N = 1, Cross-Over Trials and Balanced Designs

1. N = 1, Cross-Over Trials and Balanced Designs

2. N = 1 Trials • Trials can be undertaken with just one participant. If the condition is a chronic relapsing problem the unit of randomisation can be the ‘episode’ of treatment. • For example, n = 1 trials have been used among people with rheumatoid arthritis to ‘try out’ different pain relieving drugs.

3. Migraine Trial • A trial could be mounted to allocate a test treatment for each migraine attack. • Placebo or open treatment could be used. • Once the individual senses an attack coming on he contacts an independent ‘statistician’ and she gives him the allocation.

4. Blinding of allocation • Important to blind allocation as the migraine sufferer may ask for treatment at different stages of the attack if he knew which allocation might come up. • Placebo might be useful but any effect including a ‘placebo’ effect is worthwhile.

5. Example • Some suggestion that blood sugar falls prior a migraine perhaps boosting this with glucose tablets may limit attack? • Randomised to either 2 asprin or 2 asprin plus 3 gluscose tables. • Outcome return to ‘normal’ activity.

6. Sample Size • This is kept small (18 migraines) because it is paired data (within the individual) so variance is low. • Low significance level p = 0.20 (treatment harmless, consequences of Type I error low, Type II error large).

7. Blocking? • Not used although one could stratify by severity, but due to single individual could easily lead to prediction of block size. • A simple allocation schedule used which is a single balanced block of 20 migraines to avoid prediction of last few allocations.

8. Time lines • Average migraine once a month so trial is unavoidably long (approx 2 years). • But dramatic effect could lead to early stopping. • Sadly no such occurrence.

9. Cross-over trials • Cross-over studies are when participants are randomly allocated to one treatment for a period of time and then ‘cross-over’ to the other treatment for the second period of time. • Some HRT trials for treatment of menopausal symptoms have used cross over designs.

10. Advantages • The advantages of a cross-over trial is that is substantially strengthens the inference of an effect as essentially one is repeating the trial twice and because data are paired within individuals usually smaller sample sizes are needed.

11. Disadvantages • Can’t be used in many if not most circumstances. • Can’t cross people over from a cancer treatment to a placebo and vice versa. • Even when they are feasible there is can be a problem with ‘washout’ or contamination if the drug remains in the patients’ system will dilute the effects.

12. Disadvantages (cont) • If there is a dramatic effect of treatment people may refuse to cross over to new treatment. • For example, an RCT of dietary exclusion of foods for IBS suffers versus sham diet, found a big effect in the first period BUT those in the ‘real’ diet who benefited refused to cross to a sham diet.

13. Balanced Incomplete Block Designs • Trials of guidelines or educational packages may use an incomplete block design.

14. Avoiding Hawthorne Effects • One way of avoiding a Hawthorne effect is to use a balanced design whereby the ‘control’ group also gets an intervention. • This can be done using a balanced design.

15. Example • A guidelines trial might look at the effectiveness of guidelines for Angina. Just giving guidelines to GPs might affect practice irrespective of the ‘benefit’ of guidelines. • We can balance this possibility by giving ‘guidelines’ say on diabetes to the control group.

16. Balanced Design • This design will balance out the Hawthorne effects of guidelines being given to one group alone. • Also allows the evaluation of two sets of guidelines for the ‘price’ of one study.

17. Example • Verstappen et al wanted to control for the Hawthorne effect in a trial looking at an intervention for test ordering from GPs. • They also undertook some methodological research to see if the ‘Hawthorne’ effect was real. Verstappen et al. J Clin Epidemiol 2004;57:1119

18. Design • GP teams randomised into 3 arms. • A tests = feedback on test ordering for cardiovascular, upper and lower abdominal problems; • B tests = feedback on test ordering for: pulmonary, non-organ related and joint complaints. • C = minimal intervention control group.

19. Results • A comparison between B vs A test groups shows a small (not significant, p = 0.29) difference. B vs C shows a much bigger nearly statistically significant difference ( p = 0.07)

20. Conclusion • The change in test ordering was exaggerated by process of research compared with the actual intervention itself. Whilst the intervention had an effect this was relatively modest.

21. Summary • Both n of 1 trials and cross-over studies are not widely used. N of 1 trials can be combined with several individuals leading to a greater power and shorter study. • Balanced designs are useful in eliminating the possibility of Hawthorne effects.