Package 'txshift' reference manual

Title:	Efficient Estimation of the Causal Effects of Stochastic Interventions
Description:	Efficient estimation of the population-level causal effects of stochastic interventions on a continuous-valued exposure. Both one-step and targeted minimum loss estimators are implemented for the counterfactual mean value of an outcome of interest under an additive modified treatment policy, a stochastic intervention that may depend on the natural value of the exposure. To accommodate settings with outcome-dependent two-phase sampling, procedures incorporating inverse probability of censoring weighting are provided to facilitate the construction of inefficient and efficient one-step and targeted minimum loss estimators. The causal parameter and its estimation were first described by Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust estimation procedure and its application to data from two-phase sampling designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert, and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package implementation is described in NS Hejazi and DC Benkeser (2020) <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be enhanced through the Super Learner ensemble model in 'sl3', available for download from GitHub using 'remotes::install_github("tlverse/sl3")'.
Authors:	Nima Hejazi [aut, cre, cph] , David Benkeser [aut] , Iván Díaz [ctb] , Jeremy Coyle [ctb] , Mark van der Laan [ctb, ths]
Maintainer:	Nima Hejazi <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.9
Built:	2024-11-20 05:36:41 UTC
Source:	https://github.com/nhejazi/txshift

Bound Precision

Description

Bound Precision

Usage

bound_precision(vals)
bound_precision(vals)

Arguments

vals

numeric vector of values in the interval [0, 1] to be bounded within arbitrary machine precision. The most common use of this functionality is to avoid indeterminate or non-finite values after the application stats::qlogis.

Details

Bound values in the unit interval to machine precision in order to avoid numerical instability issues in downstream computation.

Value

A numeric vector of the same length as vals, where the returned values are bounded to machine precision. This is intended to avoid numerical instability issues.

Bound Generalized Propensity Score

Description

Bound Generalized Propensity Score

Usage

bound_propensity(vals, bound = 0.01)
bound_propensity(vals, bound = 0.01)

Arguments

`vals`	`numeric` vector of generalized propensity score estimates. Note that the generalized propensity score is a (conditional) density and should only be bounded/truncated away from zero.
`bound`	`numeric` atomic giving the lower limit of the generalized propensity score estimates to be tolerated. Estimates less than this will be truncated to this value (default = 0.01). Note that the default may be internally overwritten by reference to the sample size (n), so the lower bound used is the greater of the specified value and 1/n.

Details

Bound estimated values of the generalized propensity score (a conditional density) to avoid numerical instability issues arising from practical violations of the assumption of positivity.

Value

A numeric vector of the same length as vals, where the returned values are bounded such that the minimum is no lower than 1/n, for the sample size n.

Confidence Intervals for Counterfactual Mean Under Stochastic Intervention

Description

Confidence Intervals for Counterfactual Mean Under Stochastic Intervention

Usage

## S3 method for class 'txshift'
confint(object, parm = seq_len(object$psi), level = 0.95, ..., ci_mult = NULL)
## S3 method for class 'txshift'
confint(object, parm = seq_len(object$psi), level = 0.95, ..., ci_mult = NULL)

Arguments

`object`	An object of class `txshift`, as produced by invoking the function `txshift`, for which a confidence interval is to be computed.
`parm`	A `numeric` vector indicating indices of `object$est` for which to return confidence intervals.
`level`	A `numeric` indicating the level of the confidence interval to be computed.
`...`	Other arguments. Not currently used.
`ci_mult`	Pre-computed multipliers for generating confidence intervals. The default of `NULL` should generally NOT be changed and is only used by the internal machinery for creating simultaneous confidence bands.

Details

Compute confidence intervals for estimates produced by txshift.

Value

A named numeric vector containing the parameter estimate from a txshift object, alongside lower and upper Wald-style confidence intervals at a specified coverage level.

Examples

set.seed(429153)
n_obs <- 100
W <- replicate(2, rbinom(n_obs, 1, 0.5))
A <- rnorm(n_obs, mean = 2 * W, sd = 1)
Y <- rbinom(n_obs, 1, plogis(A + W + rnorm(n_obs, mean = 0, sd = 1)))
txout <- txshift(
  W = W, A = A, Y = Y, delta = 0.5,
  estimator = "tmle",
  g_exp_fit_args = list(
    fit_type = "hal", n_bins = 5,
    grid_type = "equal_mass",
    lambda_seq = exp(-1:-9)
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  )
)
confint(txout)
set.seed(429153)
n_obs <- 100
W <- replicate(2, rbinom(n_obs, 1, 0.5))
A <- rnorm(n_obs, mean = 2 * W, sd = 1)
Y <- rbinom(n_obs, 1, plogis(A + W + rnorm(n_obs, mean = 0, sd = 1)))
txout <- txshift(
  W = W, A = A, Y = Y, delta = 0.5,
  estimator = "tmle",
  g_exp_fit_args = list(
    fit_type = "hal", n_bins = 5,
    grid_type = "equal_mass",
    lambda_seq = exp(-1:-9)
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  )
)
confint(txout)

Compute the Shift Parameter Estimate and the Efficient Influence Function

Description

Compute the Shift Parameter Estimate and the Efficient Influence Function

Usage

eif(
  Y,
  Qn,
  Hn,
  estimator = c("tmle", "onestep"),
  fluc_mod_out = NULL,
  C_samp = rep(1, length(Y)),
  ipc_weights = rep(1, length(Y))
)
eif(
  Y,
  Qn,
  Hn,
  estimator = c("tmle", "onestep"),
  fluc_mod_out = NULL,
  C_samp = rep(1, length(Y)),
  ipc_weights = rep(1, length(Y))
)

Arguments

`Y`	A `numeric` vector of the observed outcomes.
`Qn`	An object providing the value of the outcome evaluated after imposing a shift in the treatment. This object is passed in after being constructed by a call to the internal function `est_Q`.
`Hn`	An object providing values of the auxiliary ("clever") covariate, constructed from the treatment mechanism and required for targeted minimum loss-based estimation. This object object should be passed in after being constructed by a call to the internal function `est_Hn`.
`estimator`	The type of estimator to be fit, either `"tmle"` for targeted maximum likelihood estimation or `"onestep"` for a one-step estimator.
`fluc_mod_out`	An object giving values of the logistic tilting model for targeted minimum loss estimation. This type of object should be the output of the internal routines to perform this step of the TML estimation procedure, as given by `fit_fluctuation`.
`C_samp`	Indicator for missingness due to exclusion from second-phase sample. Used for compatibility with the IPCW-TML estimation routine.
`ipc_weights`	A `numeric` vector that gives inverse probability of censoring weights for each observation. These are generated by invoking the routines for estimating the censoring mechanism.

