2. TREATMENT 1 Evaluation of interventions How best assess treatments /other interventions? RCT (randomised controlled trial)

3. OBJECTIVES Treatment lecture 1 Describe structure of RCT Define, calculate and interpret main measures of effect for RCTs Compare RCT design with observational study designs (Treatment lecture 2) Explain differences between efficacy and effectiveness Distinguish between explanatory and management trials)

4. What will be covered Experimental design RCT architecture Architecture of cohort and case control studies RCT analysis (measures of effect) Calculations  Examples……….

5. RCT is Main tool for assessment of treatments /other interventions Gold standard for treatment evaluation RCT design based on experiment What is an experiment? See if you name the defining characteristic(s) of an experiment

6. Experiment  Intervention group identical to control group  Random allocation of study factor (by researcher)  All other factors constant Hence any differences in outcome between the study and control groups can only be due to the intervention i.e. the study has proved that the intervention caused/prevented the outcome. Not possible to fully achieve these characteristics in research on humans. A randomised controlled trial (RCT) is as close as we can get i.e. a quasi experimental design. In an RCT  random selection  random allocation of factor  double blind procedures are used to approximate the characteristics of an experiment.

7. Randomised controlled trial population group 1 group 2 Outcome? new treatment control treatment Time

8. How assess impact of harmful factors e.g. alcohol, smoking, radiation? RCT? Not ethical to assign one group to interventions that are thought to be harmful! Observational studies Cohort study Case control study Ecological / correlation study (Observational epidemiology)

9. Cohort study population group 1 group 2 Outcome? exposed not exposed self selected Time

10. Case control study population % Exposed? Case (Has disease X) Control (Not disease X) Time

11. Case control study population % Exposed? Outcome (disease X) Not outcome (Not disease X) Time

12. RCT architecture RANDOMISATION Informed consent

13. Analyses for RCTs How compare outcomes in treated and control groups? Example 1: 2% mortality rate in treated (Rx) vs 4% in controls? The obvious way to compare these two proportions is to either Divide Subtract

14. Example 1: 2% mortality rate in Rx vs 4% in controls? Divide: 2% / 4% = 0.5 Half as many deaths in treated group Treatment is better than control Irx / Ic = 0.5 = relative risk (RR) Or Subtract: 2% - 4% = - 2% (minus) 2 fewer deaths in treated group for every 100 treated Treatment is better than control Irx - Ic = - 2% = risk difference (RD) Ic - Irx = 2% = risk reduction (RRed) Number needed to treat (NNT) to prevent 1 death = 1/risk reduction = 1 / 2% = 50

15. Example 2: 2/10,000 mortality rate in Rx vs 4/10,000 in controls? 2/10,000 / 4/10,000 = 0.5 Half as many deaths in treated group Treatment is better than control Irx / Ic = 0.5 = relative risk (RR) Or 2/10,000 - 4/10,000 = - 2/10,000 2 fewer deaths in treated group for every 10,000 treated!! Treatment is better than control - v. few deaths prevented! Irx - Ic = - 2/10,000 = risk difference (RD) Ic - Irx = 2/10,000 = risk reduction (RRed) NNT = 1 / 2/10,000 = 500

16. Note that relative risk in Examples 1 is the same as in Example 1 but that the risk difference, risk reduction and NNT are very different in the two examples. Outcome measures that based on subtraction are dependent on the magnitude of the rates (per 100 vs per 1000 etc.) whereas the magnitude is cancelled out in relative rates.

17. Hypothetical mortality rates at 10 years Try to calculate these outcome measures yourself before turning to next slide

18. Hypothetical mortality rates at 10 years

20. Incidence(E) = a/a+b Incidence (NE) = c/c+d RR = Inc(E) / Inc(NE) = a/a+b / c/c+d OR = a/b / c/d = ad/bc RRed = Inc(NE) - Inc(E) = c/c+d - a/a+b RRR = RRed (x100) Inc(NE) NNT = 1/ RRed Formula based definitions of outcome measures

21. Relative risk (RR) = incidence in treated group incidence in control group Odds ratio = Outcome/ no outcome in treated group Outcome/ no outcome in control group Risk difference (RD) (Attributable risk) = (incid. in treated group) - (incid. in control group) (Absolute) Risk reduction (RRed) = (incid. in control group) - (incid. in treated group) Relative risk reduction (RRR) (%) = risk reduction (x100) incidence in control group = 1 - RR (x 100) Number needed to treat (NNT) = 1/ risk reduction Text based definitions of outcome measures

22. Interpretation of relative risk values

23. Sustained virological response (48 wks) O. Reichard et al. Lancet 1998, 351,83-7 Randomised, double-blind, placebo-controlled trial of interferon alpha-2b with and without ribavirin for chronic hepatitis C Outcome Yes No Incidence RR = CI. 95 1 - 4.0 P = 0.7 OR = RD = RRed = RRR = NNT = Treatment n=50 1832 ? Control n=5 941 ? Calculate the outcome measures for this study Note that a sustained virological response is a good outcome so if the Rx works, we should have higher incidence in the treated group

24. Analyses for RCTs How compare outcomes in Rx and control groups? Example 3: 9% cure rate in Rx vs 3% cure rate in controls? Divide Subtract

25. Example 3: 9% / 3% = 3.0 3x as many cured in treated group as in control Treatment is better than control Irx / Ic = 3.0 = relative risk (RR) Or 9% - 3% = (+)6% 6 extra cures in treated group for every 100 treated Treatment is better than control Irx - Ic = 6% = risk difference (RD) (risk reduction not relevant)

26. Interpretation of relative risk values

27. Sustained virological response (48 wks) O. Reichard et al. Lancet 1998, 351,83-7 Randomised, double-blind, placebo-controlled trial of interferon alpha-2b with and without ribavirin for chronic hepatitis C Outcome Yes No Incidence RR = 2 CI. 95 1 - 4.0 P = 0.7 OR = 2.6 RD = 18% RRed = -18% RRR = -100% NNT = 5.6 Treatment n=50 183218/50 =36% Control n=50 9419/50 =18%