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If only care about the higher subgroup (above cutoff), only need ate.rmtl.high and hr.high so set "onlyhigh" to be TRUE
Scores are adjusted to the opposite sign if higher.y == FALSE; scores stay the same if higher.y == FALSE;
this is because estsurv() function always takes the subgroup of the top highest adjusted scores,
and higher adjusted scores should always represent high responders of trt=1
estsurv.bilevel.subgroups(
y,
d,
x.cate,
x.ps,
x.ipcw,
trt,
yf,
tau0 = tau0,
score,
higher.y,
prop,
onlyhigh,
surv.min = 0.025,
ps.method = "glm",
minPS = 0.01,
maxPS = 0.99,
ipcw.method = "breslow"
)ate.rmtl.high: estimated ATEs (ratio of RMTL) in the multiple bi-level subgroups that are in the higher-than-cutoff category;
vector of size equal to the length of prop; always returned.
ate.rmtl.low: estimated ATEs (ratio of RMTL) in the multiple bi-level subgroups that are in the lower-than-cutoff category;
vector of size equal to the length of prop; returned only when onlyhigh = TRUE.
hr.high: unadjusted hazard ratio in the multiple bi-level subgroups that are in the higher-than-cutoff category;
vector of size equal to the length of prop; always returned.
hr.low: unadjusted hazard ratio in the multiple bi-level subgroups that are in the lower-than-cutoff category;
vector of size equal to the length of prop; returned only when onlyhigh = TRUE
Observed survival or censoring time; vector of size n.
The event indicator, normally 1 = event, 0 = censored; vector of size n.
Matrix of p.cate baseline covariates specified in the outcome model; dimension n by p.cate.
Matrix of p.ps baseline covariates specified in the propensity score model; dimension n by p.ps.
Matrix of p.ipw baseline covariate specified in inverse probability of censoring weighting; dimension n by p.ipw.
Treatment received; vector of size n with treatment coded as 0/1.
Follow-up time, interpreted as the potential censoring time; vector of size n if the potential censoring time is known.
The truncation time for defining restricted mean time lost.
Estimated log CATE scores for all n observations from one of the five methods
(random forest, boosting, naive Poisson, two regressions, contrast regression); vector of size n.
A logical value indicating whether higher (TRUE) or lower (FALSE)
Proportions corresponding to percentiles in the estimated log CATE scores that define subgroups to calculate ATE for;
vector of floats in `(0, 1]` (if onlyhigh=TRUE) or in `(0, 1)` (if onlyhigh=FALSE):
Each element of prop represents the high/low cutoff in each bi-level subgroup and the length of prop
is number of bi-level subgroups
Indicator of returning only the ATEs in the higher-than-cutoff category of the bi-level subgroups; boolean.
Lower truncation limit for probability of being censored (positive and very close to 0).
A character value for the method to estimate the propensity score. Allowed values include one of:
'glm' for logistic regression with main effects only (default), or
'lasso' for a logistic regression with main effects and LASSO penalization on
two-way interactions (added to the model if interactions are not specified in ps.model).
Relevant only when ps.model has more than one variable.
A numerical value (in `[0, 1]`) below which estimated propensity scores should be
truncated. Default is 0.01.
A numerical value (in `(0, 1]`) above which estimated propensity scores should be
truncated. Must be strictly greater than minPS. Default is 0.99.
The censoring model. Allowed values are: 'breslow' (Cox regression with Breslow estimator of the baseline survivor function),
'aft (exponential)', 'aft (weibull)', 'aft (lognormal)' or 'aft (loglogistic)'. Default is 'breslow'.