Title:	Prepare Electronic Prescription Record Data to Estimate Drug Exposure
Version:	0.0.4
Maintainer:	David Selby <David.Selby@manchester.ac.uk>
BugReports:	https://github.com/belayb/drugprepr/issues
Description:	Prepare prescription data (such as from the Clinical Practice Research Datalink) into an analysis-ready format, with start and stop dates for each patient's prescriptions. Based on Pye et al (2018) <doi:10.1002/pds.4440>.
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
RoxygenNote:	7.1.2
Imports:	dplyr, doseminer, rlang, tidyr, sqldf, stringr, purrr, DescTools
Depends:	R (≥ 2.10)
Suggests:	knitr, rmarkdown, testthat, kableExtra
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2021-11-09 11:50:09 UTC; j10204ds
Author:	Belay Birlie Yimer [aut], David Selby [aut, cre], Meghna Jani [aut], Goran Nenadic [aut], Mark Lunt [aut], William G. Dixon [aut]
Repository:	CRAN
Date/Publication:	2021-11-09 18:50:05 UTC

Clean implausibly-long prescription durations

Description

Given a prescription length limit, truncate any prescriptions that appear to be longer than this, or mark them as missing.

Usage

clean_duration(data, max_months = Inf, method = c("truncate", "remove"))

Arguments

Details

The method 'truncate' causes any duration longer than max_months to be replaced with the value of max_months (albeit converted to days). The method 'remove' causes such durations to be replaced with NA. There is no explicit 'ignore' method, but if you want to 'do nothing', simply set max_months to an arbitrarily high number. By default, the maximum is infinite, so nothing should happen. (Of course, you could also just not run the function...)

Value

A data frame of the same structure as the input, possibly with some elements of the duration column changed

Note

Currently the variable name is hard-coded as 'duration', but in principle this could be parametrised for datasets where the column has a different name.

Examples

long_presc <- data.frame(duration = c(100, 300, 400, 800))
clean_duration(long_presc, 6)
clean_duration(long_presc, 12, 'remove')

Close small gaps between successive prescriptions

Description

Given a series of prescriptions in data, if one prescription (for the same patient and drug) starts \leq min_gap days after the previous one finishes, we extend the length of the previous prescription to cover the gap.

Usage

close_small_gaps(data, min_gap = 0L)

Arguments

Value

The input data frame data, possibly with some of the stop_dates changed.

Examples

gappy_data <- data.frame(
  patid = 1,
  prodcode = 'a',
  start_date = Sys.Date() + (0:6) * 7,
  stop_date = Sys.Date() + (0:6) * 7 + 4
)
close_small_gaps(gappy_data)
close_small_gaps(gappy_data, 7)

Compute numerical daily dose from free-text prescribing instructions

Description

The function calls the R package doseminer to extract dose information from free-text prescribing instructions, then computes the average numerical daily dose according to a given decision rule.

Usage

compute_ndd(data, dose_fn = mean, freq_fn = mean, interval_fn = mean)

Arguments

Details

The general formula for computing numerical daily dose (ndd) is given by

\mbox{ndd} = \mbox{DF} \times \mbox{DN} / \mbox{DI},

where

DF

is dose frequency, the number of dose 'events' per day

DN

is dose number, or number of units of drug taken during each dose 'event'

DI

is dose interval, or the number of days between 'dose days', where an interval of 1 means every day

Prescriptions can have a variable dose frequency or dose number, such as '2-4 tablets up to 3 times per day'. In this case, the user can choose to reduce these ranges to single values by taking the minimum, maximum or average of these endpoints.

Value

A data frame mapping the raw text to structured dosage information.

Examples

compute_ndd(dataset1, min, min, mean)

Example data from the Clinical Practice Research Datalink (CPRD).

Description

A dataset containing prescription information for two individuals. The dataset is a hypothetical dataset resembling the real CPRD data.

Usage

dataset1

Format

A data frame with 18 rows and 9 variables:

patid

unique identifier given to a patient in CPRD GOLD

pracid

unique identifier given to a practice in CPRD GOLD

start_date

Beginning of the prescription period

prodcode

CPRD unique code for the treatment selected by the GP

dossageid

Identifier that allows dosage information on the event to be retrieved from Common Dosages Lookup table

text

Prescription instruction for the prescribed product, as entered by the GP

qty

Total quantity entered by the GP for the prescribed product

numdays

Number of treatment days prescribed for a specific therapy event

dose_duration

an estimated prescription duration, as entered by CPRD

...

Source

https://cprdcw.cprd.com/_docs/CPRD_GOLD_Full_Data_Specification_v2.0.pdf

Decision 1: impute implausible total quantities

Description

A light wrapper around impute_qty.

