Clinical Trials generalities & specificities for epidemic diseases in LMICs

1. Clinical Trialsgeneralities & specificitiesfor epidemic diseases in LMICs P. Olliaro WHO/TDR & University of Oxford

2. GCP, GCLP requirements Registration First In Humans Policy, Practice Post- registration studies to inform policy Discovery & Pre-clinical Research Regulatory Clinical studies (Phase I, II, III) Capacities & capabilities for sustained, durable clinical research in Developing Countries

3. Full scale evalution of treatment efficacy & tolerability Comparative vs. standard treatment (or no treatment) >2 well-conducted studies needed; relatively large numbers Sometimes multi-centre; eligible to meta-analysis First in Humans Clinical Pharmacology & Tolerance (Healthy Normal Volunteers) Determine acceptable single dosage Dose-ranging; dose-escalation Regulatory review process for marketing authorization IND Phase I Phase II Phase III NDA Phase IV, PMS Define tolerability in large numbers when drug used routinely Assess effectiveness in conditions of use Pragmatic trials Investigational New Drug Application Initial clinical investigation for treatment effects Small scale in patients (mild disease) Determine appropriate dose schedule Efficacy, tolerability, PKs

4. Special features • Mission: provide affordable, adapted public health priority products • Corollary: provide data in support of registration, policy & practice; availability of quality product • Context: • Outbreak: challenge & short-window opportunity • LMIC capacities & capabilities

5. Study designs: observational vs. experimental studies • What happened? • Case-control study • What will happen? • Cohort study • Clinical trial • What’s happening? • Cross-sectional study

6. TAXONOMY OF CLINICAL RESEARCH Grimes & Schulz, Lancet 2002;359:57-61 Did investigator assign exposures? NO YES Experimental study Observational study Comparison group? Random allocation? Randomised controlled trial Non-randomised controlled trial Analytical study Descriptive study Direction? Exposure & outcome same time Exposure <- outcome Exposure -> outcome Cohort study Case-control study Cross-sectional study

7. What happened? Case-control study Exposed Cases Non-exposed Exposed Controls Non-exposed Time Onset of study Direction of enquiry

8. What is happening? Cross-sectional study With oucome Subjects Selected for Study Without outcome Time Onset of study No direction of enquiry

9. What will happen? Cohort study With outcome Exposed OR Subjects Without outcome Cohort Selected For Study With outcome Unexposed OR Controls Without outcome Time Onset of study Direction of enquiry

10. Randomised Controlled Clinical Trial With outcome Experimental Subjects Without outcome Subjects Meeting Entry Criteria With outcome Controls (Treated OR Untreated) Without outcome    Time Onset of study Intervention Direction of enquiry

12. Clinical trials & the scientific method • Define purpose of trial: state specific hypothesis • Design the trial: written protocol • Conduct trial: good organization • size; avoidance of bias • Analyse data: descriptive statistics, tests of hypothesis • Draw conclusions: publish results

13. Some statistic definitions •  (probability of a type I error) is the probability of erroneously rejecting the null hypothesis (i.e. recommending a medicine with no advantages) given that the null hypothesis is true; •  (probability of a type II error) is the probability of erroneously failing to reject the null hypothesis (i.e. keeping a good medicine away from patients) given that the alternative hypothesis is true • 1-  (power) quantifies the ability of the study to find true differences of various values of  (see below). It expresses the chance of correctly identifying the alternative hypothesis, and to correctly identifying a better medicine. •  is the minimum difference between groups that is judged to be clinically important - i.e. the minimal effect which has clinical relevance in case management. • Populations of analysis • intent-to-treat (ITT): considered the choice population for analysis; all patients randomized who gave informed consent (and received any amount of the assigned intervention at least once). Problem: it requires measurement on all patients whether or not they are still adhering to the protocol (‘losses to follow-up’). Used esp. in pragmatic (effectiveness) trials as it reflects treatment effects in conditions that are closer to those encountered in routine use, as opposed to the • per-protocol (PP): restricted to the patients without major protocol deviations who are evaluable at the planned visit for efficacy assessment. It measures the pure treatment effect ("evaluable patients' analysis"). • Modified ITT (mITT): a subset of the ITT population allowing for the exclusion of patients due to non compliance or missing outcome.

