Biostatistics

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Biostatistics

• ? ? ?
• C.F. Jeff Lin, MD. PhD.
• ? ? ? ? ? ? ? ? ? ? ?
• ? ? ? ? ? ? ? ? ? ? ? ? ?
• cflin_at_mail.ntpu.edu.tw
• http//web.ntpu.edu.tw/cflin

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Study Protocol

• Research question
• Significance background
• Study design
• Study population and sampling
• Variables and measurements
• Statistical issues
• Ethical issues
• Quality control and data management

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The Research Cycle
Develop research question
Infer conclusions
Design study
Implement study
Analyze results
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Types of Study Designs from Descriptive
Studies to Randomized Controlled Trials (RCT)

• Kirsten Bibbins-Domingo, PhD, MD
• Assistant Professor of Medicine and of
  Epidemiology and Biostatistics
• University of California, San Francisco

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Objectives

• To understand the difference between descriptive
  and analytic studies
• To identify the hierarchy of study designs, and
  the strengths and weakness of each design
• To be able to apply different study designs to
  the same research question

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Types of Studies

• Descriptive Studies
• Observational Analytic Studies
• Cross Sectional studies
• Case Control studies
• Cohort studies
• Experimental Studies
• Randomized controlled trials

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Hierarchy of Study Types
Analytic

• Descriptive
• Case report
• Case series
• Survey

• Observational
• Cross sectional
• Case-control
• Cohort studies

• Experimental
• Randomized
• controlled trials

Strength of evidence for causality between a risk
factor and outcome
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Descriptive Studies

• Getting a lay of the land
• Surveys (NHIS, MCBS)
• How many men in the U.S. filled Viagra
  prescriptions in 2004?
• Describing a novel phenomena
• Case reports or case series
• Viagra-associated serous macular detachment.
• Sildenafil-associated nonarteritic anterior
  ischemic optic neuropathy.

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Descriptive Studies

• Cannot establish causal relationships
• Still play an important role in describing trends
  and generating hypotheses about novel
  associations
• The start of HIV/AIDS research
• Squamous cell carcinoma in sexual partner of
  Kaposi sarcoma patient. Lancet. 1982 Jan
  301(8266)286.
• New outbreak of oral tumors, malignancies and
  infectious diseases strikes young male
  homosexuals. CDA J. 1982 Mar10(3)39-42.
• AIDS in the "gay" areas of San Francisco. Lancet.
  1983 Apr 231(8330)923-4.

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Analytic Studies

• Attempt to establish a causal link between a
  predictor/risk factor and an outcome.
• You are doing an analytic study if you have any
  of the following words in your research question
  
• greater than, less than, causes, leads to,
  compared with, more likely than, associated with,
  related to, similar to, correlated with

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Hierarchy of Study Types
Analytic

• Descriptive
• Case report
• Case series
• Survey

• Observational
• Cross sectional
• Case-control
• Cohort studies

• Experimental
• Randomized
• controlled trials

Strength of evidence for causality between a risk
factor and outcome
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Research Question
Is the regular consumption of Red Bull
associated with improved academic performance
among U.S. medical students?
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Rationale

• functional drink designed for periods of mental
  and physical exertion.
• performance, concentration, memory, reaction
  time, vigilance, and emotional balance
• Taurine glucuronolactone caffeine

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Background

• Alford C, Cox H, Wescott R. The effects of red
  bull energy drink on human performance and mood.
  Amino Acids. 200121(2)139-50.
• Warburton DM, Bersellini E, Sweeney E. An
  evaluation of a caffeinated taurine drink on
  mood, memory and information processing in
  healthy volunteers without caffeine abstinence.
  Psychopharmacology (Berl). 2001 Nov158(3)322-8.
• Seidl R, Peyrl A, Nicham R, Hauser E. A taurine
  and caffeine-containing drink stimulates
  cognitive performance and well-being. Amino
  Acids. 200019(3-4)635-42.
• Horne JA, Reyner LA. Beneficial effects of an
  "energy drink" given to sleepy drivers. Amino
  Acids. 200120(1)83-9.
• Kennedy DO, Scholey AB. A glucose-caffeine
  'energy drink' ameliorates subjective and
  performancedeficits during prolonged cognitive
  demand. Appetite. 2004 Jun42(3)331-3.