Usage

decision_1(data, decision = "a")

Arguments

Note

Decisions f and g are not yet implemented.

See Also

Other decision functions: decision_10(), decision_2(), decision_3(), decision_4(), decision_5(), decision_6(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 10: close small gaps between successive prescriptions

Description

Where one prescription (for the same drug and patient) starts only a short time after the previous finishes, this function can close the gap, as if the prescription was continuous over the entire period.

Usage

decision_10(data, decision = "a")

Arguments

Details

The underlying function is called close_small_gaps

See Also

Other decision functions: decision_1(), decision_2(), decision_3(), decision_4(), decision_5(), decision_6(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 2: impute missing total quantities

Description

A light wrapper around impute_qty.

Usage

decision_2(data, decision = "a")

Arguments

Note

Decisions e and f are not yet implemented.

See Also

Other decision functions: decision_10(), decision_1(), decision_3(), decision_4(), decision_5(), decision_6(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 3: impute implausible daily doses

Description

A light wrapper around impute_ndd.

Usage

decision_3(data, decision = "a")

Arguments

Note

Decisions f and g are not yet implemented.

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_4(), decision_5(), decision_6(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 4: impute missing daily doses

Description

A light wrapper around impute_ndd.

Usage

decision_4(data, decision = "a")

Arguments

Note

Decisions e and f are not yet implemented.

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_5(), decision_6(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 5: impute implausible prescription durations

Description

A light wrapper around clean_duration.

Usage

decision_5(data, decision = "a")

Arguments

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_4(), decision_6(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 6: choose method of calculating prescription duration

Description

This is just shorthand for defining a column equal to one of the specified formulae. If the column(s) corresponding to decision are missing, an error will be thrown. If you have already calculated or obtained the column duration from elsewhere, this step is not necessary.

Usage

decision_6(data, decision = "c")

Arguments

Note

This step actually takes place before decision_5.

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_4(), decision_5(), decision_7(), decision_8(), decision_9(), drug_prep()

Decision 7: impute missing prescription durations

Description

A light wrapper around impute_duration.

Usage

decision_7(data, decision = "a")

Arguments

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_4(), decision_5(), decision_6(), decision_8(), decision_9(), drug_prep()

Decision 8: disambiguate prescriptions with the same start date

Description

A light wrapper around impute_duration, followed by removing duplicate rows with the same combination of prodcode, patid and start_date.

Usage

decision_8(data, decision = "a")

Arguments

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_4(), decision_5(), decision_6(), decision_7(), decision_9(), drug_prep()

Decision 9: handle overlapping prescription periods

Description

In situations where one prescription starts before another (for the same patient and drug) finishes, this function will either implicitly sum the doses (i.e. do nothing) or it will divide the intervals into non-overlapping subsets, shifting these sub-intervals forward in time until there is no overlap.

Usage

decision_9(data, decision = "a")

Arguments

Details

The underlying algorithm for shifting overlapping intervals is implemented by the internal function shift_interval.

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_4(), decision_5(), decision_6(), decision_7(), decision_8(), drug_prep()

Run drug preparation algorithm

Description

Run drug preparation algorithm

Usage

drug_prep(data, plausible_values, decisions = rep("a", 10))

Arguments

Value

A data frame including estimated stop_date for each prescription

See Also

Other decision functions: decision_10(), decision_1(), decision_2(), decision_3(), decision_4(), decision_5(), decision_6(), decision_7(), decision_8(), decision_9()

Examples

plausible_values <- data.frame(
  prodcode = c('a', 'b', 'c'),
  min_qty = 0,
  max_qty = c(50, 100, 200),
  min_ndd = 0,
  max_ndd = c(10, 20, 30)
)
drug_prep(example_therapy,
          plausible_values,
          decisions = c('a', 'a', 'a', 'a', 'a',
                        'c', 'a', 'a', 'a', 'a'))

Example electronic prescription dataset

Description

Based on a hypothetical 'therapy' file from the Clinical Practical Research Datalink (CPRD), a UK database of primary care records.

Usage

example_therapy

Format

An object of class data.frame with 30 rows and 6 columns.

Note

This dataset is now generated deterministically, so it will not vary between sessions.

Get the mode (most common value) of a vector

Description

Get the mode (most common value) of a vector

Usage

get_mode(v, na.rm = TRUE)

Arguments

Impute missing or implausible values

Description

This is a workhorse function used by impute_ndd, impute_qty and others.

