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Randomized Trials: Design, Subjects, and Randomization

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Title: Randomized Trials: Design, Subjects, and Randomization


1
Randomized Trials Design, Subjects, and
Randomization
  • Clay Johnston, MD, PhD
  • Neurology and Epidemiology

2
Randomized Trials the Evidence in
Evidence-Based
  • Today
  • Randomized trials why bother?
  • Randomization
  • Adaptive designs
  • Selection of participants (Inclusion/exclusion)
  • Design options for trials
  • Factorial designs
  • Cross-over designs
  • Matched pairs
  • Cluster or group randomization

3
Randomized Controlled Trial (RCT)
  • An experiment in which subjects are randomly
    allocated into groups, usually called study and
    control groups, to receive or not to receive an
    experimental preventive or therapeutic procedure,
    maneuver, or intervention. The results are
    assessed by rigorous comparison of rates of
    disease, death, recovery, or other appropriate
    outcome in the study and control groups,
    respectively.

4
Number of randomized trials published
Based on Medline search restricted to
Randomized clinical trials
5
Disadvantages of RCTs
  • Expensive typically in millions
  • Time Consuming typically years
  • Can only answer a single question
  • May not apply to most patients in practice
  • May not be practical
  • Generally very difficult to get funded
  • Time consuming, organizationally complex
  • Class dismissed.

6
Alternatives to RCTs(30 second Epi. Course)
  • Case-control studies
  • Compare those with and without disease
  • Cross-sectional studies
  • Compare rates of risk factor among those with
    and without disease at a single time point.
  • Cohort studies (prospective)
  • Identify those with and without risk factor
  • Follow forward in time to see who gets
    disease
  • Case-control, cross-sectional, and cohort studies
    are observational (not experimental)

7
Reasons for doing RCTs
  • Only study design that can prove causation
  • Rodney Dangerfield syndrome for observational
    researchers
  • Required by FDA (and others) for new drugs and
    some devices
  • Most influential to clinical practice

8
Example Estrogen Replacement Therapyin
post-menopausal women
  • Important therapeutic question
  • Applies to 30 million women in US
  • Prempro (estrogen/progestin combo)may have been
    most prescribed drug in US
  • Potentially huge impact on public health
  • Complex ERT effects multiple diseases

9
Estrogen Replacement Therapy (ERT)
  • Disease Effect on Risk
  • Coronary heart disease Decrease by 40 -
    80Osteoporosis (hip fx) Decrease by 30 -
    60Breast cancer Increase by 10 -
    20Endometrial cancer Increase by 700
  • Alzheimers Decrease by ?
  • Pulmonary embolism Increase by 200 - 300deep
    vein thrombosis

From observational (case-control and cohort)
studies
10
Nurses Health Study (NEJM, 9/12/91)
  • Prospective cohort study, n 48,470
  • 337,000 person years of follow-up
  • Risk of Major Estrogen Use Coronary
    Disease Relative Risk
  • Never Used 1.4 1.0
  • Current user 0.6 0.56 (0.40-0.80)
  • Former user 1.3 0.83 (0.65-1.05)

Events per 1000 women-years of follow-up
Relative Risk (95 CI) compared to never users
11
Meta-analysis of ERT, Published 4/10/97
  • Benefits (for CHD, osteoporosis) outweigh risks
    (breast cancer) and side effectsAll
    post-menopausal women should be taking ERT

CNN, 4/10/97
12
Virtually all estrogen results werebased on
observational data
  • Women chose to take ERT
  • Are ERT users different from non-users?
  • Age
  • Health status
  • More exercise
  • Health behaviors (see Dr.)
  • SES
  • Try to adjust in analysis, but may not be
    possible
  • Randomized trials alleviate these problems

13
Heart and Estrogen-Progestin Replacement Study
(HERS)
  • Secondary prevention of heart disease
  • HRT (Prempro) vs. placebo (4-5 years)
  • 2763 women with established heart disease
  • Postmenopausal, lt 80 years, mean age 67
  • 20 clinical centers in U.S./UCSF Coordinating
    center
  • Funding by Wyeth-Ayerst (post-NIH refusal)
  • Results JAMA 8/98

14
HERS Summary of results
  • Endpoint Placebo HRT RR P
  • New CHD 176 172 0.99 0.91
  • Any fracture 138 130 0.95 0.70
  • Conclusion Randomized trials can lead to big
    surprises!

