catecvsurv: Cross-validation of the conditional average treatment effect...
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catecvsurv R Documentation
Cross-validation of the conditional average treatment effect (CATE) score for survival outcomes
Description
Provides doubly robust estimation of the average treatment effect (ATE) by the
RMTL (restricted mean time lost) ratio in nested and mutually exclusive subgroups of patients
defined by an estimated conditional average treatment effect (CATE) score via
cross-validation (CV). The CATE score can be estimated with up to 5 methods among the following:
Random forest, boosting, poisson regression, two regressions, and contrast regression
(see score.method).
Usage
catecvsurv(
data,
score.method,
cate.model,
ps.model,
ps.method = "glm",
initial.predictor.method = "randomForest",
ipcw.model = NULL,
ipcw.method = "breslow",
minPS = 0.01,
maxPS = 0.99,
followup.time = NULL,
tau0 = NULL,
higher.y = TRUE,
prop.cutoff = seq(0.5, 1, length = 6),
prop.multi = c(0, 1/3, 2/3, 1),
abc = TRUE,
train.prop = 3/4,
cv.n = 10,
error.max = 0.1,
max.iter = 5000,
surv.min = 0.025,
tree.depth = 2,
n.trees.rf = 1000,
n.trees.boosting = 200,
B = 3,
Kfold = 5,
error.maxNR = 0.001,
max.iterNR = 150,
tune = c(0.5, 2),
seed = NULL,
plot.gbmperf = TRUE,
verbose = 0
)
Arguments
data
A data frame containing the variables in the outcome, propensity score, and inverse
probability of censoring models (if specified); a data frame with n rows (1 row per observation).
score.method
A vector of one or multiple methods to estimate the CATE score.
Allowed values are: 'randomForest', 'boosting', 'poisson', 'twoReg', and
'contrastReg'.
cate.model
A standard Surv formula describing the outcome model to be fitted.
The outcome must appear on the left-hand side.
ps.model
A formula describing the propensity score (PS) model to be fitted. The treatment must
appear on the left-hand side. The treatment must be a numeric vector coded as 0/1.
If data are from a randomized controlled trial, specify ps.model = ~1 as an intercept-only model.
ps.method
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.
initial.predictor.method
A character vector for the method used to get initial
outcome predictions conditional on the covariates specified in cate.model. Only applies
when score.method includes 'twoReg' or 'contrastReg'. Allowed values include
one of 'randomForest', 'boosting' and 'logistic' (fastest).
Default is 'randomForest'.
ipcw.model
A formula describing the inverse probability of censoring weighting (IPCW)
model to be fitted. The left-hand side must be empty. Default is ipcw.model = NULL,
which corresponds to specifying the IPCW model with the same covariates as the outcome model
cate.model plus the treatment.
ipcw.method
A character value for 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)' (accelerated failure time model with different distributions for
y variable). Default is 'breslow'.
minPS
A numerical value (in '[0, 1]') below which estimated propensity scores should be
truncated. Default is 0.01.
maxPS
A numerical value (in '(0, 1]') above which estimated propensity scores should be
truncated. Must be strictly greater than minPS. Default is 0.99.
followup.time
A column name in data specifying the maximum follow-up time,
interpreted as the potential censoring time. Default is followup.time = NULL,
which corresponds to unknown potential censoring time.
tau0
The truncation time for defining restricted mean time lost. Default is NULL,
which corresponds to setting the truncation time as the maximum survival time in the data.
higher.y
A logical value indicating whether higher (TRUE) or
lower (FALSE) values of the outcome are more desirable. Default is TRUE.
prop.cutoff
A vector of numerical values (in '(0, 1]') specifying percentiles of the
estimated log CATE scores to define nested subgroups. Each element represents the cutoff to
separate observations in nested subgroups (below vs above cutoff).
The length of prop.cutoff is the number of nested subgroups.
An equally-spaced sequence of proportions ending with 1 is recommended.
Default is seq(0.5, 1, length = 6).
prop.multi
A vector of numerical values (in '[0, 1]') specifying percentiles of the
estimated log CATE scores to define mutually exclusive subgroups.
It should start with 0, end with 1, and be of length(prop.multi) > 2.
