| Name : R-autoCovariateSelection
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| Version : 1.0.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.14.7
| Date : 2024-06-14 11:52:05
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| Group : Development/Libraries/Other
| Source RPM : R-autoCovariateSelection-1.0.0-lp153.14.7.src.rpm
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| Size : 0.54 MB
| |
| Packager : (none)
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| Summary : Automated Covariate Selection Using HDPS Algorithm
|
Description :
Contains functions to implement automated covariate selection using
methods described in the high-dimensional propensity score (HDPS)
algorithm by Schneeweiss et.al. Covariate adjustment in
real-world-observational-data (RWD) is important for for estimating
adjusted outcomes and this can be done by using methods such as, but
not limited to, propensity score matching, propensity score weighting
and regression analysis. While these methods strive to statistically
adjust for confounding, the major challenge is in selecting the
potential covariates that can bias the outcomes comparison estimates in
observational RWD (Real-World-Data). This is where the utility of
automated covariate selection comes in. The functions in this package
help to implement the three major steps of automated covariate
selection as described by Schneeweiss et. al elsewhere. These three
functions, in order of the steps required to execute automated
covariate selection are, get_candidate_covariates(),
get_recurrence_covariates() and get_prioritised_covariates(). In
addition to these functions, a sample real-world-data from publicly
available de-identified medical claims data is also available for
running examples and also for further exploration. The original article
where the algorithm is described by Schneeweiss et.al. (2009)
< doi:10.1097/EDE.0b013e3181a663cc> .
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