15
Womens Health Initiative HRT study (7/10/02)
  • Randomized trial (2)
  • 16,608 women with uterus (ERT progestin vs.
    placebo)
  • 11,000 women without uterus (ERT alone vs.
    placebo)
  • Ages 50-79, mean age 64
  • Represent broad range of U.S. women
  • 40 clinical centers

16
WHI EP study 7/10/02
  • Combination therapy arm stopped early (3 years)
  • Mean 5.2 years of follow-up
  • Overall, health risks outweigh benefits
  • Significant increased risk for invasive breast
    cancer HRT users

17
WHI E P Coronary Heart Disease
years 1 2 3
4 5 6
18
HERS/WHI Trials Take Home
  • Observational studies can be wrong.
  • Cohort studies can be wrong
  • Meta-analysis of observational studies can be
    wrong.
  • What went wrong with observational studies of HRT?

19
Major Observational Study Limitation
  • CHARM
  • Candesartan in Heart failure Assessment of
    Reduction in Mortality and morbidity
  • Compared those taking vs. not taking either drug
    or placebo
  • 35 RRR in all-cause mortality
  • For drug and placebo.
  • Take home
  • Confounders are impossible to fully identify

Lancet 2005, 3662005
20
CLINICAL TRIALS IN THE NEWS
  • Lots of trials of things other than drugs
  • Surgical techniques
  • Weight loss

21
Clinical Trials in the NewsJAMA 1/5/05
Vs.
Vs.
22
RCT of 4 Popular Weight Loss Programs
Vs.
  • Compare
  • Atkins (low carbohydrate)
  • Weight Watchers (low calorie/portion size)
  • Zone (high protein/low-glycemic load)
  • Ornish (very low fat)

JAMA 1/5/05
23
Diet study Design
  • N 160
  • Randomize to 1 of 4 diets
  • Follow for 12 months
  • Endpoints
  • Weight loss
  • Heart disease risk factors (cholesterol, BP,
    triglycerides)

JAMA 1/5/05
24
Diet study Results at 12 months
  • Year Atkins Zone Weight Ornish
  • watchers .
  • Weight (kg) -3.9 -4.9 -4.6 -6.6
  • LDL (mg/dL) -13.5 -18.1 -14.2 -25.2
  • SBP (mm/Hg) 0.3 2.1 -4.1 0.9

JAMA 1/5/05
25
Diet study Summary
  • All diets lead to modest reductions in weight
    and cardiac risk factors
  • Poor compliance for all diets, especially Atkins
    and Ornish
  • Those who adhered well had better results

JAMA 1/5/05
26
Examples of major breakthroughs from RCTs
  • Protease inhibitors and AIDS
  • Aspirin and heart disease
  • Lipid lowering (statins) and heart disease

27
NINDS Trials
28
So you want to do a randomized trial
29
Steps in a Classical Randomized, Controlled
Trail (RCT)
  • 1. Select participants
  • 2. Measure baseline variables
  • 3. Randomize (to 1 or more treatments)
  • 4. Apply intervention
  • 5/6. Follow-up--measure outcomes
  • Most commonly one treatment vs. control
  • Can be used for various types of outcomes
    (binary, continuous)

30
Randomization
  • Key element of RCTs
  • Assures equal distribution of both...
  • Measured/known confounders
  • Unmeasured/unknown confounders
  • Important to do well
  • True random allocation
  • Tamper-proof (no peeking, altering order of
    participants, etc)
  • Simple randomization
  • Low tech
  • High tech

