Package {msprog}

Type: Package
Title: Reproducible Assessment of Disability Course in Multiple Sclerosis
Version: 1.0.0
Description: Analyse disability course in multiple sclerosis (MS) from longitudinal data. The package provides a flexible framework for identifying disability events under user-specified criteria, allowing adaptation to different study designs and endpoints. Tools are included to facilitate transparent and reproducible reporting of the settings used in the analysis. For an introduction to the package and illustrative applications, see Montobbio et al. (2024) <doi:10.1177/13524585241243157>.
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
Imports: stats, dplyr, rlang
Depends: R (≥ 4.1.0)
VignetteBuilder: knitr
Suggests: knitr, rmarkdown, xfun, DT
License: MIT + file LICENSE
NeedsCompilation: no
Packaged: 2026-06-18 13:56:40 UTC; nmontobbio
Author: Noemi Montobbio ORCID iD [aut, cre], Francesca Bovis [rev], Luca Carmisciano [rev]
Maintainer: Noemi Montobbio <noemi.montobbio@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-23 16:00:02 UTC

Assess multiple sclerosis disability course from longitudinal data.

Description

Detect and characterise confirmed disability worsening (CDW) or improvement (CDI) events of an outcome measure (EDSS, NHPT, T25FW, or SDMT; or any custom outcome) based on repeated assessments through time (and on the dates of acute episodes, if any). The events are detected sequentially by scanning the outcome values in chronological order. Several qualitative and quantitative options are given as arguments that can be set by the user and reported as a complement to the results to ensure reproducibility.

Usage

MSprog(
  data,
  subj_col,
  value_col,
  date_col,
  outcome,
  relapse = NULL,
  rsubj_col = NULL,
  rdate_col = NULL,
  renddate_col = NULL,
  subjects = NULL,
  delta_fun = NULL,
  worsening = NULL,
  event = c("firstCDW", "firstCDI", "multiple", "firstPIRA", "firstRAW", "first"),
  RAW_PIRA = FALSE,
  baseline = c("fixed", "roving", "roving_impr", "roving_wors"),
  proceed_from = c("firstconf", "event"),
  sub_threshold_rebl = c("none", "change", "improvement", "worsening"),
  bl_geq = FALSE,
  relapse_rebl = FALSE,
  skip_local_extrema = c("none", "strict", "all"),
  validconf_col = NULL,
  conf_days = 12 * 7,
  conf_tol_days = c(7, 2 * 365.25),
  require_sust_days = 0,
  check_intermediate = TRUE,
  relapse_to_bl = 30,
  relapse_to_event = 0,
  relapse_to_conf = 30,
  relapse_assoc = 90,
  relapse_indep = NULL,
  impute_last_visit = 0,
  date_format = NULL,
  include_dates = FALSE,
  include_values = FALSE,
  include_stable = TRUE,
  verbose = 1
)

Arguments

data

Data frame containing longitudinal data, including: subject IDs, outcome values, visit dates.

subj_col

Name of data column with subject IDs.

value_col

Name of data column with outcome values.

date_col

Name of data column with visit dates.

outcome

Specifies the outcome type. Must be one of the following:

  • "edss" (Expanded Disability Status Scale)

  • "nhpt" (Nine-Hole Peg Test)

  • "t25fw" (Timed 25-Foot Walk)

  • "sdmt" (Symbol Digit Modalities Test)

  • "custom" (only accepted when specifying custom delta_fun and worsening).

