Improving the internal validity of experiments in focal ischaemia Malcolm Macleod Centre for Clinical Brain Sciences, University of Edinburgh

1026 interventions in experimental stroke O’Collins et al Ann Neurol 2006

1026 interventions in experimental stroke 603 O’Collins et al Ann Neurol 2006 Tested in focal ischaemia

1026 interventions in experimental stroke 883 374 O’Collins et al Ann Neurol 2006 Effective in focal ischaemia

1026 interventions in experimental stroke 883 550 97 18 O’Collins et al Ann Neurol 2006 Tested in clinical trial

1026 interventions in experimental stroke 883 1 3 97 17 550 O’Collins et al Ann Neurol 2006 Effective in clinical trial

What’s my problem? I want to improve the outcome for my patients with stroke To get that, I want to conduct high quality clinical trials of interventions which have a reasonable chance of actually working in humans But which of the remaining 929 interventions should I choose?

It’s not just my problem …

“…you will meet with several observations and experiments which, though communicated for true by candid authors or undistrusted eye-witnesses, or perhaps recommended by your own experience, may, upon further trial, disappoint your expectation, either not at all succeeding, or at least varying much from what you expected” Robert Boyle (1693) Concerning the Unsuccessfulness of Experiments

What is a Valid Experiment? One which describes some biological truth in the system being studied Internal validity: the extent to which an experiment accurately describes what happened in that model system Can be inferred by extent of reporting of measures to avoid common biases

What is a Valid Translational strategy? One which considers all available supporting animal data One which considers the likelihood of publication bias One which tests a drug under circumstances similar to those in which efficacy has been demonstrated in animal models

Potential sources of bias in animal studies Internal validity Problem Solution Selection Bias Randomisation Performance Bias Allocation Concealment Detection Bias Blinded outcome assessment Attrition bias Reporting drop-outs/ ITT analysis False positive report bias Adequate sample sizes After Crossley et al, 2008, Wacholder, 2004 Are animal experiments falsely positive? Are clinical trials falsely negative? Do animal studies not model human disease with sufficient fidelity to be useful?

Internal validity Dopamine Agonists in models of PD Ferguson et al, in draft

Internal validity Dopamine Agonists in models of PD Ferguson et al, in draft

Internal validity Dopamine Agonists in models of PD Ferguson et al, in draft

Blinded outcome assessment Internal Validity Randomisation and blinding in studies of hypothermia in experimental stroke Randomisation Blinded outcome assessment Yes No Efficacy è 47% 39% 47% 37% Efficacy è van der Worp et al Brain 2007 Are animal experiments falsely positive? Are clinical trials falsely negative? Do animal studies not model human disease with sufficient fidelity to be useful? Yes No

Stem cells in experimental stroke Lees et al, in draft

Neurobehavioural score Internal Validity Randomisation, allocation concealment and blinding in studies of Stem cells in experimental stroke Infarct Volume Neurobehavioural score Lees et al, in draft

Internal Validity NXY-059 Macleod et al, 2008

Internal Validity Attrition bias

Internal Validity False positive reporting bias The positive predictive value of any test result depends on p (α) Power (1-ß) Pre-test probability of a positive result after Wacholder, 2004

Internal Validity False positive reporting bias The positive predictive value of any test result depends on p (α) (0.05) Power (1-ß) (0.30) Pre-test probability of a positive result (0.50) after Wacholder, 2004 Positive predictive value = 0.67 i.e. only 2 out of 3 statistically positive studies are truly positive

Chances that data from any given animal will be non-contributory assume simple two group experiment seeking 30% reduction in infarct volume, observed SD 40% of control infarct volume Number of animals Power % animals wasted 4 18.6% 81.4% 8 32.3% 67.7% 16 56.4% 43.6% 32 85.1% 14.9%

Chances of wasting an animal

External Validity Publication Bias for FK506 All outcomes 29 publications 109 experiments 1596 animals Improved outcome by 31% (27-35%) Precision Macleod et al, JCBFM 2005 Worse Better

External Validity Hypertension in studies of NXY-059 in experimental stroke Infarct volume: 9 publications 29 experiments 408 animals 44% (35-53%) improvement Hypertension: 7% of animal studies 77% of patients in the (neutral) SAINT II study Macleod et al, Stroke in press

External Validity Hypertension in studies of tPA in experimental stroke Infarct Volume: 113 publications 212 experiments 3301 animals Improved outcome by 24% (20-28) Hypertension: 9% of animal studies Specifically exclusion criterion in (positive) NINDS study Efficacy è 25% -2% “Normal” ÝBP Perel et al BMJ 2007 Comorbidity

Quality of Translation tPA and tirilazad Both appear to work in animals tPA works in humans but tirilazad doesn’t Time to treatment: tPA: Animals – median 90 minutes Clinical trial – median 90 minutes Time to treatment: tirilazad Animals – median 10 minutes Clinical trial - >3 hrs for >75% of patients Sena et al, Stroke 2007; Perel et al BMJ 2007 Are animal experiments falsely positive? Are clinical trials falsely negative? Do animal studies not model human disease with sufficient fidelity to be useful?

Chose your patients – tPA: Effect of time to treatment on efficacy Perel et al BMJ 2007; Lancet 2004 11% of all patients treatment initiated by 90 minutes 33% of sub-3hr patients treatment initiated by 90 minutes

How much efficacy is left? Reported efficacy Publication bias Randomisation Co-morbidity bias 26% 20% 5% 32%

Summarising data from animal experiments Studies Systematic Review And Meta-analysis Clinical Trial how powerful is the treatment? what is the quality of evidence? what is the range of evidence? is there evidence of a publication bias? What are the conditions of maximum efficacy?

Resources and acknowledgements www.camarades.info/index_files/papers.htm www.camarades.info/index_files/talks.htm www.camarades.info/index_files/resources.htm Chief Scientist Office, Scotland Emily Sena, Evie Ferguson, Jen Lees, Hanna Vesterinen David Howells, Bart van der Worp, Uli Dirnagl, Philip Bath