“let’s get down to whatis reallytypes of missingness • missing at random (mar) –given the...

“Let’s get down to whatis really

wrong”

– what makes clinical sense when a

neuroscience trial has missing data?

Summary of this presentation

• Missing at random; missing not at random.

• Deciding what you want to estimate.

• Efficacy attributable to the experimental

treatment – basing assumptions on

evidence.

Types of missingness

• Missing at random (MAR)

– Given the model & observed data, missing-

ness is independent of the missing data.

– Corollary: one can model missing outcomes

based on the observed outcomes.

• Missing not at random (MNAR)


ness is not independent of the missing data.

– Corollary: we cannot estimate missing

outcomes from observed.Rubin (1978)

Types of missingness

• Missing at random (MAR)


ness is independent of the missing data.

– Corollary: one can model missing outcomes

based on the observed outcomes.

• Missing not at random (MNAR)


ness is not independent of the missing data.

– Corollary: we cannot estimate missing

outcomes from observed.

But withdrawals are probably

different than subjects who

remain in study!

Bias from missing data?

• Missing data could bias an estimate of

treatment effect…

• but bias depends on what you want to

estimate.

What do you want to estimate?

• The “estimand”

• Estimand should serve the needs of

patients, of clinicians, of regulators.

– Estimand may imply assumptions about

missings.


• The “estimand”

• Estimand should serve the needs of

patients, of clinicians, of regulators.

– Estimand may imply assumptions about

missings.

...perhaps

counterfactual

assumptions.


• What estimand would serve the needs of

patients? Treatment effect…

– if I adhere to regimen?

– observed in study population?

– taking into account that not all subjects

completed the course of treatment?



patients? Treatment effect…



Per protocol

estimate

Completers only

– taking into account that not all subjectscompleted the course of treatment?

Withdrawals

are different:

“treatment

policy” or

MNAR

estimate




– if none had stopped treatment early


patients/clinicians? Treatment efPfeer cprto…tocolestimate

Completers only

Model withdrawals

based on

observed: MAR

– taking into account that not all subjectscompleted the course of treatment?

Withdrawals

are different:

“treatment

policy” or

MNAR

estimate



regulators? Treatment effect…














Perhaps

some form

of this

estimand?









Often referred to as

“effectiveness estimand” in the

literature

Perhaps

some form

of this

estimand?



Often referred to as

“effectiveness estimand” in the

literature

Perhaps

some form

of this

estimand?







Could also be“Treatment policy”

estimand

From estimand to estimate

• Suppose we choose estimand “the

treatment effect taking into account that

not all subjects completed the course of

treatment”.

• For example, choose “effect attributable to

study treatment” (Mallinckrodt et al. 2014).

• Historic data can help us choose plausible

assumptions for missing outcomes.

• Two neuroscience examples….

Plausible assumptions

• Scenario: superiority study; control arm is

standard of care or placebo; study

estimand is effectiveness: “effect

attributable to study treatment”.

• If subject withdraws early, this estimand

implies no continuing benefit from

experimental treatment over and above

control except what would occur due to

residual drug in the body.

Assumptions using historic data

• Can we identify likely trajectory of efficacy

after withdrawal, assuming no continuing

benefit from experimental treatment over

control except what would occur due to

residual drug in the body?

• Can we identify likely trajectory of

withdrawals, relative to that of control?

• If yes: we have justifiable assumptions for

this effectiveness estimand.

Example 1: major depressive

disorder: MDD

Historic data: typical trajectories

for MDD

Brannan et al. (2005)


for MDD


for MDDA variety of assumptions based

on trajectories of control group

are implementable via multiple

imputation


for MDD

?

“Jump to reference” (J2R)


for MDD

?

Imputations at mean of

reference


for MDD

?

Model experimental arm

using reference data only:

“Copy reference” (CR)

Using post-withdrawal data

• Motivation to collect post-withdrawal

efficacy in early studies

• If post-withdrawal data not available from

standard studies, may be available from

randomized withdrawal studies

Apr2014

Perahia et al. (2006)

Ranomized withdrawal, MDD

Ranomized withdrawal, MDD

Rapid worsening

to placebo-like efficacy

Select suitable control-based

trajectory?


trajectory?

Imputations at mean of

reference may be

clinically reasonable,

based on the historic data


trajectory?

“Jump to reference”

assumption could be a

conservative choice, also

clinically justifiable.

Using post-withdrawal data

• Based on historic data, clinically

interpretable (and clinical reasonable)

assumptions for missing data could be

– Imputations for experimental arm have the

mean of the placebo group at each visit, or

– Imputations for experimental arm follow the

“jump to control” assumption: withdrawal from

experimental arm, usually with poor

HAMDT17, will be imputed to perform

similarly poorly in the control arm.

Apr2014

Example 2: Insomnia

• Efficacy score total sleep time (TST)

– higher is better.

• About 20% withdrawals expected in each

treatment group.

• Experimental treatment has a mechanism

of action similar to the orexin receptor

antagonists (ORAs).

T ds and Innovations in Clinical Trial Statistics: Missing data, developments in practice, copyright MOKelly 2014Apr2014

• Data on post-withdrawal TST in an ORA is

available in recent randomized withdrawal

study available as FDA Briefing

Document.


Example 2: Insomnia

ORA randomized withdrawal

FDA Peripheral & Central Nervous System Drugs Advisory Committee Meeting (2013)


?Imputations at mean of reference


?Imputations at mean of reference˟


?“Jump to reference” (J2R)


“Jump to reference” (J2R)

Take account of apparent early lower TST by subtracting 10 minutes here (“delta adjustment”)?

Plausible assumptions

• Based on historic data, strategy could be

– Imputations for experimental arm follow the

“jump to control” assumption: withdrawal from

experimental arm, usually with poor TST, will

be imputed to perform similarly poorly in the

control arm, OR

– As above, but subtract 10 minutes from

imputed values for first post-withdrawal visit

(“delta-adjust” with δ = 10) to take account of

apparent early lower TST in withdrawals.


Conclusions

• Estimands and their assumptions about

missing data will be most useful if they

reflect clinical scenarios of interest:

statisticians and clinicians can work

together to identify these.

– Provide justification for the chosen strategy

for missing data in the protocol or statistical

analysis plan.

• Sensitivity analyses still essential, even for

scientifically justified MNAR estimands.

Apr2014

References

Brannan et al. (2005) Onset of action for duloxetine 60 mg once daily: double-blind,placebo-controlled

studies J. Psychiatr Res. 39 161-72.

FDA Peripheral & Central Nervous System Drugs Advisory Committee Meeting (2013) Suvorexant

Tablets Insomnia indication Advisory Committee Briefing Document for NDA204569.

Mallinckrodt et al. (2014) Recent Developments in the Prevention and Treatment of MissingData.

Therapeutic Innovation & Regulatory Science 48 68-80.

Perahia et al. (2006) Duloxetine in the prevention of relapse of major depressive disorder:Double-

blind placebo-controlled study. Br J Psychiatry. 188 346-53.

Rubin (1978) Multiple imputation in sample surveys – a phenomenological Bayesian approach to

nonresponse. Proceedings of the Survey Research Methods Section of the American Statistical

Association 1 20–34.

Back up slides

Questions?


for MDD

Brannan et al. (2005) Onset of action for duloxetine 60 mg once daily: double-blind, placebo-controlled studies

?

Match slope of reference:

“Copy increment from reference”

(CR)

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