When it's not set to "custom", outcome type triggers internal checks on value range, determines the direction of worsening (see worsening argument), and selects the default definition of clinically meaningful change given the reference value (using the built-in function compute_delta()). The latter can be replaced by a custom function using the delta_fun argument.

relapse

Optional data frame containing longitudinal data, including subject IDs and relapse onset dates.

rsubj_col

Name of subject ID column in the relapse data frame, if different from the one in data.

rdate_col

Name of relapse onset date column in the relapse data frame, if different from the date column in data.

renddate_col

Name of relapse end date column in the relapse data frame (if applicable).

subjects

Subset of subjects (a vector or list of IDs). If none is specified, all subjects listed in data are included.

delta_fun

Custom function specifying the minimum clinically meaningful change in the outcome measure from the provided reference value. The function provided must take a numeric value (reference score) as input, and return a numeric value corresponding to the minimum shift from baseline, see example below. If outcome is set to "custom", a custom delta function must be specified by the user. For other values of outcome, if no delta_fun is specified, the built-in function compute_delta() is used internally.

worsening

The direction of worsening ("increase" if higher values correspond to worse disease course, "decrease" otherwise).

The given value is only used when outcome is set to "custom". Otherwise, worsening is automatically set to "increase" if outcome is set to "edss", "nhpt", "t25fw", and to "decrease" if outcome is set to "sdmt".

event

Character string specifying which events to detect. Must be one of the following.

  • "firstCDW" (first CDW, default).

  • "firstCDI" (first CDI).

  • "multiple" (all confirmed events in chronological order).

  • "firstPIRA" (first progression independent of relapse activity, PIRA).

  • "firstRAW" (first relapse-associated worsening, RAW).

  • "first" (only the very first confirmed event – CDI or CDW).

RAW_PIRA

If TRUE, further classify CDW events based on their timing with respect to relapses (RAW, PIRA). The argument is ignored if event is set to "firstCDI" (not relevant) or to "firstRAW"/"firstPIRA" (always enabled).

baseline

Specifies the baseline scheme. Must be one of the following.

  • "fixed": first eligible outcome value; recommended for randomised data (default).

  • "roving": updated after each CDI or CDW event to the visit determined by proceed_from; suitable for a multiple-event setting (i.e., when event is set to "multiple") or when searching for a specific type of CDW (i.e., when event is set to "firstPIRA" or "firstRAW").

  • "roving_impr": updated after every CDI (to the visit determined by proceed_from); suitable for a first-CDW setting to discard fluctuations around baseline; not recommended for multiple events.

  • "roving_wors": updated after every CDW (to the visit determined by proceed_from); suitable when searching for a specific type of CDW (i.e., when event is set to "firstPIRA" or "firstRAW").

proceed_from

After detecting a confirmed disability event, continue searching:

  • from the next visit after the first qualifying confirmation visit if proceed_from="firstconf"

  • from the next visit after the event onset if proceed_from="event".

If baseline is set to "roving", "roving_impr", or "roving_wors", when rebaselining after a confirmed disability event, the baseline is moved to:

  • the first qualifying confirmation visit if proceed_from="firstconf"

  • the event visit if proceed_from="event".

sub_threshold_rebl

This argument is only used if baseline is not set to "fixed". It controls whether and which "sub-threshold" events (i.e., confirmed changes in the outcome measure below the clinically meaningful threshold) can trigger a re-baseline. Must be one of the following.

  • "none" (default): only use clinically meaningful confirmed changes for re-baseline.

  • "change": any confirmed sub-threshold change can potentially trigger a re-baseline.

  • "improvement": any confirmed sub-threshold improvement can potentially trigger a re-baseline.

  • "worsening": any confirmed sub-threshold worsening can potentially trigger a re-baseline.

See delta_fun argument and compute_delta() function for more details.

bl_geq

This argument is only used if relapse-based re-baseline is enabled (relapse_rebl=TRUE). If TRUE, the new reference value must always be greater or equal than the previous one; when it is not, the old reference value is assigned to it [2].

relapse_rebl

If TRUE, re-baseline after every relapse [2].

skip_local_extrema

This argument is only used if the baseline is moved. It controls re-baseline behaviour in the presence of local minima or maxima.

A visit i is a local minimum point for outcome x if

x[i+1]>x[i] and x[i-1]>x[i].

Local maxima are defined similarly.

A visit i is a strict local minimum point for outcome x if

x[i+1]-x[i]>=delta_fun(x[i]);

x[i-1]-x[i]>=delta_fun(x[i]).

Strict local maxima are defined similarly.

