Package 'adapt4pv' reference manual

Title:	Adaptive Approaches for Signal Detection in Pharmacovigilance
Description:	A collection of several pharmacovigilance signal detection methods based on adaptive lasso. Additional lasso-based and propensity score-based signal detection approaches are also supplied. See Courtois et al <doi:10.1186/s12874-021-01450-3>.
Authors:	Emeline Courtois [cre], Ismaïl Ahmed [aut], Hervé Perdry [ctb]
Maintainer:	Emeline Courtois <[email protected]>
License:	GPL-2
Version:	0.2-3
Built:	2024-12-13 06:46:09 UTC
Source:	CRAN

Adaptive approaches for signal detection in PharmacoVigilance

Description

This package fits adaptive lasso approaches in high dimension for signal detection in pharmacovigilance. In addition to classical implementations found in the litterature, we implemented two approaches particularly appropriated to variable selections framework, which is the one that stands in pharmacovigilance. We also supply in this package signal detection approaches based on lasso regression and propensity score in high dimension.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois <[email protected]>

fit an adaptive lasso with adaptive weights derived from lasso-bic

Description

Fit a first lasso regression and use Bayesian Information Criterion to determine ' adaptive weights (see lasso_bic function for more details), then run an adaptive lasso with this penalty weighting. BIC is used for the adaptive lasso for variable selection. Can deal with very large sparse data matrices. Intended for binary reponse only (option family = "binomial" is forced). Depends on the glmnet and relax.glmnet function from the package glmnet.

Usage

adapt_bic(x, y, gamma = 1, maxp = 50, path = TRUE, betaPos = TRUE, ...)
adapt_bic(x, y, gamma = 1, maxp = 50, path = TRUE, betaPos = TRUE, ...)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`gamma`	Tunning parameter to defined the penalty weights. See details below. Default is set to 1.
`maxp`	A limit on how many relaxed coefficients are allowed. Default is 50, in `glmnet` option default is 'n-3', where 'n' is the sample size.
`path`	Since `glmnet` does not do stepsize optimization, the Newton algorithm can get stuck and not converge, especially with relaxed fits. With `path=TRUE`, each relaxed fit on a particular set of variables is computed pathwise using the original sequence of lambda values (with a zero attached to the end). Default is `path=TRUE`.
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `penalty.factor`, `family`, `maxp` and `path`.

Details

The adaptive weight for a given covariate i is defined by

$w_i = 1/|\beta^{BIC}_i|^\gamma$

where $\beta^{BIC}_i$ is the NON PENALIZED regression coefficient associated to covariate $i$ obtained with lasso-bic.

Value

An object with S3 class "adaptive".

`aws`	Numeric vector of penalty weights derived from lasso-bic. Length equal to nvars.
`criterion`	Character, indicates which criterion is used with the adaptive lasso for variable selection. For `adapt_bic` function, `criterion` is "bic".
`beta`	Numeric vector of regression coefficients in the adaptive lasso. If `criterion` = "cv" the regression coefficients are PENALIZED, if `criterion` = "bic" the regression coefficients are UNPENALIZED. Length equal to nvars. Could be NA if adaptive weights are all equal to infinity.
`selected_variables`	Character vector, names of variable(s) selected with this adaptive approach. If `betaPos = TRUE`, this set is the covariates with a positive regression coefficient in `beta`. Else this set is the covariates with a non null regression coefficient in `beta`. Covariates are ordering according to the p-values (two-sided if `betaPos = FALSE` , one-sided if `betaPos = TRUE`) in the classical multiple logistic regression model that minimzes the BIC in the adaptive lasso.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
ab <- adapt_bic(x = drugs, y = ae, maxp = 50)


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)
ab <- adapt_bic(x = drugs, y = ae, maxp = 50)

fit an adaptive lasso with adaptive weights derived from CISL

Description

Compute the CISL procedure (see cisl for more details) to determine adaptive penalty weights, then run an adaptive lasso with this penalty weighting. BIC is used for the adaptive lasso for variable selection. Can deal with very large sparse data matrices. Intended for binary reponse only (option family = "binomial" is forced). Depends on the glmnet function from the package glmnet.

Usage

adapt_cisl(
  x,
  y,
  cisl_nB = 100,
  cisl_dfmax = 50,
  cisl_nlambda = 250,
  cisl_ncore = 1,
  maxp = 50,
  path = TRUE,
  betaPos = TRUE,
  ...
)
adapt_cisl(
  x,
  y,
  cisl_nB = 100,
  cisl_dfmax = 50,
  cisl_nlambda = 250,
  cisl_ncore = 1,
  maxp = 50,
  path = TRUE,
  betaPos = TRUE,
  ...
)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`cisl_nB`	`nB` option in `cisl` function. Default is 100.
`cisl_dfmax`	`dfmax` option in `cisl` function. Default is 50.
`cisl_nlambda`	`nlambda` option in `cisl` function. Default is 250.
`cisl_ncore`	`ncore` option in `cisl` function. Default is 1.
`maxp`	A limit on how many relaxed coefficients are allowed. Default is 50, in `glmnet` option default is 'n-3', where 'n' is the sample size.
`path`	Since `glmnet` does not do stepsize optimization, the Newton algorithm can get stuck and not converge, especially with relaxed fits. With `path=TRUE`, each relaxed fit on a particular set of variables is computed pathwise using the original sequence of lambda values (with a zero attached to the end). Default is `path=TRUE`.
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `penalty.factor`, `family`, `maxp` and `path`.

