Title:	Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies
Version:	1.5.2
Description:	Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck <doi:10.1080/01621459.2021.1955691>, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; <doi:10.1007/s10985-023-09606-7>).
Depends:	R (≥ 2.10)
License:	AGPL-3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
Imports:	stats, nnls, cli, R6, generics, origami, future (≥ 1.17.0), progressr, data.table (≥ 1.13.0), checkmate (≥ 2.1.0), SuperLearner, isotone, ife, lifecycle
URL:	https://beyondtheate.com/, https://github.com/nt-williams/lmtp
BugReports:	https://github.com/nt-williams/lmtp/issues
Suggests:	testthat (≥ 2.1.0), covr, rmarkdown, knitr, ranger, twang
NeedsCompilation:	no
Packaged:	2025-06-13 18:24:07 UTC; nicholaswilliams
Author:	Nicholas Williams [aut, cre, cph], Iván Díaz [aut, cph]
Maintainer:	Nicholas Williams <ntwilliams.personal@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-13 18:50:02 UTC

lmtp: Non-Parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies

Description

Non-parametric estimators for casual effects based on longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck doi:10.1080/01621459.2021.1955691, traditional point treatment, and traditional longitudinal effects. Continuous, binary, categorical treatments, and multivariate treatments are allowed as well are censored outcomes. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type. For both continuous and binary outcomes, additive treatment effects can be calculated and relative risks and odds ratios may be calculated for binary outcomes. Supports survival outcomes with competing risks (Diaz, Hoffman, and Hejazi; doi:10.1007/s10985-023-09606-7).

Author(s)

Maintainer: Nicholas Williams ntwilliams.personal@gmail.com (ORCID) [copyright holder]

Authors:

• Iván Díaz Ivan.Diaz@nyulangone.org (ORCID) [copyright holder]

See Also

Useful links:

• https://beyondtheate.com/

• https://github.com/nt-williams/lmtp

• Report bugs at https://github.com/nt-williams/lmtp/issues

Defunct estimators

Description

These estimators were deprecated to encourage use of lmtp_tmle() and lmtp_sdr() functions.

Usage

lmtp_sub(...)

lmtp_ipw(...)

Defunct functions

Description

No longer used in the package.

Usage

create_node_list(...)

Time To Event Last Outcome Carried Forward

Description

A helper function to prepare survival data for use with LMTP estimators by imputing outcome nodes using last outcome carried forward when an observation experiences the event before the end-of-follow-up.

Usage

event_locf(data, outcomes)

Arguments

Value

A modified dataset with future outcome nodes set to 1 if an observation experienced an event at any previous time point.

Examples

event_locf(sim_point_surv, paste0("Y.", 1:6))

IPSI Function Factory

Description

A function factory that returns a shift function for increasing or decreasing the probability of exposure when exposure is binary.

Usage

ipsi(delta)

Arguments

Value

A shift function.

See Also

lmtp_tmle(), lmtp_sdr()

Examples

data("iptwExWide", package = "twang")
a <- paste0("tx", 1:3)
baseline <- c("gender", "age")
tv <- list(c("use0"), c("use1"), c("use2"))
lmtp_sdr(iptwExWide, a, "outcome", baseline = baseline, time_vary = tv,
         shift = ipsi(0.5), outcome_type = "continuous", folds = 2)

Perform Contrasts of LMTP Fits

Description

Estimates contrasts of multiple LMTP fits compared to either a known reference value or a reference LMTP fit.

Usage

lmtp_contrast(..., ref, type = c("additive", "rr", "or"))

Arguments

Value

A list of class lmtp_contrast containing the following components:

Examples

  a <- c("A1", "A2")
  nodes <- list(c("L1"), c("L2"))
  cens <- c("C1", "C2")
  y <- "Y"

  # mean population outcome
  psi_null <- lmtp_tmle(sim_cens, a, y, time_vary = nodes, cens = cens, shift = NULL, folds = 1)

  # treatment rule, everyone is increased by 0.5
  d <- function(data, x) data[[x]] + 0.5
  psi_rule1 <- lmtp_tmle(sim_cens, a, y, time_vary = nodes, cens = cens,
                         shift = d, folds = 1, mtp = TRUE)

  # treatment rule, everyone is decreased by 0.5
  d <- function(data, x) data[[x]] - 0.5
  psi_rule2 <- lmtp_tmle(sim_cens, a, y, time_vary = nodes, cens = cens,
                         shift = d, folds = 1, mtp = TRUE)

