Binary logistic regression is one of the most useful regression models. It allows you to predict, classify, or understand explanatory relationships between a set of predictors and a binary outcome.
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Stage 4
Member Training: Introduction to Binary Logistic Regression
December 3rd, 2024 by TAF Support
Member Training: Types of Longitudinal, Repeated Measures, and Time Series
October 2nd, 2024 by TAF Support
How do you know when to use a time series and when to use a linear mixed model for longitudinal data?
What’s the difference between repeated measures data and longitudinal?
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Member Training: Analyzing Longitudinal Data: Comparing Regression and Structural Equation Modeling Approaches
July 2nd, 2024 by TAF Support
When analyzing longitudinal data, do you use regression or structural equation based approaches? There are many types of longitudinal data and different approaches to analyzing them. Two popular approaches are a regression based approach and a structural equation modeling based approach.
Member Training: Coarsened Exact Matching, an Alternative to Propensity Score Matching
February 29th, 2024 by TAF Support
The objective for quasi-experimental designs is to establish cause and effect relationships between the dependent and independent variables. However, they have one big challenge in achieving this objective: lack of an established control group.
Member Training: Frailty Models
November 2nd, 2023 by TAF Support
Most survival analysis models for time-to-event data, like Cox regression, assume independence. The survival time for one individual cannot influence the survival time for another.
This assumption doesn’t hold in many study designs. You may have animals clustered into litters, matched pairs, or patients in a multi-center trial with correlated survival times within a center.
Member Training: Moderated Mediation, Not Mediated Moderation
February 28th, 2023 by TAF Support
Moderated mediation, also known as Conditional Process Modeling, is great tool for understanding one type of complex relationship among variables.