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Statistical considerations in small proof-of-concept trials, including crossover designs

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Statistical considerations in small proof-of-concept trials, including crossover designs

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    1. (c) Stephen Senn 2008 1 Statistical considerations in small proof-of-concept trials, including crossover designs Stephen Senn

    2. (c) Stephen Senn 2008 2 People look down on marketing men Its not true that they are not scientists They work in sell biology I would like to take this opportunity to draw your attention to a book I rather like

    3. (c) Stephen Senn 2008 3 Outline Decision analysis and proof of concept Value of information perspective Place of cross-over trials Carry-over The potential for cross-over trials in studying individual response

    4. (c) Stephen Senn 2008 4 A Model

    5. (c) Stephen Senn 2008 5 Model Continued

    6. (c) Stephen Senn 2008 6 Example

    7. (c) Stephen Senn 2008 7

    8. (c) Stephen Senn 2008 8 Value of Biomarker Information in Terms of Posterior Variance Suppose that over all products for this indication the correlation of true therapeutic and biomarker outcomes is 0.9 Let the prior means be zero in this class Let the prior variances be 1 Let the data variance of a minimal experiment be also 1 Implies prior information equivalent to one minimal experiment

    9. 9 Simulated and theoretical posterior variances. The correlation between true therapeutic and biomarkers means is 0.9 The prior variance is the same as the data variance for one minimal experiment (n=1) Simulated and theoretical posterior variances. The correlation between true therapeutic and biomarkers means is 0.9 The prior variance is the same as the data variance for one minimal experiment (n=1)

    10. (c) Stephen Senn 2008 10 A Serious Warning to Statisticians

    11. (c) Stephen Senn 2008 11 My Gloomy Take on This We dont really understand this topic There may be less value in proof of concept studies than we propose Therapeutic studies may be valuable even if they have low power There is no point in undertaking POC studies unless you can see circumstance under which they would cause you to cancel projects

    12. (c) Stephen Senn 2008 12 Appropriate Attitudes for Cross-over Trials They are not suitable for all indications and questions They are extremely valuable for some indications and questions Carry-over has to be dealt with by washout Dont pre-test for carry-over Dont rely on classical statistical approaches to carry-over Cross-over trials have great potential in investigating individual response

    13. (c) Stephen Senn 2008 13

    14. (c) Stephen Senn 2008 14 The simple carry-over model.

    15. (c) Stephen Senn 2008 15 Three Period Bioequivalence Designs Three formulation designs in six sequences common. Subjects randomised in equal numbers to six possible sequences. For example, 18 subjects, three on each of the sequences ABC, ACB, BAC, BCA, CAB, CBA. A = test formulation under fasting conditions, B = test formulation under fed conditions C = reference formulation under fed conditions.

    16. (c) Stephen Senn 2008 16 These are the weights for the B-C contrast Note that they add to 1 over all cells labelled B and -1 over all cells labelled CThese are the weights for the B-C contrast Note that they add to 1 over all cells labelled B and -1 over all cells labelled C

    17. (c) Stephen Senn 2008 17 Properties of these weights Sum to 0 in any column, eliminates the period effect. Sum to 0 in any row eliminates patient effect Sum to 0 over cells labelled A A has no part in definition of contrast Sum to 1 over the cells labelled B and to -1 over the cells labelled C Estimate contrast B-C

    18. (c) Stephen Senn 2008 18 Note that these weights also sum to zero over any column and row, over zero for all cells labelled A and to 1 over all cells labelled B and -1 over all cells labelled BNote that these weights also sum to zero over any column and row, over zero for all cells labelled A and to 1 over all cells labelled B and -1 over all cells labelled B

    19. (c) Stephen Senn 2008 19 They sum to zero over all cells labelled a, b and cThey sum to zero over all cells labelled a, b and c

