Package 'SensIAT'

Produce fitted model for group (treatment or control)

Description

Produces a fitted model that may be used to produce estimates of mean and variance for the given group.

Usage

fit_SensIAT_fulldata_model(data, trt, ...)

fit_SensIAT_within_group_model(
  group.data,
  outcome_modeler,
  knots,
  id.var,
  outcome.var,
  time.var,
  alpha = 0,
  intensity.covariates = ~.,
  outcome.covariates = ~. - 1,
  End = max({
     {
         time.var
     }
 }, na.rm = TRUE) + 1,
  integration.tolerance = .Machine$double.eps^(1/3),
  intensity.bandwidth = NULL,
  ...,
  influence.args = list()
)

Arguments

data	the full data set.
trt	an expression that determine what is treated as the treatment. Everything not treatment is considered control.
...	add parameters as needed or use this to pass forward into the outcome_modeler.
group.data	The data for the group that is being analyzed. Preferably passed in as a single tibble that internally is subsetted/filtered as needed.
outcome_modeler	A separate function that may be swapped out to switch between negative-binomial, single index model, or another we will dream up in the future.
knots	knot locations for defining the spline basis.
id.var	The variable that identifies the patient.
outcome.var	The variable that contains the outcome.
time.var	The variable that contains the time.
alpha	The sensitivity parameter.
intensity.covariates	A formula representing modifications to the intensity model.
outcome.covariates	A formula representing modifications to the outcome model. The default removes the intercept term.
End	The end time for this data analysis, we need to set the default value as the max value of the time
integration.tolerance	The tolerance for the integration.
intensity.bandwidth	The bandwidth for the intensity model kernel.
influence.args	A list of additional arguments to pass to the influence function.

Details

This function should be agnostic to whether it is being provided a treatment or control group.

Value

a list with class SensIAT-fulldata-fitted-model with two components, control and treatment, each of which is an independently fitted SensIAT-within-group-fitted-model fit with the fit_within_group_model function.

Should return everything needed to define the fit of the model. This can then be used for producing the estimates of mean, variance, and in turn treatment effect. For the full data model a list with two models one each for the treatment and control groups.

Functions

• fit_SensIAT_fulldata_model(): Fit the sensitivity analysis for both treatment and control groups.

Examples

model <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = SensIAT_sim_outcome_modeler,
        alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
        id.var = Subject_ID,
        outcome.var = Outcome,
        time.var = Time,
        End = 830,
        knots = c(60,60,60,60,260,460,460,460,460),
    )

---

Compute Conditional Means

Description

Compute Conditional Means

Usage

pcori_conditional_means(model, alpha = 0, new.data = model.frame(model), ...)

Arguments

model	An object of class SensIAT::outcome-model
alpha	Sensitivity parameter
new.data	Data to compute conditional means for, defaults to the model frame for the fitted model.
...	passed onto methods.

Details

Compute the conditional expectations needed for predictions in the models. Three additional values/expectations are computed:

• ⁠$E \big[ Y(t) \exp \{ \alpha Y(t) \} | A(t)=1, \bar{O}(t) \big]$⁠, returned as E_y_past, and

• ⁠$E \big[ \exp \{ \alpha Y(t) \} \ | A(t)=1, \bar{O}(t) \big]$⁠, returned as E_exp_alphaY.

Value

The new.data frame with additional columns E_Y_past, and E_exp_alphaY appended.

---

Directly estimate the probability mass function of Y.

Description

Directly estimate the probability mass function of Y.

Usage

pcoriaccel_estimate_pmf(Xb, Y, xi, y_seq, h, kernel = "K2_Biweight")

Arguments

Xb	Numeric vector of individual linear predictors from the data
Y	Numeric vector of individual responses from the data
xi	value of the individuals linear predictor at the point of estimation
y_seq	Numeric vector of unique values of Y.
h	bandwidth of the kernel
kernel	character string specifying the kernel to use, either "dnorm", "K2_Biweight", or "K4_Biweight"

---

Compiled version of evaluate_basis() function

Description

Compiled version of evaluate_basis() function

Usage

pcoriaccel_evaluate_basis(spline_basis, x)

Arguments

spline_basis	The spline basis, S4 class orthogonalsplinebasis::SplineBasis
x	The point to evaluate

Value

Vector of the basis functions evaluated at x.

