Assessing progression independent of relapse activity (PIRA) in MS

This vignette illustrates how to use the msprog package to study disability progression independent of relapse activity (PIRA) in multiple sclerosis (MS). For a more general introduction on msprog package usage for disability course assessment, please refer to the vignette Analysing disability course in MS.

Defining PIRA

Recent studies [1–3] have highlighted the contribution of PIRA to overall disability worsening, generating growing interest in PIRA as a relevant endpoint. Classifying a confirmed disability worsening (CDW) event as relapse-independent requires careful observation of its relative timing with respect to relapses. Established definitions of PIRA [4–6] require the absence of relapses within appropriate intervals anchored to the dates of three main checkpoints:

  1. a visit preceding the event: can be (i) the current reference, (ii) the last visit before event onset, or (iii) the last visit before event onset with a clinically meaningful score distance from it 1;
  2. the initial disability worsening (event onset);
  3. an eligible confirmation visit.

For example, in [4], an absence of relapses was required between the reference visit and 30 days after the event, and during the 30 days before and 30 days after confirmation for a CDW event to be considered as PIRA; in [5], the authors required an absence of relapses during the 90 days before the event and between the event and the confirmation visit; in [6], the authors recommend an absence of relapses during the 90 days before and 30 days after the event, and during the 90 days before and 30 days after confirmation; when a high specificity is desired, they recommend an absence of relapses in the whole period between reference and confirmation; in [7], the authors recommend an absence of relapses since the last visit preceding the event (up to the event), and during the 30 days before confirmation.

Such an approach is easily integrated into the msprog::MSprog() function through its argument relapse_indep, allowing to specify custom relapse-free intervals based on any subset of the checkpoints 1-3, thus allowing to replicate any of the above-mentioned definitions. The relapse_indep argument must be provided in the form produced by function msprog::relapse_indep_from_bounds(), as follows:

output <- MSprog(...
                 relapse_indep=relapse_indep_from_bounds(p0, p1, e0, e1, c0, c1, prec_type, use_end_dates),
                 ...)

where: p0 and p1 specify the interval around the preceding visit; e0 and e1 specify the interval around the event; c0 and c1 specify the interval around the confirmation visit; see Figure 1. 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). The prec_type argument specifies which preceding visit to take into account ('baseline' for current reference, 'last' for last visit preceding event onset, 'last_delta' for last visit preceding event onset with a clinically meaningful score distance from it).2

*Figure 1. Relapse-free intervals characterising PIRA, as defined by arguments `p0`, `p1`, `e0`, `e1`, `c0`, `c1`.*

Figure 1. Relapse-free intervals characterising PIRA, as defined by arguments p0, p1, e0, e1, c0, c1.


Some examples are provided below.

  • No relapses during the 90 days before and 30 days after the event, and during the 90 days before and 30 days after confirmation [6]:

    relapse_indep <- relapse_indep_from_bounds(p0=0, p1=0,   # baseline
                                          e0=90, e1=30, # event
                                          c0=90, c1=30) # confirmation
    *Figure 2. Relapse-free intervals as recommended in @muller2023.*

    Figure 2. Relapse-free intervals as recommended in [6].


  • No relapses in the whole period between reference and confirmation [6]:

    relapse_indep <- relapse_indep_from_bounds(p0=0, p1=NULL,    # baseline
                                          e0=NULL, e1=NULL, # event
                                          c0=NULL, c1=0)    # confirmation
    *Figure 3. Relapse-free intervals as recommended in @muller2023 for high specificity.*

    Figure 3. Relapse-free intervals as recommended in [6] for high specificity.


  • No relapses between the reference visit and 30 days after the event, and during the 30 days before and 30 days after confirmation [4]:

    relapse_indep <- relapse_indep_from_bounds(p0=0, p1=NULL,  # baseline
                                          e0=NULL, e1=30, # event
                                          c0=30, c1=30)   # confirmation
    *Figure 4. Relapse-free intervals as used in @kappos2020.*

    Figure 4. Relapse-free intervals as used in [4].