  # Example 1.1
  # Additive effect of rule 1 compared to a known constant
  lmtp_contrast(psi_rule1, ref = 0.9)

  # Example 1.2
  # Additive effect of rule 1 compared to the population mean outcome
  lmtp_contrast(psi_rule1, ref = psi_null)

  # Example 1.3
  # Additive effects of rule 1 and 2 compared to the population mean outcome
  lmtp_contrast(psi_rule1, psi_rule2, ref = psi_null)

  # Example 1.4
  # Relative risk of rule 1 compared to observed exposure
  lmtp_contrast(psi_rule1, ref = psi_null, type = "rr")

  # Example 1.5
  # Odds of rule 1 compared to observed exposure
  lmtp_contrast(psi_rule1, ref = psi_null, type = "or")

Set LMTP Estimation Parameters

Description

Set LMTP Estimation Parameters

Usage

lmtp_control(
  .bound = 1e+05,
  .trim = 0.999,
  .learners_outcome_folds = 10,
  .learners_trt_folds = 10,
  .return_full_fits = FALSE,
  .discrete = TRUE,
  .info = FALSE
)

Arguments

Value

A list of parameters controlling the estimation procedure.

Examples

lmtp_control(.trim = 0.975)

LMTP Sequential Doubly Robust Estimator

Description

Sequentially doubly robust estimator for the effects of traditional causal effects and modified treatment policies for both point treatment and longitudinal data with binary, continuous, or time-to-event outcomes. Supports binary, categorical, and continuous exposures.

Usage

lmtp_sdr(
  data,
  trt,
  outcome,
  baseline = NULL,
  time_vary = NULL,
  cens = NULL,
  compete = NULL,
  shift = NULL,
  shifted = NULL,
  k = Inf,
  mtp = TRUE,
  outcome_type = c("binomial", "continuous", "survival"),
  id = NULL,
  bounds = NULL,
  learners_outcome = "SL.glm",
  learners_trt = "SL.glm",
  folds = 10,
  weights = NULL,
  control = lmtp_control()
)

Arguments

Details

Should mtp = TRUE?

A modified treatment policy (MTP) is an intervention that depends on the natural value of the exposure (the value that the treatment would have taken under no intervention). This differs from other causal effects, such as the average treatment effect (ATE), where an exposure would be increased (or decreased) deterministically. If your intervention of interest adds, subtracts, or multiplies the observed treatment values by some amount, use mtp = TRUE.

Value

A list of class lmtp containing the following components:

Examples

  set.seed(56)
  n <- 1000
  W <- rnorm(n, 10, 5)
  A <- 23 + 5*W + rnorm(n)
  Y <- 7.2*A + 3*W + rnorm(n)
  ex1_dat <- data.frame(W, A, Y)

  # Example 1.1
  # Point treatment, continuous exposure, continuous outcome, no loss-to-follow-up
  # Interested in the effect of a modified treatment policy where A is decreased by 15
  # units only among observations whose observed A was above 80.
  # The true value under this intervention is about 513.
  policy <- function(data, x) {
    (data[[x]] > 80)*(data[[x]] - 15) + (data[[x]] <= 80)*data[[x]]
  }

  lmtp_sdr(ex1_dat, "A", "Y", "W", shift = policy,
           outcome_type = "continuous", folds = 2, mtp = TRUE)

  # Example 2.1
  # Longitudinal setting, time-varying continuous exposure bounded by 0,
  # time-varying covariates, and a binary outcome with no loss-to-follow-up.
  # Interested in the effect of a treatment policy where exposure decreases by
  # one unit at every time point if an observations observed exposure is greater
  # than or equal to 2. The true value under this intervention is about 0.305.
  head(sim_t4)

  A <- c("A_1", "A_2", "A_3", "A_4")
  L <- list(c("L_1"), c("L_2"), c("L_3"), c("L_4"))

  policy <- function(data, trt) {
    a <- data[[trt]]
    (a - 1) * (a - 1 >= 1) + a * (a - 1 < 1)
  }

  # BONUS: progressr progress bars!
  progressr::handlers(global = TRUE)

  lmtp_sdr(sim_t4, A, "Y", time_vary = L, shift = policy,
           folds = 2, mtp = TRUE)

