Vascular Surgery Biostatistics Seminar

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Vascular Surgery Biostatistics Seminar

• We have a website http//www.phs.wfubmc.edu/publi
  c/edu_vascSurg.cfm
• Course is experimental
• Ask questions during lectures
• Let me know of specific statistical issues that
  you want covered
• Assignment for last 2 sessions (review of
  student-selected publications)
• Pick 2 articles for class review
• Email PDFs of them to me by October 20th

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Texts

• Gehlbach Interpreting the Medical Literature
  (ISBN 0-07-143789-4)
• Dawson Trapp Basic and Clinical Biostatistics
  (ISBN 0-07-141017-1)
• Good Hardin Common Errors in Statistics (ISBN
  0-471-79431-7)
• Huck Reading Statistics and Research (ISBN
  0-205-51067-1)
• van Belle Statistical Rules of Thumb (ISBN
  0-471-40227-3)

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Schedule
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Study DesignGehlbach Chapters 1-6
5
Hypothetical example factors affecting (causing)
renovascular disease (RVD)

• Outcomes
• Renal function (GFR, serum creatinine)
• RVD by diagnostic test (ultrasound, angiogram)
• End-stage renal disease (dialysis dependence)
• Renal-related mortality

• Exposures
• Hypertension
• RVD repair
• Open revascularization
• Percutaneous repair
• Risk factors age, race, smoking, diabetes,

Q How can we examine a specific hypothesis as it
relates to RVD? A Formulate a hypothesis and
design a study!
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Design Dilemma
Ideal question one would pose
Data one can collect or access

• From Good Hardin, Common Errors in Statistics
  and How to Avoid Them
• Before conducting the experiment, trial, survey,
  data analysis
• Write down the objectives
• Translate those objectives into testable
  hypotheses
• List potential findings and resulting conclusions

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Research Question vs. Hypothesis

• Research Question
• How does diabetes affect renal function after
  renal revascularization?

• Hypothesis
• In patients treated for RVD with endovascular
  repair, those with diabetes have poorer early
  renal function response than those without
  diabetes.

Good Hardin Formulate hypotheses to be
quantifiable, testable, and statistical in nature.
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Classification of Study Designs

• Observational studies
• Descriptive or case-series
• Retrospective (case-control)
• Cross-sectional (prevalence), surveys
• Prospective (cohort)
• Retrospective cohort

• Experimental studies
• Controlled trials
• Parallel designs
• Sequential designs
• External controls
• Studies with no controls

Meta-analyses
Adapted from Dawson Trapp, Basic Clinical
Biostatistics (4th ed)
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Observational Studies
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Retrospective Designs

• Begin with disease/condition/outcome and look
  back for features (exposure) of those with and
  without outcome
• Useful for
• Hypothesizing causes of disease
• Identifying risk factors
• Weaknesses
• Biased case and/or control selection
• Biased exposure ascertainment
• Temporal sequence of exposure/outcome

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Retrospective Designs (cont.)

• Advantages
• Data availability (design of choice for chart
  reviews)
• Usually inexpensive
• Can be performed quickly
• Matching cases and controls
• Prevents imbalance of known risk factor and
  potential confounding
• Can reduce variability (increase efficiency)
• Require special analysis techniques

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Retrospective Design (example)

• Lei et. al., Familial aggregation of renal
  disease J Am Soc Neph (1998) 91270-1276
• Recruited 689 patients with new onset ESRD
• Used random-digit dialing to recruit 361 controls
  from geographic community
• Matched cases to controls (21) using 5-year age
  groups
• Obtained information on familial history of ESRD
  and other risk factors (age, race, sex,
  socioeconomic,)
• Found patients with 2 relatives with ESRD at
  increased risk for ESRD

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Retrospective Cohort Design

• Uses previously collected data on a well-defined
  cohort
• Common approach for disease or treatment
  registries since meticulous record-keeping is
  required
• All follow-up took place in the past
• Subject to many of the same biases of other
  retrospective designs
• Allows estimation of prospective-like measures

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Retrospective Cohort (example)

• Holland and Lam, Predictors of hospitalization
  and death among pre-dialysis patients Nephrol
  Dial Transplant (2000) 15650-658
• Identified predictors of first hospitalization in
  a cohort of 362 seen in pre-dialysis clinic
• Dialysis initiation and loss to follow-up were
  censored events
• Hospitalization (for any cause) was outcome
• Risk factors examined using survival analysis
• Took advantage of records kept in pre-dialysis
  clinic

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Cross-sectional Designs

• Classifies a population or group with respect to
  both outcome and exposure at a single point in
  time
• Useful for
• Disease description
• Diagnosis and staging
• Describing disease processes, mechanisms
• Weaknesses
• Subject to sampling and recall biases
• Temporal order problem
• Cant estimate disease incidence, only prevalence

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Cross-sectional Design (example)

