Advances in Group-Based Trajectory Modeling and an SAS Procedure for Estimating Them

By B. Jones, Dan Nagin. 2007

1,171 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/193e25ae410088fff07d42ea3763bbb89c113665

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Citations in the corpus

Exploring Some Analytical Characteristics of Finite Mixture Models (2006, background): “It is described in Jones et al. (2001) and Jones and Nagin (2005) and in documentation available at http://www.ncovr.org.”
A Note on a Stata Plugin for Estimating Group-based Trajectory Models (2012, methodology): “Email: dn03@andrew.cmu.edu trajectory model demonstrated in two prior articles in this journal (Jones, Nagin, and Roeder 2001; Jones and Nagin 2007).”
Group-based Trajectory Modeling Extended to Account for Nonrandom Participant Attrition (2011, methodology): “While the Poisson option by definition accommodates the scale minimum of zero, its implementation in Proc Traj does not allow for censoring at a maximum value.”
Methods and Measures: Growth mixture modeling: A method for identifying differences in longitudinal change among unobserved groups (2009, methodology): “They are easily implemented using PROC TRAJ in SAS (Jones & Nagin, 2007; Jones, Nagin, & Roeder, 2001).”
Longitudinal Attendance Patterns: Developing High School Dropouts (2012, background): “significance of parameters and model fit (see Jones and Nagin [2007] for details).”
An Examination of First and Second Generation Immigrant Offending Trajectories (2014, methodology): “The Wald test was computed using the SAS macro trajtest (see Jones & Nagin, 2007, pp. 563- 564). within the immigrant subsamples (see research question 3).”
Hospital Phenotypes in the Management of Patients Admitted for Acute Myocardial Infarction (2016, methodology): “To identify distinct hospital phenotypes, we analyzed hospital-level, risk-standardized estimates of PCI procedure rate, CABG procedure rate, and ICU admission rate using a group-based growth-mixture modeling approach.(4,24) Because hospitals had multiple years of data, each utilization measure was ”
Delinquent Development in a Sample of High-Risk Youth (2009, methodology): “…group-based modeling method and Nagin (2004) for a nontechnical overview. group-based modeling was carried out using the SaS macro called Proc Traj (Jones & Nagin 2007; Jones, Nagin, & Roeder 2001). given that offending is less likely to occur when a respondent is incarcerated, time spent…”