  # Example 2.2
  # The previous example assumed that the outcome (as well as the treatment variables)
  # were directly affected by all other nodes in the past. In certain situations,
  # domain specific knowledge may suggest otherwise.
  # This can be controlled using the k argument.
  lmtp_sdr(sim_t4, A, "Y", time_vary = L, shift = policy,
           k = 0, folds = 2, mtp = TRUE)

  # Example 2.3
  # Using the same data as examples 2.1 and 2.2.
  # Now estimating the effect of a dynamic modified treatment policy.
  # creating a dynamic mtp that applies the shift function
  # but also depends on history and the current time
  policy <- function(data, trt) {
    mtp <- function(data, trt) {
      (data[[trt]] - 1) * (data[[trt]] - 1 >= 1) + data[[trt]] * (data[[trt]] - 1 < 1)
    }

    # if its the first time point, follow the same mtp as before
    if (trt == "A_1") return(mtp(data, trt))

    # otherwise check if the time varying covariate equals 1
    ifelse(
      data[[sub("A", "L", trt)]] == 1,
      mtp(data, trt), # if yes continue with the policy
      data[[trt]]     # otherwise do nothing
    )
  }

  lmtp_sdr(sim_t4, A, "Y", time_vary = L, mtp = TRUE,
           k = 0, shift = policy, folds = 2)

  # Example 2.4
  # Using the same data as examples 2.1, 2.2, and 2.3, but now treating the exposure
  # as an ordered categorical variable. To account for the exposure being a
  # factor we just need to modify the shift function (and the original data)
  # so as to respect this.
  tmp <- sim_t4
  for (i in A) {
    tmp[[i]] <- factor(tmp[[i]], levels = 0:5, ordered = FALSE)
  }

  policy <- function(data, trt) {
    out <- list()
    a <- data[[trt]]
    for (i in 1:length(a)) {
      if (as.character(a[i]) %in% c("0", "1")) {
        out[[i]] <- as.character(a[i])
      } else {
        out[[i]] <- as.numeric(as.character(a[i])) - 1
      }
    }
    factor(unlist(out), levels = 0:5, ordered = FALSE)
  }

  lmtp_sdr(tmp, A, "Y", time_vary = L, shift = policy,
           k = 0, folds = 2, mtp = TRUE)

  # Example 3.1
  # Longitudinal setting, time-varying binary treatment, time-varying covariates
  # and baseline covariates with no loss-to-follow-up. Interested in a "traditional"
  # causal effect where treatment is set to 1 at all time points for all observations.
  if (require("twang")) {
    data("iptwExWide", package = "twang")

    A <- paste0("tx", 1:3)
    W <- c("gender", "age")
    L <- list(c("use0"), c("use1"), c("use2"))

    lmtp_sdr(
      iptwExWide, A, "outcome", baseline = W, time_vary = L,
      shift = static_binary_on, outcome_type = "continuous",
      mtp = FALSE, folds = 2
    )
  }

  # Example 4.1
  # Longitudinal setting, time-varying continuous treatment, time-varying covariates,
  # binary outcome with right censoring. Interested in the mean population outcome under
  # the observed exposures in a hypothetical population with no loss-to-follow-up.
  head(sim_cens)

  A <- c("A1", "A2")
  L <- list(c("L1"), c("L2"))
  C <- c("C1", "C2")
  Y <- "Y"

  lmtp_sdr(sim_cens, A, Y, time_vary = L, cens = C, shift = NULL, folds = 2)

  # Example 5.1
  # Time-to-event analysis with a binary time-invariant exposure. Interested in
  # the effect of treatment being given to all observations on the cumulative
  # incidence of the outcome.
  # For a survival problem, the outcome argument now takes a vector of outcomes
  # if an observation experiences the event prior to the end of follow-up, all future
  # outcome nodes should be set to 1 (i.e., last observation carried forward).
  A <- "trt"
  Y <- paste0("Y.", 1:6)
  C <- paste0("C.", 0:5)
  W <- c("W1", "W2")

  lmtp_sdr(sim_point_surv, A, Y, W, cens = C, folds = 2,
           shift = static_binary_on, outcome_type = "survival")

  # Example 6.1
  # Intervening on multiple exposures simultaneously. Interested in the effect of
  # a modified treatment policy where variable D1 is decreased by 0.1 units and
  # variable D2 is decreased by 0.5 units simultaneously.
  A <- list(c("D1", "D2"))
  W <- paste0("C", 1:3)
  Y <- "Y"

  d <- function(data, a) {
    out = list(
      data[[a[1]]] - 0.1,
      data[[a[2]]] - 0.5
    )
    setNames(out, a)
  }

  lmtp_sdr(multivariate_data, A, Y, W, shift = d,
           outcome_type = "continuous", folds = 1, mtp = TRUE)