Details

Estimate the value of the causal parameter alongside statistical inference for the parameter estimate based on the efficient influence function of the target parameter, which takes the following form:

Value

A list containing the parameter estimate, estimated variance based on the efficient influence function (EIF), the estimate of the EIF incorporating inverse probability of censoring weights, and the estimate of the EIF without the application of such weights.

Estimate the Censoring Mechanism

Description

Estimate the Censoring Mechanism

Usage

est_g_cens(
  C_cens,
  A,
  W,
  samp_weights = rep(1, length(C_cens)),
  fit_type = c("sl", "glm"),
  glm_formula = "C_cens ~ .",
  sl_learners = NULL,
  bound = 0.02
)
est_g_cens(
  C_cens,
  A,
  W,
  samp_weights = rep(1, length(C_cens)),
  fit_type = c("sl", "glm"),
  glm_formula = "C_cens ~ .",
  sl_learners = NULL,
  bound = 0.02
)

Arguments

`C_cens`	A `numeric` vector of loss to follow-up indicators.
`A`	A `numeric` vector of observed exposure values.
`W`	A `numeric` matrix of observed baseline covariate values.
`samp_weights`	A `numeric` vector of observation-level sampling weights, as produced by the internal procedure to estimate the two-phase sampling mechanism `est_samp`.
`fit_type`	A `character` indicating whether to use GLMs or Super Learner to fit the censoring mechanism. If option "glm" is selected, the argument `glm_formula` must NOT be `NULL`, instead containing a model formula (as per `glm`) as a `character`. If the option "sl" is selected, the argument `sl_learners` must NOT be `NULL`; instead, an instantiated sl3 `Lrnr_sl` object, specifying learners and a metalearner for the Super Learner fit, must be provided. Consult the documentation of sl3 for details.
`glm_formula`	A `character` giving a `formula` for fitting a (generalized) linear model via `glm`.
`sl_learners`	Object containing a set of instantiated learners from the sl3, to be used in fitting an ensemble model.
`bound`	`numeric` giving the lower limit of censoring mechanism estimates to be tolerated (default = 0.02). Estimates below this value are truncated to this or 1/n. See `bound_propensity` for details.

Details

Compute the censoring mechanism for the observed data, in order to apply a joint intervention for removing censoring by re-weighting.

Value

A numeric vector of the propensity score for censoring.

Estimate the Exposure Mechanism via Generalized Propensity Score

Description

Estimate the Exposure Mechanism via Generalized Propensity Score

Usage

est_g_exp(
  A,
  W,
  delta = 0,
  samp_weights = rep(1, length(A)),
  fit_type = c("hal", "sl"),
  sl_learners_density = NULL,
  haldensify_args = list(grid_type = "equal_range", lambda_seq = exp(seq(-1, -13, length
    = 300)))
)
est_g_exp(
  A,
  W,
  delta = 0,
  samp_weights = rep(1, length(A)),
  fit_type = c("hal", "sl"),
  sl_learners_density = NULL,
  haldensify_args = list(grid_type = "equal_range", lambda_seq = exp(seq(-1, -13, length
    = 300)))
)

Arguments

`A`	A `numeric` vector of observed exposure values.
`W`	A `numeric` matrix of observed baseline covariate values.
`delta`	A `numeric` value identifying a shift in the observed value of the exposure under which observations are to be evaluated.
`samp_weights`	A `numeric` vector of observation-level sampling weights, as produced by the internal procedure to estimate the two-phase sampling mechanism `est_samp`.
`fit_type`	A `character` specifying whether to use Super Learner (from sl3) or the Highly Adaptive Lasso (from hal9001) to estimate the conditional exposure density.
`sl_learners_density`	Object containing a set of instantiated learners from sl3, to be used in fitting an ensemble model.
`haldensify_args`	A `list` of argument to be directly passed to `haldensify` when `fit_type` is set to `"hal"`. Note that this invokes the Highly Adaptive Lasso instead of Super Learner and is thus only feasible for relatively small data sets.

Details

Compute the propensity score (exposure mechanism) for the observed data, including the shift. This gives the propensity score for the observed data (at the observed A) the counterfactual shifted exposure levels at (A - delta), (A + delta), and (A + 2 * delta).

Value

A data.table with four columns, containing estimates of the generalized propensity score at a downshift (g(A - delta | W)), no shift (g(A | W)), an upshift (g(A + delta) | W), and an upshift of magnitude two (g(A + 2 delta) | W).

Estimate Auxiliary Covariate of Full Data Efficient Influence Function

Description

Estimate Auxiliary Covariate of Full Data Efficient Influence Function

Usage

est_Hn(gn_exp, gps_bound)
est_Hn(gn_exp, gps_bound)

Arguments

`gn_exp`	An estimate of the exposure density (a generalized propensity score) using the output provided by `est_g_exp`.
`gps_bound`	`numeric` giving the lower limit of the generalized propensity score estimates to be tolerated (default = 0.05). Estimates less than this are truncated to this or 1/n. See `bound_propensity` for details.

Details

Compute an estimate of the auxiliary covariate of the efficient influence function required to update initial estimates through logistic tilting models for targeted minimum loss estimation.

Value

A data.table with two columns, containing estimates of the auxiliary covariate at the natural value of the exposure H(A, W) and at the shifted value of the exposure H(A + delta, W).

Estimate the Outcome Mechanism

Description

Estimate the Outcome Mechanism

Usage

est_Q(
  Y,
  C_cens = rep(1, length(Y)),
  A,
  W,
  delta = 0,
  samp_weights = rep(1, length(Y)),
  fit_type = c("sl", "glm"),
  glm_formula = "Y ~ .",
  sl_learners = NULL
)
est_Q(
  Y,
  C_cens = rep(1, length(Y)),
  A,
  W,
  delta = 0,
  samp_weights = rep(1, length(Y)),
  fit_type = c("sl", "glm"),
  glm_formula = "Y ~ .",
  sl_learners = NULL
)

Arguments

`Y`	A `numeric` vector of observed outcomes.
`C_cens`	A `numeric` vector of loss to follow-up indicators.
`A`	A `numeric` vector of observed exposure values.
`W`	A `numeric` matrix of observed baseline covariate values.
`delta`	A `numeric` indicating the magnitude of the shift to be computed for the exposure `A`. This is passed to the internal `shift_additive` and is currently limited to additive shifts.
`samp_weights`	A `numeric` vector of observation-level sampling weights, as produced by the internal procedure to estimate the two-phase sampling mechanism `est_samp`.
`fit_type`	A `character` indicating whether to use GLMs or Super Learner to fit the outcome regression. If the option "glm" is selected, the argument `glm_formula` must NOT be `NULL`, instead containing a model formula (as per `glm`) as a `character`. If the option "sl" is selected, the argument `sl_learners` must NOT be `NULL`; instead, an instantiated sl3 `Lrnr_sl` object, specifying learners and a metalearner for the Super Learner fit, must be provided. Consult the documentation of sl3 for details.
`glm_formula`	A `character` giving a `formula` for fitting a (generalized) linear model via `glm`.
`sl_learners`	Object containing a set of instantiated learners from the sl3, to be used in fitting an ensemble model.

