Statistics in Drug
Development
Rong Zhou, PhD
Director, Biostatistics
Medpace
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Topics
 Introduction of pharmaceutical industry
 Drug development process and clinical trials.
 Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
 Other topics
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Drug
 Drug definition:
according to the Food, Drug, and Cosmetic Act (FD&C Act):
(1): a substance recognized in an official pharmacopoeia or formulary
(2): a substance intended for use in the diagnosis, cure, mitigation,
treatment, or prevention of disease
(3): a substance other than food intended to affect the structure or function
of the body
(4): a substance intended for use as a component of a medicine but not a
device or a component, part, or accessory of a device Novartis (60.9B),
Simple version: A Chemical Substance that Interacts with a Living System
and Produces a Biological Response
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Drug
“New Drug” vs “Generic Drug”
 New Drug patents expire 20 years from the date of filing. Many other
factors can affect the duration of a patent.
Since the company applies for a patent long before the clinical trial to assess a drug’s
safety and efficacy has commenced, the effective patent period after the drug has finally
received approval is often around seven to twelve years.
 New drugs (brand names) and generic drugs have the same
pharmacological effects (dosage, intended use, effects, side effects,
route of administration, risks, safety, etc.) and pharmacokinetics.
 For example: Glucophage vs. metformin.
“Prescription Only” vs “Over the Counter”
Other drugs (orphan drugs), medical devices, vaccine
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Pharmaceutical Industry
 List of 2013 top biotech and pharmaceutical companies (Revenue):
Johnson & Jonson (71.3B),
Novartis (60.9B),
Roche (52.4B),
Pfizer (51.6B),
Sanofi, Merck, GlaxoSmithKline, AstraZeneca, Eli Lilly, Abbott Lab.
List of 2013 top biotech and pharmaceutical companies (Net income):
Pfizer (US, 22B),
Johnson & Jonson (13.8B),
Roche (Switzerland, 12.B),
Novartis (Switzerland, 9.5B), GlaxoSmithKline (UK, 9B), Sanofi (France,
5.1B), Amgen (US, 5.1B), Eli Lilly, Novo Nordisk, Merck.
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Pharmaceutical Industry: Drug
 Example of Blockbuster drugs from AstraZeneca:
AstraZeneca's Nexium (heartburn and acid reflux medication)
Sale: 5B (2009), 5B (2010), 4.4B (2011), 3.9B (2012),
6.14B (2013, the top 2nd)
Patent expired May 2014. AstraZeneca will not release an OTC drug.
AstraZeneca's Crestor (indication: cholesterol)
Sale: 5.3B (2013, the top 4th). Patent is going to expire in 2016.
 Example of Blockbuster drugs from Pfizer:
Lipitor (Pfizer, indication: cholesterol) had sale Q3/2012 as $0.186B. The
patent expired in November, 2011.
Lipitor sales in 2010: $10.7B for the year.
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Pharmaceutical Industry: R&D
 Research and Development Cost (Forbes, 08/2013)
Company
Number of new drugs
10 year R&D spending (B$)
Abbott
1
13.2
Sanofi
6
60.8
AstraZeneca
4
38.2
Roche
8
70.9
Pfizer
10
77.8
Eli Lilly
4
26.7
GSK
11
57.6
J&J
13
67.6
The median cost per new drug is $4 ~ 5 billion dollars.
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Pharmaceutical Industry: CRO
Contract Research Organization:
A contract research organization (CRO) is an organization that provides
support to the pharmaceutical, biotechnology, and medical device
industries in the form of research services outsourced on a contract basis.
CROs that specialize in clinical-trials services can offer their clients the
expertise of moving a new drug or device from its conception to FDA/EMA
marketing approval, without the drug sponsor having to maintain a staff for
these services.
Top CROs:
Quintiles, PAREXEL, Covance, PPD, ICON
The outsourcing market is about $20-30 billions.
Quintiles (Durham NC) has 30,000+ employees in 60 countries. Year 2011
revenue is about $3.00 billion, and Year 2013 revenue is $3.8 billion.
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Agenda
√ Introduction of pharmaceutical industry
 Drug development process and clinical trials.
 Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
 Other topics
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Drug Development Process
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Clinical Trials
Visit ClinicalTrials.gov.
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Clinical Trial Phases
Phase 1:
Studies that are usually conducted with healthy volunteers and that
emphasize safety. The goal is to find out what the drug's most frequent and
serious adverse events are and, often, how the drug is metabolized and
excreted (pharmacokinetics).