When x[i]=x[i-2], visit i is not considered a local extremum point even if the above conditions hold. This controls for cases where the outcome has an undulating course. The following argument values are accepted.

  • "none" (default): local extrema are always accepted as valid baseline values.

  • "strict": the baseline cannot be placed at a strict local minimum or maximum.

  • "all": the baseline cannot be placed at a local minimum or maximum.

validconf_col

Name of data column, if any, specifying which visits can (TRUE) or cannot (FALSE) be used as confirmation visits. If not specified (validconf_col=NULL), all visits are potentially used as confirmation visits.

conf_days

Period before confirmation (days). Can be a single value, or vector of any length if considering multiple windows. If length(conf_days) > 1 (e.g., conf_days=c(12*7, 24*7)), the function detects events confirmed at either time point (e.g., "confirmed over 12 or 24 weeks") with their relative tolerance (as per conf_tol_days).

conf_tol_days

Tolerance window for confirmation visit (days). Can be an integer (equal lower and upper tolerance) or vector of length 2 (different lower and upper tolerance). The right end of the interval (upper tolerance) may be set to Inf (confirmation window unbounded on the right – e.g., "confirmed over 12 or more weeks").

require_sust_days

Minimum number of days over which a confirmed change must be sustained (i.e., confirmed at all visits occurring in the specified period) to be retained as an event. Events sustained for the remainder of the follow-up period are always retained regardless of follow-up duration. If require_sust_days=Inf, events are retained only when sustained for the remainder of the follow-up period.

(Warning: if check_intermediate is set to FALSE, sustained change will be established based only on the end of the specified period.)

check_intermediate

If TRUE (default), events are confirmed over all intermediate visits up to the confirmation visit.

If set to FALSE (not recommended in most cases, as it may discard meaningful fluctuations), events will be confirmed only at the specified confirmation visit (and only at the end of the period defined by require_sust_days, if any).

relapse_to_bl

Minimum distance (days) from the onset of a relapse for a visit to be used as baseline. Can be an integer (minimum distance from last relapse onset) or vector of length 2 (minimum distance from last relapse onset, minimum distance from next relapse onset). Note that setting the distance to zero means keeping the baseline where it is regardless of surrounding relapses.

If relapse end dates are available (renddate_col), the minimum distance from last relapse onset is automatically set to the specific relapse duration, unless relapse_to_bl (or relapse_to_bl[1]) is zero (in which case relapse timing does not affect baseline placement).

If the designated baseline does not satisfy this constraint, the baseline is moved to the next available visit.

relapse_to_event

Minimum distance (days) from the onset of a relapse for an event to be considered as such. Can be an integer (minimum distance from last relapse onset) or vector of length 2 (minimum distance from last relapse onset, minimum distance from next relapse onset). Note that setting the distance to zero means retaining the event regardless of surrounding relapses.

If relapse end dates are available (renddate_col), the minimum distance from last relapse onset is automatically set to the specific relapse duration, unless relapse_to_event (or relapse_to_event[1]) is zero (in which case relapse timing does not affect event validation).

relapse_to_conf

Minimum distance (days) from the onset of a relapse for a visit to be an eligible confirmation visit. Can be an integer (minimum distance from last relapse onset) or vector of length 2 (minimum distance from last relapse onset, minimum distance from next relapse onset). Note that setting the distance to zero means using any visit for confirmation regardless of surrounding relapses.

If relapse end dates are available (renddate_col), the minimum distance from last relapse onset is automatically set to the specific relapse duration, unless relapse_to_conf (or relapse_to_conf[1]) is zero (in which case relapse timing does not affect selection of confirmation visits).

relapse_assoc

Maximum distance (days) from the onset of a relapse for a CDW event to be classified as RAW. Can be an integer (maximum distance from last relapse onset) or vector of length 2 (maximum distance from last relapse onset, maximum distance from next relapse onset). If relapse end dates are available (renddate_col), the maximum distance from last relapse is automatically set to the specific relapse duration. The argument is ignored if RAW events are not detected (e.g., if event="firstCDW" and RAW_PIRA=FALSE).

relapse_indep

Specifies relapse-free intervals for PIRA definition. Must be a named list
list(prec=list(p0, p1), event=list(e0, e1), conf=list(c0, c1))
specifying the intervals around (any subset of) three checkpoints:

  1. a preceding visit, e.g., baseline or last visit before the worsening (p0 and p1)

  2. the event onset (e0 and e1)

  3. an eligible confirmation visit (c0 and c1).