  # Example 7.1
  # Time-to-event analysis with a binary time-invariant exposure and a competing-risk.
  lmtp_sdr(
    data = sim_competing_risks,
    trt = "A",
    cens = paste0("C", 1:5),
    compete = paste0("D", 1:5),
    baseline = paste0("W", 1:5),
    outcome = paste0("Y", 1:5),
    outcome_type = "survival",
    shift = static_binary_on,
    folds = 1
  )

LMTP Survival Curve Estimator

Description

Wrapper around lmtp_tmle and lmtp_sdr for survival outcomes to estimate the entire survival curve. Estimates are reconstructed using isotonic regression to enforce monotonicity of the survival curve. Confidence intervals correspond to marginal confidence intervals for the survival curve, not simultaneous intervals.

Usage

lmtp_survival(
  data,
  trt,
  outcomes,
  baseline = NULL,
  time_vary = NULL,
  cens = NULL,
  compete = NULL,
  shift = NULL,
  shifted = NULL,
  estimator = c("lmtp_tmle", "lmtp_sdr"),
  k = Inf,
  mtp = TRUE,
  id = NULL,
  learners_outcome = "SL.glm",
  learners_trt = "SL.glm",
  folds = 10,
  weights = NULL,
  control = lmtp_control()
)

Arguments

Value

A list of class lmtp_survival containing lmtp objects for each time point.

Examples

# Example 1.1
# Time-to-event analysis with a binary time-invariant exposure. Interested in
# the effect of treatment being given to all observations on the cumulative
# incidence of the outcome.
A <- "trt"
Y <- paste0("Y.", 1:6)
C <- paste0("C.", 0:5)
W <- c("W1", "W2")

curve <- lmtp_survival(sim_point_surv, A, Y, W, cens = C, folds = 1,
                       shift = static_binary_on, estimator = "lmtp_tmle")

tidy(curve)

# Example 1.2
# Time-to-event analysis with a binary time-invariant exposure and a competing-risk.
lmtp_survival(
  data = sim_competing_risks,
  trt = "A",
  cens = paste0("C", 1:5),
  compete = paste0("D", 1:5),
  baseline = paste0("W", 1:5),
  outcome = paste0("Y", 1:5),
  shift = static_binary_on,
  folds = 1
)

LMTP Targeted Maximum Likelihood Estimator

Description

Targeted maximum likelihood estimator for the effects of traditional causal effects and modified treatment policies for both point treatment and longitudinal data with binary, continuous, or time-to-event outcomes. Supports binary, categorical, and continuous exposures.

Usage

lmtp_tmle(
  data,
  trt,
  outcome,
  baseline = NULL,
  time_vary = NULL,
  cens = NULL,
  compete = NULL,
  shift = NULL,
  shifted = NULL,
  k = Inf,
  mtp = TRUE,
  outcome_type = c("binomial", "continuous", "survival"),
  id = NULL,
  bounds = NULL,
  learners_outcome = "SL.glm",
  learners_trt = "SL.glm",
  folds = 10,
  weights = NULL,
  control = lmtp_control()
)

Arguments

Details

Should mtp = TRUE?

A modified treatment policy (MTP) is an intervention that depends on the natural value of the exposure (the value that the treatment would have taken under no intervention). This differs from other causal effects, such as the average treatment effect (ATE), where an exposure would be increased (or decreased) deterministically. If your intervention of interest adds, subtracts, or multiplies the observed treatment values by some amount, use mtp = TRUE.

Value

A list of class lmtp containing the following components:

Examples

  set.seed(56)
  n <- 1000
  W <- rnorm(n, 10, 5)
  A <- 23 + 5*W + rnorm(n)
  Y <- 7.2*A + 3*W + rnorm(n)
  ex1_dat <- data.frame(W, A, Y)

  # Example 1.1
  # Point treatment, continuous exposure, continuous outcome, no loss-to-follow-up
  # Interested in the effect of a modified treatment policy where A is decreased by 15
  # units only among observations whose observed A was above 80.
  # The true value under this intervention is about 513.
  policy <- function(data, x) {
    (data[[x]] > 80)*(data[[x]] - 15) + (data[[x]] <= 80)*data[[x]]
  }

  lmtp_tmle(ex1_dat, "A", "Y", "W", shift = policy,
            outcome_type = "continuous", folds = 2, mtp = TRUE)

  # Example 2.1
  # Longitudinal setting, time-varying continuous exposure bounded by 0,
  # time-varying covariates, and a binary outcome with no loss-to-follow-up.
  # Interested in the effect of a treatment policy where exposure decreases by
  # one unit at every time point if an observations observed exposure is greater
  # than or equal to 2. The true value under this intervention is about 0.305.
  head(sim_t4)

  A <- c("A_1", "A_2", "A_3", "A_4")
  L <- list(c("L_1"), c("L_2"), c("L_3"), c("L_4"))
  policy <- function(data, trt) {
    a <- data[[trt]]
    (a - 1) * (a - 1 >= 1) + a * (a - 1 < 1)
  }

  # BONUS: progressr progress bars!
  progressr::handlers(global = TRUE)

  lmtp_tmle(sim_t4, A, "Y", time_vary = L, shift = policy,
            folds = 2, mtp = TRUE)

  # Example 2.2
  # The previous example assumed that the outcome (as well as the treatment variables)
  # were directly affected by all other nodes in the past. In certain situations,
  # domain specific knowledge may suggest otherwise.
  # This can be controlled using the k argument.
  lmtp_tmle(sim_t4, A, "Y", time_vary = L, shift = policy,
            k = 0, folds = 2, mtp = TRUE)

  # Example 2.3
  # Using the same data as examples 2.1 and 2.2.
  # Now estimating the effect of a dynamic modified treatment policy.
  # creating a dynamic mtp that applies the shift function
  # but also depends on history and the current time
  policy <- function(data, trt) {
    mtp <- function(data, trt) {
      (data[[trt]] - 1) * (data[[trt]] - 1 >= 1) + data[[trt]] * (data[[trt]] - 1 < 1)
    }

    # if its the first time point, follow the same mtp as before
    if (trt == "A_1") return(mtp(data, trt))

    # otherwise check if the time varying covariate equals 1
    ifelse(
      data[[sub("A", "L", trt)]] == 1,
      mtp(data, trt), # if yes continue with the policy
      data[[trt]]     # otherwise do nothing
    )
  }

  lmtp_tmle(sim_t4, A, "Y", time_vary = L, mtp = TRUE,
            k = 0, shift = policy, folds = 2)

  # Example 2.4
  # Using the same data as examples 2.1, 2.2, and 2.3, but now treating the exposure
  # as an ordered categorical variable. To account for the exposure being a
  # factor we just need to modify the shift function (and the original data)
  # so as to respect this.
  tmp <- sim_t4
  for (i in A) {
    tmp[[i]] <- factor(tmp[[i]], levels = 0:5, ordered = FALSE)
  }

  policy <- function(data, trt) {
    out <- list()
    a <- data[[trt]]
    for (i in 1:length(a)) {
      if (as.character(a[i]) %in% c("0", "1")) {
        out[[i]] <- as.character(a[i])
      } else {
        out[[i]] <- as.numeric(as.character(a[i])) - 1
      }
    }
    factor(unlist(out), levels = 0:5, ordered = FALSE)
  }

  lmtp_tmle(tmp, A, "Y", time_vary = L, shift = policy,
            k = 0, folds = 2, mtp = TRUE)

  # Example 3.1
  # Longitudinal setting, time-varying binary treatment, time-varying covariates
  # and baseline covariates with no loss-to-follow-up. Interested in a "traditional"
  # causal effect where treatment is set to 1 at all time points for all observations.
  if (require("twang")) {
    data("iptwExWide", package = "twang")

    A <- paste0("tx", 1:3)
    W <- c("gender", "age")
    L <- list(c("use0"), c("use1"), c("use2"))

    lmtp_tmle(iptwExWide, A, "outcome", baseline = W, time_vary = L,
              shift = static_binary_on, outcome_type = "continuous",
              mtp = FALSE, folds = 2)
  }

  # Example 4.1
  # Longitudinal setting, time-varying continuous treatment, time-varying covariates,
  # binary outcome with right censoring. Interested in the mean population outcome under
  # the observed exposures in a hypothetical population with no loss-to-follow-up.
  head(sim_cens)

