Trials Adrian Boyle

Objectives Design Measures of quality How to analyse data from an RCT How to appraise an RCT

New terms Explanatory vs pragmatic Surrogate end point Equipoise Factorial Cross-over Intention to treat Number needed to treat

Equipoise There should be substantial uncertainty in the clinician’s mind about which treatment is better for the patient before the patient is enrolled in a trial

Why randomise? To avoid / reduce selection bias To balance confounders across groups

Limitations of trials Internal validity high at expense of low external validity Efficacy rather than effectiveness Irrelevant narrow questions Often a ‘Shot in the dark’ Drug companies often want to compare against placebo, not standard treatment Expensive and time consuming

More jargon Phase 1 Clinical pharmacology Drug safety in volunteers Phase 2 Initial investigation of effect Effectiveness Phase 3 Full scale evaluation Compared to placebo or standard practice Phase 4 Post marketing surveillance

Trial design Efficacy Frontier of effect under ideal circumstances works Effectiveness How this intervention works in ‘the real world’ Explanatory Provide clues as to how the intervention Pragmatic Shows how well the intervention works

Basic trial design Population Randomisation Exposure1 Exposure 2 Outcome Outcome

Analysis of basic study design Relative risk incidence of outcome in group 1 DIVIDED BY incidence of outcome in group 2 Sounds dramatic and sexy

Analysis of basic study design Absolute risk reduction: incidence of outcome in group 1 MINUS incidence of outcome in group 2 Less sexy and more useful. Ask the next drug rep. Enjoy

Analysis of basic study design Number needed to treat Inverse of the absolute risk reduction

Example A trial of drug A compared to drug B found that 20 out the 50 people who received A were alive at one year compared to 15 /50 who received drug B What is the relative risk? What is the absolute risk? What is the NNT

Example Relative risk 20/50 = 0.4 15/50 = 0.3 0.4/0.3 = 1.33 Or this could be expressed as a 33% increase in survival at one year

NNT Absolute risk reduction 0.4-0.3 = 0.1 This could be expressed as a risk reduction of 10% NNT 1/0.1 = 10 That is, you need to treat 10 people to save one life in one year

Multi-variate analysis Adjust for potential confounders and bias Usually with logistic regression expressed as a odds ratio

Factorial trial design Population Randomisation Exposure2 Exposure1 Exposure1 Placebo Placebo Exposure2 Placebo Outcome Outcome Outcome Outcome

Cross over design Population Randomisation Exposure1 Exposure2 Outcome

Cluster The unit of randomisation is a group of individuals e.g. GP practices or hospitals Easier implementation of a complex design Large studies Seriously complicated statistics

Randomisation Many methods Simple Block Stratification Weighted Simple The proof of the pudding is in the table 1

Sources of bias in a trial Selection Performance Losses to follow up Detection

What bias is there here? Population 200 Randomisation Exposure 1 100 100 15 Outcome Outcome 12 10

Is this bias? Population 2000 Randomisation Exposure1 Exposure 2 100 Outcome Outcome 50 10

What bias is there here? Population 200 Randomisation Exposure1 100 100 Outcome No Outcome 60 No Outcome 80 Outcome 20 10

Outcome measure Does the outcome mean anything to you? Beware surrogate outcomes Beware composite outcome especially if industry funded

Appraising a trial Identify aims Identify study design Identify population, exposure and outcome Consider the randomisation Consider the blinding Consider the measurement What biases and confounding factors are there? What is the result and what does it mean

Assessing strength / quality JADAD scoring http://www.naturalstandard.org/explanation_columns.html