ctCoxMSM {contTimeCausal} | R Documentation |

The function estimates the effect of treatment regime (in terms of time to treatment discontinuation) for a survival outcome under a Cox proportional hazards model with time-varying confounding in the presence of dependent censoring. Studying the effect of time to treatment initiation is applicable by redefining "treatment discontinuation" in the current description to "treatment initiation".

```
ctCoxMSM(data, base = NULL, td = NULL)
```

`data` |
A data.frame object. A data.frame containing all observed data. At a minimum, this data.frame must contain columns with headers "id", "U", "V", "deltaU", and "deltaV". If time-dependent covariates are included, additional columns include "stop" and "start". See Details for further information |

`base` |
A character or integer vector or NULL. The columns of data to be included in the time-independent component of the model. If NULL, time-independent covariates are excluded from the Cox model for treatment discontinuation. |

`td` |
A character or integer vector or NULL. The columns of data to be included in the time-dependent component of the model. If NULL, time-dependent covariates are excluded from the Cox model for treatment discontinuation. |

The Cox marginal structural model (MSM) assumes that the potential failure
time `T^{\overline{a}}`

under the treatment `\overline{a}`

follows a proportional hazards
model with `\psi*a_u`

. We assume that the participant continuously
received treatment until time `V`

. The observed failure time can be
censored assuming the censoring time is independent of the failure time
given the treatment and covariate history (the so-called ignorable
censoring). The function allows for multi-dimensional
baseline covariates and/or multi-dimensional time-dependent covariates.
Variance estimates can be implemented by delete-one-group jackknifing
and recalling ctCoxMSM.

If only time-independent covariates are included, the data.frame must contain the following columns:

- id:
A unique participant identifier.

- U:
The time to the clinical event or censoring.

- deltaU:
The clinical event indicator (1 if U is the event time; 0 otherwise).

- V:
The time to optional treatment discontinuation, a clinical event, censoring, or a treatment-terminating event.

- deltaV:
The indicator of optional treatment discontinuation (1 if treatment discontinuation was optional; 0 if treatment discontinuation was due to a clinical event, censoring or a treatment-terminating event).

If time-dependent covariates are to be included, the data.frame must be a time-dependent dataset as described by package survival. Specifically, the time-dependent data must be specified for an interval (lower,upper] and the data must include the following additional columns:

- start:
The lower boundary of the time interval to which the data pertain.

- stop:
The upper boundary of the time interval to which the data pertain.

An S3 object of class ctc. Object contains element ‘psi’, the estimate of the Cox MSM parameter(s) and ‘coxPH’, the Cox regression for V.

Yang, S., A. A. Tsiatis, and M. Blazing (2018). Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach, Biometrics, 74, 900–909.

```
data(ctcData)
# sample data to reduce computation time of example
smp <- ctcData$id %in% sample(1:1000, 150, FALSE)
ctcData <- ctcData[smp,]
# analysis with both time-dependent and time-independent components
res <- ctCoxMSM(data = ctcData, base = "x", td = "xt")
# analysis with only the time-independent component
res <- ctCoxMSM(data = ctcData, base = "x")
# analysis with only the time-dependent component
res <- ctCoxMSM(data = ctcData, td = "xt")
```

[Package *contTimeCausal* version 1.0 Index]