31
Other types of randomization
  • Blocking equal after each n assignments
  • e.g., block size of 4, treatments a and b
  • abab aabb abba baba bbaa baab
  • Randomly choose blocks
  • Assure relatively equal number of ppts. to each
    treatment
  • Disadvantages of blocking (in unblinded trials)
  • Size of block 2 treatments--4 or 6
  • Very commonly used
  • Formally random, permuted blocks

32
Randomized blocks to balance prognostic variables
  • Stratified permuted blocks
  • Blocks within strata of prognostic variable
  • e.g., Stroke prevention after TIA. Time from
    event a key predictor
  • Stratum
  • lt12 hour aabb baba
  • gt12 hour baab abab .
  • Limited number of risk factors
  • Very commonly used in multicenter studies to
    balance within clinical center
  • Fancier techniques for assuring balance
  • Adaptive randomization

33
Adaptive Randomization
  • Various designs that reduce the total number of
    subjects necessary when comparing multiple groups
  • Determine assignment on the fly based on prior
    data
  • Often used for dose finding
  • Simple designs based on a priori decision rules
  • Eg, go to next highest dose if no side effects in
    first 4 treated
  • Complex designs based on recalculation of odds
  • ASTIN trial of 15 doses of neutrophil inhibitory
    factor (Stroke, 2003)

34
(No Transcript)
35
Implementation of randomization
  • Less challenging for blinded studies
  • Sealed envelopes in fixed order at clinical sites
  • List of drug numbers
  • a b a b b b a a
  • 1 2 3 4 5 6 7 8
  • Clinic receives bottles labeled only by
    numbers--assign in order
  • IVRS Interactive Voice Response System
  • Unblinded studies important to keep next
    assignment secret
  • Problem with blocks within strata

36
Randomization Summary
  • Key element of clinical trials
  • Not really very complicated (usually)

37
Who to Study Principles for Inclusion/exclusion
  • Widest possible generalizability
  • Sufficiently high event rate (for power to be
    adequate)
  • Population in whom intervention likely to be
    effective and safe
  • Ease of recruitment
  • Likelihood of compliance with treatment and F/U

38
Who to Study Principles for Inclusion/exclusion
  • Homogeneity --------------------Heterogeneity

39
Explicit criteria for inclusion in a trial
  • Typically written as inclusion/exclusion
    criteria in protocol
  • Generally, the more explicit the better
  • Want centers or investigators to be consistent
  • Examples of exclusion criteria decisions
  • 1. Women with heart disease vs. Women with CABG
    surgery or documented MI by ECG (criteria) or
    enzymes (criteria)
  • 2. Users of estrogen vs Use of oral ERT (.625 mg
    prempro) for more than 3 months over last 24 mos.

40
Valid reasons to exclude participants
  • Treatment would be unsafe
  • Adverse experience from active treatment
  • Risk of placebo
  • Active treatment cannot/unlikely to be effective
  • No risk of outcome
  • Disease type unlikely to respond
  • Competing/interfering treatment (history of?)
  • Unlikely to adhere or follow-up
  • Practical problems

41
Enrollment rates in stroke trials
42
Example of Inclusions/Exclusions in Protocol
  • Inclusion Criteria
  • Male or female gt 40 years
  • TIA (gt 10 minutes) or minor acute ischemic stroke
    (NIHSS lt 2 at time of randomization) occurring
    less than 24 hours before randomization
  • Informed consent signed
  • Able to have MRI scan within 24 hours of symptoms
    onset
  • Exclusion Criteria
  • Related to absolute contraindications to the use
    of clopidogrel and/or ASA
  • History of drug allergy to thienopyridine
    derivatives or ASA
  • Severe uncontrolled hypertension (SBP gt 160 mm Hg
    or DBP gt 110 mm Hg on two or more measures over
    the last 6 months)
  • History of clinically significant or persistent
    thrompocytopenia
  • History of clinically significant or persistent
    neutropenia
  • Women of child-bearing potential who are not
    following an effective method of contraception
  • Women who are breast-feeding

