Transcript design of trials
NEW Randomized Trials
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9 Sessions
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Grady (course director), Black (lecturer), Cummings (lecturer)
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Mechanics
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Turn in homework to Olivia Romero prior to each session
Randomized Trials: the
Evidence
“Evidence-Based” in
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Today
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Randomized trials: why bother?
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Randomization
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Selection of participants (Inclusion/exclusion)
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Design options for trials
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Dennis Black, PhD
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597-9112
NEW Feedback from last year (observed)….
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Great course but…..
HERS
NEW Feedback from this year (predicted)….
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Great course but…..
WHI
Randomized Trials: the
Evidence
“Evidence-Based” in
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Today
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Randomized trials: why bother?
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Randomization
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Selection of participants (Inclusion/exclusion)
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Design options for trials
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Dennis Black, PhD
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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
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Drug vs. placebo
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Low fat diet vs. regular diet
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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
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Expensive
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Time Consuming
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Can only answer a single question
So, why bother?
Alternatives to RCTs (30 second Epi. Course)
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Case-control studies
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Compare those with and without disease
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Cohort studies (prospective)
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Identify those with and without risk factor Follow forward in time to see who gets disease
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Cohort and case-control are
observational
(not experimental)
Reasons for doing RCTs
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Only study design that can prove causation
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Required by FDA (and others) for new drugs and some devices
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Most influential to clinical practice
Example: Estrogen Replacement Therapy in post-menopausal women
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Important therapeutic question
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Applies to 30 (?) million women in US
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Prempro
(estrogen/progestin combo) may be most prescribed drug in US
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Potentially huge impact on public health
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Complex, ERT effects multiple diseases
Estrogen Replacement Therapy (ERT) Disease Coronary heart disease Osteoporosis (hip fx) Breast cancer Endometrial cancer
Alzheimer’s Pulmonary embolism & deep vein thrombosis
Effect on Risk* Decrease by 40 - 80% Decrease by 30 - 60% Increase by 10 - 20% Increase by 700%
Decrease by ?
Increase by 200 - 300%
* From observational (case-control and cohort) studies
Nurses Health Study (NEJM, 9/12/91)
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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
Relative Risk** 1.0
0.56 (0.40-0.80) 0.83 (0.65-1.05) * Events per 1000 women-years of follow-up ** Relative Risk (95% CI) compared to never users
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
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Women chose to take ERT Are ERT users different from non-users?
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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)
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Secondary prevention of heart disease
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HRT (Prempro) vs. placebo (4-5 years)
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~ 2763 women with established heart disease
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Postmenopausal, < 80 years, mean age 67
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20 clinical centers in U.S./UCSF Coordinating center
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Funding by Wyeth-Ayerst (post-NIH refusal)
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Expected results????
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Real results: JAMA: 8/98
HERS: Summary of results Endpoint New CHD Any fracture Placebo 176 138 HRT 172 130 RR 0.99
0.95
P 0.91
0.70
Conclusion: Randomized
trials can lead to big surprises!
Women’s Health Initiative HRT study* (7/10/02)
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Randomized trial (2)
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16,608 women with uterus (ERT + progestin vs. placebo) ~11,000 women without uterus (ERT alone vs. placebo)
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Ages 50-79, mean age 64
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Represent broad range of U.S. women
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40 clinical centers
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Follow-up planned for 8.5 years, to end in 2005 *
only one component of WHI..more later
WHI HRT study: 7/10/02
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Combination therapy arm stopped early (3 years)
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Mean 5.2 years of follow-up
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Overall, health risks outweigh benefits
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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
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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
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Clinical trials needed to establish cause and effect
Beta carotene: Clinical trial #1 The Alpha-Tocopherol, Beta Carotene Cancer Prevention Study RQ: Design: Subjects: Intervention: (factorial) Outcome: Do vitamin E and beta-carotene prevent lung cancer in smokers?
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 Lung cancer incidence
Beta-carotene: Clinical Trial #1 Results Incidence per 10,000 person years Beta-Carotene Control RR* Lung Cancer Cases Lung Cancer Deaths 56.3
35.6
47.5
30.8
1.19
1.16
* Relative risk: Beta carotene vs. control
Beta carotene: Clinical trial #2 The Beta-Carotene and Retinol Efficacy Trial (CARET) RQ: Design: Subjects: Intervention: Outcome: Do vitamin A and beta-carotene prevent lung cancer in smokers?
