Treatment Switching in the VenUS IV trial

Methods to manage treatment non-compliance in RCTs with time-to-event

outcomesCaroline FairhurstYork Trials Unit

Context

• Two arm RCT• Clinical setting• Continuous treatment • Time-to-event outcome (e.g., death,

healing)

Dream or reality?

Ideal• All participants will

remain in the trial throughout follow-up

• Will be concordant with their allocated treatment

• Will provide outcome data

Reality• Participants withdraw

from the trial and are lost to follow-up

• Withdraw from treatment

• 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 switchingRandomised

n=457

Hosieryn=230

Bandagen=224

Hosiery Bandage

Non-trial treatment

n=42

Non-trial treatment

n=46n=46n=16

Treatment switching

Increase in ulcer a

rea

Compression

uncomfortable

Ulcer deterio

ration

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:

0, whilst receiving control treatment1, whilst receiving experimental

treatment

Breaks randomisation balance and so subject to selection bias if switching related to prognosis

trt=

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

Time on control treatment

Time on experimental treatment Acceleration

factor

RPSFTM

• For patients (always) treated with control treatment: Ti1=0 Þ Ti=Ui

• For patients (always) treated with experimental treatment Ti0=0 Þ Ti=Ui

• Experimental treatment ‘multiplies’ survival time by relative to control treatment

RPSFTM

Control patient

RandomisationDeath

Control patient who switches

Observed

Death

Time

Counterfactual

Expected survival time without active treatment – `shrunk’ by a factor of

Death

Counterfactual

Counterfactual

Observed

Observed

Treatment patient

RPSFTM

Grid search for :• Vary values of by a small amount between

two plausible minimum and maximum values• Transform observed survival times using

• 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

• 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

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 IVMethod 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 0.92 (0.66, 1.28)

0.63

RPSFTM_cox 0.91 (0.69, 1.21)

0.53

IPE_exp 0.89 - -

IPE_wei 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.