43
Example of Inclusions/Exclusions in Protocol
  • Inclusion Criteria
  • Male or female gt 40 years
  • TIA (gt 10 minutes) or minor acute ischemic stroke
    (NIHSS lt 2 at time of randomization) occurring
    less than 24 hours before randomization
  • Informed consent signed
  • Able to have MRI scan within 24 hours of symptoms
    onset
  • Exclusion Criteria
  • Related to absolute contraindications to the use
    of clopidogrel and/or ASA
  • Related to concomitant or planned medication(s) /
    treatment(s)
  • Related to TIA/Stroke characteristics
  • Related to the presence of other medical problems
    that would either interfere with participation in
    the trial or lead to inability to complete the
    trial

44
Design-a-trial Inclusion criteria options for
HRT
  • Study HRT and prevention of heart disease, 4
    years (HERS-like)
  • Women over age 50 years
  • Women over 60 years
  • Women over 75 years
  • Women with existing heart disease
  • Generalizability?
  • Feasible sample size?
  • Population amenable to intervention?
  • Logistic difficulties (recruitment? cost?
    adherence)

45
HERS inclusion options
  • HERS trial options (event rate)
  • Women over age 50 years (0.1/year)
  • Women over 60 years (0.5/year)
  • Women over 75 years (1/year)
  • Women with existing heart disease (4/year)

46
HERS inclusion options
  • HERS trial options (event rate) n required
  • Women over age 50 years (0.1/year) 55,000
  • Women over 60 years (0.5/year) 45,000
  • Women over 75 years (1/year) 34,000
  • Women with existing heart disease (4/year)
    3,000
  • (Choose last option as most practical common to
    generalize from secondary to primary prevention)

47
Exclusions/inclusions examples
  • Important impact on generalizability of both
    efficacy and safety
  • Example Primary Stroke Prevention Trial
  • Chlorthalidone vs. placebo
  • Stroke rates dramatically increase with age
  • Stroke more common than MI after age 65
  • Who should we include?
  • Age cut-off?
  • Atrial fibrillation?

48
Inclusion, Exclusion Conclusion
  • Many factors to balance in deciding who to
    include
  • Generally not a clear cut or single correct
    decision
  • Many academics have simplistic understanding of
    issues

49
Alternative RCT designs Large-simple vs.
standard
  • Large simple less information on more people
  • Balance loss of precision with sample size
  • Cost per patient with number of patients
  • Example
  • Primary Prevention of Stroke Trial
  • COMMIT 45,852 patients in China, adding
    clopidogrel to aspirin in acute MI
  • Standard lots of info on few
  • Example CASTIA trial

50
Alternative RCT designs Factorial design
  • Test of more than one treatment (vs. placebo)
  • Each drug alone and in combination
  • Allows multiple hypotheses in single trial
  • Efficient
  • Example Physicians Health Study
  • Test aspirin gt MI
  • beta carotene gt cancer

51
Factorial design Physicians Heath Study
Placebo
Beta-carotene
Aspirin vs. no aspirin (MI)
Aspirin plus Beta-carotene
Aspirin
Beta carotene vs. no beta carotene (cancer)
52
Music, imagery, touch, and prayer as adjuncts to
interventional cardiac care the Monitoring and
Actualisation of Noetic Trainings (MANTRA) II
randomised study
53
Factorial design Primary Stroke Prevention Trial
Placebo
Chlorthal.