  • No relapses during the 90 days before the event and between the event and the confirmation visit [5]:

    relapse_indep <- relapse_indep_from_bounds(p0=0, p1=0,     # baseline
                                          e0=90, e1=NULL, # event
                                          c0=NULL, c1=0)  # confirmation
    *Figure 5. Relapse-free intervals as used in @cagol2022.*

    Figure 5. Relapse-free intervals as used in [5].


  • No relapses since the last visit preceding the event (up to the event) [7]:

    relapse_indep <- relapse_indep_from_bounds(p0=0, p1=NULL,      # last visit before the event
                                          e0=NULL, e1=0, # event
                                          prec_type='last')
    *Figure 6. Relapse-free intervals as recommended in @muller2025.*

    Figure 6. Relapse-free intervals as recommended in [7].


The requirement of relapse-free periods is often coupled with a non-fixed baseline scheme. This can be a roving baseline, where the reference value is updated after every confirmed improvement or worsening event, as in [6] (baseline='roving' in MSprog()); or a re-baseline after the onset of each relapse, as in [4] (relapse_rebl=TRUE in MSprog()); or both, as in [7].

Detecting PIRA

To illustrate how to apply the above definitions when assessing disability course from patient data, we use the toy data provided in the msprog package. These include artificially generated EDSS and SDMT assessments (toydata_visits) and relapse dates (toydata_relapses) for four patients:

head(toydata_visits)
#> # A tibble: 6 × 5
#>   id    date       visit_day  EDSS  SDMT
#>   <chr> <date>         <dbl> <dbl> <dbl>
#> 1 1     2021-09-23         0   5.5    54
#> 2 1     2021-11-03        41   5.5    54
#> 3 1     2022-01-19       118   5.5    57
#> 4 1     2022-04-27       216   5.5    55
#> 5 1     2022-07-12       292   6      57
#> 6 1     2022-11-06       409   6      51
head(toydata_relapses)
#> # A tibble: 5 × 3
#>   id    date       visit_day
#>   <chr> <date>         <dbl>
#> 1 2     2021-06-12       198
#> 2 2     2022-10-25       698
#> 3 3     2022-12-01       409
#> 4 6     2022-12-18       426
#> 5 7     2021-09-11       185


The following code detects, for each subject in the toy dataset, the first EDSS PIRA event (event='firstPIRA') confirmed over \(\geq\) 12 or \(\geq\) 24 weeks (conf_days=c(7*12, 7*24), conf_tol_days=c(0, Inf)), for two different definitions of PIRA. We set verbose=2 to print an extended log of the computations performed.

  1. Roving baseline, and absence of relapses during the 90 days before and 30 days after the event, and during the 90 days before and 30 days after confirmation [6]:

    output <- MSprog(toydata_visits, 'id', 'EDSS', 'date', 'edss',
               relapse=toydata_relapses, 
               conf_days=c(7*12, 7*24), conf_tol_days=c(0, Inf),
               event='firstPIRA', baseline='roving',
               relapse_indep=relapse_indep_from_bounds(p0=0, p1=0, e0=90, e1=30, c0=90, c1=30),
               verbose=2)
#> 
#> Subject #1: 8 visits, 0 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.5 (2022-07-12); potential confirmation visits available: no.6, 7, 8
#> Found EDSS-CDW (PIRA) (visit no.5, 2022-07-12) confirmed at 84 days, sustained up to visit no.8 (2023-03-11)
#> Baseline at visit no.6
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> Subject #2: 10 visits, 2 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.4 (2021-06-12); potential confirmation visits available: no.5, 6, 7, 8, 9, 10
#> Found EDSS-CDW (RAW) (visit no.4, 2021-06-12) confirmed at 84 days, sustained up to visit no.5 (2021-09-04)
#> Baseline at visit no.5
#> Searching for events from visit no.6 on
#> Found EDSS change at visit no.8 (2022-05-19); potential confirmation visits available: no.9, 10
#> Found EDSS-CDW (PIRA) (visit no.8, 2022-05-19) confirmed at 84 days, sustained up to visit no.10 (2022-11-26)
#> Baseline at visit no.9
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> Subject #3: 7 visits, 1 relapse
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.2 (2021-12-01); potential confirmation visits available: no.4, 5, 7
#> Not a confirmed change: proceed with search
#> Searching for events from visit no.3 on
#> No edss change in any subsequent visit: end process
#> Event: -
#> 
#> Subject #4: 7 visits, 0 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.2 (2021-12-04); potential confirmation visits available: no.3, 4, 5, 6, 7
#> Found EDSS-CDI (visit no.2, 2021-12-04) confirmed at 84 days, sustained up to visit no.3 (2022-03-12)
#> Baseline at visit no.3
#> Searching for events from visit no.4 on
#> Found EDSS change at visit no.4 (2022-07-19); potential confirmation visits available: no.6, 7
#> Found EDSS-CDW (PIRA) (visit no.4, 2022-07-19) confirmed at 84 days, sustained up to visit no.7 (2023-04-27)
#> Baseline at visit no.6
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> Subject #5: 9 visits, 0 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.3 (2021-11-01); potential confirmation visits available: no.5, 6, 7, 8, 9
#> Found EDSS-CDW (PIRA) (visit no.3, 2021-11-01) confirmed at 84 days, sustained up to visit no.9 (2023-03-13)
#> Baseline at visit no.5
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> Subject #6: 7 visits, 1 relapse
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.3 (2022-02-15); potential confirmation visits available: no.4, 5, 7
#> Found EDSS-CDI (visit no.3, 2022-02-15) confirmed at 84 and 168 days, sustained up to visit no.5 (2022-10-05)
#> Baseline at visit no.4
#> Searching for events from visit no.5 on
#> Found EDSS change at visit no.6 (2022-12-22); potential confirmation visits available: no.
#> Not a confirmed change: proceed with search
#> Searching for events from visit no.7 on
#> Found EDSS change at visit no.7 (2023-02-21); potential confirmation visits available: no.
#> Not a confirmed change: proceed with search
#> Searching for events from visit no.- on
#> No edss change in any subsequent visit: end process
#> Event: -
#> 
#> Subject #7: 9 visits, 1 relapse
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.3 (2021-09-11); potential confirmation visits available: no.5, 6, 7, 8, 9
#> Not a confirmed change: proceed with search
#> Searching for events from visit no.4 on
#> Found EDSS change at visit no.5 (2022-03-17); potential confirmation visits available: no.6, 7, 8, 9
#> Found EDSS-CDW (PIRA) (visit no.5, 2022-03-17) confirmed at 84 days, sustained up to visit no.9 (2023-04-28)
#> Baseline at visit no.6
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> ---
#> Outcome: edss
#> Confirmation over: 84, 168 days (-0 days, +Inf days)
#> Baseline: roving
#> Baseline skipped if: <30 days from last relapse
#> Event skipped if: -
#> Confirmation visit skipped if: <30 days from last relapse
#> Events detected: firstPIRA
#> 
#> *Please use `print(output)` to display full info on event detection criteria*
#> 
#> ---
#> Total subjects: 7
#> ---
#> Subjects with PIRA: 5
# print(output$results, row.names=FALSE) # results
DT::datatable(output$results, rownames=F,
              options = list(dom='t', scrollX=T, scrollY="200px", paging = FALSE)
              )


