Ever planned RCT w/ PFS and OS as formal endpoints?
Ever simulated PFS and OS for same set of patients?
In both cases: How did you model the fact that PFS and OS are highly dependent? E.g. we know that PFS <= OS and P(PFS = OS) > 0.
A (long) thread - but it' s worth it!
1/n
Ever simulated PFS and OS for same set of patients?
In both cases: How did you model the fact that PFS and OS are highly dependent? E.g. we know that PFS <= OS and P(PFS = OS) > 0.
A (long) thread - but it' s worth it!
1/n
We are very used to model dependency of an endpoint over time (= group-sequential designs) or over nested populations (= enrichment designs), b/c then it's not difficult to write down correlation between test statistics.
However, typically in trial planning PFS and OS...
2/n
However, typically in trial planning PFS and OS...
2/n
are treated as if they were independent and proportional hazards (PH) is assumed for both.
We propose to model dependency between PFS and OS using an illness-death multistate model (IDM), building on the work of Meller et al (2019) doi.org/10.1002/sim.82…
3/n
We propose to model dependency between PFS and OS using an illness-death multistate model (IDM), building on the work of Meller et al (2019) doi.org/10.1002/sim.82…
3/n
Making assumptions on 6 transition-specific hazards (3 in control + 3 in tmt arm) we *induce* survival functions for PFS and OS, and parsimoniously model their dependence in mathematically appropriate way.
Turns out that for constant transition-specific hazards...
4/n
Turns out that for constant transition-specific hazards...
4/n
we always have PH for PFS, but that for OS hazards are only proportional under *very restrictive* assumptions that are typically not met in a clinical trial.
--> So, assuming PH for PFS and OS independently when planning RCT is too simplistic! <--
What is the implication?
5/n
--> So, assuming PH for PFS and OS independently when planning RCT is too simplistic! <--
What is the implication?
5/n
Accounting for dependency can increase power. In one of our scenarios we can reduce number of necessary OS events to perform final analysis at from 770 to 600.
However, since shape of OS survival functions is induced through assumption on transition-specific hazards...
6/n
However, since shape of OS survival functions is induced through assumption on transition-specific hazards...
6/n
modeling based on IDM can also *reduce* power for OS. So, making simplifying assumption of independence of PFS and OS can lead to RCT that is underpowered for OS.
Our paper describes all this and illustrates how RCT can be planned based on simulating from an IDM.
7/n
Our paper describes all this and illustrates how RCT can be planned based on simulating from an IDM.
7/n
R package provides easily accessible implementation.
Curious to hear comments!
#oncology #multistate #survivalanalysis #biostatistics
Link to paper: arxiv.org/abs/2301.10059
Link to R package: cran.r-project.org/web/packages/s…
The end.
Curious to hear comments!
#oncology #multistate #survivalanalysis #biostatistics
Link to paper: arxiv.org/abs/2301.10059
Link to R package: cran.r-project.org/web/packages/s…
The end.
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Link to R package on github: github.com/insightsengine…
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