14. Randomized comparative designs Comparator (reference) intervention Superiority design: to provide evidence that the test intervention is superior to the control COMPARATOR : placebo or active (standard) treatment The study may be designed to compare proportions (cure rates)between the control and test intervention, but also means. A non-significant result (i.e. no significant difference detected) does not imply that the two treatments are equal. SAMPLE SIZE CALCULATION: • The choice of the values of type one error rate, , and power, 1- (i.e. how stringent the study will be), and the expected cure rates with the control and the improvement to be detected for the test intervention will determine the sample size of the study. • Reliable efficacy data for the comparator arm are needed; wrongly estimating the efficacy of the comparator treatment may result in the study being underpowered, hence failing to produce the intended results. • When the number of arms is >2 (i.e. >1 test intervention or dose), this will have to be accounted for in sample size calculation and result in a larger sample size per group, other things being equal, in order to allow for multiple comparisons. • Allowance should be made in sample size calculation for losses to follow-up. Read: Chow SC, Liu JP (2004) Hypotheses testing and p-values. Design and analysis of clinical trials Concept and methodologies. second edition ed: Wiley & Sons. pp. 72-73.

15. The issue of placebo controls • Volunteer/provider think they are getting/giving something of value • Superiority Design = measures absolute efficacy and safety • Measures all pharmacologically mediated effects • Cleanly separates drug adverse events from background disease • Ethical when no harm can come to the volunteer by no treatment or delayed treatment • Placebo controls with design modification: • Add on • Replacement • Early Escape • Limited Placebo Period • Randomized Withdrawal • Unbalanced Randomization

16. No-treatment arm • True negative controls • Disadvantages • Cannot be fully blinded • Subject retention • Patient management • All aspects of observation • Critical decisions made by observer blinded to Rx assignment • Eligibility • Endpoint determination • Changes in management

17. Sample size calculations for comparative superiority trials

18. Randomized comparative designs Comparator (reference) intervention Non-inferiority design: to show the new intervention is no worse than the standard treatment by some margin  (the non-inferiority margin) = the largest clinically acceptable difference • Choice of the non-inferiority margin: avoid harmful treatment to be declared non-inferior, and to retain a treatment that brings a true benefit for the patient. The decision should be based on previous studies with the reference treatment and the minimally important effect that one wants to observe with the new treatment against additional benefits. • How to identify the correct , • E.g. compare (i) the two-sided 95% confidence interval of the difference between the test and the reference treatment to (ii) a two-sided 95% CI of the difference between the reference treatment and the placebo based on historical data and meta analyses (if such data are available). • also consider a clinically acceptable failure rate, in the context of other factors, e.g. duration of treatment, route of administration, costs. • Populations analysed • Traditionally PP because ITT might bias the results toward equivalence (risk of falsely claiming non-inferiority). • Current thinking and regulatory agencies demand: the study objective should be achieved in both the ITT and PP populations • Minimise losses to follow-up! Rothmann M, Li N, Chen G, Chi GY, Temple R, et al. (2003) Design and analysis of non-inferiority mortality trials in oncology. Stat Med 22: 239-264. Hasselblad V, Kong DF (2001) Statistical methods for comparison to placebo in active control trials. Drug Information Journal 35: 435-449

19. Sample size calculations for comparative non-inferiority trials Efficacy in the reference arm from 80%-95%, delta 5-10%, alpha error 0.01, power 90% Non-inferiority margin = the smallest acceptable difference with respect to the success rate with the reference treatment.

20. Sample size calculation (N per group) for non-inferiority trials Success rate ranging 60-90%; exclusions ranging 0-25%; non-inferiority margin (delta) 6%, 8% and 10%

21. Adaptive designs • Meant to allow choices amongst various drugs and regimens (dose, duration) systematically, as quickly and effectively and with as few patients as possible. • Includes: group sequential designs, sequential methods and methods to stop earlier trials with superiority or non-inferiority designs. • Allow redesigning the trial based on the information acquired through interim analyses, which may result in changing the sample size, the number of arms, or other elements. • Sequential and group-sequential trials are a special case of adaptive trials where several interim analyses are done in order to complete earlier the trial based on the accumulated information.

22. Adaptive designs: sequential methods Whitehead triangular test: a graphical methods defining with boundaries which allows for early rejection or non-rejection of H0. • most effective when early end-points or surrogate markers exist • requires an efficient (on-line) data-management system in place and a constant interface with a statistician. • allows analysing cumulated information at each step, early stopping (when treatment proves effective (p0) or ineffective (pa)), non-comparative and comparative designs, and can eventually result in shortening study duration and reducing the number of subjects to be exposed. • several treatments (or doses) can be tested in parallel. • allows combining sequentially in a single study: (1) screening of potential treatments (one-sided triangular test applied to multiple non-comparative studies as required) and (2) comparing the so selected treatment to the reference treatment (two-sided triangular test)

23. Boundaries of the one-sided triangular test pa = 0.20 pa=0.18 pa = 0.25 example of sequential analyses with modeled data

24. Sample size calculation for the one-sided and two-sided triangular test pa=0.18, 0.20 and 0.25; and two-sided test, pa=0.20

25. Study flow diagram and patient attrition according to the CONSORT statement