Details

Compute the outcome regression for the observed data, including with the shift imposed by the intervention. This returns the outcome regression for the observed data (at A) and under the counterfactual shift shift (at A + delta).

Value

A data.table with two columns, containing estimates of the outcome mechanism at the natural value of the exposure Q(A, W) and an upshift of the exposure Q(A + delta, W).

Estimate Probability of Censoring by Two-Phase Sampling

Description

Estimate Probability of Censoring by Two-Phase Sampling

Usage

est_samp(V, C_samp, fit_type = c("sl", "glm"), sl_learners = NULL)
est_samp(V, C_samp, fit_type = c("sl", "glm"), sl_learners = NULL)

Arguments

`V`	A `numeric` vector, `matrix`, `data.frame` or similar object giving the observed values of the covariates known to potentially inform the sampling mechanism.
`C_samp`	A `numeric` vector of observed values of the indicator for inclusion in the second-phase sample.
`fit_type`	A `character` indicating whether to perform the fit using GLMs or a Super Learner ensemble model. If use of Super Learner is desired, then the argument `sl_learners` must be provided.
`sl_learners`	An sl3 `Lrnr_sl` object, a Super Learner ensemble or learner instantiated externally using sl3.

Details

Compute estimates of the sampling probability for inclusion in the the second-phase via the two-phase sampling mechanism. These estimates are used for the creation of inverse probability weights.

Value

A numeric vector of the estimated sampling mechanism.

Fit One-Dimensional Fluctuation Model for Updating Initial Estimates

Description

Fit One-Dimensional Fluctuation Model for Updating Initial Estimates

Usage

fit_fluctuation(
  Y,
  Qn_scaled,
  Hn,
  ipc_weights = rep(1, length(Y)),
  method = c("standard", "weighted"),
  flucmod_tol = 50
)
fit_fluctuation(
  Y,
  Qn_scaled,
  Hn,
  ipc_weights = rep(1, length(Y)),
  method = c("standard", "weighted"),
  flucmod_tol = 50
)

Arguments

`Y`	A `numeric` vector corresponding to an outcome variable.
`Qn_scaled`	An object providing the value of the outcome evaluate after inducing a shift in the exposure. This object should be passed in after being constructed by a call to `est_Q`.
`Hn`	An object providing values of the auxiliary ("clever") covariate, constructed from the treatment mechanism and required for targeted minimum loss estimation. This object object should be passed in after being constructed by a call to `est_Hn`.
`ipc_weights`	A `numeric` vector that gives inverse probability of censoring weights for each observation. These are generated by invoking the routines for estimating the censoring mechanism.
`method`	A `character` giving the type of regression to be used in traversing the fluctuation sub-model. The available choices are "weighted" and "standard". Consult the literature for details on the differences.
`flucmod_tol`	A `numeric` indicating the largest value to be tolerated in the fluctuation model for the targeted minimum loss estimator.

Details

Procedure for fitting a one-dimensional fluctuation model to update the initial estimates of the outcome regression based on the auxiliary covariate. These updated estimates are subsequently used to construct the TML estimator of the counterfactual mean under a modified treatment policy.

Value

A list containing the fluctuation model (a glm object) produced by logistic regression, a character vector indicating the type of fluctuation (whether the auxiliary covariates was used as a weight or included directly in the model formula), the updated estimates of the outcome regression under the shifted value of the exposure, and the updated estimates of the outcome regression under the natural value of exposure.

Iterative IPCW Update Procedure of Augmented Efficient Influence Function

Description

Iterative IPCW Update Procedure of Augmented Efficient Influence Function

Usage

ipcw_eif_update(
  data_internal,
  C_samp,
  V,
  ipc_mech,
  ipc_weights,
  Qn_estim,
  Hn_estim,
  estimator = c("tmle", "onestep"),
  fluctuation = NULL,
  flucmod_tol = 50,
  eif_reg_type = c("hal", "glm")
)
ipcw_eif_update(
  data_internal,
  C_samp,
  V,
  ipc_mech,
  ipc_weights,
  Qn_estim,
  Hn_estim,
  estimator = c("tmle", "onestep"),
  fluctuation = NULL,
  flucmod_tol = 50,
  eif_reg_type = c("hal", "glm")
)

Arguments

`data_internal`	A `data.table` containing of the observations selected into the second-phase sample.
`C_samp`	A `numeric` indicator for missingness due to exclusion the from second-stage sample.
`V`	A `data.table` giving the values across all observations of all variables that play a role in the censoring mechanism.
`ipc_mech`	A `numeric` vector of the censoring mechanism estimates all of the observations, only for the two-phase sampling mechanism. Note well that these values do NOT account for censoring from loss to follow-up.
`ipc_weights`	A `numeric` vector of inverse probability of censoring weights, including such weights for censoring due to loss to follow-up. Without loss to follow-up, these are equivalent to `C_samp / ipc_mech` in an initial run of this procedure.
`Qn_estim`	A `data.table` corresponding to the outcome regression. This is produced by invoking the internal function `est_Q`.
`Hn_estim`	A `data.table` corresponding to values produced in the computation of the auxiliary ("clever") covariate. This is produced easily by invoking the internal function `est_Hn`.
`estimator`	The type of estimator to be fit, either `"tmle"` for targeted maximum likelihood estimation or `"onestep"` for a one-step estimator.
`fluctuation`	A `character` giving the type of regression to be used in traversing the fluctuation submodel. The choices are "weighted" and "standard".
`flucmod_tol`	A `numeric` indicating the largest value to be tolerated in the fluctuation model for the targeted minimum loss estimator.
`eif_reg_type`	Whether a flexible nonparametric function ought to be used in the dimension-reduced nuisance regression of the targeting step for the censored data case. By default, the method used is a nonparametric regression based on the Highly Adaptive Lasso (from hal9001). Set this to `"glm"` to instead use a simple linear regression model. In this step, the efficient influence function (EIF) is regressed against covariates contributing to the censoring mechanism (i.e., EIF ~ V \| C = 1).

