cohort study design and estimating drug exposures

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Cohort Study Design and Estimating Drug Exposures

Presenter: Dr Andrea Schaffer, Centre for Big Data Research

in Health, University of New South Wales, Australia

13th Asian Conference on Pharmacoepidemiology

October 13-15, 2021

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Disclosures

I am supported by the following:

• National Health and Medical Research Council (NHMRC) Centre of

Research Excellence in Medicines Intelligence (#1196900)

• NHMRC Early Career Fellowship (#1158763)

The Centre for Big Data Research in Health, UNSW received funding

from AbbVie Australia in 2020 to conduct research unrelated to this

current work

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Acknowledgements

Special thanks to:

• Dr Sallie-Anne Pearson, UNSW Sydney

• Dr Vin Re Lo, University of Pennsylvania

• Dr Wei Zhou, MSD & Co

• Drs Kristian Filion and Laurent Azoulay, McGill University

• Dr Tobias Gerhard, Rutgers University

• Dr Almut G Winterstein, University of Florida

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Educational objectives

Understand the selection of drug exposure and

comparators within pharmacoepidemiology databases

Understand epidemiologic study designs

Understand the strengths and limitations of cohort

studies for pharmacoepidemiology research2

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Epidemiology study designs

Adapted from Centre for Evidence Based Medicine: http://www.cebm.net

Treatment/control groups similar except treatment

assignment

How many people are taking drug X?

Is drug X associated with Y?

Does drug X cause Z?

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What is a cohort?

A well-defined group of people (who, where, when)

• Classified based on presence and absence of an exposure

Individuals in the cohort are followed forward through time and

observed for occurrence of outcome(s) of interest

What question does a cohort study answer?

Among the two subgroups defined by exposure

(exposed/unexposed), how does the incidence of a particular

outcome differ?

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Cohort studies

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Cohort

Follow-up

Exposure

Outcome

Kim et al. Pharmacoepi Drug Saf 2019;28:507

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Checklist for simple cohort study

1. Define population(s) of interest and comparator

2. Define inclusion/exclusion criteria

3. Define how people will be sampled from the underlying source population

4. Define explicitly (!!) date of cohort entry and exit

5. Classify person time by exposure

6. Sum person-time of the different exposure categories

7. Assign number of events to these categories

8. Calculate incidence and incident rates

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Incident versus prevalent users

Time

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New user design

Identify and selects only drug initiators (incident users)

Follow-up begins at initiation of therapy (time zero = t0)

Restrict to people with minimum period of non-use prior to t0 (washout

period)

Baseline characteristics obtained in specified period before initiation (t0)

This accounts for depletion of susceptibles:

• Risk of outcome is often highest shortly after initiation—can lead to discontinuation

• Prevalent users are more likely to be people who did not experience the outcome

• Can make harmful exposures appear to be protective

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13 Renoux et al. Pharmacoepi Drug Saf 2017;26:554

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Selecting appropriate comparator

Consider the study question—needs to be fit-for-purpose and clinically

meaningful

What about “non-users” as comparator?

• Non-users often differ substantially to users (confounding by indication)

• Difficult to define date of cohort entry

Inactive comparator (different indication)

• Ensures all patients are interacting with health care system

• Easier to define date of cohort entry

Active comparator (same indication)

• Patients are more similar (indication for use, comorbid disease)

• Reduces confounding

15Setoguchi et al. Circulation 2007;115:27. Dong et al. Br J Clin Phamacol 2018;84:1045..

Inactive

comparator

Active

comparator

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Cohort entry/exit

When to start follow-up (t0)

• Calendar day

• Time (x days after inclusion in registry)

• Event (date of diabetes diagnosis, first dispensing of drug)

