Clinical trial design (randomized, blinded &crossover)

This presentation provides an overview of clinical trial design, including phases of clinical trials, key study designs (randomized, blinded, crossover, etc.), ethical considerations, and regulatory aspects. It aims to help students understand how to plan, conduct, and analyze clinical trials to ensure scientific validity and patient safety.

1. Clinical Trial Design Prof. Krishna V. Supalkar Department of Pharmacology Dr. Rajendra Gode Institute of Pharmacy, Amravati

2. Risk: The probability of harm being caused; the probability (chance, odds) of an occurrence. Absolute Risk: Risk in a population of exposed people is the probability of an event affecting members of a particular population (e.g. 1 in 1,000). Absolute risk can be measured over time ( incidence ) or at a given time ( prevalence ). Attributable Risk: Difference between the risk in an exposed population (absolute risk) and the risk in an unexposed population (reference risk) is called attributable risk. Attributable risk is the result of an absolute comparison between outcome frequency measurements, such as incidence. Relative Risk: Relative risk is defined as: 'Ratio of the risk in an exposed population (absolute risk) and the risk in an unexposed population (reference risk)'. Relative risk is the result of a relative comparison between outcome frequency measurements, e.g. incidences.

3. Incidence: the  rate   of new cases  of a disease occurring in a  specific population  over a  particular period of time. Prevalence: the  number of cases  of a disease in a  specific population  at a  particular timepoint  or over a  specified period of time. Prevalence differs from incidence as prevalence includes all cases  (new and pre-existing cases) in the population at the specified time  whereas incidence is limited to new cases only. Prevalence is a useful measure of the burden of disease. Prevalence changes when people with the condition are cured or die. Bear in mind that increased prevalence doesn’t necessarily mean a bigger problem. Higher prevalence could mean a prolonged survival without cure or an increase of new cases, or both. A lower prevalence could mean that more people are dying rather than being cured, a rapid recovery, and/or a low number of new cases. Example… The number of people diagnosed with asthma each year remains relatively stable over time. Since asthma has a genetic component and is not significantly influenced by lifestyle or environmental factors, its incidence rate tends to stay constant. However, nowadays people do not die of asthma and the number of people with asthma in the population persists until they die of another cause. There is also not often a cure for asthma, it does not go away but it is just managed better. Therefore, the number of cases of asthma keeps increasing while more people are diagnosed with the condition than those with the disease that die. The prevalence of asthma in a population is therefore increasing.

4. A general practice surgery with a patient population of 40,000 people wanted to evaluate the epidemiology of COPD in its patients. The information they collected from their records is shown in the following table: Year Number of Patients with COPD Total number of patients at the practice 2018 1780 39,640 2019 1826 40,000 * For simplicity, we assume that there were no deaths or recovery of patients with COPD during 2018 and 2019, and all the patients remained in the practice. From this table we can say that: In  2018 , the number of patients at the practice ( total population ) was  39,640 . The number of people at that time with COPD was 1780. Therefore, the  prevalence  equals  1780/39640= 0.0449 . This can be expressed as 4.5% of the patient population had COPD at that time. In  2019 , the number of patients at the practice had increased to  40,000 . The number of cases of COPD in 2019 had risen to 1826. The  prevalence in 2019  therefore was  1826/40000= 0.0456  giving us 4.6% of the population. We can see the prevalence of COPD in this population only changed by approximately 0.1%. The number of new cases in 2019 compared to 2018 is 1826-1780, making the difference 46. Therefore, the  number of new cases  at the practice is  46 per year , which makes the  incidence 46/40,000 =0.00115 (1.15 per 1000 population) . (For simplicity this is assuming those 46 were all new patients with onset of COPD in 2019).

5. Cohort study Risk Lung cancer No lung cancer Total Incidence Smokers 40 20 60 40/60 = 0.666 Non-smokers 3 37 40 3/40 = 0.075 Incidence ratio = 0.666/0.075 = 8.88

6. Case control study Risk Breast cancer No breast cancer Oral contraceptives 80 50 Non O.C. 20 50 Odd ratio = 80 X 50 20 X 50 = 4 Odds: Probability of an occurrence p divided by the probability of its non-occurrence (1 - p). Odd Ratio: Ratio of the Odds in a given population and the Odds in another population.

7. Cross sectional study Prevalence in exposed: 80/100 = 0.8 Prevalence in non-exposed: 50/100 = 0.5 Prevalence ratio: 0.8/0.5 = 1.6

8. Experimental Study ( Also Known as Intervention Studies ) Best study design to prove causation. Here, investigator decides who will get the exposure and who will not. So under direct control of the investigator unlike other type prospective study where exposure is not dictated by the investigator. Epidemiologist takes some action, intervention or manipulation in contrast to descriptive studies where no action is taken but observation is done.

