This colloquium is open to upper level BME students who previously took, or are currently enrolled in, BME300 and are pursuing BME Departmental Honors. The class will meet once weekly and use out-of-class readings/podcasts with in-class discussions to delve deeper into synthesis, analysis, and especially application of biomaterials in medicine. The purpose of this course is to enhance your knowledge of translational biomaterials in a research setting by fostering your ability to read, critically analyze, and discuss relevant scientific research articles.

Students choose a topic on their own under the guidance of a faculty adviser who will then grade the final written report

Integration of linear models with experimental design and sampling, considering applications with unbalanced and missing data. In-depth discussion of mixed models including parameterizations, analysis of covariance, unequal numbers of observations per cell, missing cells. Repeated measure designs and longitudinal data analysis emphasized, with many examples illustrated using SAS. Prerequisite: BIOST&EP 640 and 691F or equivalent. Recommended: Bioepi 741 and 744.

This is not available at this time

A discovery based capstone project provides an essential culminating learning experience in the UMASS Amherst M.S program in biostatistics. This course will guide students as they carry out an individual project in 7 steps: (1) formulate a research question, (2) conduct literature review, (3) select relevant data sources, (4) choose appropriate statistical/machine learning methodology, (5) create and implement their analytical plan including data ingestion, transformation, modeling, and interpretation, (6) write a research paper, and (7) effectively communicate their project and results.

With the recent and ongoing 'data explosion', methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce a general framework for causal inference: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability, 4) choice and implementation of estimators including parametric and semi-parametric methods, and 5) interpretation of findings.

Principles of statistics applied to analysis of biological and health data, evaluation of public health and clinical programs.