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Pilot studies. Karla Hemming The University of Birmingham. Pilot or feasibility study?. No consensus on the definition Pilot study: Mini version of the full trial Tests protocol Feasibility study: Tests individual components Refines / develops intervention. Why have a pilot study?.
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Pilot studies Karla Hemming The University of Birmingham
Pilot or feasibility study? • No consensus on the definition • Pilot study: • Mini version of the full trial • Tests protocol • Feasibility study: • Tests individual components • Refines / develops intervention
Why have a pilot study? • 70% of publically funded trials fail to recruit required number of participants • NIHR increasingly require demonstration of proof of principal that the TRIAL will work (not the intervention)
What pilot studies are not about • Not around estimating effect size or CI of effect size • Why? • Pilot studies are small • Any estimate of effect will be highly uncertain • Mean estimate tells us nothing!
Objectives of a pilot study • Show proof of principal of intervention (?) • Test out data collection methods • Acceptability of questionnaires • Inform outcome • Only insofar as what is important to the patient • Estimate important parameters to inform sample size calculation • (not effect size!)
Proof of principal • Need a well defined surrogate outcome • That surrogate outcome would have to have a very clear link to important clinical outcome • Example – weight management programme • Important clinical outcome BMI • Show adherence improved in a pilot? • Difficulty – to show adherence improved might need as large a sample as for BMI
Important parameters to estimate • Examples include but are not limited to: • Recruitment rate (informs how long the trial will have to last for) • Consent rate (informs how amenable patients are to be randomised) • Retention rate (inform drop out rate) • Continuous outcomes – SD • Binary outcomes – underlying event rate
Sample size justification • Sample size should be justified • Depends on objective
Objective: estimate SD • Browne 1995 • Sample size of 30 is reasonable to give an estimate of the SD • BUT! Don’t just use mean estimate of SD in subsequent power calculations – use upper 95 percentile value
Objective: estimate a process rate • Show this rate is above some value • Or within some range (CI) • Called a precision based approach • )
Example • Want to show retention rate (p) is above some value (c) • Retention should not differ between arms (blinded trial) – so include both arms in calculation • Precision based approach • Show retention rate above c% • Need an estimate of actual retention rate p% • Specify significance or width of CI (precision)
Example cont… • Show retention rate above 60% • Estimate actual retention rate 80% • Recruit SS of 35, number retained 28 (80%) • 95% CI would be: 63% to 92% • Great! Showed retention rate above 60% and can proceed to definitive trial
Example cont… • What if retention rate only 70% • That is, recruit 35 • But, only 24 are retained • 95% CI would be 51% to 83% • Oh no… haven't shown retention rate above 60%
Power of this sample size • Method was motivated around CI or precision based approach (common in pilot studies) • But, the power of a SS of 35 to show that the retention rate is above 60% is only 50% (assuming event rate observed is 80%)
Precision based methods • Precision methods (CI) crucially depend on actual observed rate • Associated power of this approach is much lower than 80% • So, if you use the precision based approach do not incorporate stringent criteria to follow to definitive trial
Recommendations • Application viewed unfavourably if geared around effect sizes and hypothesis testing • Rather, should be focused around testing TRIAL protocol (or components) • Sample size needs to be justified • Don’t have stringent stop go criteria for full trial
Other things… • More than one objective – take the larger SS • Internal pilots • Full trial fundable? • If full trial not likely to be fundable by NIHR, RfPB unlikely to fund