Estimation of the cluster-specific treatment effects in the partially nested design.
data_in |
A data.frame containing all necessary variables.
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ttname |
[character ]
A character string of the column name of the treatment variable. The treatment variable should be dummy-coded, with 1 for the (clustered) treatment arm and 0 for the (non-clustered) control arm.
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Kname |
[character ]
A character string of the column name of the cluster assignment variable. This variable should be coded as 0 for individuals in the control arm, the arm without the cluster assignment.
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Yname |
[character ]
A character string of the column name of the outcome variable
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Xnames |
[character ]
A character vector of the column names of the baseline covariates.
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Yfamily |
[numeric(1) ] Variable type of the outcome, with Yfamily = "gaussian" for continuous outcome, and Yfamily = "binomial" for binary outcome.
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learners_tt |
[character ]
A character vector of methods for estimating the treatment model, chosen from the SuperLearner R package. Default is "SL.glm" , a generalized linear model for the binary treatment variable. Other available methods can be found using the R function SuperLearner::listWrappers() .
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learners_k |
[character ]
A character string of a method for estimating the cluster assignment model, which can be one of "SL.multinom" (default), "SL.xgboost.modified" , "SL.ranger.modified" , and "SL.nnet.modified" .
Default is "SL.multinom" , the multinomial regression (nnet::multinom ) for the categorical cluster assignment using the treatment arm data. The other options are "SL.xgboost.modified" (gradient boosted model, xgboost::xgboost ), "SL.ranger.modified" (random forest model, ranger::ranger ), and "SL.nnet.modified" (neural network model, "SL.nnet.modified" ) modified for fitting categorical response variable of type multinomial.
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learners_y |
[character ]
A character vector of methods for estimating the outcome model, chosen from the SuperLearner R package. Default is "SL.glm" , a generalized linear model for the outcome variable, with family specified by Yfamily . Other available methods can be found using the R function SuperLearner::listWrappers() .
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sensitivity |
Specification for sensitivity parameter values on the standardized mean difference scale, which can be NULL (default) or "small_to_medium" . If NULL , no sensitivity analysis will be run. If "small_to_medium" , the function will run a sensitivity analysis for the cluster assignment ignorability assumption, and the sensitivity parameter values indicate a deviation from this assumption of magnitude 0.1 and 0.3 standardized mean difference.
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cv_folds |
[numeric(1) ] The number of cross-fitting folds. Default is 4.
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seed |
An integer that is used as argument by the set.seed() for offsetting the random number generator. Default is to leave the random number generator alone.
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ate_K |
A data.frame of the estimation results.
The columns "ate_k", "std_error", "CI_lower", and "CI_upper" contain the estimate, standard error estimate, and lower and upper bounds of the 0.95 confidence interval of the cluster-specific treatment effect for the cluster (indicated by column "cluster") in the same row.
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cv_components |
A data.frame of nuisance model estimates.
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sens_results |
NULL if the argument sensitivity = NULL .
If the argument sensitivity = "small_to_medium" is specified, sens_results is a list of four data frames, containing the estimation results with the sensitivity parameter value (standardized mean difference) being 0.1, 0.3, -0.1, -0.3.
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