Clinical Trials Overview

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Clinical Trials Overview
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Clinical Trials

• A clinical trial is a prospectively planned
  experiment for the purpose of evaluating one or
  more potentially beneficial therapies or
  treatments
• In general these studies are conducted under as
  many controlled conditions as possible in order
  to provide definitive answers to well-defined
  questions

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Primary vs. Secondary Questions

• Primary
• most important, central question
• ideally, only one
• stated in advance
• basis for design and sample size
• Secondary
• related to primary
• stated in advance
• limited in number

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Examples

• Physicians Health Study (PHS) started in fall
  1982
• risks and benefits of aspirin and beta carotene
  in the prevention of cardiovascular disease and
  cancer
• low-dose aspirin vs placebo
• Primary total mortality
• Secondary fatal nonfatal myocardial infarction
• Eastern Cooperative Oncology Group (ECOG)
• tamoxifen vs placebo
• Primary tumor recurrence/relapse, disease-free
  survival
• Secondary total mortality

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Definitions

• Single Blind Study A clinical trial where the
  participant does not know the identity of the
  treatment received
• Double Blind Study A clinical trial in which
  neither the patient nor the treating
  investigators know the identity of the treatment
  being administered.

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Definitions

• Placebo
• Used as a control treatment
• 1. An inert substance made up to physically
  resemble a treatment being investigated
• 2. Best standard of care if placebo
  unethical
• 3. Sham control

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Definitions

• Adverse event
• An incident in which harm resulted to a person
  receiving health care.
• Examples Death, irreversible damage to liver,
  nausea
• Not always easy to specify in advance because
  many variables will be measured
• May be known adverse effects from earlier trials

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Adverse Events

• Challenges
• Long term follow-up versus early benefit
• Rare AEs may be seen only with very large numbers
  of exposed patients and long term follow-up
• Example COX II inhibitors
• Vioxx Celebrex
• Immediate pain reduction vs longer term increase
  in cardiovascular risk

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Surrogate Endpoints

• Response variables used to address questions
  often called endpoints
• Surrogates used as alternative to desired or
  ideal clinical response to save time and/or
  resources
• Examples
• Suppression of arrhythmia (sudden death)
• T4 cell counts (AIDS or ARC)
• Often used in therapeutic exploratory trials
• Use with caution in therapeutic confirmatory
• trials

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The General Flow of Statistical Inference
Sample Protocol to Obtain Participants
Patient Population
Observed Results
Inference about Population
Sample protocol / design key to analysis and
inference and may redefine the population for
future experiments
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Types of Clinical Trials

• Randomized
• Non-Randomized
• Single-Center
• Multi-Center
• Phase I, II, III Trials

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Phase I Trial

• Objective To determine an acceptable range of
  doses and schedules for a new drug
• Usually seeking maximum tolerated dose (MTD)
• Participants often those that have failed other
  treatments
• Important, however, that they still have normal
  organ functions

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Phase II Trial

• Objective To determine if new drug has any
  beneficial activity and thus worthy of further
  testing / investment of resources.
• Doses and schedules may not be optimum
• Begin to focus on population for whom this drug
  will likely show favorable effect

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Phase III Trial

• Objective To compare experimental or new
  therapies with standard therapy or competitive
  therapies.
• Very large, expensive studies
• Required by FDA for drug approval
• If drug approved, usually followed by Phase IV
  trials to follow-up on long-range adverse events
  concern is safety

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Characterization of Trials
Phase Single Center Single Center Multi Center Multi Center
Randomized Non-Rand. Randomized Non-Rand.
I Never Yes Never Sometimes
II Rare Yes Yes Sometimes
III Yes Use of Historical Controls Yes Use of Historical Controls
Carrying out a multi-center randomized clinical
trial is the most difficult way to generate
scientific information.
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Why Clinical Trials?

• 1. Most definitive method to determine whether a
  treatment is effective.
• Other designs have more potential biases
• One cannot determine in an uncontrolled setting
  whether an intervention has made a difference in
  the outcome.

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Observational Studies

• Correlation vs. Causation
• Examples of False Positives
• 1. High cholesterol diet and rectal cancer
• 2. Smoking and breast cancer
• 3. Vasectomy and prostate cancer
• 4. Red meat and colon cancer
• 5. Red meat and breast cancer
• 6. Drinking water frequently and bladder cancer
• 7. Not consuming olive oil and breast cancer
• Replication of observational studies may not
  overcome confounding and bias

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Why Clinical Trials?