RCT, 4.0 years 18,314 men, smokers or asbestos workers Retinol (25,000 IU) and beta carotene (15 mg) vs. placebo Lung cancer incidence
Beta-carotene: Clinical Trial #2 Results All Subjects Asbestos-exposed Smokers Lung Cancer* 1.28 (1.04-1.57) 1.40 (0.95-2.07) 1.23 (0.96-1.56) Death (all causes)* 1.17 (1.03-1.33) 1.25 (1.01-1.56) 1.13 (0.96-1.32) * Relative Risk (95% CI), treatment vs. placebo
Beta Carotene RCTs
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Beta carotene not recommended for cancer prevention
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Similar story for beta carotenes and heart disease
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RCT’s very useful
Examples of major breakthroughs from RCTs
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Protease inhibitors and AIDS
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Aspirin and heart disease
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Lipid lowering (statins) and heart disease
Steps in a “Classical” Randomized, Controlled Trail (RCT)
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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
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Key element of RCT’s
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Assure equal distribution of both...
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measured/known confounders
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unmeasured/unknown confounders
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Important to do well
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True random allocation
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Tamper-proof (no peaking, altering order of participants, etc)
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Simple randomization
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Low tech
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High tech
Other types of randomization
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Blocking*: equal after each n assignments
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e.g., block size of 4, treatments a and b abab aabb bbaa baab
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Assure relatively equal number of ppts. to each treatment
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Disadvantages of blocking
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Size of block: 2 treatments--4 or 6
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Very commonly used *Formally: random, permuted blocks
Randomization to balance prognostic variables
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Stratified permuted blocks
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Blocks within strata of prognostic variable
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e.g., HRT study of prevention of MI. High LDL at much higher risk--want to avoid more higher LDL in placebo.
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Stratum High LDL: aabb baba … Normal LDL: baab abab ….
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Limited number of risk factors
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Very commonly used in multicenter studies to balance within clinical center
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Fancier techniques for assuring balance
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Adaptive randomization (not much used)
Implementation of randomization
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Less challenging for blinded studies
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Sealed envelopes in fixed order at clinical sites
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Alternatively: list of drug numbers
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a b a b b b a a 1 2 3 4 5 6 7 8
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Clinic receives bottles labeled only by numbers--assign in order
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Unblinded studies: important to keep next assignment secret
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Problem with blocks within strata
Who to Study: Principles for Inclusion/exclusion
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Widest possible generalizability
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Sufficiently high event rate (for power to be adequate)
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Population in whom intervention likely to be effective
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Ease of recruitment
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Likelihood of compliance with treatment and FU
Explicit criteria for inclusion in a trial
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Typically written as “inclusion/exclusion” criteria in protocol
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The more explicit the better
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Want centers or investigators to be consistent
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Examples of exclusion decisions
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1. Women with heart disease vs. Women with CABG surgery or documented MI by ecg (criteria) or enzymes (criteria)
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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)
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Treatment would be unsafe
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Adverse experience from active treatment
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“Risk” of placebo (SOC)
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Active treatment cannot/unlikely to be effective
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No risk of outcome
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Disease type unlikely to respond
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Competing/interfering treatment (history of?)
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Unlikely to adhere or follow-up
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Practical problems
Design-a-trial: Inclusion criteria options for HRT
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Study HRT and prevention of heart disease, 4 years (HERS-like)
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Women over age 50 years
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Women over 60 years
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Women over 75 years
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Women with existing heart disease
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Generalizability?
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Feasible sample size?
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Population amenable to intervention?
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Logistic difficulties (recruitment? cost? adherence)
HERS inclusion options
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HERS trial options (event rate)
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Women over age 50 years (0.1%/year)
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Women over 60 years (0.5%/year)
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Women over 75 years (1%/year)
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Women with existing heart disease (4%/year)
HERS inclusion options
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HERS trial options (event rate) [n required]
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Women over age 50 years (0.1%/year) [55,000]
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Women over 60 years (0.5%/year) [45,000]
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Women over 75 years (1%/year) [34,000]
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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
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Important impact on generalizability of both efficacy and safety
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Example: Fracture Intervention Trial (FIT)
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Study of alendronate (amino-bisphosphonate) vs. placebo in women with low bone mass
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6459 women randomized to alendronate or placebo
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Fracture endpoint
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Upper GI and esophagitis concerns with bisphosphonates, esp. aminos
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Who to exclude?