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Great idea, but how do you get started.

• Interesting, novel, and relevant, but
• You only have 25,000 dollars to start
  investigating this question.
• What is feasible?

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Study Design 1

• Cross-sectional study of UCSF medical students
  taking USMLE Step 2
• Questionnaire administered when registering for
  USMLE 2
• Primary predictor self-report of gt3 cans Red
  Bull per week for the previous year
• Covariates Age, sex, undergraduate university,
  place of birth
• Outcome Score on USMLE Step 2

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Cross-sectional Study Structure
Red Bull consumption
USMLE Score
time
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Cross-sectional Study

• Descriptive value
• How many UCSF medical students drink Red Bull?
• What is the age and sex distribution of UCSF
  medical students who drink Red Bull?
• Analytic value
• Is there an association between regular Red Bull
  consumption and test scores among UCSF med
  students?
• Univariate
• Multivariate (controlling for confounders)

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Cross-sectional Study

• Other cross-sectional surveys
• AAMC
• California Health Interview Survey (NHIS, CHIS)
• National Health and Nutrition Exam Survey (NHANES)

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Cross-sectional Study Pluses

• Prevalence (not incidence)
• Fast/Inexpensive - no waiting!
• No loss to follow up
• Associations can be studied

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Measures of Association
Risk ratio (relative risk) A A B C C D
Disease Disease
Yes No
Risk Factor Yes A B
Risk Factor No C D
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Cross-sectional Study Minuses
- Cannot determine causality
Red Bull consumption
USMLE Score
time
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Cross-sectional Study Minuses
- Cannot determine causality

• ACE inhibitor use and hospitalization rates among
  those with heart failure
• Heart failure patients with a documented DNR
  status and mortality

time
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Cross-sectional Study Minuses
- Cannot determine causality
- Cannot study rare outcomes
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What if you are interested in the rare outcome?

• The association between regular Red Bull
  consumption and
• A perfect score on the USMLE Step 2
• Graduating top 1 of the medical school class
• Acceptance into a highly selective residency

ANSWER A Case-Control study
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Study Design 2

• A case-control study
• Cases 4th year med students accepted to
  residency in highly selective specialty X.
• Controls 4th year med students who applied but
  were not accepted.
• Predictor self-reported regular Red Bull
  consumption
• Additional covariates (age, sex, medical school,
  undergraduate institution)

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Case Control Studies

• Investigator works backward (from outcome to
  predictor)
• Sample chosen on the basis of outcome (cases),
  plus comparison group (controls)

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Case-control Study Structure
present
TARGET CASES Medical students accepted to highly
selective residencies
ACTUAL CASES 4th year UCSF students who matched
in highly selective specialty X
Red Bull consumption YES
Red Bull consumption NO
TARGET CONTROLS All unsuccessful applicants to
highly selective residency programs
ACTUAL CONTROLS 4th year students who failed to
match in highly selective specialty X
time
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Case Control Studies

• Determines the strength of the association
  between each predictor variable and the presence
  or absence of disease
• Cannot yield estimates of incidence or prevalence
  of disease in the population (why?)
• Odds Ratio is statistics

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Case-control Study Pluses

• Rare outcome/Long latent period
• Inexpensive and efficient may be only feasible
  option
• Establishes association (Odds ratio)
• Useful for generating hypotheses (multiple risk
  factors can be explored)

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Case-control Study - Minuses

• Causality still difficult to establish
• Selection bias (appropriate controls)
• Caffeine and Pancreatic cancer in the GI clinic
• Recall bias sampling (retrospective)
• Abortion and risk of breast cancer in Sweden
• Cannot tell about incidence or prevalence
• Studies of diagnostic tests
• Sensitivity, specificity
• Positive predictive value, negative predictive
  value