9. On the basis of study design

10. Non-Randomized Trials Also known as Quasi-Experimental Designs . It is a type of research in which the investigator manipulates the study factor but does not assign individual subjects randomly to the exposed & non- exposed groups. These are designed as: It is always not possible for ethical, administrative and other reasons to resort to a RCT. Some preventive measures apply only to groups or community-wide basis. When disease RCT require follow-up of thousands of people for a decade or more. As here randomization is not done. So, low comparability than RCT and chances of spurious results are high than RCT.

11. These studies may be of following types: Uncontrolled Trials Natural Experiments Before and after comparison studies With control Without control Non-Randomized Trials

12. 1. Uncontrolled Trials There is no comparison group. Initially may be helpful in : Evaluating whether a specific therapy appears to have any value in a particular disease. To determine an appropriate dose. To investigate adverse reactions etc. Non-Randomized Trials 2. Natural Experiments When experimental studies are not possible in humans, Natural circumstances that “mimic” an experiment are identified. Example: Group of smokers and non-smokers (naturally separated). Other population groups involved include: migrants, religious or social groups etc. John Snow’s discovery that cholera is a water borne disease was an outcome of a natural experiment.

13. 3. Before and after comparison studies without control Experiment serve as its own control. Incidence of disease before and after introduction of intervention is measured here. Standard for comparison: events which took place prior to use of new treatment or intervention. Examples: Prevention of scurvy among sailors by James Lind (1750). Studies on transmission of cholera by John Snow (1854). Prevention of polio by Salk and Sabin. Non-Randomized Trials

14. Goal of RCT Primary Goal To test whether an intervention works by comparing it to a control condition (usually either no intervention or an alternative intervention). Secondary Goals Identify factors that influence the effects of the intervention (i.e., moderators) Understand the processes through which an intervention influences change (i.e., mediators or change mechanisms that bring about the intervention effect) Randomised Trials Aims at: Achieving Internal Validity. Eliminate bias (Selection bias). Allowing comparability. Equally distributing the “other” factors which are important but not recognized or cannot be determined, equally between the two groups.

15. Randomised Trials 1. Parallel Design

16. Randomised Trials 2. Cross Over Design Latin Square Method

17. Randomised Trials It is done to ensure that the treatment and control groups are balanced on important prognostic factors that can influence the study outcome (e.g., gender, ethnicity, age, socioeconomic status). Investigator decides which strata are important and how many stratification variables can be considered given the proposed sample size. A separate simple or blocked randomization schedule is developed for each stratum. Large trials often use randomly permuted blocks within stratification groups. 3. Stratification

18. Stratified Randomization

19. Randomised Trials It is done to ensure that the treatment and control groups are balanced on important prognostic factors that can influence the study outcome (e.g., gender, ethnicity, age, socioeconomic status). Investigator decides which strata are important and how many stratification variables can be considered given the proposed sample size. A separate simple or blocked randomization schedule is developed for each stratum. Large trials often use randomly permuted blocks within stratification groups. 4. Factorial design

20. Advantages Two trials for (almost) the price of one Design is best if: two intervention have different mechanisms of actions or different outcomes Disadvantages Need to test for possibility of interaction (e.g. A differs based on the presence or absence of B) Need to consider gain in cost vs. increased complexity, recruitment and adherence issues– potential for adverse events

21. Group A [25] = Test drug A + Test B Group B [25] = Test drug A + placebo B Group C [25] = Placebo B + Test B Group D [25] = Placebo A + Placebo B

22. 5. Withdrawal Design Randomised Trials Test Drug continued Test Drug discontinued and placebo started After specific time

23. Performed when larger groups (e.g. patients of a single practitioner or hospital)are randomized instead of individual patients In a rural area with an endemic disease, we might randomize whole villages to have the intervention or not, rather than individual people. To evaluate health systems interventions Eg ; educational intervention in schools for prevention of CV risk factors To avoid treatment group contamination Administrative convenience Cluster randomized trials

24. RCT can also be described on the basis of hypothesis: Superiority studies Equivalence studies Non inferiority studies A superiority study aims to show that a new drug is more effective than the comparative treatment (placebo or current best treatment) An equivalence study is designed to prove that two drugs have the same clinical benefit. A non inferiority study aims to show that the effect of a new treatment cannot be said to be significantly weaker than that of the current treatment.