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Lecture 5: Randomised Controlled Trials

Learning outcomes. Describe the key features of randomized controlled trialsCalculate measures of association used in randomized controlled trials. Randomised Controlled Trials . Ideal design for evaluating both the effectiveness and the side effects of new forms of interventionRCTs can be used t

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Lecture 5: Randomised Controlled Trials

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    1. Lecture 5: Randomised Controlled Trials

    2. Learning outcomes Describe the key features of randomized controlled trials Calculate measures of association used in randomized controlled trials

    3. Randomised Controlled Trials Ideal design for evaluating both the effectiveness and the side effects of new forms of intervention RCTs can be used to evaluating new drugs and other treatments of disease including tests of new health and medical care technology RCTs can also be used to assess new programs for screening and early detection or new ways for organising and delivering health services

    4. RCT is an experiment in which subjects in a population are randomly allocated into groups to receive an experimental preventative or therapeutic procedure or intervention (intervention group) or to receive a placebo, no intervention or usual care (control group) and the outcomes are compared.

    5. RCTs

    6. Randomised Controlled Trials All subjects free from outcome factor/disease at the commencement of the study Study pop defined geographically, temporally and by other exclusion criteria RCTs Subjects randomly allocated to an intervention group (study factor) or non-intervention (control – placebo or established therapy) Followed over time to determine the outcome.

    7. Randomised Controlled Trials Best evidence as to whether exposure results in specific outcome Used to evaluate the efficacy of drugs, clinical and preventive intervention programs

    8. Example of Randomised Controlled Trials A placebo-controlled randomized trial might compare the effect of vitamin E treatment in schizophrenia patients (the treatment group) AGAINST the effects of a placebo on a separate group of schizophrenia patients (the control group).

    9. Phases of clinical trials Phase 1: Clinical pharmacologic studies Small studies 20-80 patients look at toxic & pharmacological effects Phase 2: Efficacy and safety 100-200 patients Phase 3: large scale RCTs for effectiveness and relative safety. Often multicentred. Followed by licensing of drug Phase 4: Post-marketing surveillance for monitoring new drugs

    10. Issues in designing RCTs Selection of subjects Random Allocation of subjects to treatment and control groups Data collection Blinding (or masking) Sample size Crossover trials Noncompliance Contamination

    11. Selection of subjects Criteria precisely defined Volunteers Representative?

    12. Random Allocation of subjects Randomization avoids bias by: Ensuring unpredictability of next assignment Reduces baseline differences in risk between treatment and control groups. should make both groups similar in terms of the distribution of risk factors larger the randomized groups, the greater the probability of equal baseline risks.

    13. Random Allocation of subjects Use computer generated random lists Or random number tables (example in text) Reduces bias caused during selection

    14. Data collection It is essential that data collected for each of the study groups be of the same quality Treatment (assigned and received) Outcome – criteria explicitly stated and measured comparably in all groups Otherwise ? observer bias

    15. Blinding (or masking) Knowledge of whether the participant is in a treatment or control group can influence behaviour Overcome through blinding the participants and/or observer and and/or data reviewer

    16. Blinding (or masking) Single blinding – subject (participant) not given any information about whether allocated to treatment or comparison group usually via a placebo (inert agent – usually indistinguishable from the active treatment)

    17. Subjects suspicion about their treatment

    18. Blinding (or masking) Double Blinding - neither subject or observer have any information about allocation of subject to treatment or comparison groups Minimises bias during assessment and care

    19. Blinding (or masking) Triple blinding –neither subject, observer or person analysing the data have any information about allocation of the subject to treatment or comparison groups Unblinded or open label studies – no attempt at blinding

    20. Blinding (or masking)

    21. Sample size Need to calculate adequate sample size to obtain meaningful results Use sample size computer program Sample size tables and statistical formulas

    22. Possible trial outcomes

    23. Crossover trials There are 2 types: Planned crossover trials subjects are randomised to therapy A or B and after being observed for a period they switch therapy. Subject become their own controls Useful as it allows ‘constant’ characteristics between the treatments

