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

Spreadsheets

Statistics Software

Graphics Software

SourceDocuments

Database

CRFs

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/PowerOnline Study Size / Power Calculator

Statistical Components of Protocols

• 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

• “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.

Project #10038: Dan Kelly & Pejman Cohan

Hypopituitarism after Moderate and Severe Head Injury

Study Size / Power : Example Cont’dThe 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 10.9 0.77 40 7.4 0.49 60 6.0 0.39 80 5.2 0.34

100 4.6 0.30 120 4.2 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’dPilot 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 non-completers (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.