C R 3 3:50p CHS A D R 9 9:50a CHS A E F 3 3:50p CHS A F T 4 4:50p CHS A G W 9 9:50a CHS A H M 9 9:50a CHS A1 241 Content. Introduction to methods and concepts of statistical analysis and sampling, with special attention to those occurring in biological sciences. Topics include descriptive measures, probability and distributions, estimation, tests of hypotheses, types of error, significance, confidence levels, sample size and power. Material: Throughout the quarter, all administrative information and course material will be posted to to view and download. Academic Integrity: In line with University policy, the guidelines and policy for academic integrity will be enforced. Please see the policy found at the following link for the policy provisions: Students with Disabilities: If you wish to request an accommodation due to a disability, please contact the Office for Students with Disabilities as soon as possible at A255 Murphy Hall, (310) , (310) (telephone device for the deaf). Website: Attendance and Class Participation Policy: Lecture attendance is not recorded; however, laboratory attendance is marked (Weeks 2 8) and forms an important element of a class participation grade. All students are expected to complete reading before coming to lab sessions, ask relevant questions, and contribute to group discussions. Homework and Laboratory Assignments. Homework sets will be distributed but will not be graded. They are, however, essential to learning the course material and will be reviewed during discussion sessions. Laboratory assignments are graded (see evaluations below). Students are urged to seek help from instructor/ta when assigned problems do not satisfactorily answer questions or explain troublesome concepts. 1

2 Required Text Rosner B. Fundamentals of Biostatistics, 8th ed. Cengage Learning, Boston, MA, Methods of Evaluation There will be 3 separate evaluations: 1) Laboratory assignments: In computer lab sessions, seven assignments will be given to assess basic techniques for analyzing data using STATA TM, doing online research and examining relevant publications for statistical content. Labs are an integral part of the course with content corresponding to lecture topics. Each lab assignment also contains exercises specifically designed to assess the 10 ASPH Competencies shown below. 2) Midterm examinations: Midterm I given on November 3, 2017, 1 2:50p and Midterm II given on November 29, 2017, 1 2:50p. Rooms TBA 3) Final examination given on December 14, 2017, 3 6p. Rooms TBA Grade Distribution All competencies must be completed in order to receive a class grade, then evaluations are compiled using the following weights: Laboratory assignments 30% Midterm exams 30% Final exam 40% Learning Objectives/ Competencies Learning Objectives 1. Introduction to statistics and its utility in the scientific, particularly the public health, environment. 2. Develop analytical skills involving distributions and measures of central tendency and spread. Understand basic informatic techniques and how they can be applied to public health situations. 3. Understand the basic concepts of probability with respect to how they apply to the fundamental interpretation of statistical data and sampling distributions. ASPH Competencies C1. Upon completion of this course, students will be able to describe the roles biostatistics serves in the discipline of public health. C2. Upon completion of this course, students will be able to distinguish among the different measurement scales and the implications for statistical descriptive methods to be used based on these distinctions. C3. Upon completion of this course, students will be able to apply descriptive techniques commonly used to summarize public health data. C5. Upon completion of this course, students will be able to apply basic informatics techniques with vital statistics and public health records in the description of public health characteristics and in public health research and evaluation. C4. Upon completion of this course, students will be able to describe basic concepts of probability. 2

3 4. To develop analytical skills involving the normal distribution and other key probability distributions. 5. To understand the concepts of estimation, confidence and confidence intervals and how they are used in statistical inference. To understand the difference between confidence and probability. C6. Upon completion of this course, students will be able to describe basic concepts of random variation and commonly used statistical probability distributions. C7. Upon completion of this course, students will be able to interpret results of statistical analyses found in public health studies. C8. Upon completion of this course, students will be able to apply descriptive and inferential methodologies according to the type of study design for answering a particular research question. C10. Upon completion of this course, students will be able to develop written presentations based on statistical analyses for both public health professionals and educated lay audiences. 6. To infer single population means and proportions with point and interval estimates. To perform hypotheses tests and to interpret results for a data set. To compare two population means with point and interval estimates. To perform hypotheses tests on the difference of two population means and two population proportions. 7. Introduction to techniques of statistical inference that do not require the use of standard assumptions such as the normal distribution. C7. Upon completion of this course, students will be able to interpret results of statistical analyses found in public health studies. C8. Upon completion of this course, students will be able to apply descriptive and inferential methodologies according to the type of study design for answering a particular research question. C10. Upon completion of this course, students will be able to develop written presentations based on statistical analyses for both public health professionals and educated lay audiences. C9. Upon completion of this course, students will be able to describe preferred methodological alternatives to commonly used statistical methods when assumptions are not met. 3

4 BIOSTATISTICS 100A COURSE SCHEDULE Fall 2017, MWF 1-2:50p, Room CHS (except as noted) Fri. 9/29/2017 Course Orientation Week 1 Mon. 10/2/2017 Lecture 1. Introduction & Descriptive Statistics Introduction 1. Population and parameters 2. Sample and statistics 3. Study design Data presentation 4. Scales and measurement 5. Percentiles 6. Exploratory data analysis Wed. 10/4/2017 Lecture 2. Descriptive Statistics Data presentation 1. Graphical techniques and tables 2. Multi variable techniques Fri. 10/6/2017 Lecture 3. Descriptive Statistics Data presentation 1. Histograms with unequal length intervals Numerical summaries of data 2. Measures of location 3. Measures of variation 4. Example Ch 1 & 2 Ch 2 Ch 2 Week 2 (Lab Session One) Mon. 10/9/2017 Lecture 4. Descriptive Statistics Summary measures for grouped data 1. Means and SDs 2. Medians and IQRs 3. Example Uniform changes to data 4. Adding to and multiplying by constants Rates 5. Rates Wed. 10/11/2017 Fri. 10/13/2017 Discussion 1. Lecture 5. Ideas of Probability & Sampling Chances Probability 1. Three definitions 2. Basic concepts 3. Adding probabilities 4. Independence 5. Conditional probabilities Ch 2 Ch 13, 5 Ch 3 4

