A primer to methodologic issues

in Pharmacoepidemiology

Reimar W. Thomsen, overlæge, lektor, ph.d.

Klinisk Epidemiologisk Afdeling, Aarhus Universitetshospital

rwt@clin.au.dk

”Farmakoepidemiologi er epidemiologiens

Saudiarabien” [Jørn Olsen]

Pharmacoepi in Denmark…

A primer to methodologic issues in pharmacoepi -

OUTLINE

1. Study designs & methods -

how to measure drug exposure in real life

2. Common types of epidemiologic bias in analytic pharmacoepi

– illustrated by some examples

– including a little bit on ”newer” analytic approaches in

pharmacoepi – propensity scores and instrumental variables

(For in-depth coverage, attend a 1-week-course,

e.g. AU course by Til Stürmer et al.!)

1. Study designs and how to measure drug exposure

Pharmacoepidemiology

• The study of distribution and determinants of drug-related events in

populations, and application of this study to efficacious drug treatment

– Last: A Dictionary of Epidemiology. Oxford University Press. New

York 1988

• The study of the use and effects of drugs in large numbers of people

– Strom: Textbook of Pharmacoepidemiology, 2006

Pharmacoepidemiology

• Descriptive methods

– Patterns of- / Determinants of- drug utilization

• Analytic methods

– Intended effects (benefit)

– Unintended effects (not only harm!)

– Comparative Effectiveness Research

Why do we need observational designs in

pharmacoepidemiology?

5 Shortcomings of RCTs (T. Stürmer)

• Too Small

– to detect rare outcomes

• Too Simple

– to detect interactions

• Too Selected

– to be generalizable to all users and all indications

• Too Specific

– to assess all relevant outcomes

• Too Short

– to detect long-term effects

11

Examples for Incidence of Adverse Drug Reaction

Drug Event Incidence

Chinidine Syncopy 1 / 100

Clozapine Agranulocytosis 1 / 1,250

Enalapril Angioedema 1 / 3,000

Lovastatin Rhabdomyolysis 1 / 3,000

Dextrane Anaphylactoid reaction 1 / 4,000

Clopidogrel Agranulocytosis 1 / 5,000

Halothane Liver cell necrosis 1 / 30,000

Choramphenicole Aplastic anemia 1 / 40,000

Cyclosporine A Malignancy ? 12

Nonexperimental Studies of Drug Effects (T. Stürmer)

• Not restricted by 5 S of RCTs

– Large enough to study rare outcomes

– Include people with co-morbidity

– Include people with co-medication

– Include elderly, children, pregnant women

– Include wider indication (e.g., less severe disease), off-

label use

– Wide variety of outcomes

– Lagged and long term effects

13

ADR Burden

...”Most of what we learn,

and will continue to learn,

about adverse drug

effects is from

observational studies”

Walker & Stampfer

Lancet 1996;348:489

Need for nonexperimental studies of drug effects

• Drugs rarely studied in RCTs:

– Inexpensive off-patent drugs

– Within-class comparisons, e.g. NSAIDs:

• Comparative effectiveness (pain)

• GI toxicity

• Cardiovascular morbidity/mortality

Effectiveness more important than efficacy in public health!

15

How to ascertain drug exposure in practice…

October 2008

Main data contents of the Danish prescription registries

The personal identifier (CPR number)

The type of drug (Anatomic Therapeutic Chemical (ATC)

classification codes)

The date of dispensing of the drug

The quantity of drug (number of packages, number of pills in the

package, quantity expressed by the defined daily dose (DDD))

NOT complete data on dosing information for each prescription

NOT complete data on the medical indication for prescribing the

drug

(A. Brookhart)

Anatomical Therapeutic Chemical (ATC) Classification

• http://www.whocc.no/atc_ddd_index/

• Tool for drug utilization research

• To improve quality of drug use

• Combined with Defined Daily Dosage (DDD)

• 1996: WHO International Working Group for Drug Statistics

Methodology, Oslo, Norway

• Strong reluctance to make changes

ATC Structure

A Alimentary tract and metabolism

B Blood and blood forming organs

C Cardiovascular system

D Dermatologicals

G Genito-urinary system and sex hormones

H Systemic hormonal preparations, excluding sex hormones and insulins

J Antiinfectives for systemic use

L Antineoplastic and immunomodulating agents

M Musculo-skeletal system

N Nervous system

P Antiparasitic products, insecticides and repellents

R Respiratory system

S Sensory organs

V Various

Level 1: Anatomical main group - consists of one letter.

There are 14 main groups: hierarchical

Hierarchical coding ideal for PE!

