,
Camila Olarte Parra [ctb] ,
Erik van Zwet [ctb]| Title: | G-Formula for Causal Inference via Multiple Imputation |
|---|---|
| Description: | Implements the G-Formula method for causal inference with time-varying treatments and confounders using Bayesian multiple imputation methods, as described by Bartlett, Olarte Parra and Daniel (2023) <arXiv:2301.12026>. It creates multiple synthetic imputed datasets under treatment regimes of interest using the 'mice' package. These can then be analysed using rules developed for analysing multiple synthetic datasets. |
| Authors: | Jonathan Bartlett [aut, cre] ,
Camila Olarte Parra [ctb] ,
Erik van Zwet [ctb] |
| Maintainer: | Jonathan Bartlett <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.1 |
| Built: | 2024-11-03 04:20:57 UTC |
| Source: | https://github.com/jwb133/gformulami |
gFormulaImpute creates multiple imputed synthetic datasets of longitudinal histories under
specified treatment regimes of interest, based on the G-formula.
gFormulaImpute( data, M = 50, trtVars, trtRegimes, nSim = NULL, micePrintFlag = FALSE, silent = FALSE, method = NULL, predictorMatrix = NULL )gFormulaImpute( data, M = 50, trtVars, trtRegimes, nSim = NULL, micePrintFlag = FALSE, silent = FALSE, method = NULL, predictorMatrix = NULL )
data |
The observed data frame |
M |
The number of imputed datasets to generate |
trtVars |
A vector of variable names indicating the time-varying treatment variables |
trtRegimes |
A vector specifying the treatment regime of interest, or a list of vectors specifying the treatment regimes of interest |
nSim |
The number of individuals to simulate in each imputed dataset. Defaults to number of individuals in observed data |
micePrintFlag |
TRUE/FALSE specifying whether the output from the call(s) to mice should be printed |
silent |
TRUE/FALSE indicating whether to print output to console (FALSE) or not (TRUE) |
method |
An optional method argument to pass to mice. If not specified, the default is to impute continuous variables using normal linear regression (norm), binary variables using logistic regression (logreg), polytomous regression for unordered factors and proportional odds model for ordered factors |
predictorMatrix |
An optional predictor matrix to specify which variables to use as predictors in the imputation models. The default is to impute sequentially, i.e. impute using all variables to the left of the variable being imputed as covariates |
gFormulaImpute creates multiple imputed synthetic datasets of longitudinal histories under
specified treatment regimes of interest, based on the G-formula, as described in
Bartlett et al 2023. Specifically, to
the observed data frame, an additional nSim rows are added in which all variables are set to
missing, except the time-varying treatment variables. The latter are set to the values
as specified in the trtRegimes argument. If multiple treatment regimes are specified,
nSim rows are added for each of the specified treatment regimes.
gFormulaImpute uses the mice package to impute the potential outcome values of the
time-varying confounders and outcome in the synthetic datasets. Imputation is performed
sequentially from left to right in the data frame. As such, the variables must be ordered
in time in the input data frame, with the time-varying confounders at each time followed
by the corresponding treatment variable at that time.
For the data argument, gFormulaImpute expects either a fully observed (complete) data frame,
or else a set of multiple imputation stored in an object of class mids (from the mice package).
Unlike with Rubin's regular multiple imputation pooling rules, it is possible
for the pooling rules developed by Raghunathan et al (2003) to give negative
variance estimates. The probability of this occurring is reduced by increasing
M and/or nSim.
gFormulaImpute returns an object of class mids. This can be analysed using the same
methods that imputed datasets from mice can be analysed with (see examples). However, Rubin's standard
pooling rules are not valid for analysis of the synthetic datasets. Instead, the synthetic
variance estimator of Raghunathan et al (2003) must be used, as implemented in the
syntheticPool function.
The development of the gFormulaMI package was supported by a grant from the UK
Medical Research Council (MR/T023953/1).
an S3 object of class mids (multiply imputed dataset)
Jonathan Bartlett [email protected]
Bartlett JW, Olarte Parra C, Daniel RM. G-formula for causal inference via multiple imputation. 2023 https://arxiv.org/abs/2301.12026
Raghunathan TE, Reiter JP, Rubin DB. 2003. Multiple imputation for statistical disclosure limitation. Journal of Official Statistics, 19(1), p.1-16.
set.seed(7626) #impute synthetic datasets under two regimes of interest imps <- gFormulaImpute(data=simDataFullyObs,M=10, trtVars=c("a0","a1","a2"), trtRegimes=list(c(0,0,0),c(1,1,1))) #fit linear model to final outcome with regime as covariate fits <- with(imps, lm(y~factor(regime))) #pool results using Raghunathan et al 2003 rules syntheticPool(fits)set.seed(7626) #impute synthetic datasets under two regimes of interest imps <- gFormulaImpute(data=simDataFullyObs,M=10, trtVars=c("a0","a1","a2"), trtRegimes=list(c(0,0,0),c(1,1,1))) #fit linear model to final outcome with regime as covariate fits <- with(imps, lm(y~factor(regime))) #pool results using Raghunathan et al 2003 rules syntheticPool(fits)
A simulated observational study data frame with no missing data.
simDataFullyObssimDataFullyObs
simDataFullyObsA data frame with 5000 rows and the following variables:
Continuous baseline confounder
Binary baseline treatment
Continuous confounder at time 1
Binary treatment at time 1
Continuous confounder at time 2
Binary treatment at time 2
Continuous final outcome
...
This function pools estimates and variances which have been obtained by analysing multiple synthetic imputations (e.g. created used gFormulaImpute) using the method developed by Raghunathan et al 2003.
syntheticPool(fits)syntheticPool(fits)
fits |
Collection of model fits produced by a call of the form
|
The only argument to syntheticPool is a set of model fits obtained by running
an analysis on an imputed dataset collection of class mids, as created
for example using the mice function in the mice package.
The function returns a table containing the overall parameter estimates, the within, between and total imputation variances, 95% confidence intervals, and p-values testing the null hypothesis that the corresponding parameters equal zero.
It is possible for the variance estimator developed by Raghunathan et al 2003 to
be negative. In this case syntheticPool stops and informs you to re-impute
using a larger number of imputations M and/or nSim.
The development of the gFormulaMI package was supported by a grant from the UK
Medical Research Council (MR/T023953/1).
A matrix containing the pooled results.
Jonathan Bartlett [email protected]
Raghunathan TE, Reiter JP, Rubin DB. 2003. Multiple imputation for statistical disclosure limitation. Journal of Official Statistics, 19(1), p.1-16.
set.seed(7626) #impute synthetic datasets under two regimes of interest using gFormulaImpute imps <- gFormulaImpute(data=simDataFullyObs,M=10, trtVars=c("a0","a1","a2"), trtRegimes=list(c(0,0,0),c(1,1,1))) #fit linear model to final outcome with regime as covariate fits <- with(imps, lm(y~factor(regime))) #pool results using Raghunathan et al 2003 rules syntheticPool(fits)set.seed(7626) #impute synthetic datasets under two regimes of interest using gFormulaImpute imps <- gFormulaImpute(data=simDataFullyObs,M=10, trtVars=c("a0","a1","a2"), trtRegimes=list(c(0,0,0),c(1,1,1))) #fit linear model to final outcome with regime as covariate fits <- with(imps, lm(y~factor(regime))) #pool results using Raghunathan et al 2003 rules syntheticPool(fits)