calc_weights | R Documentation |

`t`

.Estimates the inverse probability of censoring weights by fitting a cox-propotinal hazards model in a landmark cohort of individuals. Primarily used internally, this function has been exported to allow users to reproduce results in the vignette when estimating confidence intervals using bootstrapping manually.

```
calc_weights(
data.ms,
data.raw,
covs = NULL,
t,
s,
landmark.type = "state",
j = NULL,
max.weight = 10,
stabilised = FALSE,
max.follow = NULL
)
```

`data.ms` |
Validation data in msdata format |

`data.raw` |
Validation data in data.frame (one row per individual) |

`covs` |
Character vector of variable names to adjust for when calculating inverse probability of censoring weights |

`t` |
Follow up time at which to calculate weights |

`s` |
Landmark time at which predictions were made |

`landmark.type` |
Whether weights are estimated in all individuals uncensored at time s ('all') or only in individuals uncensored and in state j at time s ('state') |

`j` |
Landmark state at which predictions were made (only required in landmark.type = 'state') |

`max.weight` |
Maximum bound for weights |

`stabilised` |
Indicates whether weights should be stabilised or not |

`max.follow` |
Maximum follow up for model calculating inverse probability of censoring weights. Reducing this to |

Estimates inverse probability of censoring weights (Hernan M, Robins J, 2020).
Fits a cox proportional hazards model to individuals in a landmark cohort, predicting the probability of being censored
at time `t`

. This landmark cohort may either be all individuals uncensored at time `s`

, or those uncensored
and in state `j`

at time `s`

. All predictors in `w.covs`

are assumed to have a linear effect on the hazard.
Weights are estimated for all individuals in `data.raw`

, even if they will not be used in the analysis as they do not meet the landmarking
requirements. If an individual enters an absorbing state prior to `t`

, we estimate the probability of being censored
before the time of entry into the absorbing state, rather than at `t`

. Details on all the above this are provided in
vignette *overview*.

A data frame with two columns. `id`

corresponds to the patient ids from `data.raw`

. `ipcw`

contains the inverse probability
of censoring weights (specifically the inverse of the probability of being uncesored). If `stabilised = TRUE`

was specified,
a third variable `ipcw.stab`

will be returned, which is the stabilised inverse probability of censoring weights.

Hernan M, Robins J (2020). “12.2 Estimating IP weights via modeling.” In *Causal Inference:
What If*, chapter 12.2. Chapman Hall/CRC, Boca Raton.

```
# Estimate inverse probability of censoring weights for individual in cohort ebmtcal.
# Specifically the probability of being uncensored at t = 1826 days.
# Weights are estimated using a model fitted in all individuals uncensored at time s = 0.
weights.manual <-
calc_weights(data.ms = msebmtcal,
data.raw = ebmtcal,
covs = c("year", "agecl", "proph", "match"),
t = 1826,
s = 0,
landmark.type = "state",
j = 1)
str(weights.manual)
```

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