Transcript design of trials

Randomized Trials
• 9 Sessions
• Grady (course director), Black (lecturer), Cummings
(lecturer)
• Mechanics
– Turn in homework to Olivia Romero prior to each
session
NEW
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
– [email protected]
– 597-9112
Feedback from last year (observed)….
• Great course but…..
HERS
NEW
Feedback from this year (predicted)….
• Great course but…..
WHI
NEW
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
– [email protected]
– 597-9112
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
Examples of interventions
• Drug vs. placebo
• Low fat diet vs. regular diet
• Exercise vs. CPP
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”
Disadvantages of RCTs
• Expensive
• Time Consuming
• Can only answer a single question
So, why bother?
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)
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
Example: Estrogen Replacement Therapy
in 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
Estrogen Replacement Therapy (ERT)
Disease
Effect on Risk*
Coronary heart disease
Osteoporosis (hip fx)
Breast cancer
Endometrial cancer
Decrease by 40 - 80%
Decrease by 30 - 60%
Increase by 10 - 20%
Increase by 700%
Alzheimer’s
Decrease by ?
Pulmonary embolism &
deep vein thrombosis
Increase by 200 - 300%
* From observational (case-control and cohort) studies
Nurses Health Study (NEJM, 9/12/91)
• Prospective cohort study, n = 48,470
• 337,000 person years of follow-up
Estrogen Use
Never Used
Current user
Former user
Risk of Major
Coronary Disease*
1.4
0.6
1.3
* Events per 1000 women-years of follow-up
** Relative Risk (95% CI) compared to never users
Relative Risk**
1.0
0.56 (0.40-0.80)
0.83 (0.65-1.05)
Meta-analysis of ERT, Published ~4/10/97
“Benefits (for CHD, osteoporosis) outweigh risks
(breast cancer) and side effects…
All post-menopausal women should be
taking ERT”*
* CNN, 4/10/97
Virtually all estrogen results are
based 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
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, < 80 years, mean age 67
• 20 clinical centers in U.S./UCSF Coordinating center
• Funding by Wyeth-Ayerst (post-NIH refusal)
• Expected results????
– Real results: JAMA: 8/98
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!
Women’s 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
• Follow-up planned for 8.5 years, to end in 2005
* only one component of WHI..more later
WHI HRT 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
WHI: Invasive Breast Cancer
3%
2%
1%
years
1
2
3
4
5
6
7
WHI: Coronary Heart Disease
years
1
2
3
4
5
6
Other surprises:
Beta 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
Beta carotene: Clinical trial #1
The Alpha-Tocopherol, Beta Carotene
Cancer Prevention Study
RQ:
Do vitamin E and beta-carotene
prevent lung cancer in smokers?
Design:
Subjects:
Intervention:
(factorial)
RCT, factorial, 6.1 years
29,133 smokers, Finnish men aged 50-69
1. Alpha-tocopherol, 50 mg/day vs. placebo
2. Beta-carotene, 20 mg/day vs. placebo
Outcome:
Lung cancer incidence
Beta-carotene: Clinical Trial #1
Results
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
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:
Subjects:
Intervention:
RCT, 4.0 years
18,314 men, smokers or asbestos workers
Retinol (25,000 IU) and beta carotene (15 mg)
vs. placebo
Outcome:
Lung cancer incidence
Beta-carotene: Clinical Trial #2
Results
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
Beta Carotene RCTs
• Beta carotene not recommended for
cancer prevention
• Similar story for beta carotenes and heart
disease
• RCT’s very useful
Examples of major
breakthroughs from RCTs
• Protease inhibitors and AIDS
• Aspirin and heart disease
• Lipid lowering (statins) and heart disease
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)
Randomization
• Key element of RCT’s
• 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
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
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)
Implementation of randomization
• Less challenging for blinded studies
• Sealed envelopes in fixed order at clinical sites
• Alternatively: list of drug numbers
– abab bbaa
– 1234 5678
– Clinic receives bottles labeled only by
numbers--assign in order
• Unblinded studies: important to keep next
assignment secret
– Problem with blocks within strata
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
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.
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
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)
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)
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)
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?
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
Inclusion may impact effect of treatment
• FIT: Included women with baseline BMD T-score
below -1.6 (only those below -2.5 officially
osteoporotic)
• Reduction in hip fractures only among those with
more severe osteoporosis
• Similar findings in statin trials: higher lipids,
more benefit
Effect of alendronate on hip fx depends on baseline
hip BMD
Baseline BMD T-score
-1.6 – -2.5
1.84 (0.7, 5.4)
0.44 (0.18, 0.97)
< - 2.5
Overall
NEW
0.79 (0.43, 1.44)
0.1
1
Relative Hazard (± 95% CI)
10
Effect of alendronate on non-spine fx depends on
baseline hip BMD
Baseline BMD T-score
-1.6 – -2.0
1.14 (0.82, 1.60)
-2.0 – -2.5
1.03 (0.77, 1.39)
< - 2.5
0.64 (0.50, 0.82)
Overall
0.86 (0.73, 1.01)
NEW
0.1
1
Relative Hazard (± 95% CI)
10
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
NEW
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., Physician’s Health Study
– Test aspirin ==> MI
– beta caratene ==> cancer
Factorial design: Physician’s Heath Study
Placebo
Aspirin
Betacarotene
Aspirin plus
Betacarotene
Beta carotene vs.
no beta carotene
(cancer)
Aspirin vs. no
aspirin (MI)
Factorial design assumptions/limitations
• Treatments do not interact
– Effect of aspirin on MI is same with and without
beta-carotene
– Must test for interaction of treatments
– Difficult to prove, requires large sample
Factorial design assumptions/limitations
• Women’s Health Initiative (MOAS, $600M +)
– Estrogen vs. placebo (all outcomes)
– Calcium/Vit D vs. placebo (fractures)
– Low fat vs. regular diet (breast cancer)
– Effect of calcium on fractures is the
same/additive with and without estrogen..
• very shaky
NEW
3-way factorial design of WHI
HRT vs. no
HRT
NEW
Low fat vs. regular
diet
Factorial design assumptions/limitations
• Factorial designs are seductive but problematic
• Best used for unrelated RQ’s (both treatments
and outcomes)
NEW
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
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
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
Other special designs
• Cluster or grouped randomization
–Randomize groups to treatments
–Often useful especially for public healthtype interventions
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)
Previews of coming attractions
• Blinding, interventions, controls (placebo vs.
active) (1/16)
• Follow-up, compliance, etc. (1/23)
• Outcomes (efficacy and adverse effects)
• Ethical issues (many!!)
• Nuts and bolts
• Interim monitoring
• Multi-center trials and working with the evil empire
(drug cos)