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Measures of Association
Disease Disease
Yes No
Test Yes A B
Test No C D
Sensitivity A/AC Specificity D/BD
PPV A/AB NPV D/CD
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Case -- Control - the House Red

• RELY tampons and toxic shock syndrome (TSS)
• High rates of toxic shock syndrome in
  menstruating women
• Suspected OCPs or meds for PMS
• Cases 180 women with TSS in 6 geographic areas
• Controls 180 female friends of these patients
  and 180 females in the same telephone code
• Tampon associated with TSS (OR 29!)
• Super absorbency associated with TSS (OR 1.34 per
  gm increase in absorbency)
• Led to RELY brand tampons being taken off the
  market.

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Where are we?

• Preliminary results from our cross-sectional and
  case-control study suggest an association between
  Red Bull consumption and improved academic
  performance among medical students
• Whats missing? - strengthening evidence for a
  causal link between Red Bull consumption and
  academic performance
• Use results from our previous studies to apply
  for funding for a prospective cohort study!

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Study Design 3

• Prospective cohort study of UCSF medical students
  Class of 2009
• All entering medical students surveyed regarding
  beverage consumption and variety of other
  potential covariates
• Survey updated annually to record changes in Red
  Bull consumption
• Outcomes USMLE Step 1 score, USMLE Step 2
  score, match in first choice residency

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Cohort Studies

• A cohort (follow-up, longitudinal) study is a
  comparative, observational study in which
  subjects are grouped by their exposure status,
  i.e., whether or not the subject was exposed to a
  suspected risk factor
• The subjects, exposed and unexposed to the risk
  factor, are followed forward in time to determine
  if one or more new outcomes (diseases) occur
• Subjects should not have outcome variable on
  entry
• No new subjects allowed in after initial
  recruitment
• The rates of disease incidence among the exposed
  and unexposed groups are determined and compared.

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Elements of a Cohort Study

• Selection of sample from population
• Measures predictor variables in sample
• Follow population for period of time
• Measure outcome variable
• Famous cohort studies
• Framingham
• Nurses Health Study
• Physicians Health Study
• Olmsted County, Minnesota

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Prospective Cohort Study Structure
The present
The future
Top USMLE scorers
Everyone else
time
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Strengths of Cohort Studies

• Know that predictor variable was present before
  outcome variable occurred (some evidence of
  causality)
• Directly measure incidence of a disease outcome
• Can study multiple outcomes of a single exposure
  (RR is measure of association)

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Weaknesses of Cohort Studies

• Expensive and inefficient for studying rare
  outcomes
• HERS vs. WHI
• Often need long follow-up period or a very large
  population
• CARDIA
• Loss to follow-up can affect validity of findings
• Framingham

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Other Types of Cohort Studies

• Retrospective Cohort
• Identification of cohort, measurement of
  predictor variables, follow-up and measurement of
  outcomes have all occurred in the past
• Much less costly than prospective cohorts
• Investigator has minimal control over study design

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Other Types of Cohort Studies

• Nested Case-control Study
• Case-control study embedded in a cohort study
• Controls are drawn randomly from study sample
• Double Cohort
• Used to compare two separate cohorts with
  different levels of exposure to predictor
  variable (e.g., occupational groups)

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What Type of Study is This?

• Among individuals with coronary disease, what is
  the association between baseline levels of B-type
  natriuretic peptide and subsequent risk of heart
  failure?
• Among individuals presenting to heart failure
  clinic, what is the association between
  self-reported symptoms and risk of
  hospitalization for heart failure?
• Using data from HERS (RCT of HRT in women with
  coronary disease)
• Determine the risk factors for developing
  incident heart failure among women without heart
  failure at baseline.
• Determine whether HRT is associated with
  mortality among women with heart failure.
• Determine genetic markers for development of
  heart failure among black women in HERS.