Details

An adaptation of the IPCW-TMLE for iteratively constructing an efficient inverse probability of censoring weighted TML or one-step estimator. The efficient influence function of the parameter and updating the IPC weights in an iterative process, until a convergence criteria is satisfied.

Value

A list containing the estimated outcome mechanism, the fitted fluctuation model for TML updates, the updated inverse probability of censoring weights (IPCW), the updated estimate of the efficient influence function, and the estimated IPCW component of the EIF.

Working marginal structural model for causal effects of an intervention grid

Description

Working marginal structural model for causal effects of an intervention grid

Usage

msm_vimshift(
  W,
  A,
  C_cens = rep(1, length(Y)),
  Y,
  C_samp = rep(1, length(Y)),
  V = NULL,
  delta_grid = seq(-0.5, 0.5, 0.5),
  msm_form = list(type = "linear", knot = NA),
  estimator = c("tmle", "onestep"),
  weighting = c("identity", "variance"),
  ci_level = 0.95,
  ci_type = c("marginal", "simultaneous"),
  ...
)
msm_vimshift(
  W,
  A,
  C_cens = rep(1, length(Y)),
  Y,
  C_samp = rep(1, length(Y)),
  V = NULL,
  delta_grid = seq(-0.5, 0.5, 0.5),
  msm_form = list(type = "linear", knot = NA),
  estimator = c("tmle", "onestep"),
  weighting = c("identity", "variance"),
  ci_level = 0.95,
  ci_type = c("marginal", "simultaneous"),
  ...
)

Arguments

`W`	A `matrix`, `data.frame`, or similar containing a set of baseline covariates.
`A`	A `numeric` vector corresponding to a treatment variable. The parameter of interest is defined as a location shift of this quantity.
`C_cens`	A `numeric` indicator for whether a given observation was subject to censoring by way of loss to follow-up. The default assumes no censoring due to loss to follow-up.
`Y`	A `numeric` vector of the observed outcomes.
`C_samp`	A `numeric` indicator for whether a given observation was subject to censoring by being omitted from the second-stage sample, used to compute an inverse probability of censoring weighted estimator in such cases. The default assumes no censoring due to two-phase sampling.
`V`	The covariates that are used in determining the sampling procedure that gives rise to censoring. The default is `NULL` and corresponds to scenarios in which there is no censoring (in which case all values in the preceding argument `C` must be uniquely 1. To specify this, pass in a NAMED `list` identifying variables amongst W, A, Y that are thought to have played a role in defining the sampling/censoring mechanism (C).
`delta_grid`	A `numeric` vector giving the individual values of the shift parameter used in computing each of the estimates.
`msm_form`	A `list` specifying the type of working MSM to fit to summarize the counterfactual means. The `list` has two components: (1) `"type"`, which may be either "linear" or "piecewise", and (2) `"knot"`, which, if specified, must be a value in `delta_grid`. See examples for its use.
`estimator`	The type of estimator to be fit, either `"tmle"` for targeted maximum likelihood estimation or `"onestep"` for a one-step augmented inverse probability weighted (AIPW) estimator.
`weighting`	Whether to weight each parameter estimate by the inverse of its variance (in order to improve stability of the resultant MSM fit) or to simply weight all parameter estimates equally. The default is the option `"identity"`, weighting all estimates identically.
`ci_level`	A `numeric` indicating the desired coverage level of the confidence interval to be computed.
`ci_type`	Whether to construct a simultaneous confidence band covering all parameter estimates at once or marginal confidence intervals covering each parameter estimate separately. The default is to construct marginal confidence intervals for each parameter estimate rather than a simultaneous confidence band.
`...`	Additional arguments to be passed to `txshift`.

Details

Computes estimates of the counterfactual mean over a grid of shift stochastic interventions and fits a working marginal structural model to summarize the trend through the counterfactual means as a function of the specified shift intervention. The working marginal structural model may be linear in the shift parameter or piecewise linear with a single knot point. Provides support for two weighting schemes, may be used with either of the one-step or TML estimators, and also allows the construction of marginal or simultaneous confidence intervals.

Value

A list containing estimates of the individual counterfactual means over a grid in the shift parameters (delta_grid), alongside the estimate of a marginal structural model that summarizes a trend through these counterfactual means.

Examples

if (require("sl3")) {
  n_obs <- 100
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )

  # fit a linear spline with knot at 0
  n_obs <- 100
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(0.1 * A * (A >= 0) - 3 * A * (A < 0) - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25),
    msm_form = list(type = "piecewise", knot = 0)
  )
}
if (require("sl3")) {
  n_obs <- 100
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )

  # fit a linear spline with knot at 0
  n_obs <- 100
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(0.1 * A * (A >= 0) - 3 * A * (A < 0) - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25),
    msm_form = list(type = "piecewise", knot = 0)
  )
}

One-Step Estimate of Counterfactual Mean of Stochastic Shift Intervention

Description

One-Step Estimate of Counterfactual Mean of Stochastic Shift Intervention

Usage

onestep_txshift(
  data_internal,
  C_samp = rep(1, nrow(data_internal)),
  V = NULL,
  delta,
  samp_estim,
  gn_cens_weights,
  Qn_estim,
  Hn_estim,
  eif_reg_type = c("hal", "glm"),
  samp_fit_args,
  ipcw_efficiency = TRUE
)
onestep_txshift(
  data_internal,
  C_samp = rep(1, nrow(data_internal)),
  V = NULL,
  delta,
  samp_estim,
  gn_cens_weights,
  Qn_estim,
  Hn_estim,
  eif_reg_type = c("hal", "glm"),
  samp_fit_args,
  ipcw_efficiency = TRUE
)