  A <- c("A1", "A2")
  L <- list(c("L1"), c("L2"))
  C <- c("C1", "C2")
  Y <- "Y"

  lmtp_tmle(sim_cens, A, Y, time_vary = L, cens = C, shift = NULL, folds = 2)

  # Example 5.1
  # Time-to-event analysis with a binary time-invariant exposure. Interested in
  # the effect of treatment being given to all observations on the cumulative
  # incidence of the outcome.
  # For a survival problem, the outcome argument now takes a vector of outcomes
  # if an observation experiences the event prior to the end of follow-up, all future
  # outcome nodes should be set to 1 (i.e., last observation carried forward).
  A <- "trt"
  Y <- paste0("Y.", 1:6)
  C <- paste0("C.", 0:5)
  W <- c("W1", "W2")

  lmtp_tmle(sim_point_surv, A, Y, W, cens = C, folds = 2,
            shift = static_binary_on, outcome_type = "survival")

  # Example 6.1
  # Intervening on multiple exposures simultaneously. Interested in the effect of
  # a modified treatment policy where variable D1 is decreased by 0.1 units and
  # variable D2 is decreased by 0.5 units simultaneously.
  A <- list(c("D1", "D2"))
  W <- paste0("C", 1:3)
  Y <- "Y"

  d <- function(data, a) {
    out = list(
      data[[a[1]]] - 0.1,
      data[[a[2]]] - 0.5
    )
    setNames(out, a)
  }

  lmtp_tmle(multivariate_data, A, Y, W, shift = d,
            outcome_type = "continuous", folds = 1, mtp = TRUE)

  # Example 7.1
  # Time-to-event analysis with a binary time-invariant exposure and a competing-risk.
  lmtp_tmle(
    data = sim_competing_risks,
    trt = "A",
    cens = paste0("C", 1:5),
    compete = paste0("D", 1:5),
    baseline = paste0("W", 1:5),
    outcome = paste0("Y", 1:5),
    outcome_type = "survival",
    shift = static_binary_on,
    folds = 1
  )

Simulated Multivariate Exposure Data

Description

A dataset with a continuous outcome, three baseline covariates, and two treatment variables.

Usage

multivariate_data

Format

A data frame with 2000 rows and 6 variables:

C1

Continuous baseline variable.

C2

Continuous baseline variable.

C3

Continuous baseline variable.

D1

Treatment variable one at baseline.

D2

Treatment variable two at baseline.

Y

Continuous outcome

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

generics

tidy

Simulated Longitudinal Data With Censoring

Description

A dataset with a binary outcome, two time varying treatment nodes, two time varying covariates, and two censoring indicators.

Usage

sim_cens

Format

A data frame with 1000 rows and 10 variables:

L1

Time varying covariate time 1

A1

Treatment node at time 1, effected by L_1

C1

Censoring indicator that the observation is observed after time 1

L2

Time varying covariate at time 2, effected by L_1 and A_1

A2

Treatment node at time 2, effected by L_2 and A_1

C2

Censoring indicator that the observation is observed after time 2

Y

Binary outcome at time 3, effected by L_2 and A_2

Simulated Competing Risks Data

Description

A dataset with a time-to-event outcome, a competing risk, and point-treatment.

Usage

sim_competing_risks

Format

A data frame with 1000 rows and 21 variables.

Simulated Point-treatment Survival Data

Description

A dataset with a time-to-event outcome, two baseline nodes, a binary point treatment, six past-time outcome nodes, and six censoring indicators.

Usage

sim_point_surv

Format

A data frame with 2000 rows and 16 variables:

W1

Binary baseline variable.

W2

Categorical baseline variable.

trt

Binary treatment variable.

C.0

Censoring indicator that the observation is observed future time points.

Y.1

Outcome node at time 1.

C.1

Censoring indicator that the observation is observed future time points.

Y.2

Outcome node at time 2.

C.2

Censoring indicator that the observation is observed future time points.

Y.3

Outcome node at time 3.

C.3

Censoring indicator that the observation is observed future time points.

Y.4

Outcome node at time 4.

C.4

Censoring indicator that the observation is observed future time points.

Y.5

Outcome node at time 5.

C.5

Censoring indicator that the observation is observed future time points.

Y.6

Final outcome node.

Simulated Longitudinal Data

Description

A dataset with a binary outcome, four time varying treatment nodes, and four time varying covariates.

