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Compute cumulative event counts as time-dependent covariates

Source: R/recurrent.marginal.R
count_history.Rd

For each subject and each row of data, counts the number of prior events of specified types in the recurrent event history. The resulting count columns can be used as time-dependent covariates in subsequent models, e.g. to capture event-history dependence in the recurrent event rate.

Usage

count_history(
  data,
  status = "status",
  id = "id",
  types = 1,
  names.count = "Count",
  lag = TRUE,
  multitype = FALSE,
  marks = NULL
)

Arguments

data

A data frame in counting-process format, with one row per event interval per subject.

status

Name of the column containing event status codes. Default is "status".

id

Name of the column containing subject identifiers. Default is "id".

types

Integer vector of status codes to count. Each value in types generates one new count column (when multitype = FALSE) or contributes to a single combined count (when multitype = TRUE). Default is 1.

names.count

Prefix for the names of the new count columns. The status code is appended, e.g. "Count1", "Count2". Default is "Count".

lag

Logical. If TRUE (default), the count at each row is the number of events strictly before the current time (\(N(t-)\)). If FALSE, events at the current time are included (\(N(t)\)).

multitype

Logical. If TRUE, events with status in types are aggregated into a single count column, optionally weighted by marks. If FALSE (default), a separate count column is created for each value in types.

marks

Optional numeric vector of weights applied to events when multitype = TRUE. If NULL (default), each event has weight 1.

Value

The input data frame data with one or more new integer columns appended. With multitype = FALSE, columns are named paste0(names.count, k) for each k in types; with multitype = TRUE, a single column named paste0(names.count, types[1]) is added. An internal bookkeeping column lbnr__id is also added.

Details

When lag = TRUE (default), the count at each row reflects events that occurred strictly before the current time point (i.e. \(N(t-)\)), making it suitable as a left-continuous covariate in counting-process models.

Author

Thomas Scheike

Examples

data(hfactioncpx12)
hf <- hfactioncpx12
dtable(hf, ~status)
#> 
#> status
#>    0    1    2 
#>  617 1391  124 
#> 

## Separate counts for event types 1 and 2
rr <- count_history(hf, types = 1:2, id = "id", status = "status")
dtable(rr, ~"Count*" + status, level = 1)
#> 
#> Count1
#>   0   1   2   3   4   5   6   7 
#> 741 507 319 209 146  97  67  46 
#> 
#> Count2
#>    0 
#> 2132 
#> 
#> status
#>    0    1    2 
#>  617 1391  124 
#>