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Overview of methods in adjustedCurves

Robin Denz

Source: vignettes/method_overview.rmd
method_overview.rmd

The amount of methods implemented in this package can be overwhelming at first, making one wonder which method should be used. This small vignette exists to make this choice a little easier by providing a concise overview of the functionality of each method implemented in the adjustedsurv() and adjustedcif() functions. Note that this vignette does not contain a description of how these methods work or when. Information about that can be found in Denz et al. (2023) or the respective documentation pages and the cited literature therein.

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:

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:

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