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Clinical Trials, Epidemiology, and Biostatistics in Skin Disease Joel M. Gelfand, MD, MSCE Professor of Dermatology and Epidemiology Vice Chair for Clinical Research Medical Director, Clinical Studies Unit Director, Psoriasis and Phototherapy Treatment Center University of Pennsylvania Perelman School of Medicine

Clinical Trials, Epidemiology and Biostatistics in Skin Disease

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Clinical Trials, Epidemiology,

and Biostatistics in Skin

Disease

Joel M. Gelfand, MD, MSCE

Professor of Dermatology and Epidemiology

Vice Chair for Clinical Research

Medical Director, Clinical Studies Unit

Director, Psoriasis and Phototherapy Treatment Center

University of Pennsylvania Perelman School of Medicine

Disclosure and funding statement

• Investigator and/or consultant for Amgen, Abbvie, Jansen, Merck (DSMB),

Pfizer, Lilly, Celgene, Coherus (DSMB), Novartis, Sanofi, Valeant, and

Astrazenaca

• Patent – Resiquimod for CTCL

• This presentation is the sole work of Dr. Gelfand

Definition

• Epidemiology is the study of the distribution and determinants of health and disease in populations

• Clinical epidemiology extends the principles of

epidemiology to the critical evaluation of diagnostic and

therapeutic modalities in clinical practice

• Pharmacoepidemiology: The study of drug effects in

large populations of patients

• Epidemiology is the basic science underlying much of

public health, preventative medicine, and individual

patient care decisions

Study Design Issues:

Get help early!• Well formulated study

question

• Define exposure,

outcomes, confounding

factors

• Minimize selection and

information bias

• Plan for statistical error

• Analysis plan

To call in the statistician after

the experiment is done may

be no more than asking him

to perform a post-mortem

examination: he may be able

to say what the experiment

died of.-RA Fisher

Study Designs in Epidemiology

1. Clinical Trial

2. Cohort

3. Case-control

4. Cross-sectional/ecologic

5. Case series

6. Case reports

Analytic

Descriptive

Cross-sectional studies

• Definition – The status of an individual with respect to the presence or absence of both exposure and disease is assessed at the same point in time

• Use – to establish prevalence and hypothesis generation

• Limitation – can not establish temporal relationship

• Example – beta carotene and cancer

Study Designs in Epidemiology

1. Clinical Trial

2. Cohort

3. Case-control

4. Cross-sectional/ecologic

5. Case series

6. Case reports

Analytic

Descriptive

Population based studies: Unifying

theory for analytical studies

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oallocation

process

Exposed

Unexposed

study time

End o

f observ

atio

n p

erio

d

♦ = Study outcome

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Study population

↑Confounding & Selection Bias ↑ ↑ Information Bias ↑Error Sources:

Case- Control Studies

• Definition – A study comparing diseased patients to non disease patients, looking for differences in risk factors.

• Use – the study of multiple risk factors for a single disease, especially for rare diseases

• Limitation – bias in exposure data

• Example – Doll and Hill 1950, smoking and lung cancer

Cohort Study

• Definition – A study which selects subjects

on the basis of the presence or absence of

exposure to a factor of interest and follows

them to determine their outcome

• Use – To study multiple outcomes from an

exposure

• Limitation – Prolonged, Costly

• Example – PUVA cohort study

Case-Control

DiseasePresent Absent(case) (control)

Present A B(exposed)

Absent C D(unexposed)C

oh

ort

Stu

dy

Facto

r

Odds ratio = AD/BCRelative risk A/A+B

C/C+D

Types of Associations

• None (independent events)

• Spurious

– Chance (random variation)

– Bias (systematic variation)

• Indirect (confounding)

• Causal

Types of Associations

• None (independent events)

• Spurious

– Chance (random variation)

– Bias (systematic variation)

• Indirect (confounding)

• Causal

Biostatistics

Key Principles

• Methods allow one to estimate the probability that the observation is due to chance (P value)

• Assumes that you have drawn a random unbiased sample from the population you wish to study.

