Biostatistics: Study Design

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Biostatistics Study Design
Peter D. Christenson Biostatistician
Summer Fellowship Program
July 2, 2004
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Outline

• Example
• Statistical Issues in Research Studies
• Typical Flow of Data in Research Studies
• Biostatistical Resources at LA BioMed and GCRC
• Size and Power of Research Studies

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Example Design Issues
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Statistical Aspects of Research Projects

• Target population / sample / generalizability.
• Quantification of hypotheses, case definitions,
  endpoints.
• Control of bias confounding.
• Comparison/control group.
• Randomization, blinding.
• Justification of study size (power, precision,
  other) screened, enrolled, completed.
• Use of data from non-completers.
• Methods of analysis.
• Mid-study analyses.

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Typical Flow of Data in Research Studies
Source Documents

• Reports
• Spreadsheets
• Statistics Software
• Graphics Software

Database
CRFs
Database is the hub export to applications
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Biostatistical Resources at REI and GCRC

• Biostatistician Peter Christenson
• pchristenson_at_gcrc.rei.edu
• Study design, analysis of data
• Biostatistics short courses 6 weeks 2x/yr
• GCRC computer laboratory in RB-3
• Statistical, graphics, database software
• Contact Angel at 781-3601 for key code
• Webpage http//gcrc.humc.edu/Biostat

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NCSS Basic intuitive statistics package in GCRC
computer lab has power module
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SPSS More advanced statistics package in GCRC lab
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SAS Advanced professional statistics package in
GCRC lab
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Sigma Plot Scientific publication graphics
software in GCRC lab
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nQuery Professional study size / power software
in GCRC lab
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http//gcrc.humc.edu/Biostat
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www.statsoft.com/textbook/stathome.html
Good general statistics book by a software vendor.
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www.StatCrunch.com
NSF-funded software development. Not a download
use online from web browsers
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www.stat.uiowa.edu/rlenth/Power
Online Study Size / Power Calculator
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Statistical Aspects of Research Projects

• Target population / sample / generalizability.
• Quantification of hypotheses, case definitions,
  endpoints.
• Control of bias confounding.
• Comparison/control group.
• Randomization, blinding.
• Justification of study size (power, precision,
  other) screened, enrolled, completed.
• Use of data from non-completers.
• Methods of analysis.
• Mid-study analyses.

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Randomization

• Helps assure attributability of treatment
  effects.
• Blocked randomization assures approximate
  chronologic equality of numbers of subjects in
  each treatment group.
• Recruiters must not have access to randomization
  list.
• List can be created with a random number
  generator in software (e.g., Excel, NCSS),
  printed tables in stat texts, pick slips out of a
  hat.

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Study Size / Power Definition

• Power is the probability of declaring a treatment
  effect from the limited number of study subjects,
  if there really is an effect of a specified
  magnitude (say 10) among all persons to whom we
  are generalizing.
• Similar to diagnostic sensitivity.
• Power is not the probability that an effect (say
  10) observed in the study will be significant.

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Study Size / Power Confusion

• Reviewer comment on a protocol
• there may not be a large enough sample to see
  the effect size required for a successful
  outcome. Power calculations indicate that the
  study is looking for a 65 reduction in incidence
  of disease. Wouldnt it also be of interest
  if there were only a 50 or 40 reduction, thus
  requiring smaller numbers and making the trial
  more feasible?
• Investigator response was very polite.

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Study Size / Power Issues

• Power will be different for each outcome.
• Power depends on the statistical method.
• Five factors including power are inter-related.
  Fixing four of these determines the fifth
• Study size
• Power
• p-value cutoff (level of significance, e.g.,
  0.05)
• Magnitude of treatment effect to be detected
• Heterogeneity among subjects (std dev)

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Study Size / Power Example
Project 10038 Dan Kelly Pejman
Cohan Hypopituitarism after Moderate and Severe
Head Injury

• The primary outcomes for the hydrocortisone
  trial are changes in mean MAP and vasopressor use
  from the 12 hours prior to initiation of
  randomized treatment to the 96 hours after
  initiation.
• Mean changes in placebo subjects will be compared
  with hydrocortisone subjects using a two sample
  t-test.

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Study Size / Power Example Contd
Thus, with a total of the planned 80 subjects, we
are 80 sure to detect (plt0.05) group differences
if treatments actually differ by at least 5.2 mm
Hg in MAP change, or by a mean 0.34 change in
number of vasopressors.
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Study Size / Power Example Contd
Pilot data SD8.16 for ?MAP in 36 subjects. For
p-valuelt0.05, power80, N40/group, the
detectable ? of 5.2 in the previous table is
found as
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Study Size / Power Summary

• Power analysis assures that effects of a
  specified magnitude can be detected.
• For comparing means, need (pilot) data on
  variability of subjects for the outcome measure.
  E.g., Std dev from previous study.
• Comparing rates (s) does not require pilot
  variability data. Use if no pilot data is
  available.
• Helps support (superiority) studies with negative
  conclusions.
• To prove no effect (non-inferiority), use an
  equivalency study design.