5 Fall 2017 BIOSTATISTICS 100A COURSE SCHEDULE (Continued) Week 3 (Lab Session Two) Mon. 10/16/2017 Wed. 10/18/2017 Fri. 10/20/2017 Discussion 2. Week 4 (Lab Session Three) Mon. 10/23/2017 Wed. 10/25/2017 Fri. 10/27/2017 Discussion 3. Lecture 6. Ideas of Probability & Sampling Chances Probability 1. Odds and relative risk 2. Likelihoods 3. Bayes theorem 4. Prevalence and incidence Lecture 7. Ideas of Probability & Sampling Chances Chance sampling 1. Quotas and surveys 2. Bias 3. Simple random sample 4. Randomly selecting numbers 5. General probability sampling 6. Cluster and stratified sampling Experimental design 7. Single versus comparative study 8. Design of experiments 9. Principles of experimental design Lecture 8. Ideas of Probability & Sampling Chances Experimental design 1. Type of experiments Sampling distributions 2. Continuous and discrete distributions i. Binomial distribution ii. Normal distribution 3. Random variables Lecture 9. Ideas of Probability & Sampling Chances Rules for random variables 1. Means of random variables 2. Variances of random variables Normal distribution 3. General properties 4. Standard normal distribution 5. Standardization of units 6. Finding areas under normal curves Ch 3 Ch 13, 1 4 Ch 6, 1 4 Ch 4 & 5 Ch 5 5

6 Fall 2017 BIOSTATISTICS 100A COURSE SCHEDULE (Continued) Week 5 (Lab Session Four) Mon. 10/30/2017 Wed. 11/1/2017 Review 1. Lecture 10. Ideas of Probability & Sampling Chances Normal distribution 1. Chance errors 2. Properties of the sample mean 3. Sampling distribution of the mean 4. Central limit theorem 5. Normal approximation to binomial 6. Examples 7. Normal probability plots Fri. 11/3/2017 Midterm Examination I. *** Rooms TBA *** Week 6 (Lab Session Five) Mon. 11/6/2017 Lecture 11. Introduction to Statistical Inference General techniques 1. Statistical inference: techniques 2. Point estimates 3. Interval estimates 4. Length of intervals 5. Interval estimates: sample size Wed. 11/8/2017 Lecture 12. Statistical Inference of Means One sample 1. Hypothesis testing: tests of significance 2. Example 3. Notations and definitions 4. Test for μ, σ known 5. Computation of power 6. Relationships among α, power and n Fri. 11/10/2017 No Class Veterans Day Ch 5 Ch 6 Ch 7 6

7 Fall 2017 BIOSTATISTICS 100A COURSE SCHEDULE (Continued) Week 7 (Lab Session Six) Mon. 11/13/2017 Wed. 11/15/2017 Fri. 11/17/2017 Discussion 4. Week 8 (No Lab Session) Mon. 11/20/2017 Wed. 11/22/2017 Review 2. Lecture 13. Statistical Inference of Means One sample 1. Power curves 2. Confidence intervals for μ. σ is unknown 3. Properties of the t distribution 4. Finding areas in t distribution 5. Hypothesis testing for μ, σ is unknown 6. Power and t tests Lecture 14. Statistical Inference of Means One sample 1. Inference for nonnormal populations Two sample 2. Inference on two population means 3. Estimates of independent groups μ 1 μ 2 4. Known variances 5. Unknown but equal variances 6. Unknown and unequal variances 7. Hypothesis testing for μ 1 μ 2 Lecture 15. Statistical Inference of Means Two sample 1. Inference for two independent nonnormal populations 2. Inference on two population means: dependent samples Fri. 11/24/2017 No Classes Thanksgiving Holiday Week 9 (Lab Session Seven) Mon. 11/27/2017 Discussion 5. Wed. 11/29/2017 Midterm Examination II. *** Rooms TBA *** Fri. 12/1/2017 No Class. Ch 7 Ch 9, 1 2 Ch 8 Ch 9, 4 7

8 Fall 2017 BIOSTATISTICS 100A COURSE SCHEDULE (Continued) Week 10 (Lab Session Review) Mon. 12/4/2017 Lecture 16. Statistical Inference of Proportions Categorical data 1. Multinomial experiment 2. Chi square One sample 3. Inference on a single population proportion i. Confidence intervals ii. Hypothesis testing Wed. 12/6/2017 Lecture 17. Statistical Inference of Proportions Two sample 1. Inference concerning the difference in two independent proportions. 2. Difference in proportions derived from a single sample. Ch 7 Ch 10 Fri. 12/8/2017 Review 3. Handouts 1 & 2 Week Final Thu. 12/14/2017 Final Examination (3 6p). *** Rooms TBA *** 8