---

Predict mean and variance of the outcome for a SensIAT within-group model

Description

Predict mean and variance of the outcome for a SensIAT within-group model

Usage

## S3 method for class 'SensIAT_fulldata_model'
predict(object, time, ...)

## S3 method for class 'SensIAT_within_group_model'
predict(object, time, include.var = TRUE, ..., base = object$base)

Arguments

object	SensIAT_within_group_model object
time	Time points of interest
...	Currently ignored.
include.var	Logical. If TRUE, the variance of the outcome is also returned
base	A SplineBasis object used to evaluate the basis functions.

Value

If include.var is TRUE, a tibble with columns time, mean, and var is returned. otherwise if include.var is FALSE, only the mean vector is returned.

Functions

• predict(SensIAT_fulldata_model): For each combination of time and alpha estimate the mean response and variance for each group as well as estimate the mean treatment effect and variance.

Examples

model <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = SensIAT_sim_outcome_modeler,
        alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
        id.var = Subject_ID,
        outcome.var = Outcome,
        time.var = Time,
        End = 830,
        knots = c(60,60,60,60,260,460,460,460,460),
    )
predict(model, time = c(90, 180))

---

SensIAT Example Data

Description

A simulated dataset for use in the SensIAT tutorial, testing and documentation.

Usage

SensIAT_example_data

Format

A data frame with 779 rows and 4 variables consisting of 200 simulated patients. Each row in the data represents a visit for the patient. The columns are:

Subject_ID

A unique identifier for each patient.

Visit

The ordinal number of the visit for the patient. Baseline observation is 0.

Time

The time of the visit in days, since baseline.

Outcome

The outcome of interest.

---

Estimate response with jackknife resampling

Description

Estimate response with jackknife resampling

Usage

SensIAT_jackknife(original.object, time, ...)

Arguments

original.object	A SensIAT_within_group_model object.
time	Time points for which to estimate the response.
...	currently ignored.

Value

A tibble with columns alpha, time, jackknife_mean, and jackknife_var, where jackknife_mean is the mean of the jackknife estimates and jackknife_var is the estimated variances of the response at the given time points for the specified alpha values.

Examples

## Not run: 
original.object <-
fit_SensIAT_within_group_model(
    group.data = SensIAT_example_data,
    outcome_modeler = SensIAT_sim_outcome_modeler,
    alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
    id.var = Subject_ID,
    outcome.var = Outcome,
    time.var = Time,
    intensity.bandwidth = 30,
    knots = c(60,60,60,60,260,460,460,460,460),
    End = 830
)
jackknife.estimates <- SensIAT_jackknife(original.object, time = c(90, 180, 270, 360, 450))

## End(Not run)

---

Outcome Modeler for SensIAT Single Index Model.

Description

Outcome Modeler for SensIAT Single Index Model.

Usage

SensIAT_sim_outcome_modeler(
  formula,
  data,
  kernel = "K2_Biweight",
  method = "nmk",
  id = ..id..,
  ...
)

Arguments

formula	The outcome model formula
data	The data to fit the outcome model to. Should only include follow-up data, i.e. time > 0.
kernel	The kernel to use for the outcome model.
method	The optimization method to use for the outcome model, either "optim", "nlminb", or "nmk".
id	The patient identifier variable for the data.
...	Currently ignored, included for future compatibility.

Value

Object of class SensIAT::Single-index-outcome-model which contains the outcome model portion.

Examples

model <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = SensIAT_sim_outcome_modeler,
        alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
        id.var = Subject_ID,
        outcome.var = Outcome,
        time.var = Time,
        End = 830,
        knots = c(60,60,60,60,260,460,460,460,460),
    )

---