Chlorthal. plus K Citr
K Citr
54
3-way factorial design of WHI
HRT vs. no HRT
Calcium vs. no calcium
Low fat vs. regular diet
55
Factorial design issues
  • Do treatments interact?
  • Effect of chlorthalidone likely greater with
    potassium citrate on board
  • HRT may increase calcium effect on bone
  • Must test for interaction of treatments
  • If present, power is lower large sample required
  • May require more complicated stopping rules
  • May require more complicated analysis plan (eg,
    logistic regression)

56
A Factorial Trial of Six Interventions for the
Prevention of Postoperative Nausea and Vomiting
  • One third of patients have post-op N/V
  • 5200 patients randomized to test 6 individual
    medications
  • 26 64 combinations
  • Ondansetron
  • Dexamethosone
  • Droperidol
  • Propofol vs. other
  • Nitrogen vs. nitrous oxide
  • Remifentanil vs. fentanyl

Apfel, C. C. et al. N Engl J Med
20043502441-2451
57
Study Design
Apfel, C. C. et al. N Engl J Med
20043502441-2451
58
A Factorial Trial of Six Interventions for the
Prevention of Postoperative Nausea and Vomiting
  • Results
  • Ondensetron, dexamethasone, and droperidol each
    reduced N/V by 26
  • Propofol by 19
  • Nitrogen by 12
  • All agents acted independently
  • RR associated with combined interventions could
    be estimated by multiplying individual RRs.

Apfel, C. C. et al. N Engl J Med
20043502441-2451
59
Factorial design conclusions
  • Factorial designs are seductive but complicated
  • Some attraction in combining a low-risk
    hypothesis with a high-risk hypothesis
  • Must weigh benefits in efficiency against
    compounded uncertainty and complexity

60
Cross-over designs
  • Both treatments are administered sequentially to
    all subjects
  • Subject serves as own control, random order
  • Compare treatment period vs. control period
  • Increases power by reducing person-to-person
    variability

61
Cross-over designs
  • Diuretic vs. beta blocker for blood pressure
  • 1/2 get d followed by bb
  • 1/2 get bb followed by d
  • Migraine prophylaxis
  • Rx x 3 months followed by placebo
  • Placebo x 3 months followed by Rx
  • Atrial Overdrive Pacing in sleep apnea
  • AOP x 1 month followed by low-rate pacing
  • Low-rate pacing x 1 month followed by AOP

62
Cross-over assumptions/limitations
  • Transient outcomes only
  • No order effects
  • No carry-over effects
  • Need quick response and quick resolution
  • Wash out period helpful
  • More commonly used in phase I/II

63
Matched Pair Randomization
  • One of each pair to each treatment
  • Reduces risk of imbalanced randomization
  • Allows smaller sample size

64
Matched Pair Randomization
  • Example two eyes within an individual (one to
    each treatment)
  • Diabetic Retinopathy study
  • Example Filter for carotid arteries in patients
    with atrial fibrillation
  • Random side of intervention
  • Example Quality Improvement in Stroke
    Prevention (QUISP) trial
  • Randomize paired Kaiser facilities

65
Matched Pair Randomization Issues
  • Not necessary when sample size makes balanced
    randomization likely
  • Loss in efficiency
  • May not be feasible
  • Arguments about how to analyze the data
  • Maintaining the match vs. breaking it

66
Cluster or grouped randomization
  • Randomize groups to treatments
  • Often useful especially for public health-type
    interventions
  • May be only way to study a question
  • Intervention is at the group level
  • Cross-contamination between individuals

67
Cluster or grouped randomization
  • Examples
  • Quality Improvement in Stroke Prevention (QUISP)
    trial
  • DTC Advertising Trial
  • Randomize matched cities
  • Cities to public health risk factor reduction (5
    Cities Project)

68
Cluster or grouped randomization
  • Sample size complex true n is between n
    clusters and n individuals (closer to clusters)
  • Tendency to underestimate necessary sample size
  • Analysis complex
  • Must account for clustering
  • Mixed models best

69
Randomized Trials the Evidence in
Evidence-Based
  • Today
  • Randomized trials why bother?
  • Randomization
  • Adaptive designs
  • Selection of participants (Inclusion/exclusion)
  • Design options for trials
  • Factorial designs
  • Cross-over designs
  • Matched pairs
  • Cluster or group randomization

70
Previews of coming attractions
  • Statistical issues in randomized trials
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