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Package Updates
Changes in Version 1.0.4 (2024-01-30)
- Fixed an error for joint interventions on multiple treatments
- Fixed an error occurring when multiple restrictions are applied to a single variable
- Revised the
gformula()
function so that it produces a warning message rather than an error message when one of the bootstrap replicates fails. The bootstrap standard errors and 95% CIs are calculated based on the bootstrap replicates that do not fail.
- Fixed an error occurring when no interventions are supplied (i.e., only the natural course intervention is used)
- Slightly sped up the calculation of the counterfactual cumulative risks
- Expanded the error checking
Changes in Version 1.0.3 (2023-05-18)
- Fixed an error in the
gformula()
function that assumed that the name of the ID variable in obs_data
was 'id'
- Removed Travis CI
Changes in Version 1.0.2 (2023-02-27)
- Revised the plot of the estimates of the natural course risk so that it starts at (0, 0)
- Fixed an error when obtaining confidence intervals around the hazard ratio estimates
- Fixed an error in the reported standard errors of the coefficients of the fitted categorical covariate models
- Fixed an error in the reported root mean squared error values for the outcome and competing event models
- Allowed categorical covariates to be of class “numeric” (rather than requiring them to be of class “factor”)
Changes in Version 1.0.1 (2023-01-11)
- Added the “cumulative percent intervened on” and “average percent intervened on” to the output of the
gformula()
function
- Added option for users to carry forward the natural value of treatment rather than the intervened value. See the
int_visit_type
argument in the gformula()
function
- Added option for users to access the bootstrap replicates of the parametric g-formula estimates. See the
boot_diag
argument in the gformula()
function.
- Fixed an error in computing the inverse probability weighted means of the time-varying covariates
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)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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