1 HISTORY OF "CLINICAL TRIALS“ - 1, 1794 Treatment of yellow fever by bleeding, RUSH, 1794 “ I began to extract a small amount of blood each time. The appearance of the blood and the effect of the intervention made me satisfied on the safety and efficacy of treatment I have never experienced a similar feeling of sublime joy when I was able to contemplate the success of my remedy..... Thank God, the more than 100 patients that I treated personally or to which I have commanded my attention that day, I have not lost even one " Rush B. An account of the bilious remitting yellow fever as it appeared in the city of Philadelphia in Philadelphia, Dobson, 1794

HISTORY OF "CLINICAL TRIALS“ - 2 Louis P.C.A. (1835) Analysis of treatment with "bleeding" in various diseases : Pneumonia (78 cases) Erysipelas (33 cases ) Inflammation of the chest (23 cases ) Division into two groups, treated and untreated Result = no average number of better results of treatment compared with no treatment Louis P.C.A.: Recherches sur les Effects de la Saignée, Paris, De Mignaret Ed. 1835

HISTORY OF "CLINICAL TRIALS“ - 3 Lister J. (1870) Surgical treatment of gangrene according to the traditional method in comparison with the method that involved using antisepsis of the operative field by means of carbolic acid 2 groups of patients 35 and 40 cases: a)Traditional technique mortality = 43% b) New method mortality = 15% Lister J.: On the effects of the antiseptic system upon the salubrity of a surgical hospital. Lancet, i, 4, 1870

CRITERIA FOR EVALUATION OF A HEALTH INTERVENTION EFFICACY EFFICIENCY SAFETY

TYPES OF CLINICAL STUDIES ON TREATMENT OBSERVATIONAL NOT CONTROLLED STUDIES a) case series studies b) before-after studies or with historical controls QUASI-EXPERIMENTAL STUDIES a) controlled studies with contemporary controls, but not randomized EXPERIMENTAL STUDIES a) randomized controlled b) quasi-randomized controlled

HCT: Historical control trial RCT: Randomized control trial Conclusions of "HCT" and "RCT" on 6 different therapeutic problems HCTHCT after adjustment for prognostic factors RCT Result (outcome) + - Result + - Result

TREATMENTS MUST BE ASSESSED COMPARATIVELY Against another treatment, already proven effective, in terms of increased effectiveness, efficiency or safety. Against an inert substance (placebo) when there is no proven effective treatment.

CONTEMPORARY ASSESSMENT OF THE EFFICACY The judgment of efficacy must be based on a comparison between the natural history of the disease in relation to two different treatments: experimental treatment comparator treatment

NEED OF STATISTICS The evaluation of efficacy must depend on comparison of the frequency of significant events in a group of treated subjects and in a control group. The comparison can not disregard a probabilistic assessment and, then, from a statistical analysis of the data.

THE SAMPLE SIZE THE PROBABILISTIC APPROACH AND SUBSEQUENT USE OF STATISTICS IMPLIES PRE-DETERMINATION OF THE SAMPLE SIZE REQUIRED TO EVALUATE THE EFFECT OF A HEALTH INTERVENTION. SEVERAL ASPECTS MUST BE TAKEN INTO ACCOUNT.

The probabilistic approach Concept of statistical inference 95% confidence intervals Significant difference

Whole sample 23 red /64 =35% Two samples 7 reds/27=25% 16 reds/37=43% Four samples 7 reds/18=22% 10 reds/20=50% 1 red/10=10% 5 reds/15=30%

Whole sample 23 reds/64 =35% 26 samples from 1 red/1= 100% to 0 red/3= 0%

95% Confidence intervals of the Mean The minimum and maximum values that can be found in 95% of our samples The larger the sample the more the value found is close to reality The larger the sample the more narrow the confidence interval

DIFFERENT STAGES OF THE CLINICAL DRUG "TRIAL" PHASE I: clinical pharmacology and toxicology PHASE II: preliminary clinical efficacy and safety of treatment, "dose ranging", pharmacokinetics and pharmacodynamics in human. PHASE III: definitive study of the effect of treatment-RCT PHASE IV: "post-marketing” monitoring

Phase III: Randomized Controlled Trial Some preliminary concept: internal /external validity randomization masking power of the study

Phase III: Randomized Controlled Trial Randomization: to avoid the "selection bias” Must be "concealed"

Phase III studies: double blind RCT Masking: to avoid "bias" in the evaluation Expectation bias Co-interventions Single, double, triple... better describe who is blinded to treatment

Phase III studies: Randomized Controlled Trial Power of the study No. of patients sufficient to show any effect of the treatment with a good probability and with few probability of being wrong.