Usage

impute(
  data,
  variable,
  method = c("ignore", "mean", "median", "mode", "replace", "min", "max", "sum"),
  where = is.na,
  group,
  ...,
  replace_with = NA_real_
)

Arguments

Details

The argument where indicates which values are to be imputed. It can be specified as either a vector or as a function. Thus you can specify, for example, is.na to impute all missing values, or you can pass in a vector, if it depends on something else rather than just the current values of the variable to imputed. This design may change in future. In particular, if we want to impute implausible values and impute missing values separately, it's important that these steps are independent.

Value

A data frame of the same structure as data, with values imputed

Replace missing or implausible prescription durations

Description

Instead of replacing missing stop dates, we impute the durations and then infer the stop dates from there.

Usage

impute_duration(
  data,
  method,
  where = is.na,
  group = c("patid", "start_date"),
  ...
)

Arguments

Details

We can fix clashing start dates by setting group to start_date and patid, i.e. average over groups with more than one member; any metric should return the original values if the group size is one.

Value

A data frame of the same structure as data, with values imputed

Examples

example_duration <- transform(example_therapy, duration = qty / ndd)
impute_duration(example_duration, method = 'mean', group = 'patid')

Replace implausible or missing numerical daily doses (NDD)

Description

Replace implausible or missing numerical daily doses (NDD)

Usage

impute_ndd(data, method, where = is.na, group = "population", ...)

Arguments

Value

A data frame of the same structure as data, with values imputed

Examples

impute_ndd(example_therapy, 'mean')

Find implausible entries Replace implausible or missing prescription quantities

Description

Find implausible entries Replace implausible or missing prescription quantities

Usage

impute_qty(data, method, where = is.na, group = "population", ...)

Arguments

Value

A data frame of the same structure as data, with values imputed

Examples

impute_qty(example_therapy, 'mean')

Separating overlapping prescription periods

Description

Run this function and then you can either simply discard overlapping intervals or shift them around using an appropriate algorithm.

Usage

isolate_overlaps(data)

Arguments

Details

The older implementation used isolateoverlaps from the intervalaverage package and Overlap from the DescTools package. Here we refactor it using functions from tidyverse instead.

Value

A data frame of patid, prodcode, start_date and stop_date, where intervals are either exactly overlapping or mutually non-overlapping (but not partially overlapping), such that the union of such intervals is equivalent to those originally provided in data

Note

This function currently doesn't use any keys except patid and prodcode. It may be desirable to add a row ID, for matching each partial interval back to the original interval from which it was derived. This may be relevant to models using weighted dosages.

See Also

intervalaverage::isolateoverlaps, foverlaps

Examples

set.seed(1)
overlapping_data <- data.frame(
  rowid = 1:20,
  patid = 1:2,
  prodcode = 'a',
  start_date = Sys.Date() + c(round(rexp(19, 1/7)), -20),
  qty = rpois(20, 64),
  ndd = sample(seq(.5, 12, by = .5), 20, replace = TRUE),
  stringsAsFactors = FALSE
)
overlapping_data <- transform(overlapping_data,
  stop_date = start_date + qty / ndd
)
isolate_overlaps(overlapping_data)

Human-friendly interface to the drug prep algorithm

Description

A helper function that allows specifying decision rules using English words rather than alphanumeric codes. Translates the rules into the corresponding codes and then passes them to drug_prep functions.

Usage

make_decisions(
  implausible_qty,
  missing_qty,
  implausible_ndd,
  missing_ndd,
  implausible_duration,
  calculate_duration,
  missing_duration,
  clash_start,
  overlapping,
  small_gaps
)

Arguments

Value

A character vector suitable for passing to the decisions argument of the drug_prep function.

Examples

make_decisions('ignore',
               'mean population',
               'missing',
               'mean practice',
               'truncate 6',
               'qty / ndd',
               'mean individual',
               'mean',
               'allow',
               'close 15')

Example min-max data.

Description

A dataset containing minimum and maximum possible values for quantity and number of daily dose for given prescription. The dataset is hypothetical.

Usage

min_max_dat

Format

A data frame with 2 rows and 5 variables:

prodcode

CPRD unique code for the treatment selected by the GP

max_qty

maximum possible quantity to be prescribed for the product

min_qty

minimum possible quantity to be prescribed for the product

max_ndd

maximum possible number of daily dose to be prescribed for the product

min_ndd

minimum possible number of daily dose to be prescribed for the product

...

Do values fall outside a specified 'plausible' range?

Description

A utility function for indicating if elements of a vector are implausible.

Usage

outside_range(x, lower, upper, open = TRUE)

Arguments

Details

Though the function between already exists, it is not vectorised over the bounds.

Shift time intervals until they no longer overlap

Description

This is a function used by decision_9.

Usage

shift_interval(x)

Arguments

Value

A data frame with time intervals moved such that they no longer overlap