• 2. Help determine incidence of side effects and
  complications.
• Example Coronary Drug Project
• A. Detection of side effect (Cardiac
  Arrhythmias)
• Clofibrate 33.3
• Niacin 32.7 pgt.05
• Placebo 38.2
• B. Natural occurring side effect (nausea)
• Clofibrate 7.6
• Placebo 6.2

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Typical Side Effect Report - Lyrica
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Why Clinical Trials?

• 3. Theory not always best path
• Intermittent positive pressure breathing (IPPB) ?
  reduced use, no benefit
• High O2 in premature infants ? Retrolental
  Fibroplasia, Harmful
• Tonsillectomy ? Reduced use
• Bypass Surgery ? Restricted use

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Phase I Design Strategy

• Designs based largely on tradition
• Typically do some sort of dose escalation to
  reach maximum tolerated dose (MTD)
• Has been shown to be safe and reasonably
  effective
• Dose escalation often based on Fibonacci series
• 1 2 3 5 8 13 . . . .

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Dose-response curve (animal study)
 
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Typical Scheme

• 1. Enter 3 patients at a given dose
• 2. If no toxicity, go to next dosage and repeat
  step 1
• 3. a. If 1 patient has serious toxicity, add 3
  more patients at that dose (go to 4)
• b. If 2/3 have serious toxicity, consider MTD
• 4. a. If 2 or more of 6 patients have toxicity,
• MTD reached
• b. If 1 of 6 has toxicity, increase dose and go
  back to step 1

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Summary of Schemes (Storer, Biometrics
45925-37, 1989)

• A. Standard
• Observe group of 3 patients
• No toxicity? increase dose
• Any toxicity ? observe 3 or more
• One toxicity out of 6 ? increase dose
• Two or more toxicity ? stop
• B. 1 Up, 1 Down
• Observe single patients
• No toxicity ? increase dose
• Toxicity ? decrease dose

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Summary of Schemes(Storer, Biometrics 45925-37,
1989)

• C. 2 Up, 1 Down
• Observe single patients
• No toxicity in two consecutive ? increase dose
• Toxicity ? decrease dose
• D. Extended Standard
• Observe groups of 3 patients
• No toxicity ? increase dose
• One toxicity ? dose unchanged
• Two or three toxicity ? decrease dose

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Summary of Schemes (Storer, Biometrics
45925-37, 1989)

• E. 2 Up, 2 Down
• Observe groups of 2 patients
• No toxicity ? increase dose
• One toxicity ? dose unchanged
• Both toxicity ? decrease dose
• B, C, D, E - fixed sample sizes ranging from
  12 to 32 patients
• Can speed up process to get to target dose
  range
• F. Bayesian sequential/adaptive designs

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Phase II Designs

• References
• Gehan (1961) Journal of Chronic Disorders
• Fleming (1982) Biometrics
• Storer (1989) Statistics in Medicine
• Goal
• Screen for therapeutic activity
• Further evaluate toxicity
• Test using MTD from Phase I
• If drug passes screen, test further

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Phase II Design

• Design of Gehan
• No control (is this wise?)
• Two-stage (small initial sample, observe at least
  one benefit take a
  second larger sample)
• Goal is to reject ineffective drugs ASAP
• Decision I Drug is unlikely to be effective in
  ? x of patients
• Decision II Drug could be effective
• in ? x of patients

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Phase II Design

• Example Gehan Design
• Let x 20 want to check if drug likely to
  work in at least 20 of patients
• 1. Enter 14 patients
• 2. If 0/14 responds, stop and
• declare true drug response ?20
• 3. If 1/14 respond, add 15-40
• more patients
• 4. Estimate response rate C.I.

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Gehan Design

• Why 14 patients initially?
• If drug ? 20 effective, there would be 95.6
  chance of at least one success
• If 0/14 success observed, reject drug

Patient Prob 1 0.8 2 0.64 (0.8 x
0.8) 3 0.512 (0.8 x 0.8 x 0.8) --- --- 8 0.1
6 --- --- 14 0.044
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Phase II Design

• Stage I Sample Size - Gehan
• Table I
• Rejection Effectiveness ()
• Error 5 10 15 20 25 40 50
• 5 59 29 19 14 11 6 5
• 10 45 22 15 11 9 5 4

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Stage II Sample Size

• Based on desired precision of effectiveness
  estimate
• r1 of successes in Stage 1
• n1 of patients in Stage 1
• Now precision of total sample N(n1 n2)

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Stage II Sample Size

• To be conservative, Gehan suggested
• The upper 75 confidence limit from first sample
• Thus, we can generate a table for size of
• second stage (n2) based on desired precision

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Additional Patients for Stage II(n2, a1.05)
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Phase II Trial Designs

• Many cancer Phase II trials follow Gehan design
• Many other diseases could there seems to be no
  standard non-cancer Phase II design
• Might also randomize patients into multiple arms
  each with a different dose can then get a dose
  response curve
• Other two-stage designs based on determining
  p1-p0 gt x where p0 is the standard care
  combination

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Phase III Trial Designs

• The foundation for the design of controlled
  experiments established for agricultural
  experiments
• The need for control groups in clinical studies
  recognized, but not widely accepted until 1950s
• No comparison groups needed when results
  dramatic
• Penicillin for pneumococcal pneumonia
• Rabies vaccine
• Use of proper control group necessary due to
• Natural history of most diseases
• Variability of a patient's response to
  intervention

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Phase III Design

• Comparative Studies
• Experimental Group vs. Control Group
• Establishing a Control
• 1. Historical
• 2. Concurrent
• 3. Randomized
• Randomized Control Trial (RCT) is the gold
  standard
• Eliminates several sources of bias

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Purpose of Control Group

• To allow discrimination of patient outcomes
  caused by test treatment from those caused by
  other factors
• Natural progression of disease
• Observer/patient expectations
• Other treatment
• Fair comparisons
• Necessary to be informative

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Goals of Phase III Clinical Trial

• Superiority Trials
• A controlled trial may demonstrate efficacy of
  the test treatment by showing that it is superior
  to the control
• No treatment (placebo)
• Best standard of current care

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Goals of Phase III Clinical Trials

• Non-Inferiority Trials
• Controlled trial may demonstrate efficacy by
  showing the test treatment is similar in efficacy
  to a known effective treatment
• The active control has to be effective under the
  conditions of the trials
• New treatment cannot be worse by a pre-specified
  amount
• New treatment may not be better than the standard
  but may have other advantages
• Cost
• Toxicity and/or side effects
• Invasiveness

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Significance of Control Group

• Inference drawn from the trial
• Ethical acceptability of the trial
• Degree to which bias is minimized
• Type of subjects
• Kind of endpoints that can be studied
• Credibility of the results
• Acceptability of the results by regulatory
  authorities
• Other features of the trial, its conduct, and
  interpretation

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Use of Placebo Control

• The placebo effect is well documented (as
  high as 33 according to some studies)
• Could be
• No treatment placebo
• Standard care placebo
• Matched placebos are necessary so patients and
  investigators cannot decode the treatment
  assignment
• E.g. Vitamin C trial for common cold
• Placebo was used, but was distinguishable
• Many on placebo dropped out of study not
  blinded
• Those who knew they were on vitamin C reported
  fewer cold symptoms and duration than those on
  vitamin who didn't know

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Unbiased Evaluation

• Subject Bias (NIH Cold Study)
• (Karlowski, 1975)
• Duration of Cold (Days)
• Blinded Unblinded
• Subjects Subjects
• Placebo 6.3 8.6
• Ascorbic Acid 6.5 4.8

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Historical Control Study

• A new treatment used in a series of subjects
• Outcome compared with previous series of
  comparable subjects
• Non-randomized
• Rapid, inexpensive, good for initial testing of
  new
• treatments
• Vulnerable to biases
• Different underlying populations
• Criteria for selecting patients
• Patient care
• Diagnostic or evaluating criteria

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Historical Control Study

• When might we consider a historical control
  study?
• When preliminary data strongly suggest efficacy.
• When course of disease predictable, generally a
  consistently poor outcome.
• When endpoints objective, like death or
  metastisization.
• When impact of baseline and other variables on
  endpoint is well characterized.

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Randomized ControlClinical Trial

• Reference Byar et al. (1976)
• New England Journal of Medicine
• Patients assigned at random to either
  treatment(s) or control
• Considered to be Gold Standard

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Disadvantages of Randomized Control Clinical Trial

• 1. Generalizable Results?
• Subjects may not represent general patient
  population volunteer effect
• 2. Recruitment
• Twice as many new patients
• 3. Acceptability of Randomization Process
• Some physicians will refuse
• Some patients will refuse
• 4. Administrative Complexity

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Ethics of Randomization

• Statistician/clinical trialist must sell benefits
  of randomization
• Ethics Þ MD should do what he thinks is best for
  his patient
• Two MD's might ethically treat same patient quite
  differently
• Chalmers Shaw (1970) Annals New York Academy of
  Science
• 1. If MD "knows" best treatment, should not
  participate in trial
• 2. If in doubt, randomization gives each patient
  equal chance to
• receive one of therapies (i.e. best)
• 3. More ethical way of practicing medicine
• Bayesian Adaptive designs ? More likely assign
  better treatment

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Comparing Treatments

• Fundamental principle
• Groups must be alike in all important aspects and
  only differ in the treatment each group receives
• In practical terms, comparable treatment groups
  meansalike on the average
• Randomization
• Each patient has the same chance of receiving any
  of thetreatments under study
• Allocation of treatments to participants is
  carried out using a chance mechanism so that
  neither the patient nor the physician know in
  advance which therapy will be assigned
• Blinding
• Avoidance of psychological influence
• Fair evaluation of outcomes

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Randomized Phase III Experimental Designs

• Assume
• Patients enrolled in trial have satisfied
  eligibility criteria and have given consent
• Balanced randomization each treatment group will
  be assigned an equal number of patients
• Issue
• Different experimental designs can be used to
  answer different therapeutic questions

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Commonly Used Phase III Designs

• Parallel
• Withdrawal
• Group/Cluster
• Randomized Consent
• Cross Over
• Factorial
• Large Simple
• Equivalence/Non-inferiority
• Sequential

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Parallel Design

• Screen
• Trt A
• Randomize -
• Trt B
• H0 A vs. B
• Advantage
• Simple, General Use
• Valid Comparison
• Disadvantage
• Few Questions/Study

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Fundamental Design
R A N D O M I Z E
Yes
Yes
A
Eligible
Consent
No
B
No
Dropped
Dropped
Comment Compare A with B
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Run-In Design

• Problem
• Non-compliance by patient may seriously impair
  efficiency and possibly distort conclusions.
• Possible Solution Drug Trials
• Assign all eligible patients a placebo to be
  taken for a brief period of time. Patients who
  are judged compliant are enrolled into the
  study. This is often referred to as the Placebo
  Run-In period.
• Can also use active drug to test for compliance.

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Run-In Design
R A N D O M I Z E
Screen Consent
Run-In Period
Satisfactory
A
B
Unsatisfactory
Dropped
Note It is assumed that all patient entering the
run-in period are eligible and have given consent
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Withdrawal Study

• Treatment A
• Treament A ?
• Not Treatment A
  (placebo)
• Advantage
• Easy Access to subjects
• Show if continued treatment is beneficial
• Disadvantage
• Selected Population
• Different Disease Stage

randomize
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Cluster Randomization Designs

• Groups (clinics, communities) are randomized to
  treatment or control
• Examples
• Community trials on fluoridization of water
• Breast self-examination programs in different
  clinic settings in USSR
• Smoking cessation intervention trial in different
  school districtsin the state of Washington
• Advantages
• Sometimes logistically more feasible
• Avoid contamination
• Allow mass intervention, thus public health
  trial
• Disadvantages
• Effective sample size less than number of
  subjects
• Many units must participate to overcome
  unit-to-unit variation,thus requires larger
  sample size
• Need cluster sampling methods

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Cross Over DesignH0 A vs. B

• Scheme
• Period
• Group I II
• AB 1 TRT A TRT B
• BA 2 TRT B TRT A
• Advantage
• Each patient their own control
• Smaller sample size
• Disadvantage
• Not useful for acute disease
• Disease must be stable
• Assumes no period carry over
• If carryover, have a study half sized
• (Period I A vs. Period I B)

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Superiority vs. Non-Inferiority Trials

• Superiority Design Show that new treatment is
  better than the control or standard (maybe a
  placebo)
• Non-inferiority Show that the new treatment
• Is not worse that the standard by more than some
  margin
• Would have beaten placebo if a placebo arm had
  been included (regulatory)

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Equivalence/Non-inferiority Trial

• Trial with active (positive) controls.
• The question is whether new (easier or cheaper)
  treatment is as good as the current treatment.
• Must specify margin of equivalence or
  non-inferiority
• Can't statistically prove equivalency -- only
  show that difference is less than something with
  specified probability.
• Historical evidence of sensitivity to treatment
• Sample size issues are crucial.
• Small sample size, leading to low power and
  subsequently lack of significant difference, does
  not imply equivalence.

