100A. Introduction to Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Prerequisites: upper division standing, one biological or physical sciences course. Students who have completed courses in statistics may enroll only with consent of instructor. Not open for credit to students with credit for course 110A. Introduction to methods and concepts of statistical analysis. Sampling situations, with special attention to those occurring in biological sciences. Topics include distributions, tests of hypotheses, estimation, types of error, significance and confidence levels, sample size.
100B. Introduction to Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Prerequisite: course 100A or equivalent. Not open for credit to students with credit for course 110B. Introduction to analysis of variance, linear regression, and correlation analysis.
100C. Introduction to Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Prerequisite: course 100B or equivalent. Design of experiments, analysis of variance, multiple and polynomial regression analysis with biomedical applications.
110A. Basic Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Prerequisite: Mathematics 31B or equivalent. Not open for credit to students with credit for course 100A. Basic concepts of statistical analysis applied to biological sciences. Topics include random variables, sampling distributions, parameter estimates, statistical inference.
110B. Basic Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Prerequisite: course 110A. Not open for credit to students with credit for course 100B. Topics include elementary analysis of variance, simple linear regression; topics related to analysis of variance and experimental designs.
115. Topics in Estimation. (Formerly numbered 110C.) Lecture, three hours; discussion, one hour. Prerequisites: Statistics M152A, 152B. Small and large sample properties of common estimation techniques arising in biostatistical application.
M153A-M153B. Introduction to Computational Statistics. (Same as Biomathematics M153A-M153B and Statistics M153A-M153B.) Lecture, three hours; discussion, one hour. Prerequisites: Mathematics 115A, Statistics 152B. Linear and nonlinear regression analysis using package programs. Emphasis on relation between statistical theory, numerical results, and analysis of data. M153A. BMDP, SAS, and SPSS regression programs; general linear model theory; linear regression analysis; transforming and weighting; regression diagnostics; model building. M153B. Analysis of variance and covariance; nonlinear regression programs, analysis, and applications; maximum likelihood analysis; robust regression.
199. Special Studies (2 to 4 units). Prerequisites: senior standing, consent of instructor and department chair (based on written proposal outlining course of study). Individual undergraduate guided studies under direct faculty supervision. Study to be structured by instructor and student at time of initial enrollment. Only four units may be taken each term.
200A. Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Requisites: courses 100A and 100B, or 110A and 110B. Topics in methodology of applied statistics, such as design, analysis of variance, regression. S/U or letter grading.
200B-200C. Biostatistics. Lecture, three hours; discussion, one hour; laboratory, one hour. Prerequisites: courses M153A, 200A. S/U or letter grading. 200B. Multiple linear regression, including model validation, influence of observations, regression diagnostics; discriminant analysis; principal components; factor analysis and clinical trials. 200C. Measures of association and analysis of categorical data, theory of generalized linear models.
201. Topics in Applied Regression. Further studies in multiple linear regression, including model assessment principle components and errors in variables. Additional topics include estimation hypothesis testing in K4 matching for propensity score and introduction to logistic regression and its usefulness in propensity methodology.
M206A-M206B-M206C. Statistics in Psychiatric and Biobehavioral Research (2 units each). (Same as Psychiatry M286A-M286B-M286C.) Seminar, 90 minutes. Requisite: course 100B. Designed for graduate students. Examples from psychiatric literature used to illustrate statistical ideas and analysis strategies. Topics include experimental designs, sample size calculations, parametric versus nonparametric tests, regression, ANOVA, factor analysis, defining composite variables, causal inference. Computer used to illustrate basic data analysis. S/U or letter grading.
M210. Statistical Methods for Categorical Data. (Same as Biomathematics M231.) Lecture, three hours; discussion, one hour. Requisites: course 100B or 110B, Statistics 152B. Statistical techniques for analysis of categorical data; discussion and illustration of their applications and limitations.
212. Distribution Free Methods. Lecture, three hours; discussion, one hour. Requisites: course 100B or 110B, Statistics 152B. Theory and application of distribution free methods in biostatistics. S/U or letter grading.
213. Statistical Simulation Techniques. Lecture, three hours; discussion, one hour. Preparation: one computer programming course. Requisite: Statistics 152B. Techniques for simulating important statistical distributions, with applications in biostatistics. S/U or letter grading.