ADME (Absorption, Distribution, Metabolism, and Excretion) will be studies.
Phase 1 clinical trials include:
 FIM (first-in-man) and/or single ascending dose cohorts study
 Multiple ascending dose cohorts study
 Food effect study
 Drug-drug interaction study
 Bioavailability and bioequivalence
 PK study based on special populations
 Thorough QT study
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Clinical Trial Phases
Phase 2:
Studies that gather preliminary data on effectiveness (whether the drug
works in people who have a certain disease or condition). Safety continues
to be evaluated, and short-term adverse events are studied.
Phase 3:
Studies that gather more information about safety and effectiveness by
studying different populations and different dosages and by using the drug
in comb
Phase 4:
Studies are done after the drug or treatment has been marketed to gather
information on the drug's effect in various populations and any side effects
associated with long-term use.
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Conduct of Clinical Trials
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Agenda
√ Introduction of pharmaceutical industry
√ Drug development process and clinical trials.
 Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
 Other topics
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Biostatistics: Responsibilities
Design the study and develop the protocol
Design the randomization algorithm if needed
Draft the statistical analysis plan (SAP)
Programming the study data based on the SAP: tables/
figures/ listings (TFLs).
Present the results and write the interpretation of study
results in clinical study report (CSR).
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Biostatistics (1): Study Classification
Phase I PK/PD study
Thorough QT study
Superiority study
Active control and equivalence/non-inferiority study
Dose-response study
Safety follow-up study
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Biostatistics (2): Study Design
Conventional study designs:
 Parallel Group Design
 Crossover Design
Other study designs:
 Titration Design
 Cluster Randomized Designs
 Group Sequential Design and other adaptive designs
Oncology studies: up-and-down design, continual
reassessment method, etc.
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Study Design: Parallel Groups
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Study Design: Cross-Over
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Things to Consider
Selection of control:
 Placebo control
 Positive control
Randomization
 Is randomization necessary?
 Cluster randomization?
 Is randomization practical at certain level (such as site)?
 Randomization algorithm: single site vs. multiple site, permutedblock method vs. dynamic method with minimization algorithm
Blinding
Sample size calculation
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Biostatistics (3): Clinical Data
Continuous data
 Lab measures & vital signs: blood pressure, blood glucose, etc.
 Instruments: questionnaire scores, etc.
 Calculated parameters: AUC, etc.
Categorical data
 Binary outcome: response (Yes or no), fatal, etc.
 Multiple level outcome: event severity, number of events, etc.
Censored data (time-to-event)
 Time to disease progress
 Time to heal
Other data format, such as imaging data, questionnaires, ROC
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Biostatistics (4): Case Studies
Case Study 1: Diabetes
Case Study 2: Stroke Response
Case Study 3: Cardiovascular Endpoint Study
Case Study 4: Pharmacokinetics Study
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Case Study 1: Diabetes
Thirty six weeks open-label study for diabetes patients.
Objective: To evaluate the effects of insulin glargine with
metformin over NPH insulin with metformin on glycated
hemoglobin (HbA1C).
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Case Study 1
The primary efficacy variable is the change in HbA1c value
from baseline (0 weeks) to last visit (36 weeks).
The primary analysis will be performed using an analysis of
covariance (ANCOVA) model with HbA1c change from
baseline as a response variable, treatment group and
center as fixed effects, and the baseline value as a
covariate.
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Case Study 1
How to define the endpoint:
 End of study (EOS) measures (Week 36): what if missing, what if off drug for
more than two weeks? What if…
 Change from baseline to EOS
 Percent change from baseline to EOS
 Two data points vs. one to reduce variability?
 Log transformation or other transformations: still make senses?
Statistical modeling:
 Modeling diagnosis
 Need to add more factors/predictors or remove some: country, sex, race, etc.?
 Non-parametric approach?
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Case Study 2: Stroke
 This is a Phase 2, randomized, double-blind, placebo-controlled,
multicenter study. The total trial duration is 12 months.
 Approximately 200 subjects who experienced stroke will be
randomized (100:100) into the trial.
 The modified Rankin Scale (mRS) score will be assessed at Day 90
and used as efficacy. The mRS is a commonly used scale for
measuring the degree of disability or dependence in the daily
activities of people who have suffered a stroke or other causes of
neurological disability, and it has become the most widely used
clinical outcome measure for stroke clinical trials.
