Treatment Switching in the - PowerPoint Presentation

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 RCTClinical settingContinuous treatment Time-to-event outcome (e.g., death, healing)

Dream or reality?IdealAll participants will remain in the trial throughout follow-up Will be concordant with their allocated treatmentWill provide outcome data Reality Participants withdraw from the trial and a re lost to follow-up Withdraw from treatmentDeviate 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 Hosiery n=230 Bandage n=224 Hosiery BandageNon-trial treatmentn=42Non-trial treatmentn=46 n =46 n=16

Treatment switching Increase in ulcer area Compression uncomfortable Ulcer deterioration

Simple methods - ITTIntention-to-treatITT recommended (ICH E9) Compares individuals in the treatment groups to which they were randomisedEstimates 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 - PPPer-protocol1. 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 treatment 1, whilst receiving experimental treatment Breaks randomisation balance and so subject to selection bias if switching related to prognosis trt=

Complex methodsRank Preserving Structural Failure Time Model Attempt to estimate survival time lost/gained by exposure to experimental treatmentRelate the observed survival time, Ti , to the counterfactual survival time, U i by Time on control treatmentTime on experimental treatment Acceleration factor

RPSFTMFor patients (always) treated with control treatment: T i1=0 Þ Ti= U i For patients (always) treated with experimental treatment T i0 =0 Þ Ti=Ui Experimental treatment ‘multiplies’ survival time by relative to control treatment 

RPSFTM Control patient Randomisation Death 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

RPSFTMGrid 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  

AssumptionsRandomisation based treatment effect estimatorRank 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 methodsIterative parameter estimation algorithm Extension of RPSFTM methodsAssume the same causal model relating actual and counterfactual survival times Different estimation process for  

IPEA 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, U 1 , using  

IPE Same parametric accelerated failure time model is fit to the counterfactual survival timeNew 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 h r 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 HR0.99(0.79, 1.25)0.96PP_EXCHR1.10(0.86, 1.41)0.43PP_CENSHR1.23(0.98, 1.54)0.08TTVHR1.20(0.95, 1.50)0.13RPSFTM_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 - - 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 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 - -

SimulationA 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

ConclusionITT analysis recommended as primary analysisConsider 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

AcknowledgementsYork Trials UnitVenUS IV trial team Supervisor, Professor Mike Campbell (ScHARR, Sheffield)

ReferencesAshby, 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.