The auxiliary function relapse_indep_from_bounds() can be used to organise interval bounds into a named list correctly, by calling:
relapse_indep_from_bounds(p0, p1, e0, e1, c0, c1)
See relapse_indep_from_bounds() function docs for more details on how to define the intervals. If relapse end dates are available (renddate_col), it is possible to also define PIRA based on those by setting use_end_dates=TRUE in relapse_indep_from_bounds(). The argument is ignored if PIRA events are not detected (e.g., if event="firstCDW" and RAW_PIRA=FALSE).

impute_last_visit

Imputation probability for worsening events occurring at the last available visit (i.e., with no confirmation). Unconfirmed worsening events occurring at the last visit are never imputed if impute_last_visit=0; they are always imputed if impute_last_visit=1; they are imputed with probability p, ⁠0<p<1⁠, if impute_last_visit=p. If a value N>1 is passed, unconfirmed worsening events are imputed only if occurring within N days of follow-up (e.g., in case of early discontinuation).

date_format

Format of dates in the date_col and rdate_col columns of the input data. Can be specified as:

  • Standard format for dates (e.g., "%d-%m-%Y"; see strptime() docs for correct syntax).

  • "day" if dates in are given as "days from start" (the starting point can be different for each subject – e.g., days from randomisation in a clinical trial); negative values are accepted.

If not specified, function as.Date() will try to infer it automatically.

include_dates

If TRUE, output$results will include the dates of:

  • event onset ("date" column)

  • the current baseline ("bl_date" column)

  • the last visit before event onset with a clinically meaningful score difference from it ("last_delta_date" column)

  • the confirmation visit(s) ("conf<c>_date" column and, when relevant, "PIRA_conf<c>_date" column for each c in conf_days)

include_values

If TRUE, output$results will include the outcome value at:

  • event onset ("value" column)

  • the current baseline ("bl_value" column)

  • the last visit before event onset with a clinically meaningful score difference from it ("last_delta_value" column)

  • the confirmation visit(s) ("conf<c>_value" column and, when relevant, "PIRA_conf<c>_value" column for each c in conf_days)

include_stable

If TRUE, subjects with no confirmed events are included in output$results, with time2event = total follow up.

verbose

One of:

  • 0 (print no info);

  • 1 (print concise info, default);

  • 2 (print extended info).

Value

An object of class MSprogOutput with the following attributes:

For a detailed description of output data frames, see ?MSprogOutput.

References

[1] Müller J, Cagol A, Lorscheider J, Tsagkas C, Benkert P, Yaldizli Ö, et al. Harmonizing definitions for progression independent of relapse activity in multiple sclerosis: A systematic review. JAMA Neurol. 2023;80:1232–45.

[2] Kappos L, Wolinsky JS, Giovannoni G, Arnold DL, Wang Q, Bernasconi C, et al. Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol. 2020;77:1132–40.

Examples

# 1. EDSS course
output <- MSprog(toydata_visits, subj_col="id", value_col="EDSS", date_col="date", outcome="edss",
    relapse=toydata_relapses, conf_days=12*7, conf_tol_days=30,
    event="multiple", baseline="roving", verbose=1)
print(output$results) # extended info on each event for all subjects
print(output$event_count) # event counts for each subject
# 2. SDMT course
output <- MSprog(toydata_visits, subj_col="id", value_col="SDMT", date_col="date", outcome="sdmt",
    relapse=toydata_relapses, conf_days=12*7, conf_tol_days=30,
    event="multiple", baseline="roving", verbose=1)
print(output$results) # extended info on each event for all subjects
print(output$event_count) # event counts for each subject
# 3. SDMT course, with a custom delta function
my_sdmt_delta <- function(reference_value) {min(c(reference_value/10, 3))}
output <- MSprog(toydata_visits, subj_col="id", value_col="SDMT", date_col="date", outcome="sdmt",
    delta_fun=my_sdmt_delta,
    relapse=toydata_relapses, conf_days=12*7, conf_tol_days=30,
    event="multiple", baseline="roving", verbose=1)
print(output$results) # extended info on each event for all subjects
print(output$event_count) # event counts for each subject

