# Calculate inverse probability of censoring weights at time `t`

.

`calc_weights.Rd`

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.

## Usage

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

## Arguments

- 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

`t`

+ 1 may aid in the proportional hazards assumption being met in this model.

## Value

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.

## Details

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*.

## References

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.

## Examples

```
# 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)
#> 'data.frame': 2279 obs. of 2 variables:
#> $ id : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ ipcw: num NA 1.14 NA 1.01 1.03 ...
```