ISSUES RELATED TO THE SAMPLE SIZE WHICH AIMS HAS THE STUDY? WHAT MEASURE IS USED FOR EVALUATING THE OUTCOME OF THE PATIENT? HOW IS ANALYZED DATA TO IDENTIFY A POSSIBLE DIFFERENCE BETWEEN TREATMENTS? WHAT IS THE EXPECTED RESULT OF THE "STANDARD" TREATMENT OR THE PLACEBO? WHAT DIFFERENCE BETWEEN THE TWO TYPES OF TREATMENTS COMPARED IS SUFFICIENT TO CONVINCE US THAT ONE IS BETTER THAN THE OTHER? WHAT TYPE I ERROR AND TYPE II ERROR VALUES DO WE DECIDE TO CHOOSE?

EXAMPLE OF THE SAMPLE SIZE CALCULATION 1) WHICH AIMS HAS THE STUDY? To DETERMINE IF A NEW ANTIPLATELET DRUG IS BETTER THAN “ASA” FOR SECONDARY PREVENTION OF ISCHEMIC STROKE 2) WHAT MEASURE OF "OUTCOME" WOULD YOU LIKE TO USE? MORTALITY + DISABLING STROKE WITHIN 2 YEARS AFTER A PREVIOUS TIA

EXAMPLE OF THE SAMPLE SIZE CALCULATION 3) HOW DO WE ANALYZE DATA FOR ASSESSING THE TREATMENT? ASCERTAINMENT OF NEW STROKES AND DEATHS THROUGH A SYSTEMATIC FOLLOW- UP – COMPARISON OF THE FREQUENCY OF EVENTS IN THE TWO GROUPS. 4) WHICH RESULT IS EXPECTED WITH ASA: MORTALITY + disabling stroke EXPECTED WITHIN 2 YEARS WITH ASA = 10%

EXAMPLE OF THE SAMPLE SIZE CALCULATION 5) WHICH FREQUENCY OF EVENTS DO I EXPECT WITH THE NEW DRUG TO DEFINE IT MORE EFFECTIVE THAN THE COMPARATOR? EXPECTED RATE OF MORTALITY + EXPECTED RATE OF DISABLING STROKE WITHIN 2 YEARS WITH THE NEW ANTIPLATELET = 5% 6) WHAT IS THE EXPECTED DIFFERENCE OF THE FREQUENCY PERCENTAGE OF EVENTS? 10% (ASA) - 5% (NEW DRUG) = 5 %

EXAMPLE OF THE SAMPLE SIZE CALCULATION: ERROR TYPE I 6) WHICH VALUE OF  DO I INTEND TO USE? 5 % (p < 0,05)  = probability that the result of the study may be positive due to chance

EXAMPLE OF THE SAMPLE CALCULATION: ERROR TIPE II 7) WHICH VALUE OF ß DO I CHOOSE? 10 % ß = probability that is possibly negative result of the study due to chance 1- ß = power of the study (90%)

EXAMPLE OF THE SAMPLE SIZE CALCULATION: Size (N) of each group to be studied = 578 (Total subjects = 1156) P1 = 10%P2 = 5%  = 5% ß = 10%

Classification of RCTs According to the intervention aspects you want to check According to the manner in which the participants are exposed to be treated According to the number of participants According to the participants and investigators knowledge about the treatment According to the objective

Classification of RCTs “ explanatory ” have the purpose of assessing whether a treatment works or not independently in other variables high internal validity “ pragmatic ” have the purpose of assessing whether the treatment works or not in conditions similar to practice high external validity

“ explanatory ” trial efficacy internal validity “ pragmatic trial ” effectiveness external validity CLASSIFICATION OF RCTs

Classification of RCTs Parallel (parallel groups) Cross-over Factorial No. of 1 trial

Clinical Trial  Randomized and Controlled Clinical Trial with parallel groups Selected sample Randomization Treatment A Treatment B Analysis of results

Clinical Trial  Randomized and Controlled Clinical Trial with Cross-over Selected sample Randomization Treatment A Treatment B Analysis of results Treatment B Treatment A

Clinical Trial  Clinical Trial with Factorial Design Selected sample Treatment A Analysis of results Treatment B Treatment A+B Placebo

Classification of RCTs open single-blind double-blind triple, quadruple blind

Classification of RCTs “ mega-trial ” (very large simple pragmatic trial) sequential Pre-defined size

Classification of RCTs Superiority trials Equivalence trials Non-inferiority trials

0 Control Better Treatment Better Difference between treatments 95% confidence interval P = 0.05 P = 0.2 Demonstrated superiority Not demonstrated superiority P = Strongly demonstrated superiority

0 Control Better Treatment Better Difference between treatments Equivalence not demonstrated -  +  Equivalence Demonstrated Equivalence margin (10%)

0 Control Better Treatment Better Difference between treatments Non-inferiority Not demonstrated -  Non-inferiority Demonstrated Non-inferiority margin (5%)

Statistical Analysis For protocol Intention to treat concept of “drop-out” concept of lost to follow-up

Whole sample 100 points 35% reds 26 samples from 2 reds/3=66% to 1 red/6=16%