  2. Post-relapse re-baseline, and absence of relapses between the reference visit and 30 days after the event, and during the 30 days before and 30 days after confirmation [4]:

    output <- MSprog(toydata_visits, 'id', 'EDSS', 'date', 'edss',
               relapse=toydata_relapses,
               conf_days=c(7*12, 7*24), conf_tol_days=c(0, Inf), 
               event='firstPIRA', baseline='fixed', relapse_rebl=TRUE,
               relapse_indep=relapse_indep_from_bounds(p0=0, p1=NULL, e0=NULL, e1=30, c0=30, c1=30),
               verbose=2)
#> 
#> Subject #1: 8 visits, 0 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.5 (2022-07-12); potential confirmation visits available: no.6, 7, 8
#> Found EDSS-CDW (PIRA) (visit no.5, 2022-07-12) confirmed at 84 days, sustained up to visit no.8 (2023-03-11)
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> Subject #2: 10 visits, 2 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.4 (2021-06-12); potential confirmation visits available: no.5, 6, 7, 8, 9, 10
#> Searching for events from visit no.5 on
#> [post-relapse rebaseline] Baseline moved to visit no.4
#> Baseline (visit no.4) is within relapse influence: moved to visit no.5
#> Searching for events from visit no.6 on
#> Found EDSS change at visit no.8 (2022-05-19); potential confirmation visits available: no.9, 10
#> Found EDSS-CDW (PIRA) (visit no.8, 2022-05-19) confirmed at 84 days, sustained up to visit no.10 (2022-11-26)
#> [post-relapse rebaseline] Baseline moved to visit no.10
#> [post-relapse rebaseline] Not enough visits after current baseline: end process
#> Event: PIRA
#> 
#> Subject #3: 7 visits, 1 relapse
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.2 (2021-12-01); potential confirmation visits available: no.4, 5, 7
#> Not a confirmed PIRA: proceed with search
#> Searching for events from visit no.3 on
#> No edss change in any subsequent visit: end process
#> Event: -
#> 
#> Subject #4: 7 visits, 0 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.2 (2021-12-04); potential confirmation visits available: no.3, 4, 5, 6, 7
#> Not a confirmed PIRA: proceed with search
#> Searching for events from visit no.3 on
#> Found EDSS change at visit no.3 (2022-03-12); potential confirmation visits available: no.4, 5, 6, 7
#> Not a confirmed PIRA: proceed with search
#> Searching for events from visit no.4 on
#> Found EDSS change at visit no.6 (2023-01-16); potential confirmation visits available: no.7
#> Not a confirmed PIRA: proceed with search
#> Searching for events from visit no.7 on
#> No edss change in any subsequent visit: end process
#> Event: -
#> 
#> Subject #5: 9 visits, 0 relapses
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.3 (2021-11-01); potential confirmation visits available: no.5, 6, 7, 8, 9
#> Found EDSS-CDW (PIRA) (visit no.3, 2021-11-01) confirmed at 84 days, sustained up to visit no.9 (2023-03-13)
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> Subject #6: 7 visits, 1 relapse
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.3 (2022-02-15); potential confirmation visits available: no.4, 5, 7
#> Not a confirmed PIRA: proceed with search
#> Searching for events from visit no.4 on
#> Found EDSS change at visit no.4 (2022-09-03); potential confirmation visits available: no.7
#> Not a confirmed PIRA: proceed with search
#> Searching for events from visit no.5 on
#> Found EDSS change at visit no.5 (2022-10-05); potential confirmation visits available: no.7
#> Not a confirmed PIRA: proceed with search
#> [post-relapse rebaseline] Baseline moved to visit no.6
#> Baseline (visit no.6) is within relapse influence: moved to visit no.7
#> Not enough visits left: end process
#> Searching for events from visit no.- on
#> No edss change in any subsequent visit: end process
#> Event: -
#> 
#> Subject #7: 9 visits, 1 relapse
#> Baseline at visit no.1
#> Searching for events from visit no.2 on
#> Found EDSS change at visit no.3 (2021-09-11); potential confirmation visits available: no.5, 6, 7, 8, 9
#> Not a confirmed PIRA: proceed with search
#> [post-relapse rebaseline] Baseline moved to visit no.3
#> Baseline (visit no.3) is within relapse influence: moved to visit no.4
#> Searching for events from visit no.5 on
#> Found EDSS change at visit no.5 (2022-03-17); potential confirmation visits available: no.6, 7, 8, 9
#> Found EDSS-CDW (PIRA) (visit no.5, 2022-03-17) confirmed at 84 days, sustained up to visit no.9 (2023-04-28)
#> event="firstPIRA" already found: end process
#> Event: PIRA
#> 
#> ---
#> Outcome: edss
#> Confirmation over: 84, 168 days (-0 days, +Inf days)
#> Baseline: fixed, and post-relapse re-baseline
#> Baseline skipped if: <30 days from last relapse
#> Event skipped if: -
#> Confirmation visit skipped if: <30 days from last relapse
#> Events detected: firstPIRA
#> 
#> *Please use `print(output)` to display full info on event detection criteria*
#> 
#> ---
#> Total subjects: 7
#> ---
#> Subjects with PIRA: 4
# print(output$results, row.names=FALSE) # results
DT::datatable(output$results, rownames=F,
              options = list(dom='t', scrollX=T, scrollY="200px", paging = FALSE)
              )