Arguments

`data_internal`	A `data.table` constructed internally by a call to `txshift`. This contains the data elements needed for computing the one-step estimator.
`C_samp`	A `numeric` indicator for whether a given observation was included in the second-stage sample, used to compute an IPC-weighted one-step estimator in cases where two-stage sampling is performed. Default assumes no censoring due to sampling.
`V`	The covariates that are used in determining the sampling procedure that gives rise to censoring. The default is `NULL` and corresponds to scenarios in which there is no censoring (in which case all values in the preceding argument `C` must be uniquely 1. To specify this, pass in a NAMED `list` identifying variables amongst W, A, Y that are thought to have played a role in defining the sampling/censoring mechanism (C).
`delta`	A `numeric` value indicating the shift in the treatment to be used in defining the target parameter. This is defined with respect to the scale of the treatment (A).
`samp_estim`	An object providing the value of the censoring mechanism evaluated across the full data. This object is passed in after being constructed by a call to the internal function `est_samp`.
`gn_cens_weights`	An object providing the value of inverse probability of censoring weights, the inverse of the censoring mechanism estimate. The weights are used as part of the IPCW-EIF procedure to implement a joint intervention that removes the contribution of the censoring process.
`Qn_estim`	An object providing the value of the outcome evaluated after imposing a shift in the treatment. This object is passed in after being constructed by a call to the internal function `est_Q`.
`Hn_estim`	An object providing values of the auxiliary ("clever") covariate, constructed from the treatment mechanism and required for targeted minimum loss estimation. This object object should be passed in after being constructed by a call to the internal function `est_Hn`.
`eif_reg_type`	Whether a flexible nonparametric function ought to be used in the dimension-reduced nuisance regression of the targeting step for the censored data case. By default, the method used is a nonparametric regression based on the Highly Adaptive Lasso (from hal9001). Set this to `"glm"` to instead use a simple linear regression model. In this step, the efficient influence function (EIF) is regressed against covariates contributing to the censoring mechanism (i.e., EIF ~ V \| C = 1).
`samp_fit_args`	A `list` of arguments, all but one of which are passed to `est_samp`. For details, consult the documentation for `est_samp`. The first element (i.e., `fit_type`) is used to determine how this regression is fit: "glm" for generalized linear model, "sl" for a Super Learner, and "external" for a user-specified input of the form produced by `est_samp`.
`ipcw_efficiency`	Whether to invoke an augmentation of the IPCW-TMLE procedure that performs an iterative process to ensure efficiency of the resulting estimate. The default is `TRUE`; set to `FALSE` to use an IPC-weighted loss rather than the IPC-augmented influence function.

Details

Invokes the procedure to construct a one-step estimate of the counterfactual mean under a modified treatment policy.

Value

S3 object of class txshift containing the results of the procedure to compute a one-step estimate of the treatment shift parameter.

Plot working MSM for causal effects of an intervention grid

Description

Plot working MSM for causal effects of an intervention grid

Usage

## S3 method for class 'txshift_msm'
plot(x, ...)
## S3 method for class 'txshift_msm'
plot(x, ...)

Arguments

`x`	Object of class `txshift_msm` as produced by a call to `msm_vimshift`.
`...`	Additional arguments passed to `plot` as necessary.

Details

Creates a visualization of the intervention-specific counterfactual means as well as the working marginal structural model summarizing the trend across posited values of the intervention.

Examples

if (require("sl3")) {
  set.seed(3287)
  n_obs <- 1000
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )
  plot(msm)

  # fit a linear spline with knot at 0
  set.seed(8293)
  n_obs <- 1000
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(0.1 * A * (A >= 0) - 3 * A * (A < 0) - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25),
    msm_form = list(type = "piecewise", knot = 0)
  )
  plot(msm)
}
if (require("sl3")) {
  set.seed(3287)
  n_obs <- 1000
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )
  plot(msm)

  # fit a linear spline with knot at 0
  set.seed(8293)
  n_obs <- 1000
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(0.1 * A * (A >= 0) - 3 * A * (A < 0) - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25),
    msm_form = list(type = "piecewise", knot = 0)
  )
  plot(msm)
}

Print Method for Counterfactual Mean of Stochastic Shift Intervention

Description

Print Method for Counterfactual Mean of Stochastic Shift Intervention

Usage

## S3 method for class 'txshift'
print(x, ..., ci_level = 0.95)
## S3 method for class 'txshift'
print(x, ..., ci_level = 0.95)

Arguments

`x`	An object of class `txshift`.
`...`	Other options (not currently used).
`ci_level`	A `numeric` indicating the level of the confidence interval to be computed.

Details

The print method for objects of class txshift.

Value

None. Called for the side effect of printing an informative summary of slots of objects of class txshift.

Examples

if (require("sl3")) {
  set.seed(429153)
  n_obs <- 100
  W <- replicate(2, rbinom(n_obs, 1, 0.5))
  A <- rnorm(n_obs, mean = 2 * W, sd = 1)
  Y <- rbinom(n_obs, 1, plogis(A + W + rnorm(n_obs, mean = 0, sd = 1)))
  txout <- txshift(
    W = W, A = A, Y = Y, delta = 0.5,
    estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    )
  )
  print(txout)
}
if (require("sl3")) {
  set.seed(429153)
  n_obs <- 100
  W <- replicate(2, rbinom(n_obs, 1, 0.5))
  A <- rnorm(n_obs, mean = 2 * W, sd = 1)
  Y <- rbinom(n_obs, 1, plogis(A + W + rnorm(n_obs, mean = 0, sd = 1)))
  txout <- txshift(
    W = W, A = A, Y = Y, delta = 0.5,
    estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    )
  )
  print(txout)
}

Print Method for Marginal Structural Models

Description

Print Method for Marginal Structural Models

Usage

## S3 method for class 'txshift_msm'
print(x, ...)
## S3 method for class 'txshift_msm'
print(x, ...)

Arguments

`x`	An object of class `txshift_msm`.
`...`	Other options (not currently used).

Details

The print method for objects of class txshift_msm.

Value

None. Called for the side effect of printing an informative summary of slots of objects of class txshift_msm.

Examples

if (require("sl3")) {
  set.seed(3287)
  n_obs <- 1000
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )
  print(msm)
}
if (require("sl3")) {
  set.seed(3287)
  n_obs <- 1000
  W <- as.numeric(replicate(1, rbinom(n_obs, 1, 0.5)))
  A <- as.numeric(rnorm(n_obs, mean = 2 * W, sd = 1))
  Y <- rbinom(n_obs, 1, plogis(2 * A - W))
  msm <- msm_vimshift(
    W = W, A = A, Y = Y, estimator = "tmle",
    g_exp_fit_args = list(
      fit_type = "sl",
      sl_learners_density = Lrnr_density_hse$new(Lrnr_glm$new())
    ),
    Q_fit_args = list(
      fit_type = "glm",
      glm_formula = "Y ~ ."
    ),
    delta_grid = seq(-1, 1, 0.25)
  )
  print(msm)
}

Transform values from the unit interval back to their original scale

Description

Transform values from the unit interval back to their original scale

Usage

scale_to_original(scaled_vals, max_orig, min_orig)
scale_to_original(scaled_vals, max_orig, min_orig)

Arguments

`scaled_vals`	A `numeric` vector corresponding to re-scaled values in the unit interval, to be re-scaled to the original interval.
`max_orig`	A `numeric` scalar value giving the maximum of the values on the original scale.
`min_orig`	A `numeric` scalar value giving the minimum of the values on the original scale.

