The presentation highlights the importance of biostatistics in pharmaceutical programming, covering phases of drug development, clinical trial design, sample size determination, and statistical analyses. It discusses multiple endpoints such as overall response rate (ORR) and progression-free survival (PFS), alongside methods for hypothesis testing and survival estimation. The content is based on the presenter's expertise and various literature sources, aiming to enhance understanding in biostatistics for drug discovery and development.

1. BIO-STATISTICS
-AN IMPORTANT TOOL IN PHARMACUETICAL
PROGRAMMING
Soma Sekhar Sriadibhatla, GStat
09/25/2018
Presented to Syneos Health & Bristol-Myers Squibb Employees at Hopewell, NJ

2. The information in this presentation is based on presenter’s expertise, experience and
literature survey from several journals and research articles. The presenter has right to use
Gstat logo based on current accreditation from American Statistical Association.
DISCLAIMER

3. DRUG DEVELOPMENT
Phase 0 Phase I
Phase
IIB/III
Phase IV
Dosing/Toxicity
levels Safety/Dosage
Efficacy/Monitoring
of Adverse Events
Safety/Efficacy
Marketing
IND NDA
Drug
Discovery
70%
25-
30%
*Information and statistics from FDA
sBLA
Patients in
oncology
Results in
Lesser pt.

4. SCOPE
Design of Clinical trials
Sample Size determination,
Sampling, Study Population,
Selection Bias
Treatment Arms,
Randomization
Test of Hypothesis,
Descriptive Statistics
Endpoints like ORR using
International Myeloma Working
Group
Analysis : Baseline characteristics,
Safety analysis : G3/4 infusions
reactions
RANUNI
*Information from a study protocol

5. ENDPOINT
S
OS
Rand. To Death
from any cause
Precisely
measured;
Universally
Accepted
Larger
Studies; Non-
cancerous
Deaths
PFS/TTP
Rand. To Prog. Of
disease/Death
Smaller
Sample Size;
objective &
quantitative
Tumor
Assessment,
Deaths rand. Rel.
to tumor
progression
Survival, ITT
population
Not precise as
subjected to
selection bias/open
labelled; definitions
vary, Sensitive
https://www.fda.gov/downloads/drugsGuidanceComplianceRegulatoyInformation/Guidance/UCM071590.pdf

6. ORR
Single
arm/Rand.
Studies
ENDPOINTS …
Pt. tumor size
reduction/
predefined
amount/min. time
Sum of Partial
responses plus
complete responses
Can be
assessed in
single arm,
drug-
antitumor
Not
comprehensive
measure of drug
activity
DFS
Rand. To recurrence
of tumor or death
*https://www.fda.gov/downloads/drugsGuidanceComplianceRegulatoyInformation/Guidance/UCM071590.pdf

7. CENSOR =0;
EVENT DeathDt. – RANDt. + 1
CENSOR = 1 LassDt. – RANDt. + 1
Prevent loss of info. / Retain Org. Sample
Size
SURVIVAL
Estimation of Survivorship
Prob. Of survival
to a given time
point
Proc lifetest data =adpfs method = km;
Time pfstime*censor (0);
Strata trt;
Run;
No. of Events/KM PFS
https://www.pharmasug.org/proceedings/2016/DG/PharmaSUG-2016-DG03.pdf

8. TUMOR
ASSESSMENT
% change in
tumor size from
baseline at a visit
Water Fall; ORR
SURVIVAL…
Prob. of Events in
TRTA vs TRTB
1 vs 3 vs 0.33/ HAZARD’S
RATIO
Proc Phreg data = ;
Model sruvtime*censor(1);
Strata ;
Run;
Decrease/Increase in rate for parameter of
interest
https://www.pharmasug.org/proceedings/2016/DG/PharmaSUG-2016-DG03.pdf
Not valid when
both treatments
cross-interact(KM
plot)

9. Power
Analysis
80-90%
Ho: µa = µb
Ha: µa ne µb(one
sided/two sided)
STATS 101
=0.05;
µa-b/SD
Proc Power;
Twosamplemeans test = diff;
Meandiff =;
Alpha=;
Ntotal=;
Power=.;
Run;
Test of
Hypothesis
Ho: Ma = Mb
Ha: Ma > Mb(one
sided/two sided)
PROC
ANOVA/
GLM
Pvalue < 0.05 reject H0
Balanced vs Unbalanced/ Proc Mixed
PROC
ttest for
Means
http://support.sas.com/documentation/cdl/en/statugpssintroduction/61767/PDF/default/statugpssintroduction.pdf
One tailed vs
two tailed
Z vs T vs
F
SD??
Size??
Multiple
hypothesis; gate
keeping
procedure

10. STATS 101…
Pearson's
Coefficient
Correlation between two
variables
Proc freq;
tables /chisq (or CHM);
exact pcorr (or fisher) (OR);
Run;
Independence
Fisher’s
exact test
Association between two
cat.variables
Pvalue (when sample size is small)
Chi square (when sample size is large)
Two sample
vs One
Sample ttest
Two variables/one variable
When two variables are not independent/
Paired ttest
http://www2.sas.com/proceedings/sugi25/25/btu/25p069.pdf
CHM: Smoking vs
Lung Cancer;
stratified cat.data
Cell has zero??

11. KEEP LEARNING BIOSTATISTICS!!!
Design of
Experiments
Sampling
techniques Descriptive
Statistics
Significance
Inferential
Statistics
Survival
analysis,
Hazards Ratio
Count more;
Proc Freq;
Run;