214. Finite Population Sampling. Lecture, three hours. Prerequisites: course 110B, Statistics 152B. Theory and methods for sampling finite populations and estimating population characteristics. S/U or letter grading.
M215. Survival Analysis. (Same as Biomathematics M281.) Lecture, three hours; discussion, one hour. Requisites: course 110B, Statistics 152B. Statistical methods for analysis of survival data. S/U or letter grading.
216. Introduction to Statistical Methods for Biological Assays. Lecture, three hours. Prerequisite: Statistics 152B. Topics include standard statistical procedures for estimation of relative potency, density of microorganisms, and density of radioactivity, models used for these procedures, and statistical considerations for designing such assays. S/U or letter grading.
219. Special Topics: Supplemental Topics. Lecture, three hours; discussion, one hour. Requisite: course 115. Topics in biostatistics not covered in other courses.
M220. Experimental Statistics. (Same as Physiological Science CM200.) Lecture, four hours; outside study, eight hours. Introduction to statistics with focus on computer simulation instead of formulas. Bootstrap and Monte Carlo methods used to analyze physiological data. S/U or letter grading.
230. Statistical Graphics. Lecture, three hours; discussion, two hours. Prerequisites: courses 110A, 110B, 200A, or equivalent. Graphical data analysis emphasizes use of visual displays of quantitative data to gain insight into data structure by exploring patterns and relationships, and to enhance classical numerical analyses, especially assumption validity checking. Principles of graph construction, graphical methods, and perception issues. S/U or letter grading.
231. Simultaneous Statistical Inference. Lecture, three hours; discussion, one hour. Requisites: course 200C, Statistics 152B. Methods and theory of simultaneous statistical inference.
M232. Statistical Analysis of Incomplete Data. (Same as Biomathematics M232.) Lecture, three hours; discussion, one hour. Requisite: Statistics 152B. Discussion of statistical analysis of incomplete data sets, with material from sample survey, econometric, biometric, psychometric, and general statistical literature. Topics include treatment of missing data in statistical packages, missing data in ANOVA and regression imputation, weighting, likelihood-based methods, and nonrandom nonresponse models. Emphasis on application of methods to applied problems, as well as on underlying theory. S/U or letter grading.
233. Statistical Methods in AIDS (2 units). Requisites: courses 110A, 110B, M215. Coverage of methods necessary to address statistical problems in AIDS research, including projection methods for the size of AIDS epidemic and methods for estimating incubation distribution. S/U or letter grading.
M234. Applied Bayesian Inference. (Same as Biomathematics M234.) Lecture, three hours; discussion, one hour. Requisites: course 200B, Statistics 152B. Bayesian approach to statistical inference, with emphasis on biomedical applications and concepts rather than mathematical theory. Topics include large sample Bayes inference from likelihoods, noninformative and conjugate priors, empirical Bayes, Bayesian approaches to linear and nonlinear regression, model selection, Bayesian hypothesis testing, and numerical methods. S/U or letter grading.
M235. Causal Inference. (Formerly numbered 235.) (Same as Psychiatry M232.) Lecture, three hours; discussion, one hour. Requisite: course 200A. Selection bias, confounding, ecological paradox, contributions of Fisher and Neyman. Rubin model for causal inference, propensity scores. Analysis of clinical trials with noncompliance. Addressing confounding in longitudinal studies. Path analysis, structural equation, and graphical models. Decision making when causality is disputed.
M236. Analysis of Repeated Measures Designs. (Same as Biomathematics M282.) Lecture, three hours; discussion, one hour. Requisites: courses 200A, 200B. Presentation of classical and modern theories for analysis of repeated measures designs, with focus on computation and robustness. S/U or letter grading.
240A. Research Resources in Biostatistics (2 units). Lecture, three hours. Introduction to various resources available in statistical research, such as how to obtain access to current index in statistics and introduction to SUN workstation laboratory. Report on research paper in a recent statistics journal required. S/U or letter grading.
240B. Seminar for Second-Year Biostatistics Master's Students (2 units). Seminar, three hours. How to give an oral presentation on research results, including audiovisual techniques for a scientific talk and organization for short and long talks. Presentation of a paper from their current research related to their master's report required of students. S/U or letter grading.
245. Advanced Seminar: Biostatistics (2 units). Prerequisite: course 200C. Current research in biostatistics. May be repeated for credit. S/U grading.
