Helen LookerMPH Course

October 21st 2014

Understanding Understanding Clinical TrialsClinical Trials

A B

SuperDrug

WonderDrug

Why clinical trials

Design features

Main problems

quart of cider

3 servings elixir of vitriol

6 spoonfuls of vinegar

3 portions of nutmeg

2 oranges, 1 lemon

Experimental treatments (per day)

Limitations of theory

Previous disasters

Spontaneous improvements

Importance of small effects

Intervention Impact of intervention

Sleep baby on its front SIDS frequency increased 200%

Anti-oxidants to reduce mortality

5% increase in mortality

Juvenile delinquents exposed to prison – scared straight

Increase in offending rates

School based driver education

Increase in accident rates

Service organisation

Quality of direct care

Ancillary care

Initial severity

Co-morbidity

Adherence

Social support

Lifestyle

Drugs

Surgery

Type of management

Public health interventions

Phase I clinical pharmacology

Phase II initial clinical assessment

Phase III rigorous testing

Phase IV post-marketing surveillance

Trials are experiments on people

Must be real doubt (clinical equipoise)

Obtain informed consent

Preserve clinical freedom

Historical controls◦health care has moved on◦same diagnostic criteria??

Concurrent control◦why were some not selected

Randomized controls◦ new treatment◦ placebo/ conventional treatment

Streptomycin and Pulmonary TB

Intervention Control

Recruited 55 52

Dead at 6 months

7%

27%

Published 1948

Like tossing a coin

Avoids choosing

Permits fair comparison◦ two groups the same at baseline

two groups the same at baseline◦ group A: 1x , 4 x , 2 x

◦ group B: 1x , 4 x , 2 x

A A A A A AB BB B B A B B

Factors which might influence outcome

◦ Illness severity at entry

◦Current treatment

◦Disease duration

◦Relevant previous medical history

New treatment

Control

% advanced disease

47

49

% married 65 70

mean age (years)

64.2

48.9

Severe

Moderate

Moderate

A

B

A

B

A

B

When powerful predictor of outcome

Construct groups low to high

Randomise within groups

Achieve balance on the predictor

Keep it simple

Inclusion criteria◦ likely to benefit from treatment

definitely has the disease patient is likely to respond

◦unlikely to be harmed no known adverse reactions/ contraindications

Aim◦well defined, homogeneous group◦ increased likelihood of detecting an effect ◦smaller, cheaper trials

Exclusion criteria◦clear preference for intervention or

control by patient or doctor

◦??? patient unlikely to adhere to treatment complete the follow-up

◦many other factors

Entry Criteria

◦Diastolic BP 90-109

◦Age 35-64

◦Men and women

Common exclusion criteria

◦Comorbidities

◦Recently cardiovascular disease

◦Age: 50+ yrs, some 85+ years

◦Severe hypertension

◦Dementia

◦Depression

◦Substance abuseVan Spall et al 2007Uijen et al 2007

Compared to primary care patients with CVD, trial participants were:

◦Younger

◦More men

◦Lower risk CVD

◦Fewer with history of CVD

Uijen et al 2007

Death rateTrial participant 3.6%

Eligible, not enrolled

7.1%

Not eligible 11.4%

Steg et al 2007

Global Registry of Acute Coronary Events

Systematic reviews

% of ineligible patients (median)

Asthma 94%

COPD 95%

Wound healing 85%

Well-defined

Easily delivered

Same for all patients

Prior evidence of effect

Measurable outcome

Drug

Surgery

Psychotherapy

Counselling

Complex intervention

Difficulty of standardising

Death

Symptoms Quality of life

Clinical measurement

Clinically relevant

Easily measured

Accurately measured◦ measured in the same way for intervention and control

groups

Specified in trial protocols◦ primary out identified◦ others called secondary outcomes

Single blind Vs.

Double blind

Increase in effect size

Poor randomisation 41%

Not double blind 17%

Schultz et al 1995

Healthy ones may emigrate

Sick ones may be admitted to hospital

Rule of thumb◦ less than 20% lost◦similar losses for intervention and control

Specify treatment

Define study group

Random allocation

Blinded outcome assessment

Fair interpretation

Like tossing a coin

Avoids choosing

Protects against unknown confounding

Permits fair comparison

Too few patients

Performance bias

Losing patients

Flawed analysis/interpretation

Expected effect size◦ The bigger the effect you are trying to measure the fewer

people needed

How certain do you need to be that a detected difference is true?◦ Typically aim for a significance criteria of 0.05 (ie if you

find a difference between groups you have a 95% confidence that the difference is true)

Power to detect a difference when there is a true difference ?◦ Typically select a range of 80-90%

Systematic differences in the care provided to the participants in the comparison groups other than the intervention under investigation.

Usually intervention get more attention than controls

Example in psychotherapy ◦ intervention group may get more attention or more

drugs than controls◦ Example in diabetes

◦ Increased clinical contact improves diabetes control

Why do patients drop out?

Treatment side-effects

Lack of desired improvement

Too much time/effort to continue

Maintained contact with study

Analyse by intention to treat◦ change in treatment may be related to

efficacy ◦ exclusion will lead to bias

Beware sub-group analysis◦ for every 20 groups explored one will be

spuriously significant

◦ 30% reported severity of RA

◦ Randomisation described in 11%

◦ 8% of reported double blind were not

◦ 6% nominated main outcome measure

◦ 1% report sample size calculation

◦ 42% made doubtful/invalid statements

Bero and Rennie 1996

857 significance tests

48 were significant

43 expected by chance

What You Should KnowWhat You Should Know New treatments need careful

assessment

The RCT is study design of choice randomisation enables fair comparison controls for confounding: known and unknown also need fair outcome assessment

Problems often occur unequal at baseline, lack of blinding , loss to follow-up, poor outcome measurement, flawed analysis

Papers may mislead

◦ Who is being studied

◦ What treatments are being used

◦ Are treatment groups comparable at baseline

◦ Are sensible outcome measures used

◦ Are outcomes assessed blind

◦ How many patients dropped out

◦ What do the results really mean