    20. (c) Stephen Senn 2008 20

    21. (c) Stephen Senn 2008 21

    22. (c) Stephen Senn 2008 22 The Dangers of Pre-testing Situation with AB/BA design Two-stage procedure is very badly biased CARRY and PAR are highly correlated 1/?2 < ? < 1 Three treatment design Same problem Carry-over and adjusted estimates correlated ? = 0.45

    23. (c) Stephen Senn 2008 23 The Phoenix Bioequivalence Trials Analysed by DAngelo, Potvin & Turgeon * 20 drug classes 1989-1999 12 or more subjects 96 three period designs 324 two period designs

    24. 24 Abstract There is now general agreement that pre-testing for carry-over in the AB/BA design is harmful and that efficient analysis of this design must proceed on the assumption that carry-over has not affected the results to any appreciable degree. A general consensus has not been achieved in the case of higher-order designs. Since particular forms of carry-over can be estimated on a within patient basis and unbiased within patient treatment estimators are possible, some statisticians favour pre-testing and some favour automatic adjustment for carry-over. We present theoretical arguments that show that, just as in the AB/BA case, the strategy of pre-testing is biased as a whole and also that the loss in terms of efficiency in adjusting is not negligible. We also present data from two large series of bioequivalence studies to provide empirical evidence that in this context carry-over is either absent or rare. We conclude that adjusting or testing for carry-over in bioequivalence studies is at worst harmful and at best pointless, and that this may also apply to other kinds of study.Abstract There is now general agreement that pre-testing for carry-over in the AB/BA design is harmful and that efficient analysis of this design must proceed on the assumption that carry-over has not affected the results to any appreciable degree. A general consensus has not been achieved in the case of higher-order designs. Since particular forms of carry-over can be estimated on a within patient basis and unbiased within patient treatment estimators are possible, some statisticians favour pre-testing and some favour automatic adjustment for carry-over. We present theoretical arguments that show that, just as in the AB/BA case, the strategy of pre-testing is biased as a whole and also that the loss in terms of efficiency in adjusting is not negligible. We also present data from two large series of bioequivalence studies to provide empirical evidence that in this context carry-over is either absent or rare. We conclude that adjusting or testing for carry-over in bioequivalence studies is at worst harmful and at best pointless, and that this may also apply to other kinds of study.

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    26. (c) Stephen Senn 2008 26

    27. (c) Stephen Senn 2008 27

    28. (c) Stephen Senn 2008 28 Conclusions Distribution of P-values uniform no evidence of carry-over Carry-over a priori implausible presence testable by assay No point is testing for it leads to bias Or adjusting for it increased variance

    29. (c) Stephen Senn 2008 29 Possible Strategy Run multi-period cross-overs Patient by treatment interaction becomes identifiable This provides an upper bound for gene by treatment interaction Because patients differ by more than their genes

    30. (c) Stephen Senn 2008 30 Here we have perfect correlation. Here we have perfect correlation.

    31. (c) Stephen Senn 2008 31 Here we have complete independence Note, however, that the margins are the same as previously. See Senn, S. J. (2004). "Individual response to treatment: is it a valid assumption?" BMJ 329(7472): 966-968. Here we have complete independence Note, however, that the margins are the same as previously. See Senn, S. J. (2004). "Individual response to treatment: is it a valid assumption?" BMJ 329(7472): 966-968.

    32. (c) Stephen Senn 2008 32 Advantages and Disadvantages PRO CON Cheap Low tech Insight into sources of variation gained Only suitable for chronic diseases Demanding of patients time Unglamorous Does not produce diagnostic patents

    33. (c) Stephen Senn 2008 33 An Overlooked Source of Genetic Variability Humans may be classified into two important genetic subtypes. One of these suffers from a massive chromosomal deficiency. This is expressed in. Important phenotypic differences. A massive disadvantage in life expectancy. Many treatment strategies take no account of this. The names of these subtypes are...

    34. (c) Stephen Senn 2008 34

    35. (c) Stephen Senn 2008 35

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