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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.

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Presentation on theme: "Treatment Switching in the VenUS IV trial Methods to manage treatment non-compliance in RCTs with time-to-event outcomes Caroline Fairhurst York Trials."— Presentation transcript:

1 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

2 Context Two arm RCT Clinical setting Continuous treatment Time-to-event outcome (e.g., death, healing)

3 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

4 Treatment switching

5 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

6 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

7 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

8 Treatment switching Randomised n=457 Hosiery n=230 Bandage n=224 HosieryBandage Non-trial treatment n=42 Non-trial treatment n=46 n=16

9 Treatment switching Increase in ulcer area Compression uncomfortable Ulcer deterioration

10 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?

11 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

12 Simple methods - TTV Treatment as a time-varying covariate Time-to-event model adjusted for time-dependent treatment covariate: 0, whilst receiving control treatment 1, whilst receiving experimental treatment Breaks randomisation balance and so subject to selection bias if switching related to prognosis trt=

13 Complex methods Time on control treatment Time on experimental treatment Acceleration factor

14 RPSFTM

15 Control patient Randomisation Death Control patient who switches Observed Death Time Counterfactual Death Counterfactual Observed Treatment patient

16 RPSFTM

17 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

18 Complex methods

19 IPE

20

21 AF or HR?

22 Application to VenUS IV MethodTreatment effect form Estimate95% CIP-value ITTHR0.99(0.79, 1.25)0.96 PP_EXCHR1.10(0.86, 1.41)0.43 PP_CENSHR1.23(0.98, 1.54)0.08 TTVHR1.20(0.95, 1.50)0.13 RPSFTM_log0.92(0.66, 1.28)0.63 RPSFTM_cox0.91(0.69, 1.21)0.53 IPE_exp IPE_wei0.88--

23 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

24 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

25 Acknowledgements York Trials Unit VenUS IV trial team Supervisor, Professor Mike Campbell (ScHARR, Sheffield)

26 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): 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): White, I., et al. (1999). "Randomization-based methods for correcting for treatment changes: Examples from the Concorde trial." Statistics in Medicine 18(19): White, I., et al. (2002). "strbee: Randomization-based efficacy estimator." The Stata Journal 2(Number 2): Branson, M. and J. Whitehead (2002). "Estimating a treatment effect in survival studies in which patients switch treatment." Statistics in Medicine 21:


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