• Example: Insulin glargine

• A Alimentary tract & metabolism

• A 10 Drugs used in diabetes

• A 10 A Insulin and analogues

• A 10 A E Insulins and analogues for injection, long-acting

• A 10 A E 04 Insulin glargine

All these difficult terms in pharmacoepi…

• Drug exposure

• DDDs

• Current, former, or never drug use

• New use

• Wash-out period

• Adherence

• Medication Possession Ratio

• Persistence

• Drug Stop

• Augmenting / Intensification

• Switching

• Gap / Drug holiday

• Grace period

• (…)

Drug utilization: Incidence and prevalence

Example: Pottegård A et al: Use of exenatide and liraglutide in

Denmark: a drug utilization study. Eur J Clin Pharmacol 2013.

How is ”drug use” defined?

(from: Pottegaard et al, Eur J Clin Pharmacol 2013)

• “For the first day in each quarter, the number of

persons currently treated (point prevalence) was

estimated by finding the number of unique persons that

had redeemed a prescription that covered this day”

• “As the prescribed daily dose is not recorded in our

data, we defined the duration of the single prescription

as the redeemed quantity divided by the minimum

recommended daily dose (1.2mg for liraglutide and 10

μg for exenatide) and adding 20 % to account for

noncompliance and irregular prescription renewal”

Average daily dose

Pottegaard et al, Eur J Clin Pharmacol 2013

• “The amount of drug used per day in a period

between two dispensings was calculated as the

amount of active drug substance redeemed at the first

prescription divided by the number of days between

the two prescriptions”

• “The ‘“current dose used” was then calculated as a

moving average of the drug used per day in the last

three periods, weighed by the length of each period”

Adherence

2. Common types of epidemiologic bias in analytic

pharmacoepidemiology

• Illustrated by some examples

No Yes

No

Likely

Yes

Unlikely

Cause

Bias in selection or

measurement

Chance

Confounding

Cause

Explanation Finding

Important biases in analytic pharmacoepidemiology

• Misclassification of drug intake

– Recall bias (e.g., pregnant women with/without malformed child)

– Prescriptions: independent data, but prescription received or even

redeemed is ≠ actual drug use

• Usually bias towards the null, but CAVE if comparing drugs

• Confounding by indication

– “Drugs are usually given for a reason”

-> indication is complex and multifactorial, and often associated with the

outcome (can be considered as selection bias)

• Healthy user effects

– Healthy initiator, healthy adherer, sick stopper - CAVE preventive drugs

• Reverse causality / protopathic bias

– E.g. cancer and NSAIDs, pancreatitis and diabetes drugs

• Example 1: Glucose-lowering drugs and risk of acute

pancreatitis

• Example 2: Statin use and pneumonia prognosis

Nationwide data linkage in Denmark

Personal

Identification

Number

Civil Registry

System 1968-

(address, death)

Prescription

Data 1991-, 1996-

(2004-)

Primary care

health services

late 1990s- (vaccines etc.)

Laboratory

databases

late 1990s-

Hospital

Discharge

Registry 1977-

Clinical database

(e.g. DD2 2010-)

GP data / DAMD

2010s-

Example 1: Glucose-lowering drugs and risk of

acute pancreatitis

Modified from: ADA 74th Scientific Sessions, San

Fransisco 2014

Diabetes Care, in press

Incretin Therapy and Risk of Acute Pancreatitis:

A Nationwide Population-based Case-control Study

Background • Incretin-based therapies (the ‘incretins’ =

glucagon-like peptide 1 (GLP-1) receptor

agonists and dipeptidyl peptidase 4 (DPP4)

inhibitors) increasingly used in type 2 DM 1

• Concerns that incretins may cause

pancreatitis through stimulation of GLP-1

receptors in the pancreas 2

• However, type 2 diabetes per se has been

associated with a 1.5- to 2-fold increased risk

of acute pancreatitis 3

• A number of observational studies found

pancreatitis RRs close to 1.0 in users of

DPP4 inhibitors or GLP-1 receptor agonists,

vs. other glucose-lowering drug (GLD) use 4

• Some found increased RRs with incretins 5

We examined the association between

use of incretins and other glucose-

lowering drugs (GLDs) and risk of acute

pancreatitis in a large nationwide

population-based study

1. Holst JJ, Mol Cell Endocrinol 2009 2. Elashoff M, Gastroenterology 2011 3. Gonzalez-Perez A, Diabetes Care 2010 4. Li L, BMJ 2014 5. Singh S, JAMA Intern Med 2013

Methods: Case-control study

• Cases: 12,868 patients with a first-time

hospitalization with ICD-10 diagnosis of acute

pancreatitis (K85) identified in the Danish

national patient registry 2005-2012

• Controls: 128,680 age-, gender-, index date-,

and residence-matched population controls

selected 1:10 by incidence density sampling

Exposure

• Complete individual-level data on all glucose-lowering drug use from nationwide prescription database