Here, nevent is the cumulative event count for each subject, and event_type characterises the event (since event is set to firstPIRA, the count is either 0 or 1 and the event type, when present, is always PIRA); time2event is the number of days from start of follow-up to event; bl2event is the number of days from current baseline to event; conf84 and conf168 report whether the event was confirmed as a CDW over 12 or 24 weeks (84 or 168 days); PIRAconf84 and PIRAconf168 report whether the event was confirmed as PIRA over 12 or 24 weeks (84 or 168 days); sust_days is the number of days for which the event was sustained; sust_last reports whether the event was sustained until the last visit.

References

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3. Kuhlmann T, Moccia M, Coetzee T, Cohen JA, Correale J, Graves J, et al. Multiple sclerosis progression: Time for a new mechanism-driven framework. Lancet Neurol. Institute of Neuropathology, University Hospital Münster, Münster, Germany; Neuroimmunology Unit, Montreal Neurological Institute, McGill University, Montreal, QC, Canada. Electronic address: [email protected].; 2023;22:78–88. https://doi.org/10.1016/S1474-4422(22)00289-7
4. 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. University Hospital Basel, University of Basel, Basel, Switzerland.; 2020;77:1132–40. https://doi.org/10.1001/jamaneurol.2020.1568
5. Cagol A, Schaedelin S, Barakovic M, Benkert P, Todea R-A, Rahmanzadeh R, et al. Association of Brain Atrophy With Disease Progression Independent of Relapse Activity in Patients With Relapsing Multiple Sclerosis. JAMA Neurology [Internet]. 2022;79:682–92. https://doi.org/10.1001/jamaneurol.2022.1025
6. 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. Research Center for Clinical Neuroimmunology; Neuroscience Basel (RC2NB), University Hospital Basel; University of Basel, Basel, Switzerland.; 2023;80:1232–45. https://doi.org/10.1001/jamaneurol.2023.3331
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Department of Neurology; Center of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague; General University Hospital, Prague, Czech Republic.; CHUM MS Center; Universite de Montreal, Montreal, Quebec, Canada.; CHUM MS Center; Universite de Montreal, Montreal, Quebec, Canada.; Perron Institute for Neurological; Translational Science, University of Western Australia, Nedlands, Western Australia, Australia.; Perron Institute for Neurological; Translational Science, University of Western Australia, Nedlands, Western Australia, Australia.; Department of Neurosciences, Box Hill Hospital, Melbourne, Victoria, Australia.; Department of Neurology, LR 18SP03, Clinical Investigation Centre Neurosciences; Mental Health, Razi University Hospital, Tunis, Tunisia.; Department of Neuroscience, Hospital Germans Trias i Pujol, Badalona, Spain.; Department of Neuroscience, Hospital Germans Trias i Pujol, Badalona, Spain.; Department of Neurology, ASL3 Genovese, Genova, Italy.; Department of Neurology, University Hospital Center Zagreb; University of Zagreb, School of Medicine, Zagreb, Croatia.; University MS Centre, Hasselt-Pelt, Noorderhart, Rehabilitation & MS, Pelt; Hasselt University, Hasselt, Belgium.; Nemocnice Jihlava, Jihlava, Czech Republic.; Austin Health, Melbourne, Victoria, Australia.; Department of Neurology, Hospital Clı́nico Universitario San Carlos, IdISSC, Madrid, Spain.; Groene Hart Ziekenhuis, Gouda, the Netherlands.; Department of Neurology, University