Objects of class "MSprogOutput"

Description

Objects of class "MSprogOutput" are returned by MSprog(). They contain the results of event detection and associated metadata.

Structure

Objects of class "MSprogOutput" are lists with the following elements:

event_count

A data frame containing event counts for each subject (and the event sequence in case of multiple events).

results

A data frame with extended info on each event for all subjects.

settings

A list containing all the arguments used to compute the output.

unconfirmed

A data frame with info on unconfirmed events (initial change from baseline, but no confirmation) for all subjects.

event_count data frame

event_sequence

Sequence of detected events (if any), in chronological order (only displayed in multiple-event setting).

CDI

Count of confirmed disability improvement (CDI) events.

CDW

Count of confirmed disability worsening (CDW) events.

RAW

Count of relapse-associated worsening (RAW) events.

PIRA

Count of progression independent of relapse activity (PIRA) events.

Note: depending on computation settings, some of the above columns may not be included in the data frame (e.g., no CDI column if event='firstCDW' in MSprog()).

results data frame

⁠<subj_col>⁠

Subject ID.

nevent

Cumulative count of events for the subject.

event_type

Type of event (CDI or CDW).

CDW_type

Type of CDW (PIRA, RAW, undefined).

total_fu

Total length of follow-up period in days.

time2event

Number of days from start of follow-up to event onset.

bl2event

Number of days from baseline to event onset.

date

Date of event onset.

value

Outcome value at event onset.

bl_date

Date of baseline for the event.

bl_value

Outcome value at baseline.

last_delta_date

Date of last visit before event onset at a clinically meaningful score distance from it.

last_delta_value

Last outcome value before event onset at a clinically meaningful score distance from it.

conf⁠<conf_days>⁠_date

Date of ⁠<conf_days>⁠-day confirmation visit for the event.

conf⁠<conf_days>⁠_value

Outcome value at ⁠<conf_days>⁠-day confirmation visit.

PIRA_conf⁠<conf_days>⁠_date

When relevant, date of ⁠<conf_days>⁠-day confirmation visit for PIRA (could be different from CDW confirmation visit if the PIRA definition includes confirmation-related constraints on relapses).

PIRA_conf⁠<conf_days>⁠_value

When relevant, outcome value at ⁠<conf_days>⁠-day confirmation visit for PIRA (could be different from CDW confirmation visit if the PIRA definition includes confirmation-related constraints on relapses).

sust_days

Number of days for which the event was sustained.

sust_last

Whether the event was sustained up to the end of follow-up.

Note: some of the columns may not be included, depending on computation settings (e.g., no date columns if include_dates=F in MSprog()).

unconfirmed data frame

⁠<subj_col>⁠

Subject ID.

date

Date of initial outcome change.

value

Outcome value at initial outcome change.

bl_date

Date of baseline.

bl_value

Outcome value at baseline.

closest_rel_before

Distance in days from the last relapse before the outcome change (if any).

closest_rel_after

Distance in days from the next relapse after the outcome change (if any).

Note: in first-event scenarios (e.g., event='firstCDW'), the event search stops as soon as the first confirmed event is found – in this case, the unconfirmed data frame only reports unconfirmed outcome changes occurring before detecting a confirmed event.

Default minimum clinically meaningful shift for different scales.

Description

Compute the minimum clinically meaningful score change as a function of the reference value for some widely used scales (EDSS, NHPT, T25FW, or SDMT), according to the most established rule for each of these outcomes.

