R: Build a Treatment Rule

BuildRule {DevTreatRules}

R Documentation

Build a Treatment Rule

Description

Perform principled development of a treatment rule (using the IPW approach to account for potential confounding) on a development dataset (i.e. training set) that is independent of datasets used for model selection (i.e. validation set) and rule evaluation (i.e. test set).

Usage

BuildRule(
  development.data,
  study.design,
  prediction.approach,
  name.outcome,
  type.outcome,
  name.treatment,
  names.influencing.treatment = NULL,
  names.influencing.rule,
  desirable.outcome,
  rule.method = NULL,
  propensity.method,
  additional.weights = rep(1, nrow(development.data)),
  truncate.propensity.score = TRUE,
  truncate.propensity.score.threshold = 0.05,
  type.observation.weights = NULL,
  propensity.k.cv.folds = 10,
  rule.k.cv.folds = 10,
  lambda.choice = c("min", "1se"),
  OWL.lambda.seq = NULL,
  OWL.kernel = "linear",
  OWL.kparam.seq = NULL,
  OWL.cvFolds = 10,
  OWL.verbose = TRUE,
  OWL.framework.shift.by.min = TRUE,
  direct.interactions.center.continuous.Y = TRUE,
  direct.interactions.exclude.A.from.penalty = TRUE
)

Arguments

`development.data`	A data frame representing the development dataset (i.e. training set) used for building a treatment rule.
`study.design`	Either ‘observational’, ‘RCT’, or ‘naive’. For the `observational` design, the function uses inverse-probability-of-treatment observation weights (IPW) based on estimated propensity scores with predictors `names.influencing.treatment`; for the `RCT` design, the function uses IPW based on propensity scores equal to the observed sample proportions; for the `naive` design, all observation weights will be uniformly equal to 1.
`prediction.approach`	One of ‘split.regression’, ‘direct.interactions’, ‘OWL’, or ‘OWL.framework’.
`name.outcome`	A character indicating the name of the outcome variable in `development.data`.
`type.outcome`	Either ‘binary’ or ‘continuous’, the form of `name.outcome`.
`name.treatment`	A character indicating the name of the treatment variable in `development.data`.
`names.influencing.treatment`	A character vector (or single element) indicating the names of the variables in `development.data` that are expected to influence treatment assignment in the current dataset. Required for `study.design=`‘observational’.
`names.influencing.rule`	A character vector (or single element) indicating the names of the variables in `development.data` that may influence response to treatment and are expected to be observed in future clinical settings.
`desirable.outcome`	A logical equal to `TRUE` if higher values of the outcome are considered desirable (e.g. for a binary outcome, a 1 is more desirable than a 0). The `OWL.framework` and `OWL` prediction approaches require a desirable outcome.
`rule.method`	One of ‘glm.regression’, ‘lasso’, or ‘ridge’. For `type.outcome=`‘binary’, ‘glm.regression’ leads to logistic regression; for a `type.outcome=`‘continuous’, ‘glm.regression’ specifies linear regression. This is the underlying regression model used to develop the treatment rule.
`propensity.method`	One of ‘logistic.regression’, ‘lasso’, or ‘ridge’. This is the underlying regression model used to estimate propensity scores for `study.design=`‘observational’.
`additional.weights`	A numeric vector of observation weights that will be multiplied by IPW weights in the rule development stage, with length equal to the number of rows in `development.data`. This can be used, for example, to account for a non-representative sampling design or to apply an IPW adjustment for missingness. The default is a vector of 1s.
`truncate.propensity.score`	A logical variable dictating whether estimated propensity scores less than `truncate.propensity.score.threshold` away from 0 or 1 should be truncated to be no more than `truncate.propensity.score.threshold` away from 0 or 1.
`truncate.propensity.score.threshold`	A numeric value between 0 and 0.25.
`type.observation.weights`	Default is NULL, but other choices are ‘IPW.L’, ‘IPW.L.and.X’, and ‘IPW.ratio’, where L indicates `names.influencing.treatment`, X indicates `names.influencing.rule`. The default behavior is to use the ‘IPW.ratio’ observation weights (propensity score based on X divided by propensity score based on L and X) for `prediction.approach=`‘split.regression’ and to use ‘IPW.L’ observation weights (inverse of propensity score based on L) for the ‘direct.interactions’, ‘OWL’, and ‘OWL.framework’ prediction approaches.
`propensity.k.cv.folds`	An integer specifying how many folds to use for K-fold cross-validation that chooses the tuning parameters when `propensity.method` is ‘lasso’ or ‘ridge’. Default is 10.
`rule.k.cv.folds`	An integer specifying how many folds to use for K-fold cross-validation that chooses the tuning parameter when `rule.method` is `lasso` or ‘ridge’. Default is 10.
`lambda.choice`	Either ‘min’ or ‘1se’, corresponding to the `s` argument in `predict.cv.glmnet()` from the `glmnet` package. Only used when `propensity.method` or `rule.method` is ‘lasso’ or ‘ridge’. Default is ‘min’.
`OWL.lambda.seq`	Used when `prediction.approach=`‘OWL’, a numeric vector that corresponds to the `lambdas` argument in the `owl()` function from the `DynTxRegime` package. Defaults to `2^seq(-5, 5, 1)`.
`OWL.kernel`	Used when `prediction.approach=`‘OWL’, a character equal to either ‘linear’ or ‘radial’. Corresponds to the `kernel` argument in the `owl()` function from the `DynTxRegime` package. Default is ‘linear’.
`OWL.kparam.seq`	Used when `prediction.approach=`‘OWL’ and `OWL.kernel=`‘radial’. Corresponds to the `kparam` argument in the `owl()` function from the `DynTxRegime` package. Defaults to `2^seq(-10, 10, 1)`.
`OWL.cvFolds`	Used when `prediction.approach=`‘OWL’, an integer corresponding to the `cvFolds` argument in the `owl()` function from the `DynTxRegime` package. Defaults to 10.
`OWL.verbose`	Used when `prediction.approach=`‘OWL’, a logical corresponding to the `verbose` argument in the `owl()` function from the `DynTxRegime` package. Defaults to `TRUE`.
`OWL.framework.shift.by.min`	Logical, set to `TRUE` by default in recognition of our empirical observation that, with a continuous outcome, OWL framework performs far better in simulation studies when the outcome was shifted to have a minimum of just above 0.
`direct.interactions.center.continuous.Y`	Logical, set to `TRUE` by default in recognition of our empirical observation that, with a continuous outcome, direct-interactions performed far better in simulation studies when the outcome was mean-centered.
`direct.interactions.exclude.A.from.penalty`	Logical, set to `TRUE` by default in recognition of our empirical observation that, with a continuous outcome and lasso/ridge used specified as the `rule.method`, direct-interactions performed far better in simulation studies when the coefficient corresponding to the treatment variable was excluded from the penalty function.