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Non-Inferiority Challenges

• Requires high quality trial
• Poor execution favors non-inferiority
• Treatment margin somewhat arbitrary

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Sequential Design

• Continue to randomize subjects until H0 is either
  rejected or accepted
• A large statistical literature for classical
  sequential designs
• Developed for industrial setting
• Modified for clinical trials
• (e.g. Armitage 1975, Sequential Medical Trials)

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Classical Sequential Design

• Continue to randomize subjects until H0 is either
  rejected or accepted
• Classic

Trt Better
Continue
Net Treatment Effect
20
Accept H0
?
0
Continue
-20
Trt Worse
100
200
300
No. of Paired Observations
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Sample Size Considerations
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Comparing Time to Event Distributions

• Primary endpoint is the time to an event
• Compare the survival distributions
• Measure of treatment effect is the ratio of the
  hazard rates
• Must also consider the length of follow-up

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Exponential Survival Distributions

• Surivival function P(T gt t) e-lt

• George Desu (1974)
• Assumes all patients followed to an event (no
  censoring)
• Assumes all patients immediately entered

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Converting Number of Events (D) to Required
Sample Size (2N)

• d 2N x P(event) 2N d/P(event)
• P(event) is a function of the length of total
  follow-up at time of analysis and the average
  hazard rate
• Let AR accrual rate (patients per year)
• A period of uniform accrual (2N AR x A)
• F period of follow-up after accrual complete
• A/2 F average total follow-up at planned
  analysis
• average hazard rate
• Then P(event) 1 P(no event)

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Time to Failure

• In many clinical trials
• 1. Not all patients are followed to an event
• (i.e. censoring)
• 2. Patients are recruited over some period of
  time
• (i.e. staggered entry)
• More General Model (Lachin, 1981)
• where .

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• 1. Instant Recruitment Study Censored At Time T
• 2. Continuous Recruiting (O,T) Censored at T
• 3. Recruitment (O, T0) Study Censored at T (T
  gt T0)

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• Example
• Assume ? .05 (2-sided) 1 - ? .90
• ?C .3 and ?I .2
• T 5 years follow-up
• T0 3
• 0. No Censoring, Instant Recruiting
• N 128
• 1. Censoring at T, Instant Recruiting
• N 188
• 2. Censoring at T, Continual Recruitment
• N 310
• 3. Censoring at T, Recruitment to T0
• N 233

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Sample Size Adjustment for Non-Compliance

• References
• 1. Shork Remington (1967) Journal of Chronic
  Disease
• 2. Halperin et al (1968) Journal of Chronic
  Disease
• 3. Wu, Fisher DeMets (1988) Controlled
  Clinical Trials
• Problem
• Some patients may not adhere to treatment
  protocol
• Impact
• Dilute whatever true treatment effect exists

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Sample Size Adjustment for Non-Compliance

• Fundamental Principle
• Analyze All Subjects Randomized
• Called Intent-to-Treat (ITT) Principle
• Noncompliance will dilute treatment effect
• A Solution
• Adjust sample size to compensate for dilution
  effect (reduced power)
• Definitions of Noncompliance
• Dropout Patient in treatment group stops taking
  therapy
• Dropin Patient in control group starts taking
  experimental therapy

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• Comparing Two Proportions
• Assumes event rates will be altered by
  non-compliance
• Define
• PT adjusted treatment group rate
• PC adjusted control group rate
• If PT lt PC,

1.0
0
PC
PT
PC
PT
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Adjusted Sample Size

• Simple Model -
• Compute unadjusted N
• Assume no dropins
• Assume dropout proportion R
• Thus PC PC
• PT (1-R) PT R PC
• Then adjust N
• Example
• R 1/(1-R)2 Increase
• .1 1.23 23
• .25 1.78 78

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Sample Size Adjustment for Non-Compliance

• Dropouts dropins (R0, RI)
• Example
• R0 R1 1/(1- R0- R1)2 Increase
• .1 .1 1.56 56
• .25 .25 4.0 4 times

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Sample Size Adjustments

• More Complex Model
• Ref Wu, Fisher, DeMets (1980)
• Further Assumptions
• Length of follow-up divided into intervals
• Hazard rate may vary
• Dropout rate may vary
• Dropin rate may vary
• Lag in time for treatment to be fully effective

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Sample Size Summary

• Ethically, the size of the study must be large
  enough to achieve the stated goals with
  reasonable probability (power)
• Sample size estimates are only approximate due to
  uncertainty in assumptions
• Need to be conservative but realistic