    24. Cross over trials

    25. Planned Crossover trials Advantages Comparisons within individuals Subjective outcomes can be used (with blinding) Reduces sample size needed Disadvantages: Carryover Washout period Need to consider effect of the order of interventions (psychological response) Dropouts from 2nd treatment

    26. Unplanned crossover in study of bypass surgery

    27. Noncompliance Reduces the power for the detection of an effect Limiting the analysis to only those who comply may bias the results

    28. Noncompliance - Example Trial of cancer-prevention diet vs placebo diet. Treatment Group some subjects had GI symptoms (these were actually precursors of the cancer) they did not comply with the diet If these non compliant people were excluded from the analysis ? Greater effect of the cancer-prevention diet

    29. Intention to treat Performing the analysis of RCTs according to original randomised assignment Not according to whether they comply with treatment or not Optimal estimate of the true benefit of the intervention

    30. Contamination that people in control group will receive part or all of intervention that is used for the intervention group ? reduces any differences between the two groups ? decreases the likelihood of identifying these differences.

    31. Contamination may be caused by service providers or trials ‘inadvertently’ applying ‘trial interventions’ to the control group individual participants seeking additional care from providers outside of the trial. other influences on ‘usual care’ which are out of the control of the trial organisers.

    32. Contamination cannot usually be prevented it may be reduced by modifications to trial design (eg. using randomization by service providers). where it cannot be prevented or reduced then it should be measured and the information used when assessing the results.

    33. Advantages Disadvantages Best evidence for causality ensures that individuals are allocated to the intervention or control groups without prejudice Minimizes/ eliminates unequal distribution of factors that influence clinical outcome between groups Facilitates statistical analysis. Expensive (time and money). Organisationally difficult. Difficult to recruit health professionals to participate. Not always generalisable. Sometimes ethical problems.

    34. Non-randomised Controlled Trial (NRCT) an experiment in which subjects in a population are allocated into groups (using methods other than randomisation) to receive or not receive a therapeutic procedure or intervention and the outcomes are compared. all other features may be similar

    35. Example

    36. Advantages Disadvantages Poorer evidence for causality Can minimizes unequal distribution of factors that influence clinical outcome between groups Facilitates statistical analysis Less expensive than RCTs Individuals may be allocated to the intervention or control groups with prejudice Expensive (time and money). Not always generalisable.

    37. Example

    38. Measures of Association used in Intervention Trials When comparing 2 groups of participants in a RCT, the control group (those who did not receive treatment) and the intervention group, (those who were exposed to the treatment), you can use the following measures to help you judge the size of the effect of the intervention: The relative risk reduction (RRR) The absolute risk reduction (ARR) The numbers needed to treat (NNT)

    39. Relative Risk Reduction (RRR) Is the extent to which a treatment reduces a risk, in comparison with patients not receiving the treatment of interest. is the commonest reported measure of dichotomous treatment effect however does not discriminate between huge absolute treatment effects and trivial ones.

    40. Relative Risk Reduction (RRR) RRR = Event rate – Event rate in control grp in intervention grp Event rate in the control group

    41. Absolute Risk Reduction (ARR) is the difference in the absolute risk (rates of adverse events) between study and control populations ARR = Event rate – Event rate in control grp in intervention grp

    42. Number needed to treat (NNT) Is the number of patients who must be exposed to an intervention before the clinical outcome of interest occurred; for example, the number of patients needed to treat to prevent one adverse outcome.

    43. Examples NNT = 1 ARR Example: control intervention mortality rate 17% 12% NNT = 1 = 1 = 20 17%-12% 0.05 We need to treat 20 people to prevent one death

    44. DCCT – Diabetic Neuropathy

    45. Measuring the precision of the results: Tests for Statistical Significance show you the precision of the results of a study by examining the confidence intervals or the p values help you to evaluate whether the study was statistically significant.

    46. Confidence intervals show a range within which the true effect of the intervention is likely to be. a confidence interval that includes the value of no effect (e.g. RR=1 or RRR=0) shows that the intervention group is not statistically significantly different from the control.