Hospital Ghent, Ghent, Belgium.; St Michael’s Hospital, Toronto, Ontario, Canada.; Department of Neurology, Westmead Hospital, Sydney, New South Wales, Australia.; South Eastern HSC Trust, Belfast, United Kingdom.; Department of Medicine; Surgery, University Hospital Reina Sofia, Cordoba, Spain.; Department of Neurology, Hospital de Galdakao-Usansolo, Galdakao, Spain.; Hospital Universitario Donostia, San Sebastián, Spain.; Department of Neurology, Faculty of Medicine, AHEPA University Hospital, Thessaloniki, Greece.; Aarhus University Hospital, Arhus C, Aarhus, Denmark.; Department of Neurology, Concord Repatriation General Hospital, Sydney, New South Wales, Australia.; Department of Neurology, Concord Repatriation General Hospital, Sydney, New South Wales, Australia.; Department of Neurology, Concord Repatriation General Hospital, Sydney, New South Wales, Australia.; Translational Neuroimmunology Group, Kids Neuroscience Centre; Brain; Mind Centre, Sydney, New South Wales, Australia.; Faculty of Medicine; Health, Sydney Medical School, Concord Repatriation General Hospital, Sydney, New South Wales, Australia.; College of Medicine & Health Sciences; Sultan Qaboos University Hospital, Sultan Qaboos University, Al-Khodh, Oman.; Department of Neurology, Timisoara, Romania.; Department of Neurology, Booalisina Hospital, Sari, Iran.; Multiple Sclerosis Centre Kamillus-Klinik, Asbach, Germany.; Neurology Department, King Fahad Specialist Hospital-Dammam, Khobar, Saudi Arabia.; Hospital Universitario de CEMIC, Buenos Aires, Argentina.; Department of Neurology, Hospital Fernandez, Capital Federal, Argentina.; Unit of Neuroimmunology, Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospital, Geneva, Switzerland.; Neurology, Az Sint-Jan Brugge, Bruges, Belgium.; Department of Neurology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary.; St. Vincent’s Hospital, Fitzroy, Melbourne, Victoria, Australia.; Department of Neurology, Antwerp University Hospital, Edegem, Belgium.; Translational Neurosciences Research Group, Faculty of Medicine; Health Sciences, University of Antwerp, Antwerp, Belgium.; Department of Neuroscience, Barwon Health, Geelong, Australia.; AZ Alma Ziekenhuis, Sijsele, Damme, Belgium.; St. Vincent’s Hospital Sydney, Sydney, New South Wales, Australia.; Neurologic Clinic; Policlinic, MS Center; Research Center for Clinical Neuroimmunology; Neuroscience Basel (RC2NB), University Hospital Basel; University of Basel, Basel, Switzerland.; Neurologic Clinic; Policlinic, MS Center; Research Center for Clinical Neuroimmunology; Neuroscience Basel (RC2NB), University Hospital Basel; University of Basel, Basel, Switzerland.; Neurologic Clinic; Policlinic, MS Center; Research Center for Clinical Neuroimmunology; Neuroscience Basel (RC2NB), University Hospital Basel; University of Basel, Basel, Switzerland.; 2025; https://doi.org/10.1001/jamaneurol.2025.0495

  1. if skip_local_extrema is not "none" in msprog::MSprog(), local extrema are excluded from (iii).↩︎

  2. If end dates of relapses are available (may be provided as an additional column in the relapse file whose name is specified by argument renddate_col in MSprog()), the use_end_dates argument in relapse_indep_from_bounds() controls whether they are used for PIRA or not. The default is use_end_dates=F. If use_end_dates=T, the provided intervals are ignored and PIRA is defined by the event or the confirmation not falling within the duration (onset to end) of a relapse.↩︎