Adjusting effects for
treatment switching in HTA
Why, when, which and how?
Claire Watkins
Director and Consultant Statistician, Clarostat Consulting Ltd
PSI Conference, 16th May 2017
Background
What do we mean by treatment switch/crossover?
Gefitinib
Doublet
chemo
Sunitinib
Standard
clinical
practice
(inc gef)
Survival
2
Placebo
Disease prog
• e.g. Gefitinib IPASS trial (Fukuoka, 2011)
Randomise
• Or it might happen spontaneously
due to clinical practice in the
region, if the treatment is already
on the market
Sunitinib
Survival
• e.g. Sunitinib GIST trial (Demetri, 2012)
Randomise
• We might build this into the
trial protocol
Disease prog
Patients in a parallel group RCT may switch or
“crossover” to the alternative treatment at some point
before an endpoint of interest occurs.
Switching – Regulatory vs HTA viewpoint
Regulatory agency
• Evaluate efficacy in clinical trial
Health Technology Assessment
(HTA) Agency
• Evaluate effectiveness in real world
setting
• Switch happened in clinical trial
• Switch ≠ real world (mostly)
• Do not adjust OS to remove switch
- ITT primary
• Use plausible methods* to adjust
OS to remove switch
• May consider switch adjusted OS
as a supportive analysis
• If no plausible methods, use ITT
• Primary endpoint may not be OS
anyway
• Key endpoint for lifetime cost
effectiveness calculations is OS
* Views of “plausible methods” differ by
agency!
3
Why does switching matter for HTA?
It all depends on the decision problem
In HTA, the decision problem is often to compare:
Current clinical
practice without new
therapy
vs
Potential future clinical
practice including new
therapy
If there is switching and the new therapy is effective,
ITT underestimates this difference
 How to estimate long term efficacy without switch?
4
Commonly used methods to estimate control
arm survival in absence of switch (Latimer 2014, Watkins 2013)
“Naive” methods
1. Exclude switchers
2. Censor at switch
3. Time varying covariate
Simple to apply
High levels of bias
Assumption: no confounders (variables
that influence switch and survival)
“Complex” methods
1. Inverse Probability of Censoring Weighting
(IPCW; observational)
2. Rank Preserving Structural Failure Time
(RPSFT; randomisation based)
3. Iterative Parameter Estimation
(IPE; randomisation based)
4. Two-stage Accelerated Failure Time (AFT)
External data
5
Harder to apply
Try to reduce bias
Complex 1: IPCW (weight non-switched times)
Control arm survival
Compare to observed experimental arm survival
Observed (ITT) control arm
(Robins 2000)
IPC weighted
WEIGHT
Non
switchers
Non
switchers
WEIGHT
S
Switchers
Switchers
S
Key
Death time
Censor time
S Switch time
WEIGHT
WEIGHT
Assumption: The variables in the weight
calculation fully capture all reasons for
switching that are also linked to survival
(i.e. no unmeasured confounders)
6
Weights represent how “switch-like” a patient is that has not yet switched
Complex 2/3: RPSFT/IPE (adjust post switch times)
(Robins 1991, White 2002, Branson 2002)
Control arm survival
Compare to observed experimental arm survival
Observed (ITT) control arm
Non
switchers
RPSFT/IPE adjusted
Non
switchers
S
Switchers
Switchers
S
Key
Time off
experimental
Death time
Censor time
S Switch time
7
Time on
experimental
Time off
Treatment
Time on
x
experimental multiplier experimental
Assumption: Each cycle of treatment
extends survival by a constant amount.
(i.e. constant/common treatment effect)
Estimated by non-parametric G-estimation (RPSFT) or parametric model (IPE)
Complex (4): 2-Stage AFT (observational study)
Control arm survival
Compare to observed experimental arm survival
Observed (ITT) control arm
Non
switchers
P
P
P
Switchers
PS
Key
Death time
Censor time
S Switch time
P Progression time
S
(Latimer 2014)
2-stage AFT adjusted
• Treat control arm as observational
study post progression
• Re-baseline at progression
• Collect covariate data at progression
• Calculate effect of switch treatment
adjusting for covariates
• Adjust switcher data and compare
randomised arms
Assumptions: The covariates fully
capture all reasons for switching that
are also linked to survival
No time-dependent confounding
between P and S
(i.e. no unmeasured confounders)
8
Not valid if switch can occur before progression
Method selection process
Specific process proposed in
NICE DSU Technical Support
Document 16 (Latimer 2014)
Key steps (in general):
1. Can the model be fitted with
available data?
2. If yes, is the model appropriate
given the switching
mechanism?
3. If yes, are the assumptions
reasonable?
4. If yes, are the results plausible?
1. Data collection requirements for commonly
used switch adjustment methods (Watkins 2016)
10
* for “on-treatment” approach only where efficacy stops at
treatment end
^ if switch is only allowed after this point
IPCW
All time varying covariates that may
influence switch decision and OS, collected
until switch or death/censoring
All time varying covariates that may
influence switch decision and OS, collected
until secondary baseline
Date of secondary baseline (e.g. disease
progression)
2-stage
All baseline covariates that may influence
switch decision and OS
RPSFTM
/IPE
Date of stopping switch treatment
Time
varying
covariate
Date of death/censoring
Censor
switchers
Data required
Date of starting switch treatment
Exclude
switchers
Method





