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P-valueModel: T i (0) = T ioff+ exp(ψ) × T ion• When ψ=0 we have T i (0) = T i• So the test statistic is the same as for the observeddata• Thus the P-value for the RPSFTM is the same as for theITT analysis– provided the same test is used for both• The estimation procedure is “randomisation-respecting”– it is based only on the comparison of groups asrandomised– I’ve suggested the term randomisation-basedefficacy estimator, RBEE (White, 2005)36
Censoring• Censoring introduces complications in RPSFTMestimation– censoring on the T(0) scale is informative– requires re-censoring which can lead to strangeresults – see White et al (1999)37
Page 1 and 2: Methods for handling treatmentswitc
Page 3 and 4: Note on terminology• I’ve calle
Page 5 and 6: Plan of the dayIan WhiteSusie DoddJ
Page 7 and 8: Defining the question• What is th
Page 9 and 10: A hypothetical (& simplistic) trial
Page 11 and 12: Intention-To-Treat (ITT) Analysis
Page 13 and 14: Per Protocol (PP) Analysis• Censo
Page 15 and 16: Inverse-probability-of-censoring we
Page 17 and 18: Inverse probability weights• Half
Page 19 and 20: Choice of covariates for IPCW• Re
Page 21 and 22: Note on IPCW• Can handle more tha
Page 23 and 24: Marginal Structural Models (MSMs)
Page 25 and 26: Towards the RPSFTM• focussing on
Page 27 and 28: RPSFTM (2)• The RPSFTM relates ob
Page 29 and 30: Interpretation of ψModel: T i (0)
Page 31 and 32: G-estimation: an unusual estimation
Page 33 and 34: Hypothetical data(switches occur on
Page 35: Estimating ψ: is e ψ = 0.5?Treate
Page 39 and 40: Non-standard RPSFTM-like analysisto
Page 41 and 42: Arguments used: IPCWFor• Gives HR
Page 43 and 44: Key messages: design• Collect fol
Page 45 and 46: Summary & questions• IPCW, MSM an

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