Each element represents the cutoff to separate the observations into
length(prop.multi) - 1 mutually exclusive subgroups.
Default is c(0, 1/3, 2/3, 1).
abc
A logical value indicating whether the area between curves (ABC) should be calculated
at each cross-validation iterations, for each score.method. Default is TRUE.
train.prop
A numerical value (in '(0, 1)') indicating the proportion of total data used
for training. Default is 3/4.
cv.n
A positive integer value indicating the number of cross-validation iterations.
Default is 10.
error.max
A numerical value > 0 indicating the tolerance (maximum value of error)
for the largest standardized absolute difference in the covariate distributions or in the
doubly robust estimated rate ratios between the training and validation sets. This is used
to define a balanced training-validation splitting. Default is 0.1.
max.iter
A positive integer value indicating the maximum number of iterations when
searching for a balanced training-validation split. Default is 5,000.
surv.min
Lower truncation limit for the probability of being censored.
It must be a positive value and should be chosen close to 0. Default is 0.025.
tree.depth
A positive integer specifying the depth of individual trees in boosting
(usually 2-3). Used only if score.method = 'boosting' or
if initial.predictor.method = 'boosting' with score.method = 'twoReg' or
'contrastReg'. Default is 2.
n.trees.rf
A positive integer specifying the maximum number of trees in random forest.
Used if score.method = 'ranfomForest' or if initial.predictor.method = 'randomForest'
with score.method = 'twoReg' or 'contrastReg'. Only applies for survival outcomes.
Default is 1000.
n.trees.boosting
A positive integer specifying the maximum number of trees in boosting
(usually 100-1000). Used if score.method = 'boosting' or
if initial.predictor.method = 'boosting' with score.method = 'twoReg' or
'contrastReg'. Default is 200.
B
A positive integer specifying the number of time cross-fitting is repeated in
score.method = 'twoReg' and 'contrastReg'. Default is 3.
Kfold
A positive integer specifying the number of folds used in cross-fitting
to partition the data in score.method = 'twoReg' and 'contrastReg'.
Default is 5.
error.maxNR
A numerical value > 0 indicating the minimum value of the mean absolute
error in Newton Raphson algorithm. Used only if score.method = 'contrastReg'.
Default is 0.001.
max.iterNR
A positive integer indicating the maximum number of iterations in the
Newton Raphson algorithm. Used only if score.method = 'contrastReg'.
Default is 150.
tune
A vector of 2 numerical values > 0 specifying tuning parameters for the
Newton Raphson algorithm. tune[1] is the step size, tune[2] specifies a
quantity to be added to diagonal of the slope matrix to prevent singularity.
Used only if score.method = 'contrastReg'. Default is c(0.5, 2).
seed
An optional integer specifying an initial randomization seed for reproducibility.
Default is NULL, corresponding to no seed.
plot.gbmperf
A logical value indicating whether to plot the performance measures in
boosting. Used only if score.method = 'boosting' or if score.method = 'twoReg'
or 'contrastReg' and initial.predictor.method = 'boosting'. Default is TRUE.
verbose
An integer value indicating what kind of intermediate progress messages should
be printed. 0 means no outputs. 1 means only progress bar and run time.
2 means progress bar, run time, and all errors and warnings. Default is 0.
Details
The CATE score represents an individual-level treatment effect expressed as the
restricted mean survival time (RMTL) ratio) for survival outcomes. It can be estimated with boosting,
Poisson regression, random forest, and the doubly robust estimator two regressions (Yadlowsky, 2020)
applied separately by treatment group or with the other doubly robust estimator contrast regression
(Yadlowsky, 2020) applied to the entire data set.
Internal CV is applied to reduce optimism in choosing the CATE estimation method that
captures the most treatment effect heterogeneity. The CV is applied by repeating the
following steps cv.n times:
Split the data into a training and validation set according to train.prop.
The training and validation sets must be balanced with respect to covariate distributions
and doubly robust RMTL ratio estimates (see error.max).
Estimate the CATE score in the training set with the specified scoring method.
Predict the CATE score in the validation set using the scoring model fitted from
the training set.
