Analysis of Longitudinal Data with Irregular Observation Times
Create an abacus plot Creates an abacus plot, depicting visits per sub...
Multiple outputation for longitudinal data subject to irregular observ...
Create an outputted dataset for use with multiple outputation.
Add rows corresponding to censoring times to a longitudinal dataset
Create a single bootstrap sample for clustered data For clustered data...
Measures of extent of visit irregularity Provides visual and numeric m...
Given a proportional hazards model for visit intensities, compute inve...
Compute inverse-intensity weights.
Fit an inverse-intensity weighted GEE.
Create lagged versions the variables in data
Fit a semi-parametric joint model
Fit a semi-parametric joint model, incorporating intercept estimation
Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).