M250A-M250B. Linear Statistical Models. (Same as Mathematics M279A-M279B.) Lecture, three hours; discussion, one hour. Prerequisite: one upper division three-term theoretical statistics course. Topics include linear algebra applied to linear statistical models, distribution of quadratic forms, Gauss/Markov theorem, fixed and random component models, balanced and unbalanced designs. S/U or letter grading.
251. Multivariate Biostatistics. Lecture, three hours; discussion, one hour. Prerequisite: course M250A or equivalent. Multivariate analysis as used in biological and medical situations. Topics from multivariate distributions, component analysis, factor analysis, discriminant analysis, MANOVA, MANCOVA, longitudinal models with random coefficients. S/U or letter grading.
255. Advanced Topics and Probability in Biostatistics. Lecture, three hours; discussion, one hour. Prerequisites: Mathematics 276A-276B or consent of instructor. Topics include conditioning, modes of convergence, basic limit results for empirical processes, von-Mises calculus, and notions of efficiency in statistics. Applications cover M-L-R estimation in two-sample and regression models, goodness of fit methods, smoothing techniques, and bootstrap.
270. Stochastic Processes. Lecture, three hours. Prerequisites: upper division mathematics, including statistics and probability. Stochastic processes applicable to medical and biological research.
271. Mathematical Epidemiology. Lecture, three hours. Preparation: upper division mathematics (including statistics and probability). Mathematical theory of epidemiology with deterministic and stochastic models and problems involved in applying the theory.
272. Statistical Genetics. Lecture, three hours; discussion, one hour. Preparation: upper division probability and statistics; knowledge of basic genetics principles helpful but not required. Introduction to statistical analysis of genetic data from experimental crosses, populations, and human pedigrees. Topics include segregation analysis, recombination and linkage, genetic mapping, inbreeding systems, population genetics, pedigree analysis, quantitative trait analysis, and molecular phylogeny.
275. Advanced Survival Analysis. Lecture, three hours; discussion, one hour. Prerequisites: course 255, Mathematics 276A-276B. Recommended: course M215. Censoring and truncation, single sample problems, K-sample comparisons, Cox regression model, hazard rate and density estimation, estimation in Markov chains and Markov renewal processes, multivariate models, competing risks. S/U or letter grading.
276. Inferential Techniques that Use Simulation. Lecture, three hours; discussion, one hour. Prerequisites: Mathematics 276A-276B. Recommended: Biostatistics 213. Theory and application of recently developed techniques for statistical inference that use computer simulation. Topics include bootstrap, multiple imputation, data augmentation, stochastic relaxation, and sampling/importance resampling algorithm.
277. Robustness and Modern Nonparametrics. Lecture, three hours. Prerequisite: Mathematics 276A. Topics include M-estimation, influence curves, breakdown point, bootstrap, jackknife, smoothing, nonparametric regression, generalized additive models, density estimation.
M280. Statistical Computing. (Same as Biomathematics M280 and Mathematics M280.) Lecture, three hours. Prerequisites: Mathematics 115A, Statistics 152C, or equivalent. Introduction to theory and design of statistical programs: computing methods for linear and nonlinear regression, dealing with constraints, robust estimation, and general maximum likelihood methods.
285. Advanced Topics: Recent Developments. Lecture, three hours; discussion, one hour. Advanced topics and developments in biostatistics not covered in Biostatistics M210 through 219 or 270 through 276 or in other courses. Possible topics include time-series analysis, classification procedures, correspondence analysis, etc. S/U or letter grading.
295. Application of Statistical Theories in Biomedical Research. Lecture, three hours; discussion, one hour. Requisite: Statistics 152B. Review of statistical theories essential to biostatistics. Illustration of applications by examples. Topics include delta method, order statistics, asymptotic properties of MLEs, iterative algorithms for MLEs, generalized likelihood ratio tests for categorical data, and transformations.
296. Seminar: Research Topics in Biostatistics. Discussion, two hours. Advanced study and analysis of current topics in biostatistics. Discussion of current research and literature in research specialty of faculty member teaching course. S/U grading.
400. Field Studies in Biostatistics (2 or 4 units). Field observation and studies in selected community organizations for health promotion or medical care. Students must file field placement and program training documentation on form available from Student Affairs Office. May not be applied toward M.S. minimum course requirement; four units may be applied toward 44-unit minimum total required for M.P.H. degree.