Build nested subgroups of treatment responders in the training and validation sets,
separately, and estimate the ATE within each nested subgroup. For each element i of
prop.cutoff (e.g., prop.cutoff[i] = 0.6), take the following steps:
Identify high responders as observations with the 60%
(i.e., prop.cutoff[i]x100%) highest (if higher.y = FALSE) or
lowest (if higher.y = TRUE) estimated CATE scores.
Estimate the ATE in the subgroup of high responders using a doubly robust estimator.
Conversely, identify low responders as observations with the 40%
(i.e., 1 - prop.cutoff[i]x100%) lowest (if higher.y = FALSE) or
highest (if higher.y = TRUE) estimated CATE scores.
Estimate the ATE in the subgroup of low responders using a doubly robust estimator.
If abc = TRUE, calculate the area between the ATE and the series of ATEs in
nested subgroups of high responders in the validation set.
Build mutually exclusive subgroups of treatment responders in the training and
validation sets, separately, and estimate the ATE within each subgroup. Mutually exclusive
subgroups are built by splitting the estimated CATE scores according to prop.multi.
Value
Returns a list containing the following components saved as a "precmed" object:
ate.randomForest: A list of ATE output measured by the RMTL ratio if
score.method includes 'randomForest':
ate.est.train.high.cv: A matrix of numerical values with
length(prop.cutoff) rows and cv.n columns.
The ith column/jth row cell contains the estimated ATE in the nested subgroup of high responders
defined by CATE score above (if higher.y = FALSE) or below (if higher.y = TRUE) the
prop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith
cross-validation iteration.
ate.est.train.low.cv: A matrix of numerical values with
length(prop.cutoff) - 1 rows and cv.n columns.
TThe ith column/jth row cell contains the estimated ATE in the nested subgroup of low responders
defined by CATE score below (if higher.y = FALSE) or above (if higher.y = TRUE) the
prop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith
cross-validation iteration.
ate.est.valid.high.cv: Same as ate.est.train.high.cv,
but in the validation set.
ate.est.valid.low.cv: Same as ate.est.train.low.cv,
but in the validation set.
ate.est.train.group.cv: A matrix of numerical values with
length(prop.multi) - 1 rows and cv.n columns.
The ith column contains the estimated ATE in length(prop.multi) - 1
mutually exclusive subgroups defined by prop.multi in the training set in ith
cross-validation iteration.
ate.est.valid.group.cv: Same as ate.est.train.group.cv, but in the
validation set.
abc.valid: A vector of numerical values of length cv.n,
The ith element returns the ABC of the validation curve in the ith cross-validation
iteration. Only returned if abc = TRUE.
ate.boosting: A list of results similar to ate.randomForest output
if score.method includes 'boosting'.
ate.poisson: A list of results similar to ate.randomForest output
if score.method includes 'poisson'.
ate.twoReg: A list of results similar to ate.randomForest output
if score.method includes 'twoReg'.
ate.contrastReg: A list of results similar to ate.randomForest output
if score.method includes 'contrastReg'.
This method has an additional element in the list of results:
converge.contrastReg.cv: A vector of logical value of length cv.n.
The ith element indicates whether the algorithm converged in the ith cross-validation
iteration.
hr.randomForest: A list of adjusted hazard ratio if score.method includes
'randomForest':
hr.est.train.high.cv: A matrix of numerical values with
length(prop.cutoff) rows and cv.n columns.
The ith column/jth row cell contains the estimated HR in the nested subgroup of high responders
defined by CATE score above (if higher.y = FALSE) or below (if higher.y = TRUE) the
prop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith
cross-validation iteration.
hr.est.train.low.cv: A matrix of numerical values with
length(prop.cutoff) - 1 rows and cv.n columns.
TThe ith column/jth row cell contains the estimated HR in the nested subgroup of low responders
defined by CATE score below (if higher.y = FALSE) or above (if higher.y = TRUE) the
prop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith
cross-validation iteration.
hr.est.valid.high.cv: Same as hr.est.train.high.cv,
but in the validation set.
hr.est.valid.low.cv: Same as hr.est.train.low.cv,
but in the validation set.
hr.est.train.group.cv: A matrix of numerical values with
length(prop.multi) - 1 rows and cv.n columns.
