Issues in Randomization

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Issues in Randomization

• Laura Lee Johnson, Ph.D.
• Biostatistician
• National Cancer Institute
• Introduction to the Principles and Practice of
  Clinical Research
• Monday, October 25, 2004

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Biostatistics

• Randomization
• Hypothesis Testing
• Sample Size and Power
• Survival Analysis

3
Objectives

• Intuitive understanding of the statistics used in
  clinical research
• Understand and perform some simple but useful
  analyses sample size calculations
• Learn how to collaborate effectively with
  statisticians

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Objectives Randomization Lecture

• Reasons for randomization
• Randomization theory and mechanisms
• Types of randomized study designs
• Compare randomized experimental studies to
  nonrandomzied observational studies
• Nonrandomized experimental studies

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Outline

• Introductory Statistical Definitions
• What is Randomization?
• Randomized Study Design
• Experimental vs. Observational
• Non-Randomized Study Design
• Statistical Software, Books, Articles

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Words I Might Use

• Phase I, II, IIb, III, IV Trial
• Model y ß0 ß1x1 ß2x2 e
• Covariate
• Effect Modifier
• Confounder

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Confounding

• Two or more variables
• Known or unknown to the researchers
• Confounded when their effects on a common
  response variable or outcome are mixed together

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Confounding Example

• Relationship between coffee and pancreatic
  cancer, BUT
• Smoking is a known risk factor for pancreatic
  cancer
• Smoking is associated with coffee drinking but it
  is not a result of coffee drinking.

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What is confounding?

• If an association is observed between coffee
  drinking and pancreatic cancer
• Coffee actually causes pancreatic cancer, or
• The coffee drinking and pancreatic cancer
  association is the result of confounding by
  cigarette smoking.

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How to handle confounding

• If you know something is a possible confounder,
  in the data analysis use
• Stratification, or
• Adjustment
• Fear the unknown!

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Study Design Taxonomy

• Treatment vs. Observational
• Prospective vs. Retrospective
• Longitudinal vs. Cross-sectional
• Randomized vs. Non-Randomized
• Blinded/Masked or Not
• Single-blind, Double blind, Unblinded

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Outline

• Introductory Statistical Definitions
• What is Randomization?
• Randomized Study Design
• Experimental vs. Observational
• Non-Randomized Study Design
• Stat Software, Books, Articles

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Randomization Definition

• Random Allocation
• known chance receiving a treatment
• cannot predict the treatment to be given
• Eliminate Selection Bias
• Similar Treatment Groups

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ONE Factor is Different

• Randomization tries to ensure that ONE factor is
  different between two or more groups.
• Observe the Consequences
• Attribute Causality

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Types of Randomization

• Standard ways
• Random number tables (see text)
• Computer programs
• NOT legitimate
• Birth date
• Last digit of the medical record number
• Odd/even room number

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Types of Randomization

• Simple
• Blocked Randomization
• Stratified Randomization

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Simple Randomization

• Randomize each patient to a treatment with a
  known probability
• Corresponds to flipping a coin
• Could have imbalance in / group or trends in
  group assignment
• Could have different distributions of a trait
  like gender in the two arms

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Block Randomization

• Insure the of patients assigned to each
  treatment is not far out of balance
• Variable block size
• An additional layer of blindness
• Different distributions of a trait like gender in
  the two arms possible

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Stratified Randomization

• A priori certain factors likely important (e.g.
  Age, Gender)
• Randomize so different levels of the factor are
  balanced between treatment groups
• Cannot evaluate the stratification variable

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Stratified Randomization

• For each subgroup or strata perform a separate
  block randomization
• Common strata
• Clinical center, Age, Gender
• Stratification MUST be taken into account in the
  data analysis!

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When to Randomize?

• When the treatment must change!
• SWOG 1 vs. 2 years of CMFVP adjuvant
  chemotherapy in axillary node-positive and
  estrogen receptor-negative patients.
• JCO, Vol 11 No. 9 (Sept), 1993

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Randomize at the Time Trial Arms Diverge

• SWOG randomized at beginning of treatment
• Discontinued treatment before relapse or death
• 17 on 1 year arm
• 59 on 2 year arm
• Main reason was patient refusal

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Outline

• Introductory Statistical Definitions
• What is Randomization?
• Randomized Study Design
• Experimental vs. Observational
• Non-Randomized Study Design
• Stat Software, Books, Articles

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Types of Randomized Studies

• Parallel Group
• Sequential Trials
• Group Sequential trials
• Cross-over
• Factorial Designs

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Parallel Group

• Randomize patients to one of k treatments
• Response
• Measure at end of study
• Delta or change from baseline
• Repeated measures
• Function of multiple measures

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Ideal Study - Gold Standard

• Double blind
• Randomized
• Parallel groups

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Sequential Trials

• Not for a fixed period
• Terminates when
• One treatment shows a clear superiority or
• It is highly unlikely any important difference
  will be seen
• Special statistical design methods

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Group Sequential Trials

• Popular
• Analyze data after certain proportions of results
  available
• Early stopping
• If one treatment clearly superior
• Adverse events
• Careful planning and statistical design

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Crossover Trial

• E.g. 2 treatments 2 period crossover
• Use each patient as own control
• Must eliminate carryover effects
• Need sufficient washout period

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Factorial Design

• Each level of a factor (treatment or condition)
  occurs with every level of every other factor
• Selenomethionine and Celecoxib Gastroenterology
  2002 122A71

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Incomplete Factorial Trial

• Nutritional Intervention Trial (NIT)
• 4x4 incomplete factorial
• A,B,C,D
• Did not look at all possible interactions
• Not of interest (at the time)
• Sample size prohibitive

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Outline

• Introductory Statistical Definitions
• What is Randomization?
• Randomized Study Design
• Experimental vs. Observational
• Non-Randomized Study Design
• Stat Software, Books, Articles

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Observational Experimental

• Can ONLY show Association
• You will never know all the possible confounders!

