Randomized Trials: the Evidence in EvidenceBased

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Randomized Trials the Evidence in
Evidence-Based

• Today
• Randomized trials why bother?
• Randomization
• Selection of participants (Inclusion/exclusion)
• Design options for trials
• Dennis Black, PhD
• Dblack_at_psg.ucsf.edu
• 597-9112

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Randomized Controlled Trial (RCT)

• A study design in which subjects are randomized
  to intervention or control and followed for
  occurrence of disease
• Experimental (as opposed to observational)
• Definitive test of intervention
• Confounders are equally distributed across
  intervention groups
• Treated not younger, richer, healthier,
  better dieters

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Examples of interventions

• Drug vs. placebo
• Low fat diet vs. regular diet
• Exercise vs. CPP

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Number of randomized trials published
8000
7000
6000
5000
4000
3000
2000
1986
1988
1990
1992
1994
1996
1998
Based on Medline search for Randomized
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Disadvantages of RCTs

• Expensive
• Time Consuming
• Can only answer a single question
• So, why bother?

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Alternatives to RCTs(30 second Epi. Course)

• Case-control studies
• Compare those with and without disease
• Cohort studies (prospective)
• Identify those with and without risk factor
• Follow forward in time to see who gets
  disease
• Cohort and case-control are observational (not
  experimental)

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Reasons for doing RCTs

• Only study design that can prove causation
• Required by FDA (and others) for new drugs and
  some devices
• Most influential to clinical practice

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Example Estrogen Replacement Therapyin
post-menopausal women

• Important therapeutic question
• Applies to 30 (?) million women in US
• Prempro (estrogen/progestin combo)may be most
  prescribed drug in US
• Potentially huge impact on public health
• Complex, ERT effects multiple diseases

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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
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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
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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
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Virtually all estrogen results arebased 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

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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,
• 20 clinical centers in U.S./UCSF Coordinating
  center
• Funding by Wyeth-Ayerst (post-NIH refusal)
• Expected results????
• Real results JAMA 8/98

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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!

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Other surprisesBeta Carotene and cancer

• Strong suggestions that beta carotene would
  prevent cancer
• 1. Observational epi. (diets high in fruits
  and vegetables with beta carotene
  lower cancer risk)
• 2. Pathophysiology
• Clinical trials needed to establish cause and
  effect

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Beta carotene Clinical trial 1

• The Alpha-Tocopherol, Beta CaroteneCancer
  Prevention Study
• RQ Do vitamin E and beta-carotene prevent lung
  cancer in smokers?
• Design RCT, factorial, 6.1 years
• Subjects 29,133 smokers, Finnish men aged 50-69
• Intervention 1. Alpha-tocopherol, 50 mg/day vs.
  placebo(factorial) 2. Beta-carotene, 20 mg/day
  vs. placebo
• Outcome Lung cancer incidence

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Beta-carotene Clinical Trial 1Results
Incidence per 10,000 person years

• Beta-Carotene Control RR
• Lung Cancer Cases 56.3 47.5 1.19
• Lung Cancer Deaths 35.6 30.8 1.16
• Relative risk Beta carotene vs. control

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Beta carotene Clinical trial 2

• The Beta-Carotene and Retinol Efficacy Trial
  (CARET)
• RQ Do vitamin A and beta-carotene prevent
  lung cancer in smokers?
• Design RCT, 4.0 years
• Subjects 18,314 men, smokers or asbestos workers
• Intervention Retinol (25,000 IU) and beta
  carotene (15 mg) vs. placebo
• Outcome Lung cancer incidence

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Beta-carotene Clinical Trial 2Results

• Lung Cancer Death (all causes)
• All Subjects 1.28 (1.04-1.57) 1.17 (1.03-1.33)
• Asbestos-exposed 1.40 (0.95-2.07) 1.25
  (1.01-1.56)
• Smokers 1.23 (0.96-1.56) 1.13 (0.96-1.32)

Relative Risk (95 CI), treatment vs. placebo
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Beta Carotene RCTs

• Beta carotene not recommended for cancer
  prevention
• Similar story for beta carotenes and heart
  disease
• RCTs very useful

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Examples of major breakthroughs from RCTs

• Protease inhibitors and AIDS
• Aspirin and heart disease
• Lipid lowering (statins) and heart disease

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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)

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Randomization

• Key element of RCTs
• Assure equal distribution of both...
• measured/known confounders
• unmeasured/unknown confounders
• Important to do well
• True random allocation
• Tamper-proof (no peaking, altering order of
  participants, etc)
• Simple randomization
• Low tech
• High tech

