tmle3_Spec_mopttx_blip_revere: TMLE for the Mean Under the Optimal Individualized Rule

In tlverse/tmle3mopttx: Targeted Maximum Likelihood Estimation of the Mean under Optimal Individualized Treatment

TMLE for the Mean Under the Optimal Individualized Rule

Description

The functions contained in the class define a TMLE for the Mean Under the Optimal Individualized Rule with Categorical Treatment, learned and estimated under Revere CV-TMLE. For learning the Optimal Rule, see 'Optimal_Rule_Revere' class.

Format

An R6Class object inheriting from tmle3_Spec.

Value

A tmle3 object inheriting from tmle3_Spec with methods for obtaining the TMLE for the Mean Under the Optimal Individualized Rule. For a full list of the available functionality, see the complete documentation of tmle3_Spec.

Parameters

- V: User-specified list of covariates used to define the rule. - type: Blip type, corresponding to different ways of defining the reference category in learning the blip; mostly applies to categorical treatment. Available categories include "blip1" (reference level of treatment), "blip2" (average level of treatment) and "blip3" (weighted average level of treatment). - learners: List of user-defined learners for relevant parts of the likelihood. - maximize: Should the average outcome be maximized of minimized? Default is maximize=TRUE. - complex: If TRUE, the returned mean under the Optimal Rule is based on the full set of covariates provided by the user (parameter "V"). If FALSE, simpler rules (including the static rules), are evaluated as well; the returned mean under the Optimal Rule is then a potentially more parsimonious rule, if the mean performance is similar. - realistic: If TRUE, the optimal rule returned takes into account the probability of treatment given covariates. - resource: Indicates the percent of initially estimated individuals who should be given treatment that get treatment, based on their blip estimate. If resource = 1 all estimated individuals to benefit from treatment get treatment, if resource = 0 none get treatment. - interpret: If TRUE, returns a HAL fit of the blip, explaining the rule. - reference: reference category for blip1. Default is the smallest numerical category or factor.

Super class

tmle3::tmle3_Spec -> tmle3_Spec_mopttx_blip_revere

Methods

Public methods

• tmle3_Spec_mopttx_blip_revere$new()

• tmle3_Spec_mopttx_blip_revere$vals_from_factor()

• tmle3_Spec_mopttx_blip_revere$make_tmle_task()

• tmle3_Spec_mopttx_blip_revere$make_initial_likelihood()

• tmle3_Spec_mopttx_blip_revere$predict_rule()

• tmle3_Spec_mopttx_blip_revere$make_rules()

• tmle3_Spec_mopttx_blip_revere$make_est_fin()

• tmle3_Spec_mopttx_blip_revere$set_opt()

• tmle3_Spec_mopttx_blip_revere$set_rule()

• tmle3_Spec_mopttx_blip_revere$data_adapt_psi()

• tmle3_Spec_mopttx_blip_revere$get_blip_fit()

• tmle3_Spec_mopttx_blip_revere$get_blip_pred()

• tmle3_Spec_mopttx_blip_revere$make_params()

• tmle3_Spec_mopttx_blip_revere$clone()

Inherited methods

• tmle3::tmle3_Spec$make_targeted_likelihood()
• tmle3::tmle3_Spec$make_updater()

---

Method new()

Usage

tmle3_Spec_mopttx_blip_revere$new(
  V = NULL,
  type,
  learners,
  maximize = TRUE,
  complex = TRUE,
  realistic = FALSE,
  resource = 1,
  interpret = FALSE,
  likelihood_override = NULL,
  reference = NULL,
  ...
)

---

Method vals_from_factor()

Usage

tmle3_Spec_mopttx_blip_revere$vals_from_factor(x)

---

Method make_tmle_task()

Usage

tmle3_Spec_mopttx_blip_revere$make_tmle_task(data, node_list, ...)

---

Method make_initial_likelihood()

Usage

tmle3_Spec_mopttx_blip_revere$make_initial_likelihood(
  tmle_task,
  learner_list = NULL
)

---

Method predict_rule()

Usage

tmle3_Spec_mopttx_blip_revere$predict_rule(tmle_task_new)

---

Method make_rules()

Usage

tmle3_Spec_mopttx_blip_revere$make_rules(V)

---

Method make_est_fin()

Usage

tmle3_Spec_mopttx_blip_revere$make_est_fin(fit, max, p.value = 0.35)

---

Method set_opt()

Usage

tmle3_Spec_mopttx_blip_revere$set_opt(opt)

---

Method set_rule()

Usage

tmle3_Spec_mopttx_blip_revere$set_rule(rule)

---

Method data_adapt_psi()

Usage

tmle3_Spec_mopttx_blip_revere$data_adapt_psi(data_tda, node_list, Qbar0)

---

Method get_blip_fit()

Usage

tmle3_Spec_mopttx_blip_revere$get_blip_fit()

---

Method get_blip_pred()

Usage

tmle3_Spec_mopttx_blip_revere$get_blip_pred(tmle_task, fold_number = "full")

---

Method make_params()

Usage

tmle3_Spec_mopttx_blip_revere$make_params(tmle_task, likelihood)

---

Method clone()

The objects of this class are cloneable with this method.

Usage

tmle3_Spec_mopttx_blip_revere$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## Not run: 
library(sl3)
library(tmle3)
library(data.table)

data("data_bin")
data <- data_bin

Q_lib <- make_learner_stack("Lrnr_mean", "Lrnr_glm_fast")
g_lib <- make_learner_stack("Lrnr_mean", "Lrnr_glm_fast")
B_lib <- make_learner_stack("Lrnr_glm_fast", "Lrnr_xgboost")

metalearner <- make_learner(Lrnr_nnls)
Q_learner <- make_learner(Lrnr_sl, Q_lib, metalearner)
g_learner <- make_learner(Lrnr_sl, g_lib, metalearner)
B_learner <- make_learner(Lrnr_sl, B_lib, metalearner)

learner_list <- list(Y = Q_learner, A = g_learner, B = B_learner)

node_list <- list(W = c("W1", "W2", "W3"), A = "A", Y = "Y")

tmle_spec <- tmle3_mopttx_blip_revere(
  V = c("W1", "W2", "W3"),
  type = "blip1", learners = learner_list, maximize = TRUE,
  complex = TRUE, realistic = TRUE
)

## End(Not run)

tlverse/tmle3mopttx documentation built on Aug. 9, 2022, 3:31 p.m.