 The mRS score runs from 0-6, running from perfect health without
symptoms to death. 0 - No symptoms. 1 - No significant disability. 2 Slight disability. 3 - Moderate disability. 4 - Moderately severe
disability. 5 - Severe disability. 6 - Dead.
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Case Study 2
The primary efficacy variable is dichotomized outcome at Day
90:
◦ favorable outcome (mRS≤2)
◦ unfavorable outcome (mRS>2)
The proportion of subjects with a favorable outcome will be
compared between the investigational product and the
placebo using the Cochran-Mantel-Haenszel test, controlling
for baseline NIHSS score category.
Any other models to be considered?
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Case Study 3: Cardiovascular
A Double-Blind, Randomized, Placebo-Controlled Study of
atorvastatin as Prevention of Cardiovascular Events in
Patients With a Previous Stroke.
Visit S1
Visits T2 to T14
Placebo
80 mg atorvastatin
Screening Visit
Double-Blind Treatment Period
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Case Study 3
The primary efficacy variable is the time from randomization
to the first occurrence of a primary clinical endpoint (fatal or
nonfatal stroke)
A Cox proportional hazards regression analysis will be
performed on the time from randomization to the first
occurrence of a primary clinical endpoint. The primary model
will contain the following covariates: treatment, center, and
entry event (stroke). SAS procedure PROC PHREG will be used.
Kaplan–Meier estimator (the product limit estimator) of the
survival function will be plotted.
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Case Study 3
Data Handling:
 Data from all randomized patients will be analyzed.
 Patients who experience a primary clinical endpoint are
considered as completer.
 Censoring occurs for patients who do not experience a primary
clinical endpoint prior to the completion of the study. The
censoring time will correspond to the study day on which the
patient completed the study or was last contacted during the
study following a withdrawal.
 If a patient dies from a cause other than stroke, the survival
time will be censored as if the patient had been lost to followup at that point.
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Case Study 4: Pharmacokinetics
A Phase I, randomized, double-blind, two period, two
sequence crossover BE study to evaluate the effect of
DRUG_A and DRUG_R on DRUG pharmacokinetics in healthy
adult subjects after single dose administration.
Sequence
Sample
Size
Period 1:
Days 1-3
Period 2:
Days 15-17
1
32
Treatment DRUG_A
Treatment DRUG_R
2
32
Treatment DRUG_R
Treatment DRUG_A
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Case Study 4
The primary efficacy variable is pharmacokinetic parameters
AUC(0-inf), AUC(0-72hr) and Cmax of DRUG.
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Case Study 4
The statistical analysis will be performed on the logtransformed pharmacokinetic parameters.
Analysis of variance will be performed using SAS Mixed Linear
Models procedure. Subject will be fitted as a random effect
and treatment, sequence, period will be fitted as fixed effects
in the model. The ratio and associated 90% CI will be
estimated for the PK parameters.
An example of SAS code is included here.
Proc Mixed;
class subject treatment;
model logPKvar = treatment seq period;
random subject;
lsmeans treatment;
estimate 'test vs ref' treatment -1 1/cl alpha=0.1;
run;
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Agenda
√ Introduction of pharmaceutical industry
√ Drug development process and clinical trials.
√ Biostatistics in clinical trials:
◦ Study design
◦ Clinical data
◦ Statistical modeling
 Other topics
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Oncology Studies
National Cancer Institute at NIH:
The recent breakthroughs in understanding the molecular
biology of cancer have led to an unprecedented number of
new targets in oncology drug development. There are more
cancer drugs in the research pipeline than any other type of
therapy, corresponding directly with the number of oncology
clinical trials.
Different study designs and data analysis methods for
oncology studies than for other drugs.
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Current Stat Research Topics
 Adaptive design
 Missing data imputation
 Dose finding by the Continual Reassessment Method (CRM)
 Randomization algorithm and randomization tests
 Risk-based monitoring and Fraud Detection
Design and analysis of non-inferiority studies
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Medpace Biostatistics
Departments and Groups:
 Biostatistician
 Statistical analyst
 Data manager, data coordinator
 Clinical database programmer, SAS programmer
 IVRS manager, coordinator
 ECG core lab, imaging core lab
Biostatistics Department:
 Around 40 biostatisticians and statistical analysts
 Two in Scotland, UK, and the rest in Cincinnati office
 SAS is the primary software. Currently using Version 9.3
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Q&A
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