R: Precision Medicine

# Precision Medicine

## Documentation for package ‘precmed’ version 1.1.0

- DESCRIPTION file
- Package NEWS
- Package README

## Help Pages

abc | Compute the area between curves from the '"precmed"' object
abc.default | 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