*


*








 ^
Increasing data collection burden
2. Methods appropriate to switching mechanism
Censor
switchers
Time
varying
covariate
RPSFTM
/IPE
2-stage
IPCW
Switching mechanism
<10% switch
Exclude
switchers
Method
✗
✗
✗
✗
✗
✗
✗
✗
✗
>80% switch/perfect switch predictor
Switch occurs before progression for some
patients
Switch occurs a long time after progression
for some patients
Time on/off treatment or ITT survival is
similar between arms (HR ≈ 1)
11
✗ = method not appropriate
✗
✗
3. Summary of key assumptions for switch
adjustment methods
Method
Key assumptions
ITT
Switch treatment ineffective
Exclude switchers
No confounders (unlikely)
Censor switchers
No confounders (unlikely)
Time varying
covariate
IPCW
No confounders (unlikely)
2-stage
No unmeasured confounders
(stronger assumption than IPCW
as fewer covariates in model)
Constant treatment effect
RPSFTM
IPE
12
No unmeasured confounders
Constant treatment effect
Parametric distribution
The 6 core analyses
Produce as routine to understand data and switch patterns
13
A
When switch occurred relative to
randomisation, progression,
stopping randomised treatment,
death/censoring, e.g. via patient
profile plots
B
Number/% switched per arm,
overall & of patients eligible for
switch (e.g. exclude censored/died
without prog) (IPCW/2-stage
unreliable if too high)
C
Control arm patient characteristics
split by switching status, to
determine covariates that
influence switch
D
Analyse covariates that
influence survival across all
patients in the study regardless
of treatment
E
Compare control arm switchers
and non-switchers for
endpoints linked to survival
but not switch-affected, e.g.
progression-free survival
F
Summarise time on and off
treatment by randomised arm
(if similar, RPSFT/IPE
unreliable)
Patient profile plot
Example – control arm patients
14
Assessing the assumptions: No confounders
Naïve methods
Determine if core Analysis C (covariates that influence switch) and Analysis D
(covariates that influence survival) find any of the same covariates
Determine if core Analysis E (non-switch affected endpoints linked to survival)
shows a difference between switchers and non-switchers
Ask a medical expert if disease progression or other variables are likely
confounders.
15
Assessing the assumptions: No unmeasured
confounders
IPCW, 2-stage
Determine if Analysis E (non-switch affected endpoints linked to survival)
adjusted for the covariates (measured potential confounders) in the statistical
model shows a difference between switchers and non-switchers
Review patient profile plots from Analysis A for large time gaps between last key
covariate data collection and switch (concern for IPCW,) or secondary baseline
and switch (concern for 2-stage)
Ask a medical expert and review literature for potential unmeasured
confounders
16
Assessing the assumptions: Constant/common
treatment effect
RPSFT, IPE
This is the hardest assumption to assess quantitatively, so seek medical expert
opinion.
“Tipping point” sensitivity analysis relaxing this assumption – set the
treatment effect in switchers to be smaller than for those initially randomized, until
the adjusted survival gets close to the ITT result. Determine if that reduction in
effect for switchers is clinically plausible.
For trials with previous interim analyses, determine if the switch adjusted
treatment effect from these methods is different for earlier and later analysis
times
Assuming no unmeasured confounders holds, determine if the treatment effect
from second stage of 2-stage model is different to that from RPSFTM/IPE
17
Summary – choice of switch adjustment method
• First things first
•
•
•
•
Be clear on the decision problem(s) and target audience(s)
Switch adjustment is not always necessary
Define switch treatment carefully
Determine what data were collected
• No method is universally “best”
• Be methodical and justify your choice
1.
2.
3.
4.
18
Can the model be fitted with available data?
If yes, is the model appropriate given the switching mechanism?
If yes, are the assumptions reasonable?
If yes, are the results plausible?
References
Demetri GD et al. Complete longitudinal analyses of the randomized, placebo-controlled, Phase III trial of sunitinib in
patients with gastrointestinal stromal tumor following imatinib failure. Clin Cancer Res 2012; 18:3170-3179
Fukuoka M et al. Biomarker analyses and final overall survival results from a Phase III, randomized, open label, first-line
study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non-small cell lung cancer in
Asia (IPASS). J Clin Oncol 2011; 29(21):2866-2874
Watkins C et al. Adjusting overall survival for treatment switches: Commonly used methods and practical application.
Pharm Stats 2013 Nov-Dec;12(6):348-57
Latimer NR and Abrams KR. NICE DSU Technical Support Document 16: Adjusting survival time estimates in the
presence of treatment switching. (2014). Available from http://www.nicedsu.org.uk
Robins J and Finkelstein D. Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse
Probability of Censoring Weighted (IPCW) Log-Rank Tests. Biometrics 2000; 56(3):779-788
Robins JM and Tsiatis A. Correcting for non-compliers in randomised trials using rank-preserving structural failure time
models. Communications in Statistics - Theory and Methods 1991; 20:2609-2631.
White IR et al. strbee: Randomization-based efficacy estimator. The Stata Journal 2002; 2(2):140-150
Branson M, Whitehead J. Estimating a treatment effect in survival strudies in which patients switch treatment. Statistics in
Medicine 2002; 21:2449–2463.
Watkins CL, Latimer N, Wang J, Wright EJ. Guidance on selecting appropriate methods when considering adjusting
overall survival for treatment switch in oncology studies. Value in Health 2016; 19(7): A398
19
Q&A
20