summary(W.out$ps)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.01826 0.22575 0.39324 0.42094 0.59037 0.98809
SL.library = c("SL.glm", "SL.glmnet","SL.earth")
proxy.list <- names(out3$autoselected_covariate_df[,-1])
ObsData.noYA <- hdps.data[,c(investigator.specified.covariates,
proxy.list)]16 TMLE
16.1 Obtain OR with superlearner
We use the same propensity score model that was fitted based on super learning algorithm.
tmle.fit <- tmle::tmle(Y = hdps.data$outcome,
A = hdps.data$exposure,
W = ObsData.noYA,
family = "binomial",
V.Q = 3,
V.g = 3,
Q.SL.library = SL.library,
g1W = W.out$ps)If you want to know more about TMLE, look at other tutorials.
estOR.tmle <- tmle.fit$estimates$OR
estOR.tmle
#> $psi
#> [1] 1.433163
#>
#> $log.psi
#> [1] 0.3598838
#>
#> $CI
#> [1] 1.228595 1.671792
#>
#> $pvalue
#> [1] 4.65237e-06
#>
#> $var.log.psi
#> [1] 0.0061747
#>
#> $bs.var.log.psi
#> [1] NA
#>
#> $bs.CI.twosided
#> [1] NA NA
#>
#> $bs.CI.onesided.lower
#> [1] -Inf NA
#>
#> $bs.CI.onesided.upper
#> [1] NA InfSummary of results (OR).
16.2 Obtain OR without superlearner
summary(W.out0$ps)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 0.0000003 0.2445201 0.4308397 0.4481213 0.6321148 0.9975438
SL.library = c("SL.glm")
proxy.list <- names(out3$autoselected_covariate_df[,-1])
ObsData.noYA <- hdps.data[,c(investigator.specified.covariates,
proxy.list)]We use the same propensity score model that was fitted based on hdPS variables via logistic regression (no other learners).
tmle.fit0 <- tmle::tmle(Y = hdps.data$outcome,
A = hdps.data$exposure,
W = ObsData.noYA,
family = "binomial",
V.Q = 3,
V.g = 3,
Q.SL.library = SL.library,
g1W = W.out$ps)estOR.tmle0 <- tmle.fit0$estimates$OR
estOR.tmle0
#> $psi
#> [1] 1.459564
#>
#> $log.psi
#> [1] 0.3781375
#>
#> $CI
#> [1] 1.256360 1.695633
#>
#> $pvalue
#> [1] 7.669587e-07
#>
#> $var.log.psi
#> [1] 0.005850784
#>
#> $bs.var.log.psi
#> [1] NA
#>
#> $bs.CI.twosided
#> [1] NA NA
#>
#> $bs.CI.onesided.lower
#> [1] -Inf NA
#>
#> $bs.CI.onesided.upper
#> [1] NA InfSummary of results (OR).