Details

A back-transformation that returns values computed in the unit interval to their original scale. This is used in re-scaling updated TML estimates back to their natural scale. Undoes scale_to_unit.

Value

A numeric vector of the same length as scaled_vals, where the values are re-scaled to lie in their original/natural interval.

Transform values by scaling to the unit interval

Description

Transform values by scaling to the unit interval

Usage

scale_to_unit(vals)
scale_to_unit(vals)

Arguments

vals

A numeric vector corresponding to the observed values of the variable of interest, to be re-scaled to the unit interval [0,1].

Details

A transformation that scales an arbitrary set of input values to the unit interval. See scale_to_original for a corresponding backtransformation.

Value

A numeric vector of the same length as vals, where the values are re-scaled to lie in unit interval [0, 1].

Simple Additive Modified Treatment Policy

Description

Simple Additive Modified Treatment Policy

Usage

shift_additive(A, W = NULL, delta)
shift_additive(A, W = NULL, delta)

Arguments

`A`	A `numeric` vector of observed treatment values.
`W`	A `numeric` matrix of observed baseline covariate values.
`delta`	A `numeric` indicating the magnitude of the shift to be computed for the treatment `A`.

Details

A simple modified treatment policy that modifes the observed value of the exposure by shifting it by a value delta. Note that this shifting function assumes support of A|W across all strata of W.

Value

A numeric vector containing the shifted exposure values.

Targeted Minimum Loss Estimate of Counterfactual Mean of Stochastic Shift Intervention

Description

Targeted Minimum Loss Estimate of Counterfactual Mean of Stochastic Shift Intervention

Usage

tmle_txshift(
  data_internal,
  C_samp = rep(1, nrow(data_internal)),
  V = NULL,
  delta,
  samp_estim,
  gn_cens_weights,
  Qn_estim,
  Hn_estim,
  fluctuation = c("standard", "weighted"),
  max_iter = 10,
  eif_reg_type = c("hal", "glm"),
  samp_fit_args,
  ipcw_efficiency = TRUE
)
tmle_txshift(
  data_internal,
  C_samp = rep(1, nrow(data_internal)),
  V = NULL,
  delta,
  samp_estim,
  gn_cens_weights,
  Qn_estim,
  Hn_estim,
  fluctuation = c("standard", "weighted"),
  max_iter = 10,
  eif_reg_type = c("hal", "glm"),
  samp_fit_args,
  ipcw_efficiency = TRUE
)

Arguments

`data_internal`	A `data.table` constructed internally by a call to `txshift`. This contains most of the data for computing the targeted minimum loss (TML) estimator.
`C_samp`	A `numeric` indicator for whether a given observation was included in the second-stage sample, used to compute an IPC-weighted one-step estimator in cases where two-stage sampling is performed. Default assumes no censoring due to sampling.
`V`	The covariates that are used in determining the sampling procedure that gives rise to censoring. The default is `NULL` and corresponds to scenarios in which there is no censoring (in which case all values in the preceding argument `C_samp` must be 1. To specify this, pass in a NAMED `list` identifying variables amongst W, A, Y that are thought to have played a role in defining the sampling mechanism.
`delta`	A `numeric` value indicating the shift in the treatment to be used in defining the target parameter. This is defined with respect to the scale of the treatment (A).
`samp_estim`	An object providing the value of the sampling mechanism evaluated across the full data. This object is passed in after being constructed by a call to the internal function `est_samp`.
`gn_cens_weights`	An object providing the value of inverse probability of censoring weights, the inverse of the censoring mechanism estimate. The weights are used as part of the IPCW-TMLE procedure to implement a joint intervention that removes the contribution of the censoring process.
`Qn_estim`	An object providing the value of the outcome evaluated after imposing a shift in the treatment. This object is passed in after being constructed by a call to the internal function `est_Q`.
`Hn_estim`	An object providing values of the auxiliary ("clever") covariate, constructed from the treatment mechanism and required for targeted minimum loss-based estimation. This object object should be passed in after being constructed by a call to `est_Hn`.
`fluctuation`	The method to be used in the submodel fluctuation step (targeting step) to compute the TML estimator. The choices are "standard" and "weighted" for where to place the auxiliary covariate in the logistic tilting regression.
`max_iter`	A `numeric` integer giving the maximum number of steps to be taken in iterating to a solution of the efficient influence function.
`eif_reg_type`	Whether a flexible nonparametric function ought to be used in the dimension-reduced nuisance regression of the targeting step for the censored data case. By default, the method used is a nonparametric regression based on the Highly Adaptive Lasso (from hal9001). Set this to `"glm"` to instead use a simple linear regression model. In this step, the efficient influence function (EIF) is regressed against covariates contributing to the censoring mechanism (i.e., EIF ~ V \| C = 1).
`samp_fit_args`	A `list` of arguments, all but one of which are passed to `est_samp`. For details, consult the documentation for `est_samp`. The first element (i.e., `fit_type`) is used to determine how this regression is fit: "glm" for generalized linear model, "sl" for a Super Learner, and "external" for a user-specified input of the form produced by `est_samp`.
`ipcw_efficiency`	Whether to invoke an augmentation of the IPCW-TMLE procedure that performs an iterative process to ensure efficiency of the resulting estimate. The default is `TRUE`; set to `FALSE` to use an IPC-weighted loss rather than the IPC-augmented influence function.

Details

Invokes the procedure to construct a targeted minimum loss estimate (TMLE) of the counterfactual mean under a modified treatment policy.

Value

S3 object of class txshift containing the results of the procedure to compute a TML estimate of the treatment shift parameter.