• It addresses the variability inherent in drawing samples from populations.

Key Questions

• Type of data

• Distribution of data

• Inferential techniques

• Multivariable techniques

• Type 1 (alpha error) and

Type 2 (beta error/power)

• Don’t over rely on P

values!

Types of Associations

• None (independent events)

• Spurious

– Chance (random variation)

– Bias (systematic variation)

• Indirect (confounding)

• Causal

Bias

• Definition – A systematic error in collecting or interpreting data

• Selection bias – A distortion in the estimate of effect resulting from the manner in which subjects are selected for the study

• Information bias - A distortion in the estimate of effect due to measurement error or misclassification of subjects on one or more variables– Recall bias

Types of Associations

• None (independent events)

• Spurious

– Chance (random variation)

– Bias (systematic variation)

• Indirect (confounding)

• Causal

Confounding

• Definition – An observed association (or

lack of association) that is due to a mixing

of effects between exposure, the outcome,

and a third factor.

E D

F

A confounder is associated

with the exposure of interest,

and independent of that

exposure, is a risk factor for

the disease

Types of Associations

• None (independent events)

• Spurious

– Chance (random variation)

– Bias (systematic variation)

• Indirect (confounding)

• Causal

Criteria for Causation

• Strength of association (OR, RR)

• Biologic credibility

• Consistency with other studies

• Time sequence

• Dose response

• Study design

What percent of observational studies of

treatment effect are confirmed by RCTs

1. 10%

2. 25%

3. 50%

4. 75%

Probability of observational studies

being confirmed by RCTs

1. 10%

2. 25%

3. 50%

4. 75%

Confirmation rate of

preclinical research

Confirmation rate of

observational research

Begley CG and Ellis LM Nature 2012:483:531-533

Ioannidis JPA et al JAMA 2001:286;821-830

Clinical Trial

• Definition - The investigator determines

which patients receive an exposure and

then follows the patients for the outcome

• Use- Gold standard to establish causality

• Limitations – generalizability, ethical

issues

• Example – polio vaccine trials (1950) RCT

of >1 million school age children

Ethical Issues

• A conflict exists between the role of

physician (commitment entirely to patient)

and investigator (commitment to

research).

• Concept of Equipoise – the benefit of a

treatment relative to placebo is unknown

Ethical Issues and the IRB

• 1999 Jesse Gelsinger who had ornithine

transcarbamylase (OTC) deficiency, a rare

but controllable metabolic disorder, dies is

a phase I gene therapy trial at PENN.

• June 2001 Ellen Roche, a 24 year old

healthy woman dies in study at Hopkins’

• Multiple FDA letter’s censuring/banning

investigators

Lessons (re)learned from

Efalizumab• 2003 FDA approved Efalizumab

– 2764 patients treated, 218 treated > 1 year

• 2009 Withdrawn– 46,000 patients treated

– 3000 treated for ≥ 2 years

– 3 confirmed and one suspected case of PML spontaneously reported

• Estimated risk of PML in efalizumab treated

patients:

– Overall: 1 in 15,000 per year

– Patients treated > 2 years: 1 in 1000

– Likely an underestimate due to incomplete reporting

Perspective

“All scientific work is incomplete, whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us the freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time”

- Sir Austin Bradford Hill

Resources

• JAMA Evidence: http://jamaevidence.mhmedical.com/book.aspx?bookID=847

• Cohrane reviews: http://www.cochrane.org/

• American Dermatoepidemiology Network (ADEN) http://www.adenet.us/

• Hennekens and Buring. Epidemiology in Medicine. Little, Brown and Company. Boston. 1987

• Barzilai, et al. Dermatoepidemiology.J Am Acad Dermatol. 2005 Apr;52(4):559-73

for dermatoepidemiology!