Analytic and Design Methods for Translational Research

This program is provided by Biostatistics, Epidemiology, and Research Design (BERD) in collaboration with the Department of Biostatistics and Training and Education (TRANSFORM).


Translation is the process of turning observations in the laboratory, clinic and community into interventions that improve the health of individuals and the public. Topics listed on this page are those that help translate observations into interventions, and are organized into three categories: analytic methods, design, and software & applications. (NCATS)

Analytic Methods

Competing Risk Analysis

Competing risk analysis refers to a special type of survival analysis that aims to correctly estimate marginal probability of an event in the presence of competing events.

Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts.

Difference-in-Difference Estimation

DID is a quasi-experimental design that makes use of longitudinal data from treatment and control groups to obtain an appropriate counterfactual to estimate a causal effect. DID is typically used to estimate the effect of a specific intervention or treatment (such as a passage of law, enactment of policy, or large-scale program implementation) by comparing the changes in outcomes over time between a population that is enrolled in a program (the intervention group) and a population that is not (the control group).

Exploratory Factor Analysis

Factor analysis is a family of techniques used to identify the structure/dimensionality of observed data and reveal the underlying constructs that give rise to observed phenomena. The techniques identify and examine clusters of inter-correlated variables; these clusters are called “factors” or “latent variables.”

Machine Learning (videos)

Machine learning is a branch of artificial intelligence which can be broadly defined as computer algorithms which learn and self-improve from finding patterns in data.

Missing Data and Multiple Imputation

Types of missing data and options for data analysis.

Mixed Methods (videos)

Mixed methods research strategically integrates quantitative and qualitative research methods to gain greater insight.

Multi-Level Modeling

Multilevel models are appropriate for research designs where data for participants are organized at more than one level. Usually, the units of analysis are individuals who are nested within aggregate units at a higher level.

Propensity Score Analysis

Propensity score analysis (PSA) arose as a way to achieve exchangeability between exposed and unexposed groups in observational studies without relying on traditional model building. Exchangeability is critical to our causal inference.

Ridge Regression

Ridge regression is a method of estimating coefficients of regression models in which the independent variables are highly correlated.

Trajectory Analysis

A trajectory describes the course of a measured variable over age or time. Investigators in epidemiology and other fields are often interested not only in the trajectory of variables over time, but also in how covariates may affect their shape.

Software and Applications


REDCap (Research Electronic Data Capture) is a secure web application for building and managing online surveys and  databases. While there are several installations of REDCap at Columbia University Irving Medical Center intended for the use of individual departments and other organizations, the Irving Institute's installation is made available for a modest fee to any researcher at Columbia University.

For more information, see the BERD REDCap resources.

Not sure if REDCap is right for you? Request a Data Management Consultation


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