Usage

compute_delta(baseline, outcome = "edss")

Arguments

baseline

Outcome value at baseline.

outcome

One of:

  • "edss" (Extended Disability Status Scale, default);

  • "nhpt" (Nine-Hole Peg Test);

  • "t25fw" (Timed 25-Foot Walk);

  • "sdmt" (Symbol Digit Modalities Test).

Details

Default thresholds are meant to apply to all versions of each test (e.g., dominant or non-dominant hand for NHPT, best time or mean of two trials, etc.).

Value

Minimum clinically meaningful change from the provided baseline value. Specifically:

Examples

compute_delta(4.5) # default outcome is "edss"
compute_delta(55, outcome="sdmt")

Compare value to reference.

Description

Check if an outcome value determines a valid worsening, or improvement, or change, from a given reference value.

Usage

is_event(
  x,
  baseline,
  type,
  outcome = "edss",
  worsening = NULL,
  delta_fun = NULL,
  sub_threshold = FALSE
)

Arguments

x

Outcome value to test.

baseline

Outcome value at baseline.

type

One of:

  • "wors" (worsening);

  • "impr" (improvement);

  • "change" (any valid change).

outcome

One of:

  • "edss" (Extended Disability Status Scale, default);

  • "nhpt" (Nine-Hole Peg Test);

  • "t25fw" (Timed 25-Foot Walk);

  • "sdmt" (Symbol Digit Modalities Test);

  • "custom" (only accepted when specifying non-NULL worsening – and delta_fun as well, if sub_threshold=FALSE).

Outcome type determines the direction of worsening (see worsening argument) and selects the default definition of clinically meaningful change given the reference value (using the built-in function compute_delta()). The latter can be replaced by a custom function using the delta_fun argument.

worsening

The direction of worsening ("increase" if higher values correspond to worse disease course, "decrease" otherwise). This argument is only used when outcome is set to "custom". Otherwise, worsening is automatically set to "increase" for EDSS, NHPT, T25FW, and to "decrease" for SDMT.

delta_fun

Custom function specifying the minimum clinically meaningful change in the outcome measure from the provided reference value. The function provided must take a numeric value (reference score) as input, and return a numeric value corresponding to the minimum relevant shift from baseline, see example below. If outcome is set to "custom", a custom delta function must be specified by the user. For other values of outcome, if no delta_fun is specified, the built-in function compute_delta() is used internally. The argument is ignored if sub_threshold=TRUE.

sub_threshold

If TRUE, any confirmed worsening, or improvement, or change in the outcome measure is valid, regardless of delta_fun.

Value

A boolean value specifying if a valid event was found.

Examples

is_event(x=4.5, baseline=4, type="wors", outcome="edss")
is_event(x=50, baseline=57, type="wors", outcome="sdmt")
is_event(x=3, baseline=3.5, type="impr", outcome="edss", sub_threshold=TRUE)

Textual description of criteria used to assess disability course.

Description

print method for class "MSprogOutput".

Usage

## S3 method for class 'MSprogOutput'
print(x, ...)

Arguments

x

An object of class "MSprogOutput" (result of a call to MSprog()).

...

Optional arguments for print methods. They are ignored in this function.

Details

The method prints out (1) the package version, (2) a full list of function arguments, and (3) a short paragraph describing the full set of criteria used to obtain the output.

Value

Invisibly returns x.

Examples

output <- MSprog(toydata_visits, "id", "EDSS", "date", "edss",
    relapse=toydata_relapses, conf_days=7*12, conf_tol_days=30,
    event="multiple", baseline="roving", verbose=2)
print(output) # textual description of parameters used to obtain output

Define relapse-free intervals for PIRA definition.

Description

Organise the given interval bounds into a named list to be given as argument relapse_indep to function MSprog(). The relapse-free intervals may be anchored to (any subset of) the following three data-driven checkpoints.