Details

The CISL procedureis first implemented with its default value except for dfmax and nlambda through parameters cisl_dfmax and cisl_nlambda. In addition, the betaPos parameter is set to FALSE in cisl. For each covariate $i$ , cisl_nB values of the CISL quantity $\tau_i$ are estimated. The adaptive weight for a given covariate $i$ is defined by

$w_i = 1- 1/cisl_nB \sum_{b=1, .., cisl_nB} 1 [ \tau^b_i >0 ]$

If $\tau_i$ is the null vector, the associated adaptve weights in infinty. If $\tau_i$ is always positive, rather than "forcing" the variable into the model, we set the corresponding adaptive weight to 1/cisl_nB.

Value

An object with S3 class "adaptive".

`aws`	Numeric vector of penalty weights derived from CISL. Length equal to nvars.
`criterion`	Character, indicates which criterion is used with the adaptive lasso for variable selection. For `adapt_cisl` function, `criterion` is "bic".
`beta`	Numeric vector of regression coefficients in the adaptive lasso. If `criterion` = "cv" the regression coefficients are PENALIZED, if `criterion` = "bic" the regression coefficients are UNPENALIZED. Length equal to nvars. Could be NA if adaptive weights are all equal to infinity.
`selected_variables`	Character vector, names of variable(s) selected with this adaptive approach. If `betaPos = TRUE`, this set is the covariates with a positive regression coefficient in `beta`. Else this set is the covariates with a non null regression coefficient in `beta`. Covariates are ordering according to the p-values (two-sided if `betaPos = FALSE` , one-sided if `betaPos = TRUE`) in the classical multiple logistic regression model that minimzes the BIC in the adaptive lasso.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
acisl <- adapt_cisl(x = drugs, y = ae, cisl_nB = 50, maxp=10)


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)
acisl <- adapt_cisl(x = drugs, y = ae, cisl_nB = 50, maxp=10)

fit an adaptive lasso with adaptive weights derived from lasso-cv

Description

Fit a first lasso regression with cross-validation to determine adaptive weights. Run a cross-validation to determine an optimal lambda. Two options for implementing cross-validation for the adaptive lasso are possible through the type_cv parameter (see bellow). Can deal with very large sparse data matrices. Intended for binary reponse only (option family = "binomial" is forced). The cross-validation criterion used is deviance. Depends on the cv.glmnet function from the package glmnet.

Usage

adapt_cv(
  x,
  y,
  gamma = 1,
  nfolds = 5,
  foldid = NULL,
  type_cv = "proper",
  betaPos = TRUE,
  ...
)
adapt_cv(
  x,
  y,
  gamma = 1,
  nfolds = 5,
  foldid = NULL,
  type_cv = "proper",
  betaPos = TRUE,
  ...
)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`gamma`	Tunning parameter to defined the penalty weights. See details below. Default is set to 1.
`nfolds`	Number of folds - default is 5. Although `nfolds` can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is `nfolds=3`.
`foldid`	An optional vector of values between 1 and `nfolds` identifying what fold each observation is in. If supplied, `nfolds` can be missing.
`type_cv`	Character, indicates which implementation of cross-validation is performed for the adaptive lasso: a "naive" one, where adaptive weights obtained on the full data are used, and a "proper" one, where adaptive weights are calculated for each training sets. Could be either "naive" or "proper". Default is "proper".
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `nfolds`, `foldid`, `penalty.factor`, `standardize`, `intercept` and `family`.

Details

The adaptive weight for a given covariate i is defined by

$w_i = 1/|\beta^{CV}_i|^\gamma$

where $\beta^{CV}_i$ is the PENALIZED regression coefficient associated to covariate $i$ obtained with cross-validation.

Value

An object with S3 class "adaptive".

`aws`	Numeric vector of penalty weights derived from cross-validation. Length equal to nvars.
`criterion`	Character, indicates which criterion is used with the adaptive lasso for variable selection. For `adapt_cv` function, `criterion` is "cv".
`beta`	Numeric vector of regression coefficients in the adaptive lasso. If `criterion` = "cv" the regression coefficients are PENALIZED, if `criterion` = "bic" the regression coefficients are UNPENALIZED. Length equal to nvars. Could be NA if adaptive weights are all equal to infinity.
`selected_variables`	Character vector, names of variable(s) selected with this adaptive approach. If `betaPos = TRUE`, this set is the covariates with a positive regression coefficient in `beta`. Else this set is the covariates with a non null regression coefficient in `beta`. Covariates are ordering according to magnitude of their regression coefficients absolute value in the adaptive lasso.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
acv <- adapt_cv(x = drugs, y = ae, nfolds = 5)


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)
acv <- adapt_cv(x = drugs, y = ae, nfolds = 5)

fit an adaptive lasso with adaptive weights derived from univariate coefficients

Description

Compute odd-ratios between each covariate of x and y then derived adaptive weights to incorporate in an adaptive lasso. BIC or cross-validation could either be used for the adaptive lasso for variable selection. Two options for implementing cross-validation for the adaptive lasso are possible through the type_cv parameter (see bellow). Can deal with very large sparse data matrices. Intended for binary reponse only (option family = "binomial" is forced). The cross-validation criterion used is deviance. Depends on the glmnet and relax.glmnet function from the package glmnet.

Usage

adapt_univ(
  x,
  y,
  gamma = 1,
  criterion = "bic",
  maxp = 50,
  path = TRUE,
  nfolds = 5,
  foldid = NULL,
  type_cv = "proper",
  betaPos = TRUE,
  ...
)
adapt_univ(
  x,
  y,
  gamma = 1,
  criterion = "bic",
  maxp = 50,
  path = TRUE,
  nfolds = 5,
  foldid = NULL,
  type_cv = "proper",
  betaPos = TRUE,
  ...
)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`gamma`	Tunning parameter to defined the penalty weights. See details below. Default is set to 1.
`criterion`	Character, indicates which criterion is used with the adaptive lasso for variable selection. Could be either "bic" or "cv". Default is "bic"
`maxp`	Used only if `criterion` = "bic", ignored if `criterion` = "cv". A limit on how many relaxed coefficients are allowed. Default is 50, in `glmnet` option default is 'n-3', where 'n' is the sample size.
`path`	Used only if `criterion` = "bic", ignored if `criterion` = "cv". Since `glmnet` does not do stepsize optimization, the Newton algorithm can get stuck and not converge, especially with relaxed fits. With `path=TRUE`, each relaxed fit on a particular set of variables is computed pathwise using the original sequence of lambda values (with a zero attached to the end). Default is `path=TRUE`.
`nfolds`	Used only if `criterion` = "cv", ignored if `criterion` = "bic". Number of folds - default is 5. Although `nfolds` can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is `nfolds=3`.
`foldid`	Used only if `criterion` = "cv", ignored if `criterion` = "bic". An optional vector of values between 1 and `nfolds` identifying what fold each observation is in. If supplied, `nfolds` can be missing.
`type_cv`	Used only if `criterion` = "cv", ignored if `criterion` = "bic". Character, indicates which implementation of cross-validation is performed for the adaptive lasso: a "naive" one, where adaptive weights obtained on the full data are used, and a "proper" one, where adaptive weights are calculated for each training sets. Could be either "naive" or "proper". Default is "proper".
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `family`, `maxp`, `standardize`, `intercept`

Details

The adaptive weight for a given covariate i is defined by

$w_i = 1/|\beta^{univ}_i|^\gamma$

where $\beta^{univ}_i = log(OR_i)$ , with $OR_i$ is the odd-ratio associated to covariate $i$ with the outcome.

Value

An object with S3 class "adaptive".

`aws`	Numeric vector of penalty weights derived from odds-ratios. Length equal to nvars.
`criterion`	Character, same as input. Could be either "bic" or "cv".
`beta`	Numeric vector of regression coefficients in the adaptive lasso. If `criterion` = "cv" the regression coefficients are PENALIZED, if `criterion` = "bic" the regression coefficients are UNPENALIZED. Length equal to nvars. Could be NA if adaptive weights are all equal to infinity.
`selected_variables`	Character vector, names of variable(s) selected with this adaptive approach. If `betaPos = TRUE`, this set is the covariates with a positive regression coefficient in `beta`. Else this set is the covariates with a non null regression coefficient in `beta`. If `criterion` = "bic", covariates are ordering according to magnitude of their regression coefficients absolute value in the adaptive lasso. If `criterion` = "bic", covariates are ordering according to the p-values (two-sided if `betaPos = FALSE` , one-sided if `betaPos = TRUE`) in the classical multiple logistic regression model that minimzes the BIC in the adaptive lasso.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
au <- adapt_univ(x = drugs, y = ae, criterion ="cv", nfolds = 3)

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)
au <- adapt_univ(x = drugs, y = ae, criterion ="cv", nfolds = 3)

Class Imbalanced Subsampling Lasso

Description

Implementation of CISL and the stability selection according to subsampling options.

Usage

cisl(
  x,
  y,
  r = 4,
  nB = 100,
  dfmax = 50,
  nlambda = 250,
  nMin = 0,
  replace = TRUE,
  betaPos = TRUE,
  ncore = 1
)
cisl(
  x,
  y,
  r = 4,
  nB = 100,
  dfmax = 50,
  nlambda = 250,
  nMin = 0,
  replace = TRUE,
  betaPos = TRUE,
  ncore = 1
)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`r`	Number of control in the CISL sampling. Default is 4. See details below for other implementations.
`nB`	Number of sub-samples. Default is 100.
`dfmax`	Corresponds to the maximum size of the models visited with the lasso (E in the paper). Default is 50.
`nlambda`	Number of lambda values as is `glmnet` documentation. Default is 250.
`nMin`	Minimum number of events for a covariate to be considered. Default is 0, all the covariates from `x` are considered.
`replace`	Should sampling be with replacement? Default is TRUE.
`betaPos`	If `betaPos=TRUE`, variable selection is based on positive regression coefficient. Else, variable selection is based on non-zero regression coefficient. Default is TRUE.
`ncore`	The number of calcul units used for parallel computing. This has to be set to 1 if the `parallel` package is not available. Default is 1. WARNING: parallel computing is not supported for windows machines!

Details

CISL is a variation of the stability method adapted to characteristics of pharmacovigilance databases. Tunning r = 4 and replace = TRUE are used to implement our CISL sampling. For instance, r = NULL and replace = FALSE can be used to implement the $n \over 2$ sampling in Stability Selection.

Value

An object with S3 class "cisl".

`prob`	Matrix of dimension nvars x `nB`. Quantity compute by CISL for each covariate, for each subsample.
`q05`	5 $\%$ quantile of the CISL quantity for each covariates. Numeric, length equal to nvars.
`q10`	10 $\%$ quantile of the CISL quantity for each covariates. Numeric, length equal to nvars.
`q15`	15 $\%$ quantile of the CISL quantity for each covariates. Numeric, length equal to nvars.
`q20`	20 $\%$ quantile of the CISL quantity for each covariates. Numeric, length equal to nvars.

Author(s)

Ismail Ahmed

References

Ahmed, I., Pariente, A., & Tubert-Bitter, P. (2018). "Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions". Statistical Methods in Medical Research. 27(3), 785–797, doi:10.1177/0962280216643116

Examples


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)
lcisl <- cisl(x = drugs, y = ae, nB = 50)

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)
lcisl <- cisl(x = drugs, y = ae, nB = 50)

Simulated data for the adapt4pv package

Description

Simple simulated data, used to demonstrate the features of functions from adapt4cv package.

Format

X: large sparse and binary matrix with 117160 rows and 300 columns. Drug matrix exposure: each row corresponds to an individual and each column corresponds to a drug.
Y: large spase and binary vector of length 117160. Indicator of the presence/absence of an adverse event for ech individual. Only the first 30 drugs (out of the 300) are associated with the outcome.

Examples


data(ExamplePvData)


data(ExamplePvData)

propensity score estimation in high dimension with automated covariates selection using lasso-bic

Description

Estimate a propensity score to a given drug exposure by (i) selecting among other drug covariates in x which ones to include in the PS estimation model automatically using lasso-bic approach, (ii) estimating a score using a classical logistic regression with the afore selected covariates. Internal function, not supposed to be used directly.

Usage

est_ps_bic(idx_expo, x, penalty = rep(1, nvars - 1), ...)
est_ps_bic(idx_expo, x, penalty = rep(1, nvars - 1), ...)

Arguments

`idx_expo`	Index of the column in `x` that corresponds to the drug covariate for which we aim at estimating the PS.
`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`penalty`	TEST OPTION penalty weights in the variable selection to include in the PS.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `penalty.factor`, `family`, `maxp` and `path`.

Details

betaPos option of lasso_bic function is set to FALSE and maxp is set to 20. For optimal storage, the returned elements indicator_expo and score are Matrix with ncol = 1.

Value

An object with S3 class "ps", "bic".

expo_name

Character, name of the drug exposure for which the PS was estimated. Correspond to colnames(x)[idx_expo]

indicator_expo

One-column Matrix object. Indicator of the drug exposure for which the PS was estimated. Defined by x[, idx_expo].

`score_variables`	Character vector, names of covariates(s) selected with the lasso-bic approach to include in the PS estimation model. Could be empty.
`score`	One-column Matrix object, the estimated score.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
psb2 <- est_ps_bic(idx_expo = 2, x = drugs)
psb2$score_variables #selected variables to include in the PS model of drug_2

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)
psb2 <- est_ps_bic(idx_expo = 2, x = drugs)
psb2$score_variables #selected variables to include in the PS model of drug_2

propensity score estimation in high dimension with automated covariates selection using hdPS

Description

Estimate a propensity score to a given drug exposure by (i) selecting among other drug covariates in x which ones to include in the PS estimation model automatically using hdPS algorithm, (ii) estimating a score using a classical logistic regression with the afore selected covariates. Internal function, not supposed to be used directly.

Usage

est_ps_hdps(idx_expo, x, y, keep_total = 20)
est_ps_hdps(idx_expo, x, y, keep_total = 20)

Arguments

`idx_expo`	Index of the column in `x` that corresponds to the drug covariate for which we aim at estimating the PS.
`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`keep_total`	number of covariates to include in the PS estimation model according to the hdps algorithm ordering. Default is 20.

Details

Compared to the situation of the classic use of hdps (i) there is only one dimension (the co-exposition matrix) (ii) no need to expand covariates since they are already binary. In other words, in our situation hdps consists in the "prioritize covariates" step from the original algorithm, using Bross formula. We consider the correction on the interpretation on this formula made by Richard Wyss (drug epi).

Value

An object with S3 class "ps", "hdps".

expo_name

Character, name of the drug exposure for which the PS was estimated. Correspond to colnames(x)[idx_expo]

indicator_expo

One-column Matrix object. Indicator of the drug exposure for which the PS was estimated. Defined by x[, idx_expo].

`score_variables`	Character vector, names of covariates(s) selected with the hdPS algorithm to include in the PS estimation model. Could be empty.
`score`	One-column Matrix object, the estimated score.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

References

Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun, H., Brookhart, M. A. (2009). "High-dimensional propensity score adjustment in studies of treatment effects using health care claims data". Epidemiology. 20, 512–522, doi:10.1097/EDE.0b013e3181a663cc

Examples


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)
pshdps2 <- est_ps_hdps(idx_expo = 2, x = drugs, y = ae, keep_total = 10)
pshdps2$score_variables #selected variables to include in the PS model of drug_2

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)
pshdps2 <- est_ps_hdps(idx_expo = 2, x = drugs, y = ae, keep_total = 10)
pshdps2$score_variables #selected variables to include in the PS model of drug_2

propensity score estimation in high dimension using gradient tree boosting

Description

Estimate a propensity score to a given drug exposure (treatment) with extreme gradient boosting. Depends on xgboost package. Internal function, not supposed to be used directly.

Usage

est_ps_xgb(
  idx_expo,
  x,
  parameters = list(eta = 0.1, max_depth = 6, objective = "binary:logistic", nthread =
    1),
  nrounds = 200,
  ...
)
est_ps_xgb(
  idx_expo,
  x,
  parameters = list(eta = 0.1, max_depth = 6, objective = "binary:logistic", nthread =
    1),
  nrounds = 200,
  ...
)

Arguments

`idx_expo`	Index of the column in `x` that corresponds to the drug covariate for which we aim at estimating the PS.
`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`parameters`	correspond to `params` in `xgb.train` function. The complete list of parameters is available at http://xgboost.readthedocs.io/en/latest/parameter.html. Default is a list with `eta=0.1` (learning rate), `max_depth = 6` (maximum length of a tree), `objective = "binary:logistic"` and `nthread = 1` (number of threads for parallelization).
`nrounds`	Maximum number of boosting iterations. Default is 200.
`...`	Other arguments that can be passed to `xgb.train` function.

Value

An object with S3 class "ps", "xgb".

expo_name

Character, name of the drug exposure for which the PS was estimated. Correspond to colnames(x)[idx_expo]

indicator_expo

One-column Matrix object. Indicator of the drug exposure for which the PS was estimated. Defined by x[, idx_expo].

`score_variables`	Character vector, names of covariates(s) used in a at list one tree in the gradient tree boosting algorithm. Obtained with `xgb.importance` function from `xgboost` package.
`score`	One-column Matrix object, the estimated score.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
psxgb2 <- est_ps_xgb(idx_expo = 2, x = drugs, nrounds = 100)
psxgb2$score_variables #selected variables to include in the PS model of drug_2


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)
psxgb2 <- est_ps_xgb(idx_expo = 2, x = drugs, nrounds = 100)
psxgb2$score_variables #selected variables to include in the PS model of drug_2

fit a lasso regression and use standard BIC for variable selection

Description

Fit a lasso regression and use the Bayesian Information Criterion (BIC) to select a subset of selected covariates. Can deal with very large sparse data matrices. Intended for binary reponse only (option family = "binomial" is forced). Depends on the glmnet and relax.glmnet functions from the package glmnet.

Usage

lasso_bic(x, y, maxp = 50, path = TRUE, betaPos = TRUE, ...)
lasso_bic(x, y, maxp = 50, path = TRUE, betaPos = TRUE, ...)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`maxp`	A limit on how many relaxed coefficients are allowed. Default is 50, in `glmnet` option default is 'n-3', where 'n' is the sample size.
`path`	Since `glmnet` does not do stepsize optimization, the Newton algorithm can get stuck and not converge, especially with relaxed fits. With `path=TRUE`, each relaxed fit on a particular set of variables is computed pathwise using the original sequence of lambda values (with a zero attached to the end). Default is `path=TRUE`.
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `family`, `maxp` and `path`.

Details

For each tested penalisation parameter $\lambda$ , a standard version of the BIC is implemented.

$BIC_\lambda = - 2 l_\lambda + df(\lambda) * ln (N)$

where $l_\lambda$ is the log-likelihood of the non-penalized multiple logistic regression model that includes the set of covariates with a non-zero coefficient in the penalised regression coefficient vector associated to $\lambda$ , and $df(\lambda)$ is the number of covariates with a non-zero coefficient in the penalised regression coefficient vector associated to $\lambda$ , The optimal set of covariates according to this approach is the one associated with the classical multiple logistic regression model which minimizes the BIC.

Value

An object with S3 class "log.lasso".

beta

Numeric vector of regression coefficients in the lasso. In lasso_bic function, the regression coefficients are UNPENALIZED. Length equal to nvars.

selected_variables

Character vector, names of variable(s) selected with the lasso-bic approach. If betaPos = TRUE, this set is the covariates with a positive regression coefficient in beta. Else this set is the covariates with a non null regression coefficient in beta. Covariates are ordering according to the p-values (two-sided if betaPos = FALSE , one-sided if betaPos = TRUE) in the classical multiple logistic regression model that minimzes the BIC.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
lb <- lasso_bic(x = drugs, y = ae, maxp = 20)


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)
lb <- lasso_bic(x = drugs, y = ae, maxp = 20)

wrap function for `cv.glmnet`

Description

Fit a first cross-validation on lasso regression and return selected covariates. Can deal with very large sparse data matrices. Intended for binary reponse only (option family = "binomial" is forced). Depends on the cv.glmnet function from the package glmnet.

Usage

lasso_cv(x, y, nfolds = 5, foldid = NULL, betaPos = TRUE, ...)
lasso_cv(x, y, nfolds = 5, foldid = NULL, betaPos = TRUE, ...)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`nfolds`	Number of folds - default is 5. Although `nfolds` can be as large as the sample size (leave-one-out CV), it is not recommended for large datasets. Smallest value allowable is `nfolds=3`.
`foldid`	An optional vector of values between 1 and `nfolds` identifying what fold each observation is in. If supplied, `nfolds` can be missing.
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`...`	Other arguments that can be passed to `cv.glmnet` from package `glmnet` other than `nfolds`, `foldid`, and `family`.

Value

An object with S3 class "log.lasso".

`beta`	Numeric vector of regression coefficients in the lasso. In `lasso_cv` function, the regression coefficients are PENALIZED. Length equal to nvars.
`selected_variables`	Character vector, names of variable(s) selected with the lasso-cv approach. If `betaPos = TRUE`, this set is the covariates with a positive regression coefficient in `beta`. Else this set is the covariates with a non null regression coefficient in `beta`. Covariates are ordering according to magnitude of their regression coefficients absolute value.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
lcv <- lasso_cv(x = drugs, y = ae, nfolds = 3)


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)
lcv <- lasso_cv(x = drugs, y = ae, nfolds = 3)

fit a lasso regression and use standard permutation of the outcome for variable selection

Description

Performed K lasso logistic regression with K different permuted version of the outcome. For earch of the lasso regression, the $\lambda_max$ (i.e. the smaller $\lambda$ such as all penalized regression coefficients are shrunk to zero) is obtained. The median value of these K $\lambda_max$ is used to for variable selection in the lasso regression with the non-permuted outcome. Depends on the glmnet function from the package glmnet.

Usage

lasso_perm(x, y, K = 20, keep = NULL, betaPos = TRUE, ncore = 1, ...)
lasso_perm(x, y, K = 20, keep = NULL, betaPos = TRUE, ncore = 1, ...)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`K`	Number of permutations of `y`. Default is 20.
`keep`	Do some variables of `x` have to be permuted in the same way as `y`? Default is NULL, means no. If yes, must be a vector of covariates indices. TEST OPTION
`betaPos`	Should the covariates selected by the procedure be positively associated with the outcome ? Default is `TRUE`.
`ncore`	The number of calcul units used for parallel computing. Default is 1, no parallelization is implemented.
`...`	Other arguments that can be passed to `glmnet` from package `glmnet` other than `family`.

Details

The selected $\lambda$ with this approach is defined as the closest $\lambda$ from the median value of the K $\lambda_max$ obtained with permutation of the outcome.

Value

An object with S3 class "log.lasso".

`beta`	Numeric vector of regression coefficients in the lasso In `lasso_perm` function, the regression coefficients are PENALIZED. Length equal to nvars.
`selected_variables`	Character vector, names of variable(s) selected with the lasso-perm approach. If `betaPos = TRUE`, this set is the covariates with a positive regression coefficient in `beta`. Else this set is the covariates with a non null regression coefficient in `beta`. Covariates are ordering according to magnitude of their regression coefficients absolute value.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

References

Sabourin, J. A., Valdar, W., & Nobel, A. B. (2015). "A permutation approach for selecting the penalty parameter in penalized model selection". Biometrics. 71(4), 1185–1194, doi:10.1111/biom.12359

Examples


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)
lp <- lasso_perm(x = drugs, y = ae, K = 10)


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)
lp <- lasso_perm(x = drugs, y = ae, K = 10)

adjustment on propensity score

Description

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 co-occurence 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 p-value is obtained for each drug and a variable selection is performed over the corrected for multiple comparisons p-values.

Usage

ps_adjust(
  x,
  y,
  n_min = 3,
  betaPos = TRUE,
  est_type = "bic",
  threshold = 0.05,
  ncore = 1
)
ps_adjust(
  x,
  y,
  n_min = 3,
  betaPos = TRUE,
  est_type = "bic",
  threshold = 0.05,
  ncore = 1
)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`n_min`	Numeric, Minimal number of co-occurence 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 `TRUE`.
`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 p-values. Default is 0.05.
`ncore`	The number of calcul units used for parallel computing. Default is 1, no parallelization is implemented.

Details

The PS could be estimated in different ways: using lasso-bic 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 co-occurence with the outcome y. Adjustment on the PS is performed for these covariates and one sided or two-sided (depend on betaPos parameter) p-values are obtained. The p-values of the covariates not retained after filtering are set to 1. All these p-values are then adjusted for multiple comparaison with the Benjamini-Yekutieli 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.

Value

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 p-values if `betaPos = TRUE`, two-sided p-values if `betaPos = FALSE` adjusted for multiple testing. Numeric, length equal to nvars.
`selected_variables`	Character vector, names of variable(s) selected with the ps-adjust approach. If `betaPos = TRUE`, this set is the covariates with a corrected one-sided p-value lower than `threshold`. Else this set is the covariates with a corrected two-sided p-value lower than `threshold`. Covariates are ordering according to their corrected p-value.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

References

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.

Examples


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)

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)

adjustment on propensity score for one drug exposure

Description

Implement the adjustment on propensity score for one drug exposure. The binary outcome is regressed on the drug exposure of interest and its estimated PS. Internal function, not supposed to be used directly.

Usage

ps_adjust_one(ps_est, y)
ps_adjust_one(ps_est, y)

Arguments

ps_est

An object of class "ps", "*" where "*" is "bic", "hdps" or "xgb" according on how the score was estimated, respective outputs of internal functions est_ps_bic, est_ps_hdps, est_ps_xgb. It is a list with the following elements : * score_type: character, name of the drug exposure for which the PS was estimated. * indicator_expo: indicator of the drugs exposure for which the PS was estimated. One-column Matrix object. * score_variables: Character vector, names of covariate(s) selected to include in the PS estimation model. Could be empty. *score: One-column Matrix object, the estimated score.

y

Binary response variable, numeric.

Details

The PS could be estimated in different ways: using lasso-bic approach, the hdPS algorithm or gradient tree boosting using functions est_ps_bic, est_ps_hdps and est_ps_xgb respectivelly.

Value

An object with S3 class "ps","adjust"

`expo_name`	Character, name of the drug exposure for which the PS was estimated.
`estimate`	Regression coefficient associated with the drug exposure in adjustment on PS.
`pval_1sided`	One sided p-value associated with the drug exposure in adjustment on PS.
`pval_2sided`	Two sided p-value associated with the drug exposure in adjustment on PS.

Could return NA if the adjustment on the PS did not converge.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
pshdps2 <- est_ps_hdps(idx_expo = 2, x = drugs, y = ae, keep_total = 10)
adjps2 <- ps_adjust_one(ps_est = pshdps2, y = ae)
adjps2$estimate #estimated strength of association between drug_2 and the outcome by PS adjustment

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)
pshdps2 <- est_ps_hdps(idx_expo = 2, x = drugs, y = ae, keep_total = 10)
adjps2 <- ps_adjust_one(ps_est = pshdps2, y = ae)
adjps2$estimate #estimated strength of association between drug_2 and the outcome by PS adjustment

weihting on propensity score

Description

Implement the weighting on propensity score with Matching Weights (MW) or the Inverse Probability of Treatment Weighting (IPTW) for all the drug exposures of the input drug matrix x which have more than a given number of co-occurence with the outcome. The binary outcome is regressed on a drug exposure through a classical weighted regression, for each drug exposure considered after filtering. With this approach, a p-value is obtained for each drug and a variable selection is performed over the corrected for multiple comparisons p-values.

Usage

ps_pond(
  x,
  y,
  n_min = 3,
  betaPos = TRUE,
  weights_type = c("mw", "iptw"),
  truncation = FALSE,
  q = 0.025,
  est_type = "bic",
  threshold = 0.05,
  ncore = 1
)
ps_pond(
  x,
  y,
  n_min = 3,
  betaPos = TRUE,
  weights_type = c("mw", "iptw"),
  truncation = FALSE,
  q = 0.025,
  est_type = "bic",
  threshold = 0.05,
  ncore = 1
)

Arguments

`x`	Input matrix, of dimension nobs x nvars. Each row is an observation vector. Can be in sparse matrix format (inherit from class `"sparseMatrix"` as in package `Matrix`).
`y`	Binary response variable, numeric.
`n_min`	Numeric, Minimal number of co-occurence 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 `TRUE`.
`weights_type`	Character. Indicates which type of weighting is implemented. Could be either "mw" or "iptw".
`truncation`	Bouleen, should we do weight truncation? Default is `FALSE`.
`q`	If `truncation` is `TRUE`, quantile value for weight truncation. Ignored if `truncation` is `FALSE`. Default is 2.5 $\%$ .
`est_type`	Character, indicates which approach is used to estimate the propensity score. Could be either "bic", "hdps" or "xgb". Default is "bic".
`threshold`	Threshold for the p-values. Default is 0.05.
`ncore`	The number of calcul units used for parallel computing. Default is 1, no parallelization is implemented.

Details

The MW are defined by

$mw_i = min(PS_i, 1-PS_i)/[(expo_i) * PS_i + (1-expo_i) * (1-PS_i) ]$

and weights from IPTW by

$iptw_i = expo_i/PS_i + (1-expo_i)/(1-PS_i)$

where $expo_i$ is the drug exposure indicator. The PS could be estimated in different ways: using lasso-bic 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 co-occurence with the outcome y. Adjustment on the PS is performed for these covariates and one sided or two-sided (depend on betaPos parameter) p-values are obtained. The p-values of the covariates not retained after filtering are set to 1. All these p-values are then adjusted for multiple comparaison with the Benjamini-Yekutieli 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.

Value

An object with S3 class "ps", "*" ,"**" , where "*" is "mw" or "iptw", same as the input parameter weights_type, and "**" is "bic", "hdps" or "xgb" according on how the score was 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) weigted regression did not converge. Trying to estimate the score in a different way could help, but it's not insured.
`corrected_pvals`	One sided p-values if `betaPos = TRUE`, two-sided p-values if `betaPos = FALSE` adjusted for multiple testing. Numeric, length equal to nvars.
`selected_variables`	Character vector, names of variable(s) selected with the weighting on PS based approach. If `betaPos = TRUE`, this set is the covariates with a corrected one-sided p-value lower than `threshold`. Else this set is the covariates with a corrected two-sided p-value lower than `threshold`. Covariates are ordering according to their corrected p-value.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

References

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.

Examples


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)
pondps <- ps_pond(x = drugs, y = ae, n_min = 10, weights_type = "iptw")


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)
pondps <- ps_pond(x = drugs, y = ae, n_min = 10, weights_type = "iptw")

weihting on propensity score for one drug exposure

Description

Implement the weighting on propensity score with Matching Weights (MW) or the Inverse Probability of Treatment Weighting (IPTW) for one drug exposure. The binary outcome is regressed on the drug exposure of interest through a classical weighted regression. Internal function, not supposed to be used directly.