    47. Confidence intervals Where the confidence interval does not include the ‘no effect’ value this shows that there is a statistically significant different between the intervention and control group. Statistical significance is usually measured using a 95% confidence interval, meaning that if the study is repeated multiple times, 95% of the studies will have result within that range.

    48. Confidence intervals When a study does not show a statistical significance, it may still have a real effect. For example, small studies will often report no statistical significance but they may show very important clinical effects. Is the preferred way of calculating precision but in some studies only p values may be presented.

    49. p-value reflects the degree of certainty about the existence of a true effect. based on the supposition that the null hypothesis is true i.e. that there is no true difference between the intervention and control groups. Using the p value we calculate the likelihood that our null hypothesis is true.

    50. p-value If p is small, it is unlikely that the difference is due to chance and we reject the null hypothesis. If p is large then it is likely that the difference is due to chance and we do not reject the null hypothesis. Statistical significance is usually set at p=<0.05% or p=<0.01.

    51. Consort (Consolidated Standards of Reporting Trials) statement First published in 1996 Available at http://www.consort-statement.org/revisedstatement.htm#checklist Aim - improve quality of conduct & reporting of RCTs Developed by - international group of clinical trialists, statisticians, epidemiologists and biomedical editors

    52. Supported by a growing number of medical and health care journals, and editorial groups Endorsed by The Lancet, JAMA, Annals of Internal Medicine Comprises checklist & flow diagram for reporting RCTs Used in the writing, reviewing, or evaluating reports of simple two-group parallel RCTs

    53. Consort Flow Diagram

    54. CONSORT Checklist INTRODUCTION Background METHODS Participants Interventions Objectives Outcomes Sample size Randomisation: sequence generation, allocation concealment, implementation Blinding (masking) Statistical methods

    55. CONSORT Checklist RESULTS Participant flow Recruitment Baseline data Numbers analyzed Outcomes and estimation Ancillary analyses Adverse events DISCUSSION Interpretation Generalisability Overall evidence

    56. QUESTION 1 The purpose of double blinding or double masked study is to: Achieve comparability of treated and untreated subjects Reduces effects of sampling variation Avoid observer and subject bias Introduce observer and subject bias in the study

    57. QUESTION 2 The major purpose of random assignment in a RCT is to: Facilitate double blinding Facilitate measurement of outcome variables Ensure study groups are comparable on baseline characteristics Reduce selection bias in allocation of treatment

    58. QUESTION 3 A RCT comparing the efficacy of 2 drugs showed a difference between the two (p<0.05). Assume in reality, however, the 2 groups do not differ. This is therefore an example of: Type 1 error (a error) Type 2 error (ß error) 1- a 1- ß

    59. QUESTION 4 In a RCT, a planned crossover design Requires standardisation Must take into account possible residual effects Enhances generalisability of the study Eliminates need for monitoring compliance/non compliance

    60. Question 5 Phase 1 trials are Trials with large number of subjects are recruited Done for post marketing surveillance Multi-centered Small studies with 20-80 patients looking at toxic & pharmacological effects of drugs

    61. ANSWER 1 The purpose of double blinding or double masked study is to: Achieve comparability of treated and untreated subjects Reduces effects of sampling variation Avoid observer and subject bias Introduce observer and subject bias in the study

    62. ANSWER 2 The major purpose of random assignment in a RCT is to: Facilitate double blinding Facilitate measurement of outcome variables Ensure study groups are comparable on baseline characteristics Reduce selection bias in allocation of treatment

    63. ANSWER 3 A RCT comparing the efficacy of 2 drugs showed a difference between the two (p<0.05). Assume in reality, however, the 2 groups do not differ. This is therefore an example of: Type 1 error (a error) Type 2 error (ß error) 1- a 1- ß

    64. ANSWER 4 In a RCT, a planned crossover design Requires standardisation Must take into account possible residual effects Enhances generalisability of the study Eliminates need for monitoring compliance/non compliance

    65. ANSWER 5 Phase 1 trials are Trials with large number of subjects are recruited Done for post marketing surveillance Multi-centered Small studies with 20-80 patients looking at toxic & pharmacological effects of drugs

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