Observational Study in Multiple Sclerosis Challenges in the - - PowerPoint PPT Presentation

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Jul 17, 2023 655 likes •837 views

Observational Study in Multiple Sclerosis Challenges in the DIFUTURE Use-Case Heidi Seibold Causal inference workshop, Zurich 12/2018 MS Use-Case DIFUTURE Heidi Seibold 2/ 17 MS Use-Case DIFUTURE Heidi Seibold Notation and Definitions

SLIDE 1

Observational Study in Multiple Sclerosis — Challenges in the DIFUTURE Use-Case

Heidi Seibold Causal inference workshop, Zurich 12/2018

SLIDE 2

MS Use-Case DIFUTURE Heidi Seibold 2/ 17

SLIDE 3

MS Use-Case DIFUTURE Heidi Seibold

Notation and Definitions

MS Multiple sclerosis cMRT Cerebral magnetic resonance tomography (images of the brain) sMRT Spinal magnetic resonance tomography (images of the spine) ti Month i after first relevant cMRT (= cMRT before treatment start)

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SLIDE 4

MS Use-Case DIFUTURE Heidi Seibold

Develop a model which can be used for making individual treatment decisions in patients with MS

Treatment success if no new or enlarged lesions visible in sMRT and cMRT

between months 6 (t6) and 24 (t24)

3 treatment options:

◮ No treatment ◮ Basic treatment ◮ Strong treatment

A variety of patient characteristics can potentially influence treatment success

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SLIDE 5

MS Use-Case DIFUTURE Heidi Seibold

Data

Data collected as part of clinical routine at MRI (TU Munich). Outcome Treatment Baseline patient characteristics

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SLIDE 6

MS Use-Case DIFUTURE Heidi Seibold

Data

Outcome: New or enlarged lesions visible in sMRT and cMRT between t6 and t24 (yes/no)

No sMRT
cMRT “usually” around t0, t12 and t24 (not as needed t0, t6 and t24).

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SLIDE 7

MS Use-Case DIFUTURE Heidi Seibold

Data

1 2 3 4 6 12 24

Time in months Patient Treatment

Basic Strong Untreated

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SLIDE 8

MS Use-Case DIFUTURE Heidi Seibold

Data

1 2 3 4 6 12 24

Time in months Patient Treatment

Basic Strong Untreated cMRI

Treatment:

If no treatment within the first 2 years after diagnosis: treatment = no treatment
Otherwise: treatment = first treatment given
Treatment switches are possible

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SLIDE 9

MS Use-Case DIFUTURE Heidi Seibold

Data

1 2 3 4 6 12 24

Time in months Patient Treatment

Basic Strong Untreated cMRI

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SLIDE 10

MS Use-Case DIFUTURE Heidi Seibold

Data

Baseline patient characteristics:

Many possible characteristics available
Many missing values (potentially not missing at random)
Data quality potentially low

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SLIDE 11

Challenges

SLIDE 12

MS Use-Case DIFUTURE Heidi Seibold

How to deal with the issue of censoring in the outcome?

We are interested in new or enlarged lesions between t6 and t24
MRTs are mostly not done at these time points
Can we use imputation methods?
Can we use methods that can deal with censoring?

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SLIDE 13

MS Use-Case DIFUTURE Heidi Seibold

How to deal with the fact that for most patients we have only

ne sMRT?
Clinician say that we can assume that there is no spinal progression if no sMRT

was conducted.

Is there a way to account for the insecurity?

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SLIDE 14

MS Use-Case DIFUTURE Heidi Seibold

How to obtain causal treatment effects?

Data is observational
We want to be able to say why a patient should receive which treatment

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SLIDE 15

MS Use-Case DIFUTURE Heidi Seibold

What to do in case of treatment switches?

Patients sometimes switch from one treatment to another during the study

period

Is there a way to account for this?
Would it be ok to ignore treatment switches? If so, why?

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SLIDE 16

MS Use-Case DIFUTURE Heidi Seibold

What to do with missing values in patient characteristics?

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SLIDE 17

MS Use-Case DIFUTURE Heidi Seibold

What to do with other data issues?

Treatment starts before first cMRT
What to do if data quality in certain patient characteristics is bad?
...

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