Package Updates
Changes in Version 1.0.0 (2022-04-09)
- Added option for users to specify censoring models to compute inverse probability weights for estimating the natural course means / risk from the observed data
- Added data set
censor_data and a corresponding example application in the documentation to illustrate the application of inverse probability weighting for estimating the natural course means / risk from the observed data
- Fixed an error in calculating the means of the time-varying covariates under the natural course for survival outcomes
- Fixed errors in calculating the observed risk estimates and g-formula survival estimates when competing events are not treated like censoring events
- For categorical time-varying covariates, the
plot.gformula_survival(), gformula_continuous_eof(), and gformula_binary_eof() functions now display the nonparametric/IP weighted and parametric g-formula estimates of the probability of observing each level of the covariate. Previously, these functions displayed the counts of categorical variables.
Changes in Version 0.3.2 (2021-07-13)
- Updated computation of (lagged) cumulative averages to use the recursive formula. There should be a noticeable improvement in the computation time when using several (lagged) cumulative average terms and when the number of time points is large.
- Fixed an error for covariates of type
truncated normal (Thanks to
- Updates to the documentation
Changes in Version 0.3.1 (2020-03-22)
- Fixed error in the
coef.gformula() example
Changes in Version 0.3.0 (2020-01-30)
- Added wrapper function called
gformula() for the gformula_survival(), gformula_continuous_eof(), and gformula_binary_eof() functions. Users should now use the more general gformula() function to apply the g-formula.
- Added option for users to specify the values for lags at pre-baseline times by including rows at time -1, -2, …, -i.
- Added an example data set called
continuous_eofdata_pb, which illustrates how to prepare a data set with pre-baseline times
- Added option for users to pass in “control parameters” (e.g., maximum number of iterations, maxit, in glm.control) when fitting models for time-varying covariates via the
covparams$control argument. (Thanks to @jerzEG for the suggestion)
- Added option for users to access the fitted models for the time-varying covariates, outcome, and competing event (if applicable). See
model_fits argument of the gformula() function
- Added simulated data under the natural course to the
sim_data component of the output of the gformula() function
- Added a progress bar for the number of bootstrap samples completed. See the
show_progress argument of the gformula() function for further details
- Added
summary(), coef(), and vcov() S3 methods for objects of class ‘gformula’
- Added argument
fits in the print.gformula_survival(), print.gformula_continuous_eof(), and print.gformula_binary_eof() functions. Added argument all_times in the print.gformula_survival() function
- Fixed minor bug in the
lagavg() function
- Fixed bug occuring when not using lags of the intervention variable(s)
- Fixed bug occuring in the truncation beyond covariate ranges. (Thanks to Louisa Smith)
- Updates to the documentation
Changes in Version 0.2.1 (2019-08-24)
Changes in Version 0.2.0 (2019-08-22)
- Removed
example_intervention1(), example_intervention2(), and visit_sum_orig(), as these functions are not used internally and users should not directly apply them
- Removed export of
visit_sum() and natural(), as these functions are used internally and users should not directly apply them
- Updates to the documentation
Changes in Version 0.1.1 (2019-08-21)
- Minor updates to the documentation
Changes in Version 0.1.0 (2019-08-17)