ps_adjust {adapt4pv}  R Documentation 
Implement the adjustment on propensity score for all the drug exposures
of the input drug matrix x
which have more than a given
number of cooccurence with the outcome.
The binary outcome is regressed on a drug exposure and its
estimated PS, for each drug exposure considered after filtering.
With this approach, a pvalue is obtained for each drug and a
variable selection is performed over the corrected for multiple
comparisons pvalues.
ps_adjust( x, y, n_min = 3, betaPos = TRUE, est_type = "bic", threshold = 0.05, ncore = 1 )
x 
Input matrix, of dimension nobs x nvars. Each row is an observation
vector. Can be in sparse matrix format (inherit from class

y 
Binary response variable, numeric. 
n_min 
Numeric, Minimal number of cooccurence between a drug covariate and the outcome y to estimate its score. See details belows. Default is 3. 
betaPos 
Should the covariates selected by the procedure be
positively associated with the outcome ? Default is 
est_type 
Character, indicates which approach is used to estimate the PS. Could be either "bic", "hdps" or "xgb". Default is "bic". 
threshold 
Threshold for the pvalues. Default is 0.05. 
ncore 
The number of calcul units used for parallel computing. Default is 1, no parallelization is implemented. 
The PS could be estimated in different ways: using lassobic approach,
the hdps algorithm or gradient tree boosting.
The scores are estimated using the default parameter values of
est_ps_bic
, est_ps_hdps
and est_ps_xgb
functions
(see documentation for details).
We apply the same filter and the same multiple testing correction as in
the paper UPCOMING REFERENCE: first, PS are estimated only for drug covariates which have
more than n_min
cooccurence with the outcome y
.
Adjustment on the PS is performed for these covariates and
one sided or twosided (depend on betaPos
parameter)
pvalues are obtained.
The pvalues of the covariates not retained after filtering are set to 1.
All these pvalues are then adjusted for multiple comparaison with the
BenjaminiYekutieli correction.
COULD BE VERY LONG. Since this approach (i) estimate a score for several
drug covariates and (ii) perform an adjustment on these scores,
parallelization is highly recommanded.
An object with S3 class "ps", "adjust", "*"
, where
"*"
is "bic"
, "hdps"
or "xgb"
according on how the
score were estimated.
estimates 
Regression coefficients associated with the drug covariates. Numeric, length equal to the number of selected variables with this approach. Some elements could be NA if (i) the corresponding covariate was filtered out, (ii) adjustment model did not converge. Trying to estimate the score in a different way could help, but it's not insured. 
corrected_pvals 
One sided pvalues if 
selected_variables 
Character vector, names of variable(s)
selected with the psadjust approach.
If 
Emeline Courtois
Maintainer: Emeline Courtois
emeline.courtois@inserm.fr
Benjamini, Y., & Yekuteli, D. (2001). "The Control of the False Discovery Rate in Multiple Testing under Dependency". The Annals of Statistics. 29(4), 1165–1188, doi: doi: 10.1214/aos/1013699998.
set.seed(15) drugs < matrix(rbinom(100*20, 1, 0.2), nrow = 100, ncol = 20) colnames(drugs) < paste0("drugs",1:ncol(drugs)) ae < rbinom(100, 1, 0.3) adjps < ps_adjust(x = drugs, y = ae, n_min = 10)