The ith column contains the estimated HR in length(prop.multi) - 1
mutually exclusive subgroups defined by prop.multi in the training set in ith
cross-validation iteration.
hr.est.valid.group.cv: Same as hr.est.train.group.cv, but in the
validation set.
hr.boosting: A list of results similar to hr.randomForest output
if score.method includes 'boosting'.
hr.poisson: A list of results similar to hr.randomForest output
if score.method includes 'poisson'.
hr.twoReg: A list of results similar to hr.randomForest output
if score.method includes 'twoReg'.
hr.contrastReg: A list of results similar to hr.randomForest output
if score.method includes 'contrastReg'.
props: A list of 3 elements:
prop.onlyhigh: The original argument prop.cutoff,
reformatted as necessary.
prop.bi: The original argument prop.cutoff,
similar to prop.onlyhigh but reformatted to exclude 1.
prop.multi: The original argument prop.multi,
reformatted as necessary to include 0 and 1.
overall.ate.train: A vector of numerical values of length cv.n.
The ith element contains the ATE (RMTL ratio) in the training set of the ith cross-validation
iteration, estimated with the doubly robust estimator.
overall.hr.train: A vector of numerical values of length cv.n.
The ith element contains the ATE (HR) in the training set of the ith cross-validation
iteration.
overall.ate.valid: A vector of numerical values of length cv.n.
The ith element contains the ATE (RMTL ratio) in the validation set of the ith cross-validation
iteration, estimated with the doubly robust estimator.
overall.hr.valid: A vector of numerical values of length cv.n.
The ith element contains the ATE (HR) in the validation set of the ith cross-validation
iteration.
errors/warnings: A nested list of errors and warnings that were wrapped during the
calculation of ATE. Errors and warnings are organized by score.method and
position in the CV flow.
higher.y: The original higher.y argument.
abc: The original abc argument.
cv.n: The original cv.n argument.
response: The type of response. Always 'survival' for this function.
formulas:A list of 3 elements: (1) cate.model argument,
(2) ps.model argument and (3) original labels of the left-hand side variable in
ps.model (treatment) if it was not 0/1.
References
Yadlowsky, S., Pellegrini, F., Lionetto, F., Braune, S., & Tian, L. (2020).
Estimation and validation of ratio-based conditional average treatment effects using
observational data. Journal of the American Statistical Association, 1-18.. DOI: 10.1080/01621459.2020.1772080.
See Also
catefitsurv() function and boxplot(), abc methods for
"precmed" objects.
Examples
library(survival)
tau0 <- with(survivalExample,
min(quantile(y[trt == "drug1"], 0.95), quantile(y[trt == "drug0"], 0.95)))
catecv <- catecvsurv(data = survivalExample,
score.method = "poisson",
cate.model = Surv(y, d) ~ age + female + previous_cost +
previous_number_relapses,
ps.model = trt ~ age + previous_treatment,
initial.predictor.method = "logistic",
ipcw.model = ~ age + previous_cost + previous_treatment,
tau0 = tau0,
higher.y = TRUE,
cv.n = 5, seed = 999, verbose = 1)
# Try:
plot(catecv, ylab = "RMTL ratio of drug1 vs drug0 in each subgroup")
boxplot(catecv, ylab = "RMTL ratio of drug1 vs drug0 in each subgroup")
abc(catecv)
precmed documentation built on Oct. 6, 2024, 1:07 a.m.
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| catecvsurv | R Documentation |
Cross-validation of the conditional average treatment effect (CATE) score for survival outcomes
Description
Provides doubly robust estimation of the average treatment effect (ATE) by the
RMTL (restricted mean time lost) ratio in nested and mutually exclusive subgroups of patients
defined by an estimated conditional average treatment effect (CATE) score via
cross-validation (CV). The CATE score can be estimated with up to 5 methods among the following:
Random forest, boosting, poisson regression, two regressions, and contrast regression
(see score.method).
