Understanding real research 4.

1. Understanding real research 4. Randomised controlled trials.

2. What can studies do? Describe the situation: Descriptive. Explain the situation: Analytical. Compare approaches: Experimental.

3. Study designs • Descriptive • Cross-sectional, longitudinal. • Analytic • Case-control studies. • Cohort studies. • Quasi-experimental • Natural experiments, policy interventions. • Experimental • Randomised controlled trial.

4. Study designs

6. Randomised controlled trials • A clinical trial in which: • at least two treatments, programmes, interventions are compared. • one of these is a control group. • allocation uses a random, unbiased method.

7. Randomised controlled trials New treatment Group 1 Outcome Population Outcome Group 2 Control treatment From: Critical Appraisal Skills Programme (CASP), Oxford.

8. Explanatory trials Measure efficacy: the benefit a treatment produces under ideal conditions. e.g. Phase III drug trials.

9. Pragmatic trials Measureeffectiveness: the benefit a treatment produces in routine clinical practice. Aim to inform choices between treatments. Patients should be analysed in the group to which they were initially randomised, i.e.intention to treat analysis.

10. Intention to treat analysis All patients allocated to one arm of a RCT are analysed in that arm, whether or not they completed the prescribed treatment/regimen.

11. Two by two table

12. Appraising RCTs Methodological approach: Was assignment to the different groups randomised? Was the randomisation process/list concealed? Was everyone who entered the trial accounted for at the end? Were subjects and assessors “blind” to treatment allocation when assessing outcomes? Were groups similar at start of trial and treated similarly throughout the study?

13. Appraising RCTs Statistical reporting: Were subjects analysed in the group to which they were randomised: intention to treat analysis. Type of data – influences statistical analysis. Reporting of risk: RRR vs ARR.

14. Risks and Odds.

15. Risks and odds • When talking about the chance of something happening, e.g. death, hip fracture, we can talk about: • risk and relative risk • or • odds and odds ratio.

16. Risks and odds Risks. A proportion. Numerator / Denominator. Odds. A ratio. Numerator / (Denominator - Numerator).

17. Two by two table

18. Risk Risk is: aproportion. Risk of event in expt. group = a = EER. a+b Risk of event in control group = c = CER. c+d

19. Relative risk Relative risk (RR) is: a ratio of proportions. RR = EER CER. A measure of the chance of the event occurring in the experimental group relative to it occurring in the control group.

20. Relative risk - 2 RR <1 if group represented in the numerator is at lower “risk” of the event. Want this if the event is a bad outcome e.g. death. RR >1 if group represented in numerator is at greater “risk” of the event. Want this if the event is a good outcome e.g. smoking cessation.

21. Relative risk reduction The amount by which the risk of the event is reduced by the intervention. The difference in the risk of the event between the control and experimental groups, relative to the control group. RRR = (CER - EER)/CER. Use this term if the event is bad e.g. death.

22. Relative risk reduction - 2 An alternative way of calculating the relative risk reduction is to use the relative risk: RRR = (1 - RR). Use this term if the event is bad e.g. death.

23. Absolute risk reduction The absolute difference between the risk of the event in the control and experimental groups. ARR = CER - EER. ARR can be used to calculate the number needed to treat (NNT). Use this term if the event is bad e.g. death.

24. Relative benefit increase The amount by which the risk of the event is increased by the intervention. The difference in the risk of the event between the control and experimental groups, relative to the control group. RBI = (CER - EER)/CER. Use this term if the event is good e.g. smoking cessation.

25. Relative benefit increase - 2 An alternative way of calculating the relative benefit increase is to use the relative risk: RBI = (1 - RR). Use this term if the event is good e.g. smoking cessation.

26. Absolute benefit increase The absolute difference between the risk of the event in the control and experimental groups. ABI = CER - EER. ABI can be used to calculate the number needed to treat (NNT). Use this term if the event is good e.g. smoking cessation.

27. Number needed to treat The number of patients who needed to be treated to prevent the occurrence of one adverse event (e.g. complication, death) or promote the occurrence of one beneficial event (e.g. cessation of smoking). NNT = 1/ARR.

28. Odds. Odds is: a ratio. Odds of event in expt. group = a b. Odds of event in control group = c d.

29. Odds ratio. Odds ration (OR) is: a ratio of ratios. OR = ad bc.

30. Confidence interval The range of values within which the “true” value in the population is found. 95% CI: can be 95% confident the population value lies within those limits. Is an estimate of the “true” value.

31. Confidence interval - 2 95% CI = Sample estimate +/- 1.96 x SE The bigger the sample - the smaller the sample error (SE). Bigger samples  smaller CIs. more precise estimate of the “true” population value.