FIT inclusion/exclusion example
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Alendronate studies (pre-FIT) excluded:
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Any history of upper GI events
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Any (remote) history of ulcer
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Esophagial problems, etc.
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Reports of upper GI problems in clinical practice: 5% to 20% of patients stop alendronate. Due to:
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Use by “real world” patients?
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Use in real world?
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Psychological--due to warnings about potential problems
Inclusion may impact effect of treatment
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FIT: Included women with baseline BMD T-score below -1.6 (only those below -2.5 officially osteoporotic)
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Reduction in hip fractures only among those with more severe osteoporosis
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Similar findings in statin trials: higher lipids, more benefit
Effect of alendronate
on hip fx
hip BMD depends on baseline
Baseline BMD T-score
-1.6 – -2.5
< - 2.5
1.84 (0.7, 5.4) 0.44 (0.18, 0.97)
NEW Overall 0.1
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0.79 (0.43, 1.44)
10 Relative Hazard ( ± 95% CI)
Effect of alendronate
on non-spine fx
baseline hip BMD depends on
Baseline BMD T-score
-1.6 – -2.0
-2.0 – -2.5
< - 2.5
1.14 (0.82, 1.60) 1.03 (0.77, 1.39
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0.64 (0.50, 0.82)
NEW Overall 0.1
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0.86 (0.73, 1.01)
10 Relative Hazard ( ± 95% CI)
Inclusion, exclusion, Conclusion
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Many factors to balance in deciding who to include
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Generally not a clear cut or single correct decision
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Many academics have simplistic understanding of issues NEW
Alternative RCT designs: Factorial design
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Test of more than one treatment (vs. placebo)
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Each drug alone and in combination
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Allows multiple hypotheses in single trial
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Efficient (sort of)
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e.g., Physician’s Health Study
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Test aspirin ==> MI
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beta caratene ==> cancer
Factorial design: Physician’s Heath Study Placebo Aspirin Beta carotene Aspirin plus Beta carotene Beta carotene vs. no beta carotene (cancer) Aspirin vs. no aspirin (MI)
Factorial design assumptions/limitations
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Treatments do not interact
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Effect of aspirin on MI is same with and without beta-carotene
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Must test for interaction of treatments
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Difficult to prove, requires large sample
Factorial design assumptions/limitations
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Women’s Health Initiative (MOAS, $600M +)
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Estrogen vs. placebo (all outcomes) Calcium/Vit D vs. placebo (fractures)
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Low fat vs. regular diet (breast cancer)
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Effect of calcium on fractures is the same/additive with and without estrogen..
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NEW
NEW 3-way factorial design of WHI HRT vs. no HRT Low fat vs. regular diet
Factorial design assumptions/limitations
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Factorial designs are seductive but problematic
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Best used for unrelated RQ’s (both treatments and outcomes) NEW
Cross-over designs
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Both treatments are administered sequentially to all subjects
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Subject serves as own control, random order
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Compare treatment period vs. control period
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Diuretic vs. beta blocker for blood pressure
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1/2 get d followed by bb
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1/2 get bb followed by d
Cross-over assumptions/limitations
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Continuous variables only
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No order effects
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No carry-over effects
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Need quick response and quick resolution
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“Wash out” period helpful
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More commonly used in phase I/II
Other special designs
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Matched pairs randomized
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One of each pair to each treatment
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e.g., two eyes within an individual (one to each treatment)
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Diabetic Retinopathy study
Other special designs
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Cluster or grouped randomization
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Randomize groups to treatments
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Often useful especially for public health type interventions
Other special designs (clusters)
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Cluster or grouped randomization examples
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Medical practices to stop-smoking interventions
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Cities to public health risk factor reduction (5 Cities Project)
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Baseball teams to chewing-tobacco intervention
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Analysis complex
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Sample size complex: true n is between n clusters and n individuals (closer to clusters)
Previews of coming attractions
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Blinding, interventions, controls (placebo vs. active) (1/16)
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Follow-up, compliance, etc. (1/23)
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Outcomes (efficacy and adverse effects)
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Ethical issues (many!!)
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Nuts and bolts
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Interim monitoring
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Multi-center trials and working with the evil empire (drug cos)