Usage
catecvsurv(
data,
score.method,
cate.model,
ps.model,
ps.method = "glm",
initial.predictor.method = "randomForest",
ipcw.model = NULL,
ipcw.method = "breslow",
minPS = 0.01,
maxPS = 0.99,
followup.time = NULL,
tau0 = NULL,
higher.y = TRUE,
prop.cutoff = seq(0.5, 1, length = 6),
prop.multi = c(0, 1/3, 2/3, 1),
abc = TRUE,
train.prop = 3/4,
cv.n = 10,
error.max = 0.1,
max.iter = 5000,
surv.min = 0.025,
tree.depth = 2,
n.trees.rf = 1000,
n.trees.boosting = 200,
B = 3,
Kfold = 5,
error.maxNR = 0.001,
max.iterNR = 150,
tune = c(0.5, 2),
seed = NULL,
plot.gbmperf = TRUE,
verbose = 0
)
Arguments
data |
A data frame containing the variables in the outcome, propensity score, and inverse
probability of censoring models (if specified); a data frame with |
score.method |
A vector of one or multiple methods to estimate the CATE score.
Allowed values are: |
cate.model |
A standard |
ps.model |
A formula describing the propensity score (PS) model to be fitted. The treatment must
appear on the left-hand side. The treatment must be a numeric vector coded as 0/1.
If data are from a randomized controlled trial, specify |
ps.method |
A character value for the method to estimate the propensity score.
Allowed values include one of:
|
initial.predictor.method |
A character vector for the method used to get initial
outcome predictions conditional on the covariates specified in |
ipcw.model |
A formula describing the inverse probability of censoring weighting (IPCW)
model to be fitted. The left-hand side must be empty. Default is |
ipcw.method |
A character value for the censoring model. Allowed values are:
|
minPS |
A numerical value (in '[0, 1]') below which estimated propensity scores should be
truncated. Default is |
maxPS |
A numerical value (in '(0, 1]') above which estimated propensity scores should be
truncated. Must be strictly greater than |
followup.time |
A column name in |
tau0 |
The truncation time for defining restricted mean time lost. Default is |
higher.y |
A logical value indicating whether higher ( |
prop.cutoff |
A vector of numerical values (in '(0, 1]') specifying percentiles of the
estimated log CATE scores to define nested subgroups. Each element represents the cutoff to
separate observations in nested subgroups (below vs above cutoff).
The length of |
prop.multi |
A vector of numerical values (in '[0, 1]') specifying percentiles of the
estimated log CATE scores to define mutually exclusive subgroups.
It should start with 0, end with 1, and be of |
abc |
A logical value indicating whether the area between curves (ABC) should be calculated
at each cross-validation iterations, for each |
train.prop |
A numerical value (in '(0, 1)') indicating the proportion of total data used
for training. Default is |
cv.n |
A positive integer value indicating the number of cross-validation iterations.
Default is |
error.max |
A numerical value > 0 indicating the tolerance (maximum value of error)
for the largest standardized absolute difference in the covariate distributions or in the
doubly robust estimated rate ratios between the training and validation sets. This is used
to define a balanced training-validation splitting. Default is |
max.iter |
A positive integer value indicating the maximum number of iterations when
searching for a balanced training-validation split. Default is |
surv.min |
Lower truncation limit for the probability of being censored.
It must be a positive value and should be chosen close to 0. Default is |
tree.depth |
A positive integer specifying the depth of individual trees in boosting
(usually 2-3). Used only if |
n.trees.rf |
A positive integer specifying the maximum number of trees in random forest.
Used if |
n.trees.boosting |
A positive integer specifying the maximum number of trees in boosting
(usually 100-1000). Used if |
B |
A positive integer specifying the number of time cross-fitting is repeated in
|
Kfold |
A positive integer specifying the number of folds used in cross-fitting
to partition the data in |
error.maxNR |
A numerical value > 0 indicating the minimum value of the mean absolute
error in Newton Raphson algorithm. Used only if |
max.iterNR |
A positive integer indicating the maximum number of iterations in the
Newton Raphson algorithm. Used only if |
tune |
A vector of 2 numerical values > 0 specifying tuning parameters for the
Newton Raphson algorithm. |
seed |
An optional integer specifying an initial randomization seed for reproducibility.
