Methods in adjustedsurv()

The following table gives a general overview of all supported methods in adjustedsurv():

	Method	Supports Unmeasured Confounding	Supports Categorical Treatments	Supports Continuous Confounders	Approximate SE available	Always in Bounds	Always non-increasing	Doubly-Robust	Supports Dependent Censoring	Type of Adjustment	Is Nonparametric	Computation Speed	Dependencies
1	“direct”	no	yes	yes	yes	yes	yes	no	no	outcome	no	+	riskRegression
2	“direct_pseudo”	no	yes	yes	no	yes	no	no	yes	outcome	no	- -	geepack, prodlim
3	“iptw_km”	no	yes	yes	yes	yes	yes	no	(no)	treatment	depends	++	-
4	“iptw_cox”	no	yes	yes	no	yes	yes	no	(no)	treatment	depends	++	-
5	“iptw_pseudo”	no	yes	yes	yes	no	no	no	yes	treatment	depends	-	prodlim
6	“matching”	no	no	yes	no	yes	yes	no	no	treatment	depends	-	Matching
7	“emp_lik”	no	no	yes	no	yes	yes	no	no	treatment	yes	+	MASS
8	“aiptw”	no	no	yes	yes	no	no	yes	yes	both	no	-	riskRegression
9	“aiptw_pseudo”	no	yes	yes	yes	no	no	yes	yes	both	no	- -	geepack, prodlim
11	“strat_amato”	no	yes	no	no	yes	yes	no	no	-	yes	+++	-
12	“strat_nieto”	no	yes	no	yes	yes	yes	no	no	-	yes	+++	-
13	“strat_cupples”	no	yes	no	no	yes	yes	no	no	-	yes	+++	-
14	“iv_2SRIF”	yes	no	yes	no	yes	yes	no	no	-	no	+	-
15	“prox_iptw”	yes	no	yes	yes	no	no	no	no	treatment	no	- -	numDeriv
16	“prox_aiptw”	yes	no	yes	yes	no	no	yes	no	both	no	- -	numDeriv
17	“km”	no	yes	no	yes	yes	yes	no	no	none	yes	+++	-

For methods "iptw_km" and "iptw_cox" we wrote “(no)” in whether they support dependent censoring, because there is no direct implementation to handle it in this package. By supplying inverse probability of censoring weights to the treatment_model argument it is, however, possible to use those estimators to adjust for dependent censoring as well. If both inverse probability of treatment (or more general covariate balancing weights) and inverse probability of censoring weights should be used, the user can simply multiply the subject-level weights and supply the results to the treatment_model argument.

The following table gives an overview of the supported input to the treatment_model argument for methods that require it:

Method	Allowed Input to treatment_model argument
“iptw_km”	glm or multinom object, weights, formula for weightit()
“iptw_cox”	glm or multinom object, weights, formula for weightit()
“iptw_pseudo”	glm or multinom object, weights, formula for weightit()
“matching”	glm object or propensity scores
“aiptw”	glm object
“aiptw_pseudo”	glm or multinom object or propensity scores

After having created an adjustedsurv object using the adjustedsurv() function, the following functions can be used to create plots, transform the output or calculate further statistics:

• plot(): Plots the estimated adjusted survival curves
• adjusted_curve_diff(): Calculates differences in survival probabilities
• adjusted_curve_ratio(): Calculates ratios of survival probabilities
• plot_curve_diff(): Plots differences in survival probabilities
• plot_curve_ratio(): Plots ratios of survival probabilities
• adjusted_surv_quantile(): Calculates adjusted survival time quantiles
• adjusted_rmst(): Calculates adjusted restricted mean survival times
• plot_rmst_curve(): Plots adjusted restricted mean survival time curves
• adjusted_rmtl(): Calculates adjusted restricted mean time lost
• plot_rmtl_curve(): Plots adjusted restricted mean time lost curves
• adjusted_curve_test(): Performs a test of adjusted survival curve equality in an interval
• as_ggsurvplot_df(): Transforms the output to a concise data.frame

Methods in adjustedcif()

The following table gives a general overview of all supported methods in adjustedcif():

	Method	Supports Unmeasured Confounding	Supports Categorical Treatments	Supports Continuous Confounders	Approximate SE available	Always in Bounds	Always non-increasing	Doubly-Robust	Supports Dependent Censoring	Type of Adjustment	Is Nonparametric	Computation Speed	Dependencies
1	“direct”	no	yes	yes	yes	yes	yes	no	no	outcome	no	+	riskRegression
2	“direct_pseudo”	no	yes	yes	no	yes	no	no	no	outcome	no	- -	geepack, prodlim
3	“iptw”	no	yes	yes	yes	yes	yes	no	yes	treatment	no	+	riskRegression
4	“iptw_pseudo”	no	yes	yes	yes	no	no	no	no	treatment	depends	+	prodlim
5	“matching”	no	no	yes	no	yes	yes	no	no	treatment	depends	-	Matching
6	“aiptw”	no	no	yes	yes	no	no	yes	yes	both	no	-	riskRegression
7	“aiptw_pseudo”	no	yes	yes	yes	no	no	yes	no	both	no	- -	geepack, prodlim
9	“aalen_johansen”	no	yes	no	yes	yes	yes	no	no	none	yes	++	cmprsk

The following table gives an overview of the supported input to the treatment_model argument for methods that require it:

Method	Allowed Input to treatment_model argument
“iptw”	glm or multinom object
“iptw_pseudo”	glm or multinom object, weights, formula for weightit()
“matching”	glm object or propensity scores
“aiptw”	glm object
“aiptw_pseudo”	glm or multinom object or propensity scores

Note that method "iptw" currently does not support directly supplying weights or propensity scores. This is due to it relying on the ate function of the riskRegression package, which only accepts glm or multinom objects. This may be changed in the future.

After having created an adjustedcif object using the adjustedcif() function, the following functions can be used to create plots, transform the output or calculate further statistics:

• plot(): Plots the estimated adjusted CIFs
• adjusted_curve_diff(): Calculates differences in CIFs
• adjusted_curve_ratio(): Calculates ratios of CIFs
• plot_curve_diff(): Plots differences in CIFs over time
• plot_curve_ratio(): Plots ratios of survival probabilities
• adjusted_rmtl(): Calculates adjusted restricted mean time lost
• plot_rmtl_curve(): Plots adjusted restricted mean time lost curves
• adjusted_curve_test(): Performs a test of adjusted CIF equality in an interval

References

Robin Denz, Renate Klaaßen-Mielke, and Nina Timmesfeld (2023). “A Comparison of Different Methods to Adjust Survival Curves for Confounders”. In: Statistics in Medicine 42.10, pp. 1461-1479