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Hierarchy of Study Types
Analytic

• Descriptive
• Case report
• Case series
• Survey

• Observational
• Cross sectional
• Case-control
• Cohort studies

• Experimental
• Randomized
• controlled trials

Strength of evidence for causality between a risk
factor and outcome
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What distinguishes observational studies from
experiments?

• Ability to control for confounding

Confounder
Predictor
Outcome
Examples sex (men are more likely to drink red
bull and men are more likely to match in
neurosurgery) Undergraduate institution
(students from northwest school are more likely
to drink red bull and also more likely to score
higher on USMLE)
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But we measured all of the potential
confounders.

• In a prospective cohort study you can (maybe)
  measure all potential known confounders, but
• You cant control for unanticipated or unmeasured
  confounders

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Study Design 4

• Randomized controlled trial of daily Red Bull
  consumption among entering UCSF medical students
  Class 2009
• Randomized to daily consumption of Red Bull vs.
  daily consumption of placebo
• Outcomes USMLE Step 1 score, USMLE Step 2
  score, match in first choice residency

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Randomized Controlled Trials

• Investigator controls the predictor variable
  (intervention or treatment)
• Major advantage over observational studies is
  ability to demonstrate causality
• Randomization controls unmeasured confounding
• Only for mature research questions

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Basic Trial Design
Population
Treatment
Dx No Dx
Randomization
Sample
Placebo
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Steps in a Randomized Controlled Trial

• Select participants
• high-risk for outcome (high incidence)
• Likely to benefit and not be harmed
• Likely to adhere
• Measure baseline variables
• Randomize
• Eliminates baseline confounding
• Types (simple, stratified, block)

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Steps in a Randomized Controlled Trial

• Blinding the intervention
• As important as randomization
• Eliminates
• co intervention
• biased outcome ascertainment
• biased measurement of outcome
• Follow subjects
• Adherence to protocol
• Lost to follow up
• Measure outcome
• Clinically important measures
• Adverse events

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What is Blinding?

• Single blind - participants are not aware of
  treatment group
• Double blind - both participants and
  investigators unaware
• Triple blind - various meanings
• persons who perform tests
• outcome adjudicators
• safety monitoring group

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Why blind? Co-Interventions

• Unintended effective interventions
• participants use other therapy or change behavior
• study staff, medical providers, family or friends
  treat participants differently
• Nondifferential - decreases power
• Differential - causes bias

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Why blind? Biased Outcome Ascertainment or
Adjudication

• If group assignment is known
• participants may report symptoms or outcomes
  differently
• physicians or investigators may elicit symptoms
  or outcomes differently
• Study staff or adjudicators may classify similar
  events differently in treatment groups
• Problematic with soft outcomes
• investigator judgment
• participant reported symptoms, scales

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Analysis of Randomized Controlled Trial

• Analyzed like cohort study with RR (risk ratio)
• Intention to treat analysis
• Most conservative interpretation
• Include all persons assigned to intervention
  group (including those who did not get treatment
  or dropped out)
• Subgroup analysis
• Groups identified pre-randomization

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High Quality Randomized Trials

• Tamper-proof randomization
• Blinding of participants, study staff, lab staff,
  outcome ascertainment and adjudication
• Adherence to study intervention and protocol
• Complete follow-up

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Are Observational Studies Useless?

• NO
• generate important hypotheses
• provide only answer if trial not feasible
• generally produce correct answer
• But bias and confounding always worrisome
• Particularly problematic for interventions that
  require selection and adherence

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Hierarchy of Study Types
A study type of every budget, purpose and
research question
Analytic

• Descriptive
• Case report
• Case series
• Survey

• Observational
• Cross sectional
• Case-control
• Cohort studies

• Experimental
• Randomized
• controlled trials

Strength of evidence for causality between a risk
factor and outcome
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The Research Cycle
Develop research question
Infer conclusions
Design study
Implement study
Analyze results
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• Thanks !