Default is |
plot.gbmperf |
A logical value indicating whether to plot the performance measures in
boosting. Used only if |
verbose |
An integer value indicating what kind of intermediate progress messages should
be printed. |
Details
The CATE score represents an individual-level treatment effect expressed as the restricted mean survival time (RMTL) ratio) for survival outcomes. It can be estimated with boosting, Poisson regression, random forest, and the doubly robust estimator two regressions (Yadlowsky, 2020) applied separately by treatment group or with the other doubly robust estimator contrast regression (Yadlowsky, 2020) applied to the entire data set.
Internal CV is applied to reduce optimism in choosing the CATE estimation method that
captures the most treatment effect heterogeneity. The CV is applied by repeating the
following steps cv.n times:
Split the data into a training and validation set according to
train.prop. The training and validation sets must be balanced with respect to covariate distributions and doubly robust RMTL ratio estimates (seeerror.max).Estimate the CATE score in the training set with the specified scoring method.
Predict the CATE score in the validation set using the scoring model fitted from the training set.
Build nested subgroups of treatment responders in the training and validation sets, separately, and estimate the ATE within each nested subgroup. For each element i of
prop.cutoff(e.g.,prop.cutoff[i]= 0.6), take the following steps:Identify high responders as observations with the 60% (i.e.,
prop.cutoff[i]x100%) highest (ifhigher.y = FALSE) or lowest (ifhigher.y = TRUE) estimated CATE scores.Estimate the ATE in the subgroup of high responders using a doubly robust estimator.
Conversely, identify low responders as observations with the 40% (i.e., 1 -
prop.cutoff[i]x100%) lowest (ifhigher.y= FALSE) or highest (ifhigher.y= TRUE) estimated CATE scores.Estimate the ATE in the subgroup of low responders using a doubly robust estimator.
If
abc= TRUE, calculate the area between the ATE and the series of ATEs in nested subgroups of high responders in the validation set.Build mutually exclusive subgroups of treatment responders in the training and validation sets, separately, and estimate the ATE within each subgroup. Mutually exclusive subgroups are built by splitting the estimated CATE scores according to
prop.multi.
Value
Returns a list containing the following components saved as a "precmed" object:
ate.randomForest: A list of ATE output measured by the RMTL ratio ifscore.methodincludes'randomForest':ate.est.train.high.cv: A matrix of numerical values withlength(prop.cutoff)rows andcv.ncolumns. The ith column/jth row cell contains the estimated ATE in the nested subgroup of high responders defined by CATE score above (ifhigher.y = FALSE) or below (ifhigher.y = TRUE) theprop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith cross-validation iteration.ate.est.train.low.cv: A matrix of numerical values withlength(prop.cutoff) - 1rows andcv.ncolumns. TThe ith column/jth row cell contains the estimated ATE in the nested subgroup of low responders defined by CATE score below (ifhigher.y = FALSE) or above (ifhigher.y = TRUE) theprop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith cross-validation iteration.ate.est.valid.high.cv: Same asate.est.train.high.cv, but in the validation set.ate.est.valid.low.cv: Same asate.est.train.low.cv, but in the validation set.ate.est.train.group.cv: A matrix of numerical values withlength(prop.multi) - 1rows andcv.ncolumns. The ith column contains the estimated ATE inlength(prop.multi) - 1mutually exclusive subgroups defined byprop.multiin the training set in ith cross-validation iteration.ate.est.valid.group.cv: Same asate.est.train.group.cv, but in the validation set.abc.valid: A vector of numerical values of lengthcv.n, The ith element returns the ABC of the validation curve in the ith cross-validation iteration. Only returned ifabc = TRUE.
ate.boosting: A list of results similar toate.randomForestoutput ifscore.methodincludes'boosting'.ate.poisson: A list of results similar toate.randomForestoutput ifscore.methodincludes'poisson'.ate.twoReg: A list of results similar toate.randomForestoutput ifscore.methodincludes'twoReg'.ate.contrastReg: A list of results similar toate.randomForestoutput ifscore.methodincludes'contrastReg'. This method has an additional element in the list of results:converge.contrastReg.cv: A vector of logical value of lengthcv.n. The ith element indicates whether the algorithm converged in the ith cross-validation iteration.