• Incretins: ATC codes:

– DPP4 inhibitors: Sitagliptin: A10BH01, A10BD07; Vildagliptin: A10BH02, A10BD08; Saxagliptin: A10BH03, A10BD10; Alogliptin: A10BH04, A10BD09; Linagliptin: A10BH05, A10BD11

– GLP-1 receptor analogues: Exenatide: A10BX04; Liraglutide: A10BX07

• Other glucose-lowering drugs

• Redeemed ever before

– the date of hospital admission with acute pancreatitis

– or the index date among controls

• Current use: prescription redeemed within 100 days

• Former use: prescription redeemed >100 days ago

• New use: first ever prescription redeemed within 100 days

• Intensity of use: total cumulative number of prescriptions (no prescriptions, 1–3 prescriptions, or >3 prescriptions)

Confounders

• Other risk factors for acute pancreatitis retrieved from hospital and prescription databases: – gallstone disease

– Previous biliary tract procedures / ERCP (except <=10 days)

– Alcoholism

– Obesity

– Inflammatory bowel disease

– Cancer (except <=90 days)

– Other diseases in Charlson comorbidity index

– Steroids, NSAIDs, and other pancreatitis-associated drugs

Validity of acute pancreatitis in the Danish patient registry

• Positive predictive value

(PPV) of hospital diagnoses

of acute pancreatitis is 82% 1

• Validated by clinical

presentation with acute

pancreatitis (abdominal pain)

in the hospital record

combined with:

• either a 2-fold increase in

serum amylase or positive

findings by ultrasound or CT

scan, surgery, or autopsy

1. Floyd A, Scand J Gastroenterol 2002

Statistical analysis

• Conditional logistic regression to compute acute pancreatitis ORs,

adjusted for potential confounders

Pancreatitis cases

= 12,868

+ Incretins

no GLDs

+ other GLDs

Population controls

= 128,680

+ Incretins

no GLDs

+ other GLDs

Results

Characteristic Pancreatitis cases

(n = 12,868)

Population controls

(n = 128,680)

Ever use of GLDs 8.5% 6.1%

Incretins 0.69% (n=89) 0.53% (n=684)

Other GLDs 12.8% 8.3%

Male sex 51.6% 51.6%

Age >=60 years 47.9% 47.8%

Results: pancreatitis risk factors

Characteristic Pancreatitis cases

(n = 12,868)

Population controls

(n = 128,680)

Gallstone disease 16.8% 4.0%

Obesity 7.4% 3.1%

Alcoholism-related dx 15.4% 4.4%

Inflammatory bowel disease 2.2% 0.7%

Any cancer 9.2% 7.6%

Charlson Index >=1 33.2% 21.2%

Statins 22.4% 18.2%

Oral steroids 12.7% 7.7%

Azathioprine 1.2% 0.4%

NSAIDs 66.1% 51.4%

Antiepileptics 7.6% 3.8%

Unadjusted and adjusted ORs - incretins

Exposure Pancreatitis

cases

(12,868)

Population

controls

(128,680)

Unadjusted

RR

(95% CI)

Adjusted RR*

(95% CI)

Never use GLDs 11,777 (91.5%) 120,812 (93.9%) 1.00 (ref) 1.00 (ref)

Ever use any GLD 1,091 (8.5%) 7,868 (6.1%) 1.44 (1.35-1.54) 1.05 (0.98-1.13)

Ever use incretins 89 (0.7%) 684 (0.5%) 1.36 (1.08-1.69) 0.95 (0.75-1.21)

Ever use DPP4

inhibitors 68 (0.5%) 516 (0.4%) 1.38 (1.07-1.77) 1.04 (0.80-1.37)

Ever use GLP-1

receptor analogues 30 (0.2%) 230 (0.2%) 1.35 (0.92-1.98) 0.82 (0.54-1.23)

* Adjusted for previous diagnoses of gallstone disease, alcoholism-related conditions, obesity,

inflammatory bowel disease, or any cancer; for 3 levels of the Charlson Comorbidity Index score; and

for current use of oral glucocorticoids, azathioprine, lipid-lowering drugs, antiepileptics, or NSAIDs.

Unadjusted and adjusted ORs – other GLDs

Exposure Pancreatitis

cases

(12,868)

Population

controls

(128,680)

Unadjusted

RR

(95% CI)

Adjusted RR*

(95% CI)

Never use GLDs 11,777 (91.5%) 120,812 (93.9%) 1.00 (ref) 1.00 (ref)

Ever use any GLD 1,091 (8.5%) 7,868 (6.1%) 1.44 (1.35-1.54) 1.05 (0.98-1.13)

Ever use

metformin 732 (5.7%) 5,475 (4.3%) 1.39 (1.28-1.50) 1.01 (0.92-1.10)

Ever use

sulfonylureas 546 (4.2%) 3,748 (2.9%) 1.52 (1.38-1.66) 1.13 (1.02-1.25)

Ever use

insulin 355 (2.8%) 2,473 (1.9%) 1.49 (1.33-1.67) 0.96 (0.85-1.08)

* Adjusted for previous diagnoses of gallstone disease, alcoholism-related conditions, obesity,

inflammatory bowel disease, or any cancer; for 3 levels of the Charlson Comorbidity Index score; and

for current use of oral glucocorticoids, azathioprine, lipid-lowering drugs, antiepileptics, or NSAIDs.

Adjusted ORs by type of incretin use

Adjusted ORs by type of other GLD use

What happens with confounder adjustment?

Ever use Incretins DPP4 GLP-1 Metformin SU Insulin

Unadjusted,

age-/gender-

matched

1.36

(1.08-1.69)

1.38

(1.07-1.77)

1.35

(0.92-1.98)

1.39

(1.28-1.50)

1.52

(1.38-1.66)

1.49

(1.33-1.67)

Adjusted for:

Pancreatitis-

associated

conditions *

1.04

(0.82-1.31)

1.11

(0.85-1.45)

0.95

(0.63-1.42)

1.08

(0.99-1.18)

1.23

(1.11-1.36)

1.07

(0.95-1.21)

+ pancreatitis-

associated

drug use †

0.97

(0.77-1.23)

1.05

(0.80-1.37)

0.86

(0.57-1.30)

1.02

(0.93-1.12)

1.17

(1.06-1.29)

1.02

(0.90-1.15)

+ any

comorbidity

in Charlson

0.95

(0.75-1.21)

1.04

(0.80-1.37)

0.82

(0.54-1.23)

1.01

(0.92-1.10)

1.13

(1.02-1.25)

0.96

(0.85-1.08)

* gallstone disease, alcoholism, obesity, IBD, any cancer

†current use of oral glucocorticoids, azathioprine, lipid-lowering drugs, antiepileptics, or NSAIDs.

Strenghts

• Population-based design and setting in a comprehensive health care system with complete population coverage reduces risk of selection and referral biases

• High validity of hospital and prescription data from independent sources reduces risk of information bias

Limitations – potential biases

• Confounding by indication: Lack of exact data on diabetes duration and severity

– However, incretin therapy usually associated with more advanced diabetes and increased comorbidity

• Reverse causality / Protopathic bias: diabetes caused by pancreatic disease may lead to drug initiation

• Misclassification of exposure: prescription redemption ≠ actual drug use.

– May underestimate pancreatitis risk, bias towards the null (but CAVE if compliance differential by type of drug, when comparing drugs)

• Misclassification of acute pancreatitis – Unlikely to be related to type of drug use?

Limitations – potential biases

• Residual confounding by incompletely measured, unmeasured, or unknown confounders related to drug use

– E.g. obesity may be registered more completely in incretin and other GLD users, vs. non-users

– Limited data on socioeconomic and lifestyle factors that may be associated with use of new expensive drugs like the incretins

• Choice of confounders is critical

– Some factors may be intermediate variables (=effects of GLD use), rather than confounders (e.g. gallstones, obesity)

Interpretation

• Patients with acute pancreatitis are ~40% more likely to be users of any GLD (incretins or others), compared with other individuals

• After adjustment for available confounding factors, the use of incretin-based therapies is not associated with increased risk of acute pancreatitis

• Our null-results are consistent with two recent meta-analyses of RCTs of incretin effects, and with the vast majority of the approx. 10 observational studies on this topic conducted to date

Li L, BMJ 2014, Faillie J, BMJ 2014

Acknowledgments

• Educational material borrowed from:

• Professor Til Stürmer, UNC Gillings School of Global Public Health,

University of North Carolina, Chapel Hill, USA; and

• Professor Maurice Alan Brookhart, PhD, UNC Gillings School of Global

Public Health and UNC School of Medicine, University of North

Carolina, Chapel Hill, USA

• Jennifer Lund, PhD and Alan Kinlaw, MSPH, both UNC Gillings School

of Global Public Health

Read a book get your knowledge up…

• Pharmacoepidemiology, 5th Edition. Brian L. Strom (Editor), Stephen E

Kimmel (Editor), Sean Hennessy (Editor). ISBN: 978-0-470-65475-0.

976 pages. February 2012, Wiley-Blackwell.

Conclusions

• There are lots of possibilities, and a lot of work to do

• ”(Pharmaco-) epidemiology is not for amateurs”

TAK! rwt@clin.au.dk

Måske har jeg modsagt mig selv i aften.

Men hvad jeg har sagt, står jeg ved.