  1. "prec": a visit preceding the event: can be (i) the current baseline, (ii) the last visit before event onset, or (iii) the last visit before event onset with a clinically meaningful score difference from it (i.e., for an outcome x with "higher score" = "worse", the last visit i such that x[event] - x[i] >= delta_fun(x[i]), and same for the confirmation visit).

  2. "event": the disability worsening event onset.

  3. "conf": an eligible confirmation visit.

Usage

relapse_indep_from_bounds(
  p0 = 0,
  p1 = 0,
  e0 = 0,
  e1 = 0,
  c0 = 0,
  c1 = 0,
  prec_type = "baseline",
  use_end_dates = F
)

Arguments

p0

Days before preceding visit (⁠>=0⁠).

p1

Days after preceding visit (⁠>=0⁠), or NULL.

e0

Days before event onset (⁠>=0⁠), or NULL.

e1

Days after event onset (⁠>=0⁠), or NULL.

c0

Days before confirmation (⁠>=0⁠), or NULL.

c1

Days after confirmation (⁠>=0⁠).

prec_type

Which visit to use as "preceding visit". Must be one of:

  • "baseline": the current baseline.

  • "last": the last visit before event onset.

  • ⁠"last_delta⁠: the last visit before event onset with a clinically meaningful score difference from it – i.e., for an outcome x with "higher score" = "worse", the last visit i where
    ⁠x[event onset] - x[i] >= delta_fun(x[i])⁠
    and
    x[confirmation] - x[i] >= delta_fun(x[i]).

use_end_dates

If TRUE, only the right bounds e1 and c1 are used, as the left bounds will be defined by the onset-to-end interval of each relapse. This option is only relevant when relapse end dates are provided (see renddate_col argument in MSprog()).

Details

If both ends of an interval are 0 (e.g., if both p0=0 and p1=0), the checkpoint is ignored. To merge two intervals together, set both the right end of the first interval and the left end of the second interval to NULL (e.g., "between baseline and event onset": p1=NULL and e0=NULL). Here are some examples:

Value

A named list to be given as argument relapse_indep to function MSprog().

References

[1] Müller J, Cagol A, Lorscheider J, Tsagkas C, Benkert P, Yaldizli Ö, et al. Harmonizing definitions for progression independent of relapse activity in multiple sclerosis: A systematic review. JAMA Neurol. 2023;80:1232–45.

[2] Kappos L, Wolinsky JS, Giovannoni G, Arnold DL, Wang Q, Bernasconi C, et al. Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials. JAMA Neurol. 2020;77:1132–40.

Synthetic Relapse Data

Description

Artificially generated relapse onset dates for some example patients in toydata_visits to illustrate the use of the package.

Usage

data(toydata_relapses)

Format

An object of class data.frame, with columns:

id

Subject IDs.

date

Relapse onset dates.

References

This data set was artificially created for the msprog package.

Examples


data(toydata_relapses)
head(toydata_relapses)

Synthetic Longitudinal EDSS and SDMT Data

Description

Artificially generated toy data set including Extended Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT) scores in a small cohort of example patients to illustrate the use of the package.

Usage

data(toydata_visits)

Format

An object of class data.frame, with columns:

id

Subject IDs.

date

Visit dates.

EDSS

Synthetic EDSS scores (values between 0 and 10).

SDMT

Synthetic SDMT scores (values between 0 and 110).

References

This data set was artificially created for the msprog package.

Examples


head(toydata_visits)

Time to disability milestone.

Description

Scan the visits in chronological order to detect the first outcome value reaching or exceeding a specified disability milestone (e.g., EDSS>=6), with confirmation.