• Hansen et. al., Prevalence of renovascular
  disease in the elderly J Vasc Surg (2002)
  36443-451.
• 834 participants in the CHS Study were examined
  with RDS at a single point in time
• RVD status determined and prevalence in CHS
  cohort estimated
• Increased age, lower HDL-c, and increased SBP
  associated with RVD

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Surveys

• Single point-in-time studies many utilize
  sampling techniques to assure generalizability
• Complex survey designs (e.g., NHANES, NIS H-CUP)
  use probability sampling
• Target population is divided into clusters
  subsets of clusters are sampled randomly
• Certain clusters may be oversampled to assure
  representation
• Statistical analyses require special methods that
  correct variance for study design

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Complex Survey (example)

• Mondrall et. al., Operative mortality for renal
  artery bypass in the United States J Vasc Surg
  (2008) 48317-322
• Examined RABG from NIS/H-CUP survey, 2000-2004
• Observed 10 in-hospital post-op mortality
• Risk factors for increased mortality included
  age, female gender, Hx renal failure, CHF, lung
  disease
• In-hospital mortality higher than previously
  reported
• Used methods that accounted for survey design

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Ecologic Studies

• Use data from large groups to compare rates of
  exposure and disease
• Data are on group-level (e.g., data on air
  pollution levels in specific cities could be
  compared to rates of lung cancer)
• Can lead to ecologic fallacy, because one
  doesnt know whether the actual individuals
  disease are subject to the exposure of interest
• Subject to crackpot biases

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Ecologic Study (example)

• Reynolds et. al., Childhood cancer and
  agricultural pesticide use Environ Health
  Prospect (2002) 110319-324
• Examined incidence of childhood cancers in
  California in relation to pesticide use,
  1988-1994
• Data sources California Cancer Registry U.S.
  Census California Dept. of Pesticide Regulations
• Looked at cancer of all types, and by specific
  types
• Found a significant association between childhood
  leukemia rates in communities with highest use of
  propargite
• No other associations were observed

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Prospective Designs

• Start with well-defined cohort and follow-up for
  occurrence of disease/outcome
• Considered the optimal design for observational
  studies
• Useful for
• Finding causes and estimating incidence of
  disease
• Identification of risk factors
• Following natural history, determining prognosis

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Prospective Designs (cont.)

• Weaknesses
• Subject to selection bias (all studies are) and
  surveillance bias
• Losses to follow-up or dropouts
• Temporal changes in health habits (e.g., MRFIT)
• Can be expensive and always take time
• Advantages
• Correct temporal relationship between exposures
  and disease/outcome
• Allows estimation of disease incidence and
  relative risks

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Prospective Design (example)

• Edwards et. al., Renovascular disease and the
  risk of adverse coronary events Arch Intern Med
  (2005) 165207-213
• 840 CHS participants with RDS exams from Hansen
  et. al.
• Followed for CVD events for an average of 14
  months post-RDS
• Participants with RVD found to have nearly twice
  the rate of adverse CVD during observation period
  than those without RVD

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Observational Designs
Retrospective (Case-control)
Prospective (Cohort)
E(-)
Control
Controls
No Expo.
Today
E()
Participants, Patients, Subjects
Case
Cases
E(-)
Exposure
Control
E()
Cases
Controls
Case
Retrospective Cohort
E()
E(-)
E()
E(-)
Cross-sectional
Time
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Experimental Studies
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Clinical Trials

• Participants are assigned to an experimental
  treatment and followed for event of interest
• Clinical trials may
• be randomized or non-randomized
• include a control group or have no control group
• compare current treatment to an historical
  control
• employ parallel or cross-over design
• employ blinding of investigator and/or
  participant
• The randomized, double-blind, placebo-controlled,
  parallel design is considered to be the best to
  determine efficacy

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Clinical Trials (cont.)

• Randomization
• Purpose to balance groups on both observed and
  unobserved factors
• No guarantees balance occurs in expectation
  (i.e., there is chance that some factors will not
  be balanced)
• In cross-over design, its best to randomize
  treatment order (if possible)
• Blocking used to assure treatment arm balance at
  fixed points
• Stratification used to assure balance on a factor
  of interest

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Clinical Trial Parallel Group Design
With Outcome
Experimental Treatment
Without Outcome
Participants screened for entry criteria
With Outcome
Control Treatment
Without Outcome
Time
Screening
Baseline
Treatment
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Clinical Trial (example 1)

• Kay et. al., Acetylcysteine for prevention of
  acute deterioration of renal function JAMA
  (2003) 289553-558.
• Experiment to test efficacy of antioxidant
  acetylcysteine to prevent acute nephrotoxicity
• 200 patients with moderate renal insufficiency
  undergoing elective coronary angiography
• Randomized, double-blind, placebo-controlled
• 12 with increase in SCr in placebo group vs. 4
  in acetylcysteine group (P0.03)

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Clinical Trial Crossover Design
With Outcome
With Outcome
Experimental Treatment
Experimental Treatment
Without Outcome
Without Outcome
Participants screened for entry criteria
With Outcome
With Outcome
Control Treatment
Control Treatment
Without Outcome
Without Outcome
Treatment (Phase 1)
Treatment (Phase 2)
Screening
Washout
B/L
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Clinical Trial (example 2)

