Package index • precmed

All functions

abc(): Compute the area between curves from the "precmed" object

abc(<precmed>): Compute the area between curves from the "precmed" object

arg.checks(): Check arguments Catered to all types of outcome Apply at the beginning of pmcount(), cvcount(), drcount.inference(), catefitsurv(), catecvsurv(), and drsurv.inference()

arg.checks.common(): Check arguments that are common to all types of outcome USed inside arg.checks()

atefit(): Doubly robust estimator of and inference for the average treatment effect for count, survival and continuous data

atefitcount(): Doubly robust estimator of and inference for the average treatment effect for count data

atefitmean(): Doubly robust estimator of and inference for the average treatment effect for continuous data

atefitsurv(): Doubly robust estimator of and inference for the average treatment effect for survival data

auc(): Compute the area under the curve using linear or natural spline interpolation

balance.split(): Split the given dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

balancemean.split(): Split the given dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

balancesurv.split(): Split the given time-to-event dataset into balanced training and validation sets (within a pre-specified tolerance) Balanced means 1) The ratio of treated and controls is maintained in the training and validation sets 2) The covariate distributions are balanced between the training and validation sets

boxplot(<precmed>): A set of box plots of estimated ATEs from the "precmed" object

catecv(): Cross-validation of the conditional average treatment effect (CATE) score for count, survival or continuous outcomes

catecvcount(): Cross-validation of the conditional average treatment effect (CATE) score for count outcomes

catecvmean(): Cross-validation of the conditional average treatment effect (CATE) score for continuous outcomes

catecvsurv(): Cross-validation of the conditional average treatment effect (CATE) score for survival outcomes

catefit(): Estimation of the conditional average treatment effect (CATE) score for count, survival and continuous data

catefitcount(): Estimation of the conditional average treatment effect (CATE) score for count data

catefitmean(): Estimation of the conditional average treatment effect (CATE) score for continuous data

catefitsurv(): Estimation of the conditional average treatment effect (CATE) score for survival data

countExample: Simulated data with count outcome

cox.rmst(): Estimate restricted mean survival time (RMST) based on Cox regression model

data.preproc(): Data preprocessing Apply at the beginning of pmcount() and cvcount(), after arg.checks()

data.preproc.mean(): Data preprocessing Apply at the beginning of catefitmean() and catecvmean(), after arg.checks()

data.preproc.surv(): Data preprocessing Apply at the beginning of catefitcount(), catecvcount(), catefitsurv(), and catecvsurv(), after arg.checks()

drcount(): Doubly robust estimator of the average treatment effect for count data

drmean(): Doubly robust estimator of the average treatment effect for continuous data

drsurv(): Doubly robust estimator of the average treatment effect with Cox model for survival data

estcount.bilevel.subgroups(): Estimate the Average Treatment Effect of the log risk ratio in multiple bi-level subgroups defined by the proportions

estcount.multilevel.subgroup(): Estimate the ATE of the log RR ratio in one multilevel subgroup defined by the proportions

estmean.bilevel.subgroups(): Estimate the ATE of the mean difference in multiple bi-level subgroups defined by the proportions

estmean.multilevel.subgroup(): Estimate the ATE of the mean difference in one multilevel subgroup defined by the proportions

estsurv.bilevel.subgroups(): Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in multiple bi-level subgroups defined by the proportions

estsurv.multilevel.subgroups(): Estimate the ATE of the RMTL ratio and unadjusted hazard ratio in one multilevel subgroup defined by the proportions

generate_kfold_indices(): Generate K-fold Indices for Cross-Validation

glm.ps(): Propensity score estimation with LASSO

glm.simplereg.ps(): Propensity score estimation with a linear model

intxcount(): Estimate the CATE model using specified scoring methods

intxmean(): Estimate the CATE model using specified scoring methods

intxsurv(): Estimate the CATE model using specified scoring methods for survival outcomes

ipcw.surv(): Probability of being censored

meanCatch(): Catch errors and warnings when estimating the ATEs in the nested subgroup for continuous data

meanExample: Simulated data with a continuous outcome

onearmglmcount.dr(): Doubly robust estimators of the coefficients in the two regression

onearmglmmean.dr(): Doubly robust estimators of the coefficients in the two regression

onearmsurv.dr(): Doubly robust estimators of the coefficients in the two regression

plot(<atefit>): Histogram of bootstrap estimates

plot(<precmed>): Two side-by-side line plots of validation curves from the "precmed" object

print(<atefit>): Print function for atefit

print(<catefit>): Print function for atefit

scorecount(): Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods

scoremean(): Calculate the CATE score given the baseline covariates for specified scoring method methods

scoresurv(): Calculate the log CATE score given the baseline covariates and follow-up time for specified scoring method methods for survival outcomes

survCatch(): Catch errors and warnings when estimating the ATEs in the nested subgroup

survivalExample: Simulated data with survival outcome

twoarmglmcount.dr(): Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information

twoarmglmmean.dr(): Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix

twoarmsurv.dr(): Doubly robust estimators of the coefficients in the contrast regression as well as their covariance matrix and convergence information