Obtain a point estimate and 95% confidence interval for difference and ratio effects comparing exposed and unexposed (or treatment and non-treatment) groups using g-computation.
gComp(
data,
outcome.type = c("binary", "count", "count_nb", "rate", "rate_nb", "continuous"),
formula = NULL,
Y = NULL,
X = NULL,
Z = NULL,
subgroup = NULL,
offset = NULL,
rate.multiplier = 1,
exposure.scalar = 1,
R = 200,
clusterID = NULL,
parallel = "no",
ncpus = getOption("boot.ncpus", 1L)
)(Required) A data.frame containing variables for
Y, X, and Z or with variables matching the model
variables specified in a user-supplied formula. Data set should also
contain variables for the optional subgroup and offset, if
they are specified.
(Required) Character argument to describe the outcome
type. Acceptable responses, and the corresponding error distribution and
link function used in the glm, include:
(Default) A binomial distribution with link = 'logit' is used.
A Poisson distribution with link = 'log' is used.
A negative binomial model with link = 'log' is used, where the theta parameter is estimated internally; ideal for over-dispersed count data.
A Poisson distribution with link = 'log' is used; ideal for events/person-time outcomes.
A negative binomial model with link = 'log' is used, where the theta parameter is estimated internally; ideal for over-dispersed events/person-time outcomes.
A gaussian distribution with link = 'identity' is used.
(Optional) Default NULL. An object of class "formula" (or one that can be coerced to that class) which provides the the complete model formula, similar to the formula for the glm function in R (e.g. `Y ~ X + Z1 + Z2 + Z3`). Can be supplied as a character or formula object. If no formula is provided, Y and X must be provided.
(Optional) Default NULL. Character argument which specifies the
outcome variable. Can optionally provide a formula instead of Y and
X variables.
(Optional) Default NULL. Character argument which specifies the
exposure variable (or treatment group assignment), which can be binary,
categorical, or continuous. This variable can be supplied as a factor
variable (for binary or categorical exposures) or a continuous variable.
For binary/categorical exposures, X should be supplied as a factor
with the lowest level set to the desired referent. Numeric variables are
accepted, but will be centered (see Note). Character variables are not
accepted and will throw an error. Can optionally provide a formula
instead of Y and X variables.
(Optional) Default NULL. List or single character vector which
specifies the names of covariates or other variables to adjust for in the
glm function. All variables should either be factors, continuous,
or coded 0/1 (i.e. not character variables). Does not allow interaction terms.
(Optional) Default NULL. Character argument that indicates subgroups for stratified analysis. Effects will be reported for each category of the subgroup variable. Variable will be automatically converted to a factor if not already.
(Optional, only applicable for rate/count outcomes) Default NULL. Character argument which specifies the variable name to be used as the person-time denominator for rate outcomes to be included as an offset in the Poisson regression model. Numeric variable should be on the linear scale; function will take natural log before including in the model.
(Optional, only applicable for rate/count outcomes). Default 1. Numeric variable signifying the person-time value to use in predictions; the offset variable will be set to this when predicting under the counterfactual conditions. This value should be set to the person-time denominator desired for the rate difference measure and must be inputted in the units of the original offset variable (e.g. if the offset variable is in days and the desired rate difference is the rate per 100 person-years, rate.multiplier should be inputted as 365.25*100).
(Optional, only applicable for continuous exposure) Default 1. Numeric value to scale effects with a continuous exposure. This option facilitates reporting effects for an interpretable contrast (i.e. magnitude of difference) within the continuous exposure. For example, if the continuous exposure is age in years, a multiplier of 10 would result in estimates per 10-year increase in age rather than per a 1-year increase in age.
(Optional) Default 200. The number of data resamples to be conducted to produce the bootstrap confidence interval of the estimate.
(Optional) Default NULL. Character argument which specifies
the variable name for the unique identifier for clusters. This option
specifies that clustering should be accounted for in the calculation of
confidence intervals. The clusterID will be used as the level for
resampling in the bootstrap procedure.
(Optional) Default "no." The type of parallel operation to be used. Available
options (besides the default of no parallel processing) include "multicore" (not available
for Windows) or "snow." This argument is passed directly to boot.
See note below about setting seeds and parallel computing.
(Optional, only used if parallel is set to "multicore" or "snow") Default 1.
Integer argument for the number of CPUs available for parallel processing/ number of
parallel operations to be used. This argument is passed directly to boot
An object of class gComp which is a named list with components:
Summary providing parameter estimates and 95% confidence limits of the outcome difference and ratio (in a print-pretty format)
Data.frame with parameter estimates, 2.5% confidence limit, and 97.5% confidence limit each as a column (which can be used for easy incorporation into tables for publication)
Number of unique observations in the original dataset
Number of bootstrap iterations
Data.frame containing the results of the R
bootstrap iterations of the g-computation
Contrast levels compared
Error distribution used in the model
Model formula used to fit the glm
A data.frame with the marginal mean predicted outcomes (with 95% confidence limits) for each exposure level (i.e. under both exposed and unexposed counterfactual predictions)
The glm class object returned from the
fitted regression of the outcome on the exposure and relevant covariates.
The gComp function executes the following steps:
Calls the pointEstimate function on the data to obtain
the appropriate effect estimates (difference, ratio, etc.).
Generates R bootstrap resamples of the data, with replacement. If
the resampling is to be done at the cluster level (set using the
clusterID argument), the number of clusters will remain constant but
the total number of observations in each resampled data set might be
different if clusters are not balanced.
Calls the pointEstimate function on each of the resampled data sets.
Calculates the 95% confidence interval of the difference and ratio
estimates using the results obtained from the R resampled parameter
estimates.
As bootstrap resamples are generated with random sampling, users should
set a seed (set.seed for reproducible
confidence intervals.
While offsets are used to account for differences in follow-up time
between individuals in the glm model, rate differences are
calculated assuming equivalent follow-up of all individuals (i.e.
predictions for each exposure are based on all observations having the
same offset value). The default is 1 (specifying 1 unit of the original
offset variable) or the user can specify an offset to be used in the
predictions with the rate.multiplier argument.
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# NOT RUN {
## Obtain the risk difference and risk ratio for cardiovascular disease or death between
## patients with and without diabetes.
data(cvdd)
set.seed(538)
diabetes <- gComp(cvdd, formula = "cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP",
outcome.type = "binary", R = 20)
# }
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