Caroline Fairhurst (MS PowerPoint , 1824kb)

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Treatment Switching in the
VenUS IV trial
Methods to manage treatment non-compliance
in RCTs with time-to-event outcomes
Caroline Fairhurst
York Trials Unit
Context
•
•
•
•
Two arm RCT
Clinical setting
Continuous treatment
Time-to-event outcome (e.g., death,
healing)
Dream or reality?
Ideal
Reality
• All participants will
remain in the trial
throughout follow-up
• Participants withdraw
from the trial and are
lost to follow-up
• Will be concordant with
their allocated treatment
• Withdraw from
treatment
• Will provide outcome
data
• Deviate from their
allocated trial treatment
Treatment switching
Problem?
• Switching to the alternative trial treatment
makes randomised groups more similar
• Dilutes the treatment effect observed from
a comparison of treatment groups as
randomised ignoring deviations from
allocated treatment (ITT)
• If you want to estimate the effect had
fewer switches occurred, ITT analysis
biased towards the null of no difference
VenUS IV trial
Venous leg ulcers are wounds
that form on gaiter region of the
leg
They are painful, malodorous and
prone to infection
Difficult to heal and 12 month
recurrence rates are 18-28%
VenUS I, II, III
Four layer bandaging is current
gold standard
VenUS IV trial
• Population: Patients aged over 18 with at
least one venous leg ulcer and able to
tolerate high compression to the leg
• Intervention: Two layer high compression
hosiery
• Control: Four layer high compression
bandaging
• Outcome: Time to healing of the largest
ulcer
Treatment switching
Randomised
n=457
Hosiery
n=230
Non-trial
treatment
n=42
n=16
Hosiery
Bandage
n=224
n=46
Bandage
Non-trial
treatment
n=46
Treatment switching
Simple methods - ITT
Intention-to-treat
• ITT recommended (ICH E9)
• Compares individuals in the treatment groups to
which they were randomised
• Estimates the effect of offering the two treatment
policies to patients with whatever subsequent
changes may occur
• “pragmatic effectiveness not biological efficacy”
• But what about effect of receiving experimental
treatment?
Simple methods - PP
Per-protocol
• 1. Excludes patients who switch
Assumptions: Switchers have same prognosis as
non-switchers so selection bias not introduced
• 2. Censor patients at time of switch
Assumptions: Decision to switch not related to
prognosis so censoring non-informative
Simple methods - TTV
Treatment as a time-varying covariate
Time-to-event model adjusted for time-dependent
treatment covariate:
trt=
0, whilst receiving control treatment
1, whilst receiving experimental treatment
Breaks randomisation balance and so subject to
selection bias if switching related to prognosis
Complex methods
Rank Preserving Structural Failure Time
Model
• Attempt to estimate survival time lost/gained by
exposure to experimental treatment
• Relate the observed survival time, Ti, to the
counterfactual survival time, Ui by
 = 0 +   1
Time on control
treatment
Acceleration
factor
Time on experimental
treatment
RPSFTM
• For patients (always) treated with control
treatment: Ti1=0 Ti=Ui
• For patients (always) treated with
experimental treatment Ti0=0 Ti= − Ui
• Experimental treatment ‘multiplies’ survival
time by  − relative to control treatment
RPSFTM
Randomisation
Control
patient
Death
Observed
Counterfactual
Death
Treatment
Observed
patient
Counterfactual
Control
patient who
switches
Expected survival time without active treatment – `shrunk’ by
a factor of  −
Death
Observed
Counterfactual
Time
RPSFTM
Grid search for :
• Vary values of  by a small amount between two
plausible minimum and maximum values
• Transform observed survival times using
 = 0 +   1
• Compare the counterfactual survival times between
the two randomised groups (e.g., logrank test or Cox
model)
• Let ∗ be value of  which maximises the p-value
∗

from the test, then acceleration factor is 
Assumptions
• Randomisation based treatment effect
estimator
• Rank preserving: if patient i fails before
patient j on treatment A, then i would fail
before j on treatment B
• Assumes the treatment effect is the same
regardless of when patient starts to
receive experimental treatment
Complex methods
Iterative parameter estimation algorithm
• Extension of RPSFTM methods
• Assume the same causal model relating
actual and counterfactual survival times
 = 0 +   1
• Different estimation process for 
IPE
• A parametric accelerated failure time
model is fit to the observed survival times
(e.g., Exponential, Weibull)
• Initial estimate of acceleration factor   is
obtained
• This is used to create first counterfactual
dataset, U1, using
1
  = 10 +   11
IPE
• Same parametric accelerated failure time
model is fit to the counterfactual survival
time
• New estimate of   obtained
• New counterfactual dataset created
Until estimate of   converges (is within, say,
10-5 of the previous estimate)
AF or HR?
Note
• strbee Stata program (Ian White)
• ipe option
• hr option
• Final estimate of   , used to ‘correct’
observed survival times
• Proportional hazards model used to
estimate ‘corrected’ HR
Application to VenUS IV
Method
Treatment
effect form
Estimate
95% CI
P-value
ITT
HR
0.99
(0.79, 1.25)
0.96
PP_EXC
HR
1.10
(0.86, 1.41)
0.43
PP_CENS
HR
1.23
(0.98, 1.54)
0.08
TTV
HR
1.20
(0.95, 1.50)
0.13
RPSFTM_log
HR
0.92
(0.66, 1.28)
0.63
RPSFTM_cox
HR
0.91
(0.69, 1.21)
0.53
IPE_exp
HR
0.89
-
-
IPE_wei
HR
0.88
-
-
Simulation
• A simulation study suggested that the
simple methods can significantly
overestimate the true treatment effect,
whilst the more complex methods of
RPSFTM and IPE produce less biased
results
Conclusion
• ITT analysis recommended as primary
analysis
• Consider a method to estimate the true
effect of efficacy as secondary analysis,
but not PP
• Different methods can be used for
continuous or categorical variables, e.g.
CACE analysis
Acknowledgements
• York Trials Unit
• VenUS IV trial team
• Supervisor, Professor Mike Campbell
(ScHARR, Sheffield)
References
•
•
•
•
•
Ashby, R. L., et al. (2014). "Clinical and cost-effectiveness of compression
hosiery versus compression bandages in treatment of venous leg ulcers
(Venous leg Ulcer Study IV, VenUS IV): a randomised controlled trial." The
Lancet 383(9920): 871-879.
Robins, J. and A. Tsiatis (1991). "Correcting for non-compliance in
randomized trials using rank preserving structural failure time models."
Communications in Statistics-Theory and Methods 20(8): 2609 - 2631.
White, I., et al. (1999). "Randomization-based methods for correcting for
treatment changes: Examples from the Concorde trial." Statistics in
Medicine 18(19): 2617 - 2634.
White, I., et al. (2002). "strbee: Randomization-based efficacy estimator."
The Stata Journal 2(Number 2): 140 - 150.
Branson, M. and J. Whitehead (2002). "Estimating a treatment effect in
survival studies in which patients switch treatment." Statistics in Medicine
21: 2449 - 2463.

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