• Can show Association and Causality
• Well done randomization means unknown confounders
  should not create problems

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Observational Studies

• Cohort Study
• Follow a group for a while
• Cardiovascular Health Study
• Case-Control Study
• Groups with or without outcome
• Determine who was exposed to risk factor

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Observational Studies

• Cross-sectional
• Collect a representative sample
• Simultaneously classify by outcome and risk factor

Outcome
No disease
Disease
Y
Risk Factor
N
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Observational Studies are Useful

• May be only alternative
• Smoking in humans
• What happens in free living people
  (Cardiovascular Health Study)
• May be cheaper and faster than a trial

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Do not always agree

• HRT
• Observational trials
• WHI
• Publication bias?

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Outline

• Introductory Statistical Definitions
• What is Randomization?
• Randomized Study Design
• Experimental vs. Observational
• Non-Randomized Study Design
• Stat Software, Books, Articles

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Nonrandomized Experimental Studies

• No control group
• Early in investigation
• Concurrent control group
• Treatment assignment not by randomization
• Historically controlled
• Missing/poor data
• Non-comparability of groups

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No placebo/control problems

• Patients tend to do better by receiving some
  treatment, even placebo or standard of care (soc)
• Comparing a patient on treatment to baseline does
  not take this into account

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Additional Problems

• Researchers tend to interpret findings in favor
  of the new treatment
• Investigator bias
• Impossible to distinguish the effect of time from
  treatment effects
• Confounding

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Human Assumptions and Concurrent Control Groups

• Newer better
• Systematic allocation is unreliable and many
  times NOT systematic
• Bias
• Manipulation
• No randomization ? impossible to establish if
  comparable groups

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Historical Control Study

• Small patient pool
• Pediatrics
• Cancer research
• Responses compared to controls from previous
  studies.
• Only half the patients
• No placebo exposure

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Historical Control Problems

• Serious bias for assessing treatment efficacy
• Controls not a good comparison group

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Historical Controls and Time

• Treatments, technology, patient care changed over
  time
• Patient population characteristics have changed
  over time

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Non-randomized Phase II design problems

• Placebo effect
• Investigator bias
• Unblinded treatment
• Regression to the mean
• Natural reduction in disease activity over time

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Observational Studies

• Why can observational studies only find a weaker
  degree of connection?
• Subject to confounding
• Can correct for what you know, but nothing to be
  done about the unknown
• Sometimes it is unethical to do a randomized
  trial (e.g. smoking)

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Causation vs. Association

• Causation
• Established by experimental studies and clinical
  trials
• Association
• Observation studies can merely find association
  between a risk factor and an response

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Outline

• Introductory Statistical Definitions
• What is Randomization?
• Randomized Study Design
• Experimental vs. Observational
• Non-Randomized Study Design
• Stat Software, Books, Articles

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Statistical Resources

• Software
• Books
• Articles, etc.

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Software

• Most is expensive and some have yearly license
  fees
• NIH (through CIT) many times has the software for
  free or cheaper than retail
• Some is hard to use, some is easy

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Software Programming Options

• S-PLUS (Windows/UNIX) Strong academic and NIH
  following extensible comprehensive
• www.insightful.com
• R (Windows/Linux/UNIX/Mac) GNU similar to
  S-PLUS
• www.r-project.org
• www.bioconductor.org

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S and R

• Produce well-designed publication-quality plots
• Code from C,C, Fortran can be called
• Active user communities

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Statistical Calculators

• www.stat.ucla.edu
• Statistical Calculators

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Other Software

• STATA (Windows/Mac/UNIX)
• Good for general computation, survival,
  diagnostic testing
• Epi friendly
• GUI/menu and command driven
• Active user community
• www.stata.com

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Other Software

• SAS (Windows/UNIX)
• Command driven
• Difficult to use, but very good once you know how
  to use it
• Many users on the East coast
• www.sas.com
• SPSS, EpiCure, many others

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Books

• Statistical Rules of Thumb by Gerald van Belle
• Epidemiology by Leon Gordis
• The Statistical Evaluation of Medical Tests for
  Classification and Prediction by Margaret
  Sullivan Pepe

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More Books

• Hosmer and Lemeshow books
• Statistical Reasoning in Medicine The Intuitive
  P-Value Primer by Lemuel Moye
• Designing Clinical Research An Epidemiologic
  Approach, edited by Stephen Hulley

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And More Books

• Data Monitoring Committees in Clinical Trials A
  Practical Perspective by Ellenberg, Fleming,
  DeMets.
• Fundamentals of Clinical Trials by Friedman,
  Furberg, DeMets

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Articles

• British Medical Journal Statistics Notes
• http//www.sghms.ac.uk/depts/
• phs/staff/jmb/pbstnote.htm
• Statistics in Medicine
• NEJM Equivalence trials
• October 16, 1997

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Questions?