Efficient Estimate of Counterfactual Mean of Stochastic Shift Intervention

Description

Efficient Estimate of Counterfactual Mean of Stochastic Shift Intervention

Usage

txshift(
  W,
  A,
  C_cens = rep(1, length(A)),
  Y,
  C_samp = rep(1, length(Y)),
  V = NULL,
  delta = 0,
  estimator = c("tmle", "onestep"),
  fluctuation = c("standard", "weighted"),
  max_iter = 10,
  gps_bound = 0.01,
  samp_fit_args = list(fit_type = c("glm", "sl", "external"), sl_learners = NULL),
  g_exp_fit_args = list(fit_type = c("hal", "sl", "external"), lambda_seq = exp(seq(-1,
    -13, length = 300)), sl_learners_density = NULL),
  g_cens_fit_args = list(fit_type = c("glm", "sl", "external"), glm_formula =
    "C_cens ~ .^2", sl_learners = NULL),
  Q_fit_args = list(fit_type = c("glm", "sl", "external"), glm_formula = "Y ~ .^2",
    sl_learners = NULL),
  eif_reg_type = c("hal", "glm"),
  ipcw_efficiency = TRUE,
  samp_fit_ext = NULL,
  gn_exp_fit_ext = NULL,
  gn_cens_fit_ext = NULL,
  Qn_fit_ext = NULL
)
txshift(
  W,
  A,
  C_cens = rep(1, length(A)),
  Y,
  C_samp = rep(1, length(Y)),
  V = NULL,
  delta = 0,
  estimator = c("tmle", "onestep"),
  fluctuation = c("standard", "weighted"),
  max_iter = 10,
  gps_bound = 0.01,
  samp_fit_args = list(fit_type = c("glm", "sl", "external"), sl_learners = NULL),
  g_exp_fit_args = list(fit_type = c("hal", "sl", "external"), lambda_seq = exp(seq(-1,
    -13, length = 300)), sl_learners_density = NULL),
  g_cens_fit_args = list(fit_type = c("glm", "sl", "external"), glm_formula =
    "C_cens ~ .^2", sl_learners = NULL),
  Q_fit_args = list(fit_type = c("glm", "sl", "external"), glm_formula = "Y ~ .^2",
    sl_learners = NULL),
  eif_reg_type = c("hal", "glm"),
  ipcw_efficiency = TRUE,
  samp_fit_ext = NULL,
  gn_exp_fit_ext = NULL,
  gn_cens_fit_ext = NULL,
  Qn_fit_ext = NULL
)

Arguments

`W`	A `matrix`, `data.frame`, or similar containing a set of baseline covariates.
`A`	A `numeric` vector corresponding to a treatment variable. The parameter of interest is defined as a location shift of this quantity.
`C_cens`	A `numeric` indicator for whether a given observation was subject to censoring by way of loss to follow-up. The default assumes no censoring due to loss to follow-up.
`Y`	A `numeric` vector of the observed outcomes.
`C_samp`	A `numeric` indicator for whether a given observation was subject to censoring by being omitted from the second-stage sample, used to compute an inverse probability of censoring weighted estimator in such cases. The default assumes no censoring due to two-phase sampling.
`V`	The covariates that are used in determining the sampling procedure that gives rise to censoring. The default is `NULL` and corresponds to scenarios in which there is no censoring (in which case all values in the preceding argument `C_samp` must be uniquely 1). To specify this, pass in a `character` vector identifying variables amongst W, A, Y thought to have impacted the definition of the sampling mechanism (C_samp). This argument also accepts a `data.table` (or similar) object composed of combinations of variables W, A, Y; use of this option is NOT recommended.
`delta`	A `numeric` value indicating the shift in the treatment to be used in defining the target parameter. This is defined with respect to the scale of the treatment (A).
`estimator`	The type of estimator to be fit, either `"tmle"` for targeted maximum likelihood or `"onestep"` for a one-step estimator.
`fluctuation`	The method to be used in the submodel fluctuation step (targeting step) to compute the TML estimator. The choices are "standard" and "weighted" for where to place the auxiliary covariate in the logistic tilting regression.
`max_iter`	A `numeric` integer giving the maximum number of steps to be taken in iterating to a solution of the efficient influence function.
`gps_bound`	`numeric` giving the lower limit of the generalized propensity score estimates to be tolerated (default = 0.01). Estimates falling below this value are truncated to this or 1/n. For details, see `bound_propensity`.
`samp_fit_args`	A `list` of arguments, all but one of which are passed to `est_samp`. For details, consult the documentation of `est_samp`. The first element (i.e., `fit_type`) is used to determine how this regression is fit: generalized linear model ("glm") or Super Learner ("sl"), and "external" a user-specified input of the form produced by `est_samp`.
`g_exp_fit_args`	A `list` of arguments, all but one of which are passed to `est_g_exp`. For details, see the documentation of `est_g_exp`. The 1st element (i.e., `fit_type`) specifies how this regression is fit: `"hal"` to estimate conditional densities via the highly adaptive lasso (via haldensify), `"sl"` for sl3 learners used to fit Super Learner ensembles to densities via sl3's `Lrnr_haldensify` or similar, and `"external"` for user-specified input of the form produced by `est_g_exp`.
`g_cens_fit_args`	A `list` of arguments, all but one of which are passed to `est_g_cens`. For details, see the documentation of `est_g_cens`. The 1st element (i.e., `fit_type`) specifies how this regression is fit: `"glm"` for a generalized linear model or `"sl"` for sl3 learners used to fit a Super Learner ensemble for the censoring mechanism, and `"external"` for user-specified input of the form produced by `est_g_cens`.
`Q_fit_args`	A `list` of arguments, all but one of which are passed to `est_Q`. For details, consult the documentation for `est_Q`. The first element (i.e., `fit_type`) is used to determine how this regression is fit: `"glm"` for a generalized linear model for the outcome mechanism, `"sl"` for sl3 learners used to fit a Super Learner for the outcome mechanism, and `"external"` for user-specified input of the form produced by `est_Q`.
`eif_reg_type`	Whether a flexible nonparametric function ought to be used in the dimension-reduced nuisance regression of the targeting step for the censored data case. By default, the method used is a nonparametric regression based on the Highly Adaptive Lasso (from hal9001). Set this to `"glm"` to instead use a simple linear regression model. In this step, the efficient influence function (EIF) is regressed against covariates contributing to the censoring mechanism (i.e., EIF ~ V \| C = 1).
`ipcw_efficiency`	Whether to use an augmented inverse probability of censoring weighted EIF estimating equation to ensure efficiency of the resultant estimate. The default is `TRUE`; the inefficient estimation procedure specified by `FALSE` is only supported for completeness.
`samp_fit_ext`	The results of an external fitting procedure used to estimate the two-phase sampling mechanism, to be used in constructing the inverse probability of censoring weighted TML or one-step estimator. The input provided must match the output of `est_samp` exactly.
`gn_exp_fit_ext`	The results of an external fitting procedure used to estimate the exposure mechanism (generalized propensity score), to be used in constructing the TML or one-step estimator. The input provided must match the output of `est_g_exp` exactly.
`gn_cens_fit_ext`	The results of an external fitting procedure used to estimate the censoring mechanism (propensity score for missingness), to be used in constructing the TML or one-step estimator. The input provided must match the output of `est_g_cens` exactly.
`Qn_fit_ext`	The results of an external fitting procedure used to estimate the outcome mechanism, to be used in constructing the TML or one-step estimator. The input provided must match the output of `est_Q` exactly; use of this argument is only recommended for power users.

