calc_weights {calibmsm} | R Documentation |
Calculate inverse probability of censoring weights at time t
.
Description
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 |
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.
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.
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)