David Mendez, PhD: “Intro to trend analyses and time-series approaches for modeling” (conceptual) Description: This module will: 1) Expose you to different approaches to time series modeling; 2) Help you understand the underlying assumptions behind different time series modeling
Jihyoun Jeon, PhD, MS: “Joinpoint regression” (application) Description: Please note that part 1 of this model (conceptual) is not available for public viewing. The hands-on part of the module will teach you how to: 1) Perform a trend analysis using the Joinpoint Trend Analysis software developed by the NCI; 2) Identify trend change points which break the trend into distinct periods; 3) Compare trends between different subpopulations
Ted Holford, PhD: “Age-period-cohort modeling” (conceptual) Description: This module will teach you how to: 1) Express temporal trends from an age, period, and cohort perspective. 2) Parameterize time in a statistical model. 3) Partition temporal trends into linear and curvature effects. 4) Model trends of ever smoking prevalence. 5) Model trends in smoking initiation and cessation probabilities. 6) Combine the contributions of initiation and cessation to quantify smoking exposure. 7) Quantify the effect of smoking on population ...
Steve Cook, PhD: “Standard longitudinal data analysis” (conceptual & application) Description: The hands-on part of the module will teach you how to: 1) create censor and duration variables; 2) restructure data to create an unbalanced person-period data set; 3) estimate discrete time models; 4) incorporate replicate weights.
David Mendez, PhD: “Introduction to compartmental modeling” (conceptual) Description: This module will teach you how to: 1) Understand the basic concepts behind compartmental models and their advantages and disadvantages over individual-based (micro) models. 2) Understand a basic taxonomy and key properties of macro-dynamic models.
Andrew Brouwer, PhD, MS, MA: “Markov modeling: Multistate transition modeling” (conceptual) Description: This module will teach you how to: 1) Understand why transitions are important to many research questions for tobacco control; 2) Specify, implement, and interpret a multistate transition applied to longitudinal data of tobacco product use; 3) Graphically summarize transition probabilities and hazard ratios; 4) Apply a multistate transition model to the Population Assessment of Tobacco and Health (PATH) Study
Andrew Brouwer, PhD, MS, MA: “Markov modeling: Multistate transition modeling” (application) Description: This module will teach you how to: 1) Understand why transitions are important to many research questions for tobacco control; 2) Specify, implement, and interpret a multistate transition applied to longitudinal data of tobacco product use; 3) Graphically summarize transition probabilities and hazard ratios; 4) Apply a multistate transition model to the Population Assessment of Tobacco and Health (PATH) Study
Ritesh Mistry, PhD: “Markov modeling: Latent transition analysis” (conceptual) Description: Learning objectives: 1) Understand the conceptual underpinnings of latent transition analysis and its value to tobacco control research. 2) Describe the key analysis steps involved in conducting latent transition analysis. 3) Interpret the parameters and results generated from latent transition analysis.
David Mendez, PhD: “Bayesian estimation and the Kalman Filter” (conceptual) Description: This module will teach you how to: 1) Understand the basic principles of Bayesian estimation. 2) Understand how Bayesian estimation relates to structural time series modeling. 3) Understand the mechanics of the Kalman filter and its applications to structural time series modeling.
Thuy Le, PhD: “Bayesian estimation and the Kalman Filter” (application) Description: The hands-on part of the module will teach you how to: 1) Apply the Kalman filter to estimate the trajectory of the smoking cessation rate from smoking prevalence observations over time. 2) Learn how to implement and interpret the results of a Kalman filter estimation in R.