Usage

sim_t4

Format

A data frame with 5000 rows and 10 variables:

ID

observation ID

L_1

Time varying covariate time 1

A_1

Treatment node at time 1, effected by L_1

L_2

Time varying covariate time 1, effected by L_1 and A_1

A_2

Treatment node at time 2, effected by L_2 and A_1

L_3

Time varying covariate time 1, effected by L_2 and A_2

A_3

Treatment node at time 3, effected by L_3 and A_2

L_4

Time varying covariate time 1, effected by L_3 and A_3

A_4

Treatment node at time 3, effected by L_4 and A_3

Y

Binary outcome at time 5, effected by L_4 and A_4

Simulated Time-varying Survival Data

Description

A dataset with a time-to-event outcome, one baseline nodes, two time-varying covariates, a binary time-varying treatment, two outcome nodes, and two censoring indicators. Data-generating mechanism taken from Lendle, Schwab, Petersen, and van der Laan (https://www.jstatsoft.org/article/view/v081i01).

Usage

sim_timevary_surv

Format

A data frame with 500 rows and 11 variables:

L0.a

Continuous baseline variable.

L0.b

Time varying covariate at baseline.

L0.c

Time varying covariate at baseline.

A0

Treatment variable at baseline

C0

Censoring indicator that the observation is observed future time points.

L1.a

Time varying covariate at time 1.

L1.b

Time varying covariate at time 1.

Y1

Outcome node at time 1.

A1

Treatment variable at time 1.

C1

Censoring indicator that the observation is observed future time points.

Y2

Final outcome node.

Turn All Treatment Nodes Off

Description

A pre-packaged shift function for use with provided estimators when the exposure is binary. Used to estimate the population intervention effect when all treatment variables are set to 0.

Usage

static_binary_off(data, trt)

Arguments

Value

A dataframe with all treatment nodes set to 0.

See Also

lmtp_tmle(), lmtp_sdr()

Examples

data("iptwExWide", package = "twang")
a <- paste0("tx", 1:3)
baseline <- c("gender", "age")
tv <- list(c("use0"), c("use1"), c("use2"))
lmtp_sdr(iptwExWide, a, "outcome", baseline = baseline, time_vary = tv,
         shift = static_binary_off, outcome_type = "continuous", folds = 2)

Turn All Treatment Nodes On

Description

A pre-packaged shift function for use with provided estimators when the exposure is binary. Used to estimate the population intervention effect when all treatment variables are set to 1.

Usage

static_binary_on(data, trt)

Arguments

Value

A dataframe with all treatment nodes set to 1.

See Also

lmtp_tmle(), lmtp_sdr()

Examples

data("iptwExWide", package = "twang")
a <- paste0("tx", 1:3)
baseline <- c("gender", "age")
tv <- list(c("use0"), c("use1"), c("use2"))
lmtp_sdr(iptwExWide, a, "outcome", baseline = baseline, time_vary = tv,
         shift = static_binary_on, outcome_type = "continuous", folds = 2)

Tidy a(n) lmtp object

Description

Tidy a(n) lmtp object

Usage

## S3 method for class 'lmtp'
tidy(x, ...)

Arguments

Examples

a <- c("A1", "A2")
nodes <- list(c("L1"), c("L2"))
cens <- c("C1", "C2")
y <- "Y"
fit <- lmtp_tmle(sim_cens, a, y, time_vary = nodes, cens = cens, shift = NULL, folds = 2)
tidy(fit)

Tidy a(n) lmtp_survival object

Description

Tidy a(n) lmtp_survival object

Usage

## S3 method for class 'lmtp_survival'
tidy(x, ...)

Arguments

Examples

# Example 1.1
# Time-to-event analysis with a binary time-invariant exposure. Interested in
# the effect of treatment being given to all observations on the cumulative
# incidence of the outcome.
A <- "trt"
Y <- paste0("Y.", 1:6)
C <- paste0("C.", 0:5)
W <- c("W1", "W2")

curve <- lmtp_survival(sim_point_surv, A, Y, W, cens = C, folds = 1,
                       shift = static_binary_on, estimator = "lmtp_tmle")

tidy(curve)

# Example 1.2
# Time-to-event analysis with a binary time-invariant exposure and a competing-risk.
lmtp_survival(
  data = sim_competing_risks,
  trt = "A",
  cens = paste0("C", 1:5),
  compete = paste0("D", 1:5),
  baseline = paste0("W", 1:5),
  outcome = paste0("Y", 1:5),
  shift = static_binary_on,
  folds = 1
)