gestSingle {gesttools} | R Documentation |
G-Estimation for an End of Study Outcome
Description
Performs g-estimation of a structural nested mean model (SNMM), based on the outcome regression methods described in Sjolander and Vansteelandt (2016) and Dukes and Vansteelandt (2018). We expect a dataset that holds an end of study outcome that is either binary or continuous, time-varying and/or baseline confounders, and a time-varying exposure that is either binary, continuous or categorical.
Usage
gestSingle(
data,
idvar,
timevar,
Yn,
An,
Cn = NA,
outcomemodels,
propensitymodel,
censoringmodel = NULL,
type,
EfmVar = NA,
...
)
Arguments
data |
A data frame in long format containing the data to be analysed. See description for details. |
idvar |
Character string specifying the name of the ID variable in the data. |
timevar |
Character string specifying the name of the time variable in the data. Note that timevar must specify time periods as integer values starting from 1 (must not begin at 0). |
Yn |
Character string specifying the name of the end of study outcome variable. |
An |
Character string specifying the name of the time-varying exposure variable. |
Cn |
Optional character string specifying the name of the censoring indicator variable. The variable specified in Cn should be a numeric vector taking values 0 or 1, with 1 indicating censored. |
outcomemodels |
a list of formulas or formula objects specifying the outcome models for Yn prior to adjustment by propensity score. The i'th entry of the list specifies the outcome model for the counterfactuals up to time i. See description for details. |
propensitymodel |
A formula or formula object specifying the propensity score model for An. |
censoringmodel |
A formula or formula object specifying the censoring model for Cn. |
type |
Value from 1-4 specifying SNMM type to fit. See details. |
EfmVar |
Character string specifying the name of the effect modifying variable for types 2 or 4. |
... |
Additional arguments, currently not in use. |
Details
Given a time-varying exposure variable, and time-varying confounders,
measured over time periods
, and an end of study outcome
measured at time
,
gest
estimates the causal parameters of a SNMM of the form
if Y is continuous or
if Y is binary. The SNMMs form is defined by the parameter , which can be controlled by the input
type
as follows
type=1
sets. This implies that
is the effect of exposure at any time t on Y.
type=2
sets, and adds affect modification by
EfmVar
, which we denote. Now
where
is the effect of exposure at any time t on Y when
for all t, modified by
for each unit increase in
at all times t. Note that effect modification is currently only supported for binary (written as a numeric 0,1 vector) or continuous confounders.
-
type=3
allows for time-varying causal effects. It setsto a vector of zeros of length T with a 1 in the t'th position. Now
where
is the effect of
on Y.
type=4
allows for a time-varying causal effect that can be modified byEfmVar
, denoted, that is it allows for both time-varying effects and effect modification. It sets
to a vector of zeros of length T with
in the t'th position. Now
where
. Here
is the effect of exposure at time t on Y when
modified by
for each unit increase in
. Note that effect modification is currently only supported for binary (written as a numeric 0,1 vector) or continuous confounders.
The data must be in long format, where we assume the convention that each row with time=t
contains and
and
. Thus the censoring indicator for each row
should indicate that a user is censored AFTER time t. The end of study outcome
should be repeated on each row. If either A or Y are binary, they must be written as numeric vectors taking values either 0 or 1.
The same is true for any covariate that is used for effect modification.
The data must be rectangular with a row entry for every individual for each exposure time 1 up to T. Data rows after censoring should be empty apart from the ID and time variables. This can be done using the function FormatData
.
The input outcomemodels should be a list with T elements (the number of exposure times), where element i describes the outcome model for the counterfactuals at time i.
Value
List of the fitted causal parameters of the posited SNMM. These are labeled as follows for each SNMM type, where An
is
set to the name of the exposure variable, i is the current time period, and and EfmVar is the effect modifying variable.
type=1 |
|
type=2 |
|
.
type=3 |
|
type=4 |
|
The function also returns a summary of the propensity scores and censoring scores via PropensitySummary
and CensoringSummary
,
along with Data
, holding the original dataset with the propensity and censoring scores as a tibble dataset.
References
Vansteelandt, S., & Sjolander, A. (2016). Revisiting g-estimation of the Effect of a Time-varying Exposure Subject to Time-varying Confounding, Epidemiologic Methods, 5(1), 37-56. <doi:10.1515/em-2015-0005>.
Dukes, O., & Vansteelandt, S. (2018). A Note on g-Estimation of Causal Risk Ratios, American Journal of Epidemiology, 187(5), 1079–1084. <doi:10.1093/aje/kwx347>.
Examples
datas <- dataexamples(n = 1000, seed = 123, Censoring = FALSE)
data <- datas$datagest
data <- FormatData(
data = data, idvar = "id", timevar = "time", An = "A",
varying = c("Y", "A", "L"), GenerateHistory = TRUE, GenerateHistoryMax = 1
)
idvar <- "id"
timevar <- "time"
Yn <- "Y"
An <- "A"
Cn <- NA
outcomemodels <- list("Y~A+L+U+Lag1A", "Y~A+L+U+Lag1A", "Y~A+L+U+Lag1A")
propensitymodel <- c("A~L+U+as.factor(time)+Lag1A")
censoringmodel <- NULL
EfmVar <- NA
gestSingle(data, idvar, timevar, Yn, An, Cn, outcomemodels, propensitymodel,
censoringmodel = NULL, type = 1, EfmVar)
# Example with censoring
datas <- dataexamples(n = 1000, seed = 123, Censoring = TRUE)
data <- datas$datagest
data <- FormatData(
data = data, idvar = "id", timevar = "time", An = "A", Cn = "C",
varying = c("Y", "A", "L"), GenerateHistory = TRUE, GenerateHistoryMax = 1
)
Cn <- "C"
EfmVar <- "L"
outcomemodels <- list("Y~A+L+U+A:L+Lag1A", "Y~A+L+U+A:L+Lag1A", "Y~A+L+U+A:L")
censoringmodel <- c("C~L+U+as.factor(time)")
gestSingle(data, idvar, timevar, Yn, An, Cn, outcomemodels, propensitymodel,
censoringmodel = censoringmodel, type = 2, EfmVar)