hr.randomForest: A list of adjusted hazard ratio ifscore.methodincludes'randomForest':hr.est.train.high.cv: A matrix of numerical values withlength(prop.cutoff)rows andcv.ncolumns. The ith column/jth row cell contains the estimated HR in the nested subgroup of high responders defined by CATE score above (ifhigher.y = FALSE) or below (ifhigher.y = TRUE) theprop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith cross-validation iteration.hr.est.train.low.cv: A matrix of numerical values withlength(prop.cutoff) - 1rows andcv.ncolumns. TThe ith column/jth row cell contains the estimated HR in the nested subgroup of low responders defined by CATE score below (ifhigher.y = FALSE) or above (ifhigher.y = TRUE) theprop.cutoff[j]x100% percentile of the estimated CATE score in the training set in the ith cross-validation iteration.hr.est.valid.high.cv: Same ashr.est.train.high.cv, but in the validation set.hr.est.valid.low.cv: Same ashr.est.train.low.cv, but in the validation set.hr.est.train.group.cv: A matrix of numerical values withlength(prop.multi) - 1rows andcv.ncolumns. The ith column contains the estimated HR inlength(prop.multi) - 1mutually exclusive subgroups defined byprop.multiin the training set in ith cross-validation iteration.hr.est.valid.group.cv: Same ashr.est.train.group.cv, but in the validation set.
hr.boosting: A list of results similar tohr.randomForestoutput ifscore.methodincludes'boosting'.hr.poisson: A list of results similar tohr.randomForestoutput ifscore.methodincludes'poisson'.hr.twoReg: A list of results similar tohr.randomForestoutput ifscore.methodincludes'twoReg'.hr.contrastReg: A list of results similar tohr.randomForestoutput ifscore.methodincludes'contrastReg'.props: A list of 3 elements:prop.onlyhigh: The original argumentprop.cutoff, reformatted as necessary.prop.bi: The original argumentprop.cutoff, similar toprop.onlyhighbut reformatted to exclude 1.prop.multi: The original argumentprop.multi, reformatted as necessary to include 0 and 1.
overall.ate.train: A vector of numerical values of lengthcv.n. The ith element contains the ATE (RMTL ratio) in the training set of the ith cross-validation iteration, estimated with the doubly robust estimator.overall.hr.train: A vector of numerical values of lengthcv.n. The ith element contains the ATE (HR) in the training set of the ith cross-validation iteration.overall.ate.valid: A vector of numerical values of lengthcv.n. The ith element contains the ATE (RMTL ratio) in the validation set of the ith cross-validation iteration, estimated with the doubly robust estimator.overall.hr.valid: A vector of numerical values of lengthcv.n. The ith element contains the ATE (HR) in the validation set of the ith cross-validation iteration.errors/warnings: A nested list of errors and warnings that were wrapped during the calculation of ATE. Errors and warnings are organized byscore.methodand position in the CV flow.higher.y: The originalhigher.yargument.abc: The originalabcargument.cv.n: The originalcv.nargument.response: The type of response. Always 'survival' for this function.formulas:A list of 3 elements: (1)cate.modelargument, (2)ps.modelargument and (3) original labels of the left-hand side variable inps.model(treatment) if it was not 0/1.
References
Yadlowsky, S., Pellegrini, F., Lionetto, F., Braune, S., & Tian, L. (2020). Estimation and validation of ratio-based conditional average treatment effects using observational data. Journal of the American Statistical Association, 1-18.. DOI: 10.1080/01621459.2020.1772080.
See Also
catefitsurv() function and boxplot(), abc methods for
"precmed" objects.
Examples
library(survival)
tau0 <- with(survivalExample,
min(quantile(y[trt == "drug1"], 0.95), quantile(y[trt == "drug0"], 0.95)))
catecv <- catecvsurv(data = survivalExample,
score.method = "poisson",
cate.model = Surv(y, d) ~ age + female + previous_cost +
previous_number_relapses,
ps.model = trt ~ age + previous_treatment,
initial.predictor.method = "logistic",
ipcw.model = ~ age + previous_cost + previous_treatment,
tau0 = tau0,
higher.y = TRUE,
cv.n = 5, seed = 999, verbose = 1)
# Try:
plot(catecv, ylab = "RMTL ratio of drug1 vs drug0 in each subgroup")
boxplot(catecv, ylab = "RMTL ratio of drug1 vs drug0 in each subgroup")
abc(catecv)
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