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.

`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`

`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

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