Usage

ps_pond_one(
  ps_est,
  y,
  weights_type = c("mw", "iptw"),
  truncation = FALSE,
  q = 0.025
)
ps_pond_one(
  ps_est,
  y,
  weights_type = c("mw", "iptw"),
  truncation = FALSE,
  q = 0.025
)

Arguments

`ps_est`	An object of class `"ps", ""` where `""` is `"bic"`, `"hdps"` or `"xgb"` according on how the score was estimated, respective outputs of internal functions `est_ps_bic`, `est_ps_hdps`, `est_ps_xgb`. It is a list with the following elements : * score_type: character, name of the drug exposure for which the PS was estimated. * indicator_expo: indicator of the drugs exposure for which the PS was estimated. One-column Matrix object. * score_variables: Character vector, names of covariate(s) selected to include in the PS estimation model. Could be empty. *score: One-column Matrix object, the estimated score.
`y`	Binary response variable, numeric.
`weights_type`	Character. Indicates which type of weighting is implemented. Could be either "mw" or "iptw".
`truncation`	Bouleen, should we do weight truncation? Default is `FALSE`.
`q`	If `truncation` is `TRUE`, quantile value for weight truncation. Ignored if `truncation` is `FALSE`. Default is 2.5 $\%$ .

Details

The MW are defined by

$mw_i = min(PS_i, 1-PS_i)/[(expo_i) * PS_i + (1-expo_i) * (1-PS_i) ]$

and weights from IPTW by

$iptw_i = expo_i/PS_i + (1-expo_i)/(1-PS_i)$

where $expo_i$ is the drug exposure indicator. The PS could be estimated in different ways: using lasso-bic approach, the hdPS algorithm or gradient tree boosting using functions est_ps_bic, est_ps_hdps and est_ps_xgb respectivelly.

Value

An object with S3 class "ps","*" , where "*" is "mw" or "iptw", same as the input parameter weights_type

`expo_name`	Character, name of the drug exposure for which the PS was estimated.
`estimate`	Regression coefficient associated with the drug exposure in adjustment on PS.
`pval_1sided`	One sided p-value associated with the drug exposure in adjustment on PS.
`pval_2sided`	Two sided p-value associated with the drug exposure in adjustment on PS.

Could return NA if the adjustment on the PS did not converge.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
pshdps2 <- est_ps_hdps(idx_expo = 2, x = drugs, y = ae, keep_total = 10)
pondps2 <- ps_pond_one(ps_est = pshdps2, y = ae, weights_type = "iptw")
pondps2$estimate #estimated strength of association between drug_2 and the outcome by PS weighting

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)
pshdps2 <- est_ps_hdps(idx_expo = 2, x = drugs, y = ae, keep_total = 10)
pondps2 <- ps_pond_one(ps_est = pshdps2, y = ae, weights_type = "iptw")
pondps2$estimate #estimated strength of association between drug_2 and the outcome by PS weighting

Summary statistics for main adapt4pv package functions

Description

Return the Sensitivity and the False Discovery Rate of an approach implemeted by the main functions of adapt4pv package.

Usage

summary_stat(object, true_pos, q = 10)
summary_stat(object, true_pos, q = 10)

Arguments

`object`	An object of class `"log.lasso"`, `"cisl"`, `"adaptive"` and `"", "ps",""` where `""` is either `"adjust"`, `"iptw"` or `"mw"` and `"**"` is either `"bic"`, `"hdps"` or `"xgb"`.
`true_pos`	Character vector, names of the true positives controls
`q`	Quantile value for variable selection with an object of class `"cisl"`. Possible values are 5, 10, 15, 20. Default is 10

Value

A data frame wich details for the signal detection method implemented in object: its number of generated signals, its sensitivity and its false discovery rate.

Author(s)

Emeline Courtois
Maintainer: Emeline Courtois [email protected]

Examples


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)
lcv <- lasso_cv(x = drugs, y = ae, nfolds = 3)
summary_stat(object = lcv, true_pos = colnames(drugs)[1:10])
# the data are not simulated in such a way that there are true positives


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)
lcv <- lasso_cv(x = drugs, y = ae, nfolds = 3)
summary_stat(object = lcv, true_pos = colnames(drugs)[1:10])
# the data are not simulated in such a way that there are true positives

Package 'adapt4pv'

Help Index

Adaptive approaches for signal detection in PharmacoVigilance

Description

Author(s)

fit an adaptive lasso with adaptive weights derived from lasso-bic

Description

Usage

Arguments

Details

Value

Author(s)

Examples

fit an adaptive lasso with adaptive weights derived from CISL

Description

Usage

Arguments

Details

Value

Author(s)

Examples

fit an adaptive lasso with adaptive weights derived from lasso-cv

Description

Usage

Arguments

Details

Value

Author(s)

Examples

fit an adaptive lasso with adaptive weights derived from univariate coefficients

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Class Imbalanced Subsampling Lasso

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Simulated data for the adapt4pv package

Description

Format

Examples

propensity score estimation in high dimension with automated covariates selection using lasso-bic

Description

Usage

Arguments

Details

Value

Author(s)

Examples

propensity score estimation in high dimension with automated covariates selection using hdPS

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

propensity score estimation in high dimension using gradient tree boosting

Description

Usage

Arguments

Value

Author(s)

Examples

fit a lasso regression and use standard BIC for variable selection

Description

Usage

Arguments

Details

Value

wrap function for `cv.glmnet`