Transcript June 10, 2004

Biostatistics for Coordinators
Peter D. Christenson
REI and GCRC
Biostatistician
GCRC Lecture Series:
Strategies for Successful Clinical Trials
Session #2
June 10, 2004
Outline
• Typical Flow of Data in Clinical Studies
• Biostatistical Resources at REI and GCRC
• Statistical Components of Research Protocols
Typical Flow of Data in Clinical Studies
Reports
Source
Documents
Database
Spreadsheets
Statistics Software
CRFs
Graphics Software
Database is the hub: export to applications
Biostatistical Resources at REI and GCRC
• Biostatistician: Peter Christenson
– Assist with study design, protocol development
– Minor, limited analysis of data
– Major analysis as investigator with %FTE on
funded studies
– GCRC and non-GCRC studies
• Biostatistics short courses: 6 weeks 2x/yr
• GCRC computer laboratory in RB-3
– For GCRC studies
– Statistical, graphics, database software
• Webpage: www.gcrc.humc.edu/Biostat
NCSS: Basic intuitive statistics package in
GCRC computer lab; has power module
SPSS: More advanced statistics package
in GCRC lab
SAS: Advanced professional statistics
package in GCRC lab
Sigma Plot: Scientific publication
graphics software in GCRC lab
nQuery: Professional study size / power
software in GCRC lab
www.gcrc.humc.edu/Biostat
www.statsoft.com/textbook/stathome.html
Good general statistics book by a software vendor.
www.StatCrunch.com
NSF-funded software development.
Not a download; use online from web browsers
www.stat.uiowa.edu/~rlenth/Power
Online Study Size / Power Calculator
Statistical Components of Protocols
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Target population / sample (generalizability).
Quantification of aims, hypotheses.
Case definitions, endpoints quantified.
Randomization.
Blinding.
Study size: screen, enroll, complete.
Use of data from non-completers.
Justification of study size (power, precision,
other).
• Methods of analysis.
• Mid-study analyses.
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.
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”.
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]. Wouldn’t 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.
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 specifies 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)
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.
Study Size / Power : Example Cont’d
The following table presents detectable differences, with p=0.05 and 80%
power, for different study sizes.
Total
Number
of
Subjects
Detectable
Difference in
Change in
Mean MAP
(mm Hg)(1)
Detectable
Difference in
Change in
Mean Number
of
Vasopressors(2)
20
40
60
80
100
120
10.9
7.4
6.0
5.2
4.6
4.2
0.77
0.49
0.39
0.34
0.30
0.27
Thus, with a total of the planned 80 subjects, we are 80% sure to detect (p<0.05)
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.
Study Size / Power : Example Cont’d
Pilot data: SD=8.16 for ΔMAP in 36 subjects.
For p-value<0.05, power=80%, N=40/group, the detectable Δ of 5.2
in the previous table is found as:
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.
Non-completing Subjects
• Enrolled subjects are never “dropouts”.
• Some studies may require recording last
status at last known time.
• Protocol should specify:
– Primary analysis set (e.g., all subjects with at least
one post-baseline value for the primary endpoint,
“modified Intent-to-Treat”.)
– How final values will be assigned to noncompleters (e.g., Last Value Carried Forward).
• Study size estimates should incorporate the
number of expected non-completers.
Mid-Study Analyses
• Mid-study comparisons should not be made
before study completion unless planned for
(interim analyses). Early comparisons are
unstable, and can invalidate final
comparisons.
• Mid-study reassessment of study size is
advised for long studies. Only standard
deviations to-date are used. Often, these
analyses can be performed at the time of
DSM reports.