402A. Principles of Biostatistical Consulting (2 units). Lecture, one hour; discussion, one hour. Prerequisites: course 100B or 110B and Statistics 152B. Presentation of structural format for statistical consulting. Role of statistician and client. Reviews of actual statistician/client interactions and case studies.
402B. Biostatistical Consulting. Discussion, two hours; laboratory, two hours. Prerequisite: course 402A. Principles and practices of biostatistical consulting. May be repeated for credit. S/U grading.
403. Computer Management of Health Data. Lecture, three hours; laboratory, two hours. Prerequisites: at least one statistics course, two research methodology courses, Program in Computing 1 or equivalent, consent of instructor. Concepts of health data management, design and maintenance of large databases on tapes or disks; computing tools and techniques facilitating data retrieval for statistical analysis, tabulation and report generation useful to biostatisticians, health planners, and other health professionals.
404. Principles of Sampling. Lecture, three hours; discussion, one hour. Prerequisites: course 100B, Epidemiology 100, or equivalent. Statistical aspects of design and implementation of a sample survey. Techniques for analysis of data, including estimates and standard errors. Avoiding improper use of survey data.
405. Demographic Materials and Methods. Lecture, three hours; laboratory, two hours. Prerequisite: course 100A or 110A. Sources of demographic information; description of human populations; calculation and interpretation of statistics used to measure and describe population growth, structure, geographic distribution, mortality, natality, and migration. S/U or letter grading.
406. Applied Multivariate Biostatistics. Lecture, three hours; laboratory, one hour. Prerequisites: course 100B, at least two other upper division research courses. Use of multiple regression, principal components, factor analysis, discriminant function analysis, logistic regression, and canonical correlation in biomedical data analysis. S/U grading optional for nondivision majors.
410. Statistical Methods in Clinical Trials. Lecture, three hours; discussion, two hours. Requisite: course 200A. Design of studies in animals to assess antitumor response; randomization, historical controls, p-values, size of study, and stratification in human experimentation; various types of controls; prognostic factors, survivorship studies, and design of prognostic studies; organization of clinical trials -- administration, comparability, protocols, clinical standards, data collection and management. S/U grading optional for nonmajors.
411. Statistical Methods for Longitudinal Data. Lecture, three hours. Requisites: course 200A, Statistics 152B. Design and analysis of longitudinal or panel studies. S/U grading optional for nonmajors.
412. Statistical Methods for Case-Control Studies. Lecture, three hours. Requisite: course 200A. Statistical designs, sampling statistics, and analytic models of case-control studies. Special topics such as exploratory analyses, multiplicity of analyses, cross-validation, small sample performances of variance estimators, measurement error in the covariates, and incomplete data. S/U or letter grading.
419. Special Topics: Applied Statistics. Lecture, three hours; discussion, one hour. Prerequisite: course 100C. Special topics in applied statistics not covered in other courses in professional series.
420. Database Management Systems. Lecture, three hours; laboratory, two hours. Prerequisite: course 403 or equivalent. Database and database models applied to medical and public health studies; design of databases for efficient data retrieval and statistical analysis using package database management and statistical package programs.
495. Teacher Preparation in Biostatistics (2 units). Prerequisites: 18 units of cognate courses in area of specialization, consent of department chair. May not be applied toward master's degree minimum total course requirement. May be repeated for credit. S/U grading.
501. Cooperative Program (2 to 8 units). Prerequisite: consent of UCLA graduate adviser and graduate dean, and host campus instructor, department chair, and graduate dean. Used to record enrollment of UCLA students in courses taken under cooperative arrangements with USC. No more than eight units may be applied toward master's degree minimum total course requirement; may not be applied toward minimum graduate course requirement. S/U grading.
596. Directed Individual Study or Research (2 to 8 units). Prerequisite: graduate standing. Individual guided studies under direct faculty supervision. Only four units may be applied toward M.P.H. and M.S. minimum total course requirement. May be repeated for credit.
597. Preparation for Master's Comprehensive or Doctoral Qualifying Examinations (2 to 8 units). Prerequisite: graduate standing. May not be applied toward any degree course requirements. May be repeated for credit. S/U grading.
599. Doctoral Dissertation Research (2 to 8 units). May not be applied toward any degree course requirements. May be repeated for credit. S/U grading.