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Other types of randomization

• Blocking equal after each n assignments
• e.g., block size of 4, treatments a and b
• abab aabb bbaa baab
• Assure relatively equal number of ppts. to each
  treatment
• Disadvantages of blocking
• Size of block 2 treatments--4 or 6
• Very commonly used
• Formally random, permuted blocks

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Randomization to balance prognostic variables

• Stratified permuted blocks
• Blocks within strata of prognostic variable
• e.g., HRT study of prevention of MI. High LDL at
  much higher risk--want to avoid more higher LDL
  in placebo.
• Stratum
• High LDL aabb baba
• Normal LDL 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 (not much used)

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Implementation of randomization

• Less challenging for blinded studies
• Sealed envelopes in fixed order at clinical sites
• Alternatively 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
• Unblinded studies important to keep next
  assignment secret
• Problem with blocks within strata

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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
• Ease of recruitment
• Likelihood of compliance with treatment and FU

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Explicit criteria for inclusion in a trial

• Typically written as inclusion/exclusion
  criteria in protocol
• The more explicit the better
• Want centers or investigators to be consistent
• Examples of exclusion 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 ERT for more than 3 months over
  last 24 mos.

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Valid reasons to exclude participants (Table
10.1)

• Treatment would be unsafe
• Adverse experience from active treatment
• Risk of placebo (SOC)
• 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

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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)

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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)

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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)

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Exclusions/inclusions examples

• Important impact on generalizability of both
  efficacy and safety
• Example Fracture Intervention Trial (FIT)
• Study of alendronate (amino-bisphosphonate) vs.
  placebo in women with low bone mass
• 6459 women randomized to alendronate or placebo
• Fracture endpoint
• Upper GI and esophagitis concerns with
  bisphosphonates, esp. aminos
• Who to exclude?

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FIT inclusion/exclusion example

• Alendronate studies (pre-FIT) excluded
• Any history of upper GI events
• Any (remote) history of ulcer
• Esophagial problems, etc.
• Reports of upper GI problems in clinical
  practice 5 to 20 of patients stop alendronate.
  Due to
• Use by real world patients?
• Use in real world?
• Psychological--due to warnings about potential
  problems

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Inclusion may impact effect of treatment

• FIT reduction in hip fractures only among those
  with more severe osteoporosis
• BL BMD T-score RR for hip fracture
• - 2.0 to -1.6 0.98
• - 2.5 to -2.0 1.05
• Similar findings in statin trials higher
  lipids, more benefit

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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 (sort of)
• e.g., Physicians Health Study
• Test aspirin MI
• beta caratene cancer

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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)
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Factorial design assumptions/limitations

• Treatments do not interact
• Effect of aspirin on MI is same with and without
  beta-carotene
• Difficult to prove, requires large sample
• Womens Health Initiative (MOAS, 600M )
• Estrogen vs. placebo (all outcomes)
• Calcium/Vit D vs. placebo (fractures)
• Effect of calcium is the same/additive with and
  without estrogen..very shaky
• Best used for unrelated RQs (both treatments and
  outcomes)

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Cross-over designs

• Both treatments are administered sequentially to
  all subjects
• Subject serves as own control, random order
• Compare treatment period vs. control period
• Diuretic vs. beta blocker for blood pressure
• 1/2 get d followed by bb
• 1/2 get bb followed by d

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Cross-over assumptions/limitations

• Continuous variables 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

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Other special designs

• Matched pairs randomized
• One of each pair to each treatment
• e.g., two eyes within an individual (one to each
  treatment)
• Diabetic Retinopathy study

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Other special designs

• Cluster or grouped randomization
• Randomize groups to treatments
• Often useful especially for public health-type
  interventions

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Other special designs (clusters)

• Cluster or grouped randomization examples
• medical practices to stop-smoking interventions
• cities to public health risk factor reduction (5
  Cities Project)
• baseball teams to chewing-tobacco intervention
• Analysis complex
• Sample size complex true n is between n
  clusters and n individuals (closer to clusters)

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Previews of coming attractions

• Blinding, interventions, controls (placebo vs.
  active)
• Follow-up, compliance, analysis
• Outcomes (efficacy and adverse effects)
• Ethical issues (many!!)
• Nuts and bolts
• Multiple hypothesis testing
• Working with the evil empire (drug cos)