healthdb (version 0.5.0)

define_case: Identify diseases/events from administrative records

Description

This function is a composite of identify_row(), exclude(), restrict_n(), and restrict_date(). It is aimed to implement common case definitions in epidemiological studies using administrative database as a one-shot big query. The intended use case is for definitions in the form of, e.g., two or more physician visits with some diagnostic code at least 30 days apart within two years. The component functions mentioned above are chained in the following order if all arguments were supplied: identify_row(vals) %>% exclude(identify_row(excl_vals), by = clnt_id) %>% restrict_n() %>% restrict_date(). Only necessary steps in the chain will be ran if some arguments are missing, see the verbose output for what was done. Note that if date_var is supplied, n_per_clnt will be counted by distinct dates instead of number of records.

Usage

define_case(
  data,
  vars,
  match = "in",
  vals,
  clnt_id,
  n_per_clnt = 1,
  date_var = NULL,
  apart = NULL,
  within = NULL,
  uid = NULL,
  excl_vals = NULL,
  excl_args = NULL,
  keep = c("all", "first", "last"),
  if_all = FALSE,
  mode = c("flag", "filter"),
  force_collect = FALSE,
  verbose = getOption("healthdb.verbose"),
  ...
)

Value

A subset of input data satisfied the specified case definition.

Arguments

data

Data.frames or remote tables (e.g., from dbplyr::tbl_sql())

vars

An expression passing to dplyr::select(). It can be quoted/unquoted column names, or helper functions, such as dplyr::starts_with().

match

One of "in", "start", "regex", "like", "between", and "glue_sql". It determines how values would be matched. See identify_row() for detail.

vals

Depending on match, it takes different input. See identify_row().

clnt_id

Grouping variable (quoted/unquoted).

n_per_clnt

A single number specifying the minimum number of group size.

date_var

Variable name (quoted/unquoted) for the dates to be interpreted.

apart

An integer specifying the minimum gap (in days) between adjacent dates in a draw.

within

An integer specifying the maximum time span (in days) of a draw.

uid

Variable name for a unique row identifier. It is necessary for SQL to produce consistent result based on sorting.

excl_vals

Same as vals but clients/groups with these values are going to be removed from the result. This is intended for exclusion criteria of a case definition.

excl_args

A named list of arguments passing to the second identify_row() call for excl_vals. If not supplied, var, match and if_all of the first call will be re-used.

keep

One of:

• "first" (keeping each client's earliest record),

• "last" (keeping the latest),

• and "all" (keeping all relevant records, default).

• Note that "first"/"last" should not be used with "flag" mode.

if_all

A logical for whether combining the predicates (if multiple columns were selected by vars) with AND instead of OR. Default is FALSE, e.g., var1 in vals OR var2 in vals.

mode

Either:

• "flag" - add new columns starting with "flag_" indicating if the client met the condition,

• or "filter" - remove clients that did not meet the condition from the data.

• This will be passed to both restrict_n() AND restrict_date(). Default is "flag".

force_collect

A logical for whether force downloading the result table if it is not a local data.frame. Downloading data could be slow, so the user has to opt in; default is FALSE.

verbose

A logical for whether printing explanation for the operation. Default is fetching from options. Use options(healthdb.verbose = FALSE) to suppress once and for all.

...

Additional arguments, e.g., flag_at, passing to restrict_date().

Examples

sample_size <- 30
df <- data.frame(
  clnt_id = rep(1:3, each = 10),
  service_dt = sample(seq(as.Date("2020-01-01"), as.Date("2020-01-31"), by = 1),
    size = sample_size, replace = TRUE
  ),
  diagx = sample(letters, size = sample_size, replace = TRUE),
  diagx_1 = sample(c(NA, letters), size = sample_size, replace = TRUE),
  diagx_2 = sample(c(NA, letters), size = sample_size, replace = TRUE)
)

# define from one source
define_case(df,
  vars = starts_with("diagx"), "in", vals = letters[1:4],
  clnt_id = clnt_id, date_var = service_dt,
  excl_args = list(if_all = TRUE),
  # remove non-case
  mode = "filter",
  # keeping the first record
  keep = "first"
)

# multiple sources with purrr::pmap
# arguments with length = 1 will be recycle to match the number of sources
# wrap expressions/unquoted variables with bquote(),
# or rlang:exprs() to prevent immediate evaluation,
# or just use quoted variable names
purrr::pmap(
  list(
    data = list(df, df),
    vars = rlang::exprs(starts_with("diagx")),
    match = c("in", "start"),
    vals = list(letters[1:4], letters[5:10]),
    clnt_id = list(bquote(clnt_id)), n_per_clnt = c(2, 3),
    date_var = "service_dt",
    excl_vals = list(letters[11:13], letters[14:16]),
    excl_args = list(list(if_all = TRUE), list(if_all = FALSE))
  ),
  define_case
)