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Releases: sahirbhatnagar/casebase

casebase v0.10.5

10 Apr 15:22
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  • Skip tests when R is compiled against ATLAS BLAS
  • Set seed for reproducible tests
  • Addressed deprecation warning in ggplot2

v0.10.1

20 Oct 20:09
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What's Changed

  • Removed family = "gbm" as it wasn't properly tested.
  • Added confint.singleEventCB to compute confidence bands for the risk (or survival) function.
  • Updated ERSPC data so that the exposure variable is categorical. This may break previous code explicitly making this conversion, or somehow relying on the numerical coding.
  • fix issue 143 by @turgeonmaxime in #144
  • Issue 149 by @turgeonmaxime in #150
  • Issue 151 by @turgeonmaxime in #152

Full Changelog: v0.9.1...v0.10.1

casebase 0.9.1

07 Feb 17:16
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  • Fixed issue with plot.singleEventCB() when visreg package is not loaded.
  • Improved error message when using family = "glmnet" with a single covariate.
  • Introduced summary method for objects of class singleEventCB, and improved the output of print by displaying the appropriate function call.

casebase 0.9.0

06 Jul 14:58
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This is a Major new release

Breaking changes

  • The output of absoluteRisk() now always contains the time variable in the first column, regardless of the length of time. This will break earlier code that depended on the previous behaviour.
  • Population time plots now use ggplot2::geom_ribbon() instead of ggplot2::geom_segment().
  • Population time functions now allow for more flexible plots with user defined arguments including sequentially adding base, case, and competing event series. These are now passed as a list to the *.params arguments. Several arguments are now deprecated.
  • Removed popTimeExposure class and the corresponding plot method. popTime() now returns an exposure attribute which contains the name of the exposure variable in the dataset. The plot method for objects of class popTime will use this exposure attribute to create exposure stratified population time plots.

New features

  • Major refactoring of absoluteRisk(). Trapezoidal rule to perform numerical integration for absolute risk estimation, providing significant speed up.
  • Users now have further control on the output of absoluteRisk() using the arguments type and addZero.
  • New plotting method for time-dependent hazard functions and hazard ratios. These include confidence intervals. See plot.singleEventCB(). The hazard function plot requires the visreg package to be installed.
  • New plotting method for cumulative incidence and survival curves. See plot.absRiskCB().
  • When time is unspecified, absoluteRisk() now computes the cumulative incidence at ntimes equidistant points between 0 and the max failure time.
  • absoluteRisk() can now compute the cumulative incidence for a "typical" covariate profile with newdata = "typical". "Typical" corresponds to the median for continuous variables and the mode for factors (each variable is summarised independently).
  • Added eprchd, brcancer, support and simdat datasets to the package.
  • Implemented riskRegression::predictRisk() method for singleEventCB objects.

Minor improvements and fixes

  • No longer importing the entire namespace of data.table and ggplot2.
  • Moved from make the docs to pkgdown for package website.
  • A warning is given when family="gbm" and nonlinear functions of time or interactions are specified.
  • Add singleEventCB class to object returned by fitSmoothHazard()
  • Add absRiskCB class to object returned by absoluteRisk()
  • Use glmnet::prepareX to convert factors into indicator variables

Initial release on CRAN

30 Apr 14:14
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This is the first release of the casebase package on CRAN which implements the case-base sampling approach of Hanley and Miettinen (2009), Saarela and Arjas (2015), and Saarela (2015), for fitting flexible hazard regression models to survival data with single event type or multiple competing causes via logistic and multinomial regression. From the fitted hazard function, cumulative incidence, risk functions of time, treatment and profile can be derived. This approach accommodates any log-linear hazard function of prognostic time, treatment, and covariates, and readily allows for non-proportionality. We also provide a plot method for visualizing incidence density via population time plots. Comprehensive documentation is available at http://sahirbhatnagar.com/casebase/