Usage

value_milestone(
  data,
  milestone,
  subj_col,
  value_col,
  date_col,
  outcome,
  worsening = NULL,
  relapse = NULL,
  rsubj_col = NULL,
  rdate_col = NULL,
  validconf_col = NULL,
  conf_days = 12 * 7,
  conf_tol_days = c(7, 2 * 365.25),
  require_sust_days = 0,
  relapse_to_event = 0,
  relapse_to_conf = 30,
  impute_last_visit = 0,
  date_format = NULL,
  verbose = 0
)

Arguments

data

Data frame containing longitudinal data, including: subject IDs, outcome values, visit dates.

milestone

Disability milestone (outcome value to check data against).

subj_col

Name of data column with subject IDs.

value_col

Name of data column with outcome values.

date_col

Name of data column with visit dates.

outcome

Specifies the outcome type. Must be one of the following:

  • "edss" (Expanded Disability Status Scale)

  • "nhpt" (Nine-Hole Peg Test)

  • "t25fw" (Timed 25-Foot Walk)

  • "sdmt" (Symbol Digit Modalities Test)

  • "custom" (only accepted when specifying argument worsening).

When it's not set to "custom", outcome type triggers internal checks on value range and determines the direction of worsening (see worsening argument).

worsening

The direction of worsening ("increase" if higher values correspond to worse disease course, "decrease" otherwise).
The given value is only used when outcome is set to "custom". Otherwise, worsening is automatically set to "increase" if outcome is set to "edss", "nhpt", "t25fw", and to "decrease" if outcome is set to "sdmt".

relapse

Optional data frame containing longitudinal data, including subject IDs and relapse onset dates.

rsubj_col

Name of subject ID column in the relapse data frame, if different from the one in data.

rdate_col

Name of date column in the relapse data frame, if different from the date column in data.

validconf_col

Name of data column, if any, specifying which visits can (TRUE) or cannot (FALSE) be used as confirmation visits. If not specified (validconf_col=NULL), all visits are potentially used as confirmation visits.

conf_days

Period before confirmation (days). Can be a single value, or vector of any length if considering multiple windows. If length(conf_days) > 1 (e.g., conf_days=c(12*7, 24*7)), the function retains milestones confirmed at either time point (e.g., "confirmed over 12 or 24 weeks") with their relative tolerance (as per conf_tol_days).

conf_tol_days

Tolerance window for confirmation visit (days); can be an integer (equal lower and upper tolerance) or vector of length 2 (different lower and upper tolerance). The right end of the interval can be set to Inf (confirmation window unbounded on the right – e.g., "confirmed over 12 or more weeks").

require_sust_days

Minimum number of days over which the milestone must be sustained (i.e., confirmed at all visits occurring in the specified period). If the milestone is sustained for the remainder of the follow-up period, it is considered reached regardless of follow-up duration. If require_sust_days=Inf, values are retained only when sustained for the remainder of the follow-up period.

relapse_to_event

Minimum distance (days) from the onset of a relapse for the milestone to be considered reached. Can be an integer (minimum distance from last relapse onset) or vector of length 2 (minimum distance from last relapse onset, minimum distance from next relapse onset). Note that setting the distance to zero means retaining the event regardless of surrounding relapses.

relapse_to_conf

Minimum distance (days) from the onset of a relapse for a visit to be a valid confirmation visit. Can be an integer (minimum distance from last relapse onset) or vector of length 2 (minimum distance from last relapse onset, minimum distance from next relapse onset). Note that setting the distance to zero means using any visit for confirmation regardless of surrounding relapses.

impute_last_visit

Imputation probability when the milestone is reached at the last available visit (i.e., with no confirmation). Unconfirmed values exceeding the milestone at the last visit are never imputed if impute_last_visit=0; they are always imputed if impute_last_visit=1; they are imputed with probability p, ⁠0<p<1⁠, if impute_last_visit=p. If a value N>1 is passed, unconfirmed values exceeding the milestone are imputed only if occurring within N days of follow-up (e.g., in case of early discontinuation).

date_format

Format of dates in the date_col and rdate_col columns of the input data. Can be specified as:

  • Standard format for dates (e.g., "%d-%m-%Y"; see strptime() docs for correct syntax).

  • "day" if dates in are given as "days from start" (the starting point can be different for each subject – e.g., days from randomisation in a clinical trial); negative values are accepted.

If not specified, function as.Date() will try to infer it automatically.

verbose

One of:

  • 0 (print no info)

  • 1 (print concise info, default)

  • 2 (print extended info).

Details

Value

A data frame containing the following columns: