acoarm | R Documentation |
This function estimates parameters of proportional hazards models with gene-treatment interactions. It employs classical case-cohort estimation methods, incorporating the case-only estimators. The method was published in Dai et al. (2016) Biometrics.
acoarm(data, svtime, event, treatment, BaselineMarker,subcohort, esttype = 1, augment = 1, weight=NULL, extra = NULL)
data |
A data frame used to access the following data. |
svtime |
A character string of column name, corresponds to one column of the data frame, which is used to store the failure time variable (numeric). |
event |
A character string of column name, corresponds to one column of the data frame, which is used to store the indicator of failure event (1: failure, 0: not failure). |
treatment |
A character string of column name, corresponds to one column of the data frame, which is used to store the binary vector of treatment variable (1: treatment, 0: placebo). |
BaselineMarker |
A character string of column name, corresponds to one column of the data frame, which is used to store a vector of baseline biomarker that is under investigation for interaction with treatment. The BaselineMarker variable is missing for those who are not sampled in the case-cohort. |
subcohort |
A character string of column name, corresponds to one column of the data frame, which is used to store the indicator of sub-cohort (1: sample belong to the sub-cohort, 0: not belong to the sub-cohort) |
esttype |
The option of estimation methods (1: Self-Prentice estimator, 0: Lin-Ying estimator). |
augment |
The indicator of whether subcohort was drawn from the placebo arm (augment=0), from the active treatment arm (augment=1), or from both arms (augment=2). |
weight |
If the genotype data are obtained through case-control sampling, weight is a vector of sampling weights (inverse of sampling probability) corresponding to rows of data. If the genotype data are obtained through case-cohort sampling, weight is NULL. If a vector of weights have been supplied by user, then esttype is automatically set to 0: Lin-Ying estimator. |
extra |
A string vector of column name(s), corresponds to more or more column(s) of the data frame, which is/are used to store the extra baseline covariate(s) to be adjusted for in addition to treatment and biomarker. |
The function returns point estimates and standard error estimates of parameters in the proportional hazards model. The method was published in Dai et al. (2015) Biometrics.
beta |
Estimated parameter |
stder |
Estimated standard error of parameter estimates |
pVal |
p value |
James Y. Dai
J. Y. Dai, X. C. Zhang,C. Y. Wang, and C. Kooperberg. Augmented case-only designs for randomized clinical trials with failure time endpoints. Biometrics, DOI: 10.1111/biom.12392, 2016.
## Load the example data data(acodata) ## ACO in placebo arm rfit0 <- acoarm(data=acodata, svtime="vacc1_evinf", event="f_evinf", treatment="f_treat", BaselineMarker="fcgr2a.3", subcohort="subcoh", esttype=1, augment=0, weight=NULL, extra=c("f_agele30","f_hsv_2","f_ad5gt18","f_crcm", "any_drug","num_male_part_cat","uias","uras")) rfit0 ## ACO in active arm rfit1 <- acoarm(data=acodata, svtime="vacc1_evinf", event="f_evinf", treatment="f_treat", BaselineMarker="fcgr2a.3", subcohort="subcoh", esttype=1, augment=1, weight=NULL, extra=c("f_agele30","f_hsv_2","f_ad5gt18","f_crcm", "any_drug","num_male_part_cat","uias","uras")) rfit1 ## ACO in both arms rfit2 <- acoarm(data=acodata, svtime="vacc1_evinf", event="f_evinf", treatment="f_treat", BaselineMarker="fcgr2a.3", subcohort="subcoh", esttype=1, augment=2, weight=NULL, extra=c("f_agele30","f_hsv_2","f_ad5gt18","f_crcm", "any_drug","num_male_part_cat","uias","uras")) rfit2
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