• Whelton et. al., Effects of celecoxib and
  naproxen on renal function Arch Intern Med
  (2000) 1601465-1470
• Experiment to compare effect of celecoxib vs.
  naproxen on renal function in elderly cohort
• 29 healthy elderly subjects took either celecoxib
  or naproxen for 10 days, had 7-day washout, then
  took other med for 10 days
• Randomized treatment order, single-blind design
• At day 6, GFR change on naproxen -7.5
  mL/min/1.73m2 vs. -1.1 on celecoxib (P0.004)

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Clinical Trials (other types)

• Non-randomized trials patients not assigned to
  treatment (or treatment order) via randomization
  interpret with caution
• External or historical controls compare current
  experiment to an external control group (e.g.,
  from prior study or literature) interpret with
  caution
• Uncontrolled trial experimental group only (no
  comparison) interpret with caution

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Clinical Trial (example 3)

• Gomes et. al., Acute renal dysfunction in
  high-risk patients after angiography (1989)
  Radiology 17065-68
• 145 patients at high-risk for renal failure
  undergoing angiography after administration with
  iohexol (non-ionic contrast)
• Compared to 202 historical controls previously
  studied with ionic contrast
• Acute renal dysfunction observed in 5.5 of
  iohexol group vs. 10 of historical control group
  (PNS)
• Authors use result to argue for new, randomized
  trial of two contrast agents

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Clinical Trials (issues)

• Blinding double-blind is optimal but not always
  feasible
• Surgical trials usually impossible to blind both
  investigator and participant
• Some trials are open-label and treat
  participants to a goal others test a behavioral
  intervention
• Group interventions are typically not blinded
  must also account for clustering in
  intervention
• If possible, always blind staff performing
  measurements
• Avoid surveillance and/or ascertainment bias

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Clinical Trials (issues)

• Look out for loss to follow-up, differential
  attrition, and poor adherence to treatments
• Intention-to-treat when analyzing outcomes,
  participants are included in analyses based on
  treatment group assignment regardless of
  treatments received or adherence
• Necessary to avoid potential bias due to
  self-selection
• Preserves randomization
• Drug and device companies love to do analyses
  based on treatments received

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Meta-analysis

• Pools results across multiple studies
• A review article with quantitative summary
• Typically combines results of several
  experimental studies
• Useful for combining small studies
• Studies should have same or similar treatments
• Pools results to get single measure of effect
• Beware meta-analyses combining experimental and
  observational designs
• Dependent upon articles reporting sufficient data
  (N, effect measure, variance)

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Meta-analysis (example)

• Leertouwer et. al., Stent placement for renal
  arterial stenosis Radiology (2000) 8078-85
• Compared studies of RVD repair with stent
  placement vs. PTA alone
• Combined data on technical success rate, BP
  response, renal function response, anatomic F/U
  from 14 studies of stent placement and 10 studies
  of PTA
• Conclusion Renal artery stent placement is
  technically superior and clinically comparable to
  renal PTA alone.

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Data Collection for Statistical Analyses
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Data Collection for Statistical Analyses

• Enter all or most of the data as numbers. Avoid
  entering letters, words, string variables
  (e.g.,NA, 22, lt3.6), or anything that resembles
  a cartoon curse word, _at_,. In Excel, all
  columns, with the exception of names and text
  comments, should be formatted as numbers or dates
  (not as general or text).
• Give each column a unique, simple, 1-word name, 8
  characters or less with no spaces, beginning with
  a letter, and place this name in the first row.
• Put only one variable in a column. Do not combine
  variables in the same column.
• Enter each patient (or unit of analysis) on a
  separate line, beginning on the second line.
• Give each research participant or patient a
  unique case number (1,2,3, etc.)- in the first
  column. Delete patient name, SS, MR, and any
  identifying information before sending it to a
  statistician. Always, save the spreadsheet with a
  password.

http//biostat.mc.vanderbilt.edu/twiki/bin/view/Ma
in/DataTransmissionProcedures?CGISESSID9fe1d0d63a
71d176ca460de518acf2cf
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Data Collection for Statistical Analyses

• Enter cases and controls in the same spreadsheet.
  Use one variable to define the control group
  (TREATED 0no, 1yes or GROUP 1Drug A, 2Drug
  B).
• Quantify. Enter continuous measurements when
  possible.
• Create a simple guide (or key) using a word
  processor to explain variables abbreviations,
  value coding, and how missing values were
  entered. Be consistent.
• Think through the analysis before collecting any
  data.
• Have a biostatistician review the coding before
  data entry and again after the first 10 patients
  have been entered.

http//biostat.mc.vanderbilt.edu/twiki/bin/view/Ma
in/DataTransmissionProcedures?CGISESSID9fe1d0d63a
71d176ca460de518acf2cf
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Spreadsheet from Hell
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Spreadsheet from Heaven