Details

Construct a one-step estimate or targeted minimum loss estimate of the counterfactual mean under a modified treatment policy, automatically making adjustments for two-phase sampling when a censoring indicator is included. Ensemble machine learning may be used to construct the initial estimates of nuisance functions using sl3.

Value

S3 object of class txshift containing the results of the procedure to compute a TML or one-step estimate of the counterfactual mean under a modified treatment policy that shifts a continuous-valued exposure by a scalar amount delta. These estimates can be augmented to be consistent and efficient when two-phase sampling is performed.

Examples

set.seed(429153)
n_obs <- 100
W <- replicate(2, rbinom(n_obs, 1, 0.5))
A <- rnorm(n_obs, mean = 2 * W, sd = 1)
Y <- rbinom(n_obs, 1, plogis(A + W + rnorm(n_obs, mean = 0, sd = 1)))
C_samp <- rbinom(n_obs, 1, plogis(W + Y)) # two-phase sampling
C_cens <- rbinom(n_obs, 1, plogis(rowSums(W) + 0.5))

# construct a TML estimate, ignoring censoring
tmle <- txshift(
  W = W, A = A, Y = Y, delta = 0.5,
  estimator = "onestep",
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  )
)
## Not run: 
# construct a TML estimate, accounting for censoring
tmle <- txshift(
  W = W, A = A, C_cens = C_cens, Y = Y, delta = 0.5,
  estimator = "onestep",
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  g_cens_fit_args = list(
    fit_type = "glm",
    glm_formula = "C_cens ~ ."
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  )
)

# construct a TML estimate under two-phase sampling, ignoring censoring
ipcwtmle <- txshift(
  W = W, A = A, Y = Y, delta = 0.5,
  C_samp = C_samp, V = c("W", "Y"),
  estimator = "onestep", max_iter = 3,
  samp_fit_args = list(fit_type = "glm"),
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  ),
  eif_reg_type = "glm"
)

# construct a TML estimate acconting for two-phase sampling and censoring
ipcwtmle <- txshift(
  W = W, A = A, C_cens = C_cens, Y = Y, delta = 0.5,
  C_samp = C_samp, V = c("W", "Y"),
  estimator = "onestep", max_iter = 3,
  samp_fit_args = list(fit_type = "glm"),
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  g_cens_fit_args = list(
    fit_type = "glm",
    glm_formula = "C_cens ~ ."
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  ),
  eif_reg_type = "glm"
)

## End(Not run)
set.seed(429153)
n_obs <- 100
W <- replicate(2, rbinom(n_obs, 1, 0.5))
A <- rnorm(n_obs, mean = 2 * W, sd = 1)
Y <- rbinom(n_obs, 1, plogis(A + W + rnorm(n_obs, mean = 0, sd = 1)))
C_samp <- rbinom(n_obs, 1, plogis(W + Y)) # two-phase sampling
C_cens <- rbinom(n_obs, 1, plogis(rowSums(W) + 0.5))

# construct a TML estimate, ignoring censoring
tmle <- txshift(
  W = W, A = A, Y = Y, delta = 0.5,
  estimator = "onestep",
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  )
)
## Not run: 
# construct a TML estimate, accounting for censoring
tmle <- txshift(
  W = W, A = A, C_cens = C_cens, Y = Y, delta = 0.5,
  estimator = "onestep",
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  g_cens_fit_args = list(
    fit_type = "glm",
    glm_formula = "C_cens ~ ."
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  )
)

# construct a TML estimate under two-phase sampling, ignoring censoring
ipcwtmle <- txshift(
  W = W, A = A, Y = Y, delta = 0.5,
  C_samp = C_samp, V = c("W", "Y"),
  estimator = "onestep", max_iter = 3,
  samp_fit_args = list(fit_type = "glm"),
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  ),
  eif_reg_type = "glm"
)

# construct a TML estimate acconting for two-phase sampling and censoring
ipcwtmle <- txshift(
  W = W, A = A, C_cens = C_cens, Y = Y, delta = 0.5,
  C_samp = C_samp, V = c("W", "Y"),
  estimator = "onestep", max_iter = 3,
  samp_fit_args = list(fit_type = "glm"),
  g_exp_fit_args = list(
    fit_type = "hal",
    n_bins = 3,
    lambda_seq = exp(seq(-1, -10, length = 50))
  ),
  g_cens_fit_args = list(
    fit_type = "glm",
    glm_formula = "C_cens ~ ."
  ),
  Q_fit_args = list(
    fit_type = "glm",
    glm_formula = "Y ~ ."
  ),
  eif_reg_type = "glm"
)

## End(Not run)

Package 'txshift'

Help Index

Bound Precision

Description

Usage

Arguments

Details

Value

Bound Generalized Propensity Score

Description

Usage

Arguments

Details

Value

Confidence Intervals for Counterfactual Mean Under Stochastic Intervention

Description

Usage

Arguments

Details

Value

Examples

Compute the Shift Parameter Estimate and the Efficient Influence Function

Description

Usage

Arguments

Details

Value

Estimate the Censoring Mechanism

Description

Usage

Arguments

Details

Value

Estimate the Exposure Mechanism via Generalized Propensity Score

Description

Usage

Arguments

Details

Value

Estimate Auxiliary Covariate of Full Data Efficient Influence Function

Description

Usage

Arguments

Details

Value

Estimate the Outcome Mechanism

Description

Usage

Arguments

Details

Value

Estimate Probability of Censoring by Two-Phase Sampling

Description

Usage

Arguments

Details

Value

Fit One-Dimensional Fluctuation Model for Updating Initial Estimates

Description

Usage

Arguments

Details

Value

Iterative IPCW Update Procedure of Augmented Efficient Influence Function

Description

Usage

Arguments

Details

Value

Working marginal structural model for causal effects of an intervention grid

Description

Usage

Arguments

Details

Value

Examples

One-Step Estimate of Counterfactual Mean of Stochastic Shift Intervention

Description

Usage

Arguments