Value

A list with some combination of the following components (depending on specified prediction.approach)

type.outcome: The type.outcome specified above (used by other functions that are based on BuildRule())
prediction.approach: The prediction.approach specified above (used by other functions that are based on BuildRule())
rule.method: The rule.method specified above (used by other functions that are based on BuildRule())
lambda.choice: The lambda.choice specified above (used by other functions that are based on BuildRule())
propensity.score.object: A list containing the relevant regression object from propensity score estimation. The list has two elements for type.observation.weights=‘IPW.ratio’ (the default for prediction.approach=‘split.regression’), has one element for type.observation.weights=‘IPW.L’ (the default for ‘OWL’, ‘OWL.framework’ and ‘direct.interactions’), has one element when type.observation.weights=‘IPW.L.and.X’, and is simply equal to NA if study.design=‘RCT’ (in which case propensity score would just be the inverse of sample proportion receiving treatment).
owl.object: For prediction.approach=‘OWL’ only, the object returned by the owl() function in the DynTxRegime package.
observation.weights: The observation weights used for estimating the treatment rule
rule.object: For prediction.approach=‘OWL.framework’ or prediction.approach=‘direct.interactions’, the regression object returned from treatment rule estimation (to which the coef() function could be applied, for example)
rule.object.control: For prediction.approach=‘split.regression’ the regression object returned from treatment rule estimation (to which the coef() function could be applied, for example) that estimates the outcome variable for individuals who do not receive treatment.
rule.object.treatment: For prediction.approach=‘split.regression’ the regression object returned from treatment rule estimation (to which the coef() function could be applied, for example) that estimates the outcome variable for individuals who do receive treatment.

References

Yingqi Zhao, Donglin Zeng, A. John Rush & Michael R. Kosorok (2012) Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107:499 1106–1118.
Shuai Chen, Lu Tian, Tianxi Cai, Menggang Yu (2017) A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics, 73:4: 1199–1209.
Lu Tian, Ash A. Alizadeh, Andrew J. Gentles, Robert Tibshirani (2014) A simple method for estimating interactions between a treatment and a large number of covariates. Journal of the American Statistical Association, 109:508: 1517–1532.
Jeremy Roth and Noah Simon (2019). Using propensity scores to develop and evaluate treatment rules with observational data (Manuscript in progress)
Jeremy Roth and Noah Simon (2019). Elucidating outcome-weighted learning and its comparison to split-regression: direct vs. indirect methods in practice. (Manuscript in progress)

Examples

set.seed(123)
example.split <- SplitData(data=obsStudyGeneExpressions,
                                     n.sets=3, split.proportions=c(0.5, 0.25, 0.25))
development.data <- example.split[example.split$partition == "development",]
one.rule <- BuildRule(development.data=development.data,
                     study.design="observational",
                     prediction.approach="split.regression",
                     name.outcome="no_relapse",
                     type.outcome="binary",
                     desirable.outcome=TRUE,
                     name.treatment="intervention",
                     names.influencing.treatment=c("prognosis", "clinic", "age"),
                     names.influencing.rule=c("age", paste0("gene_", 1:10)),
                     propensity.method="logistic.regression",
                     rule.method="glm.regression")
coef(one.rule$rule.object.control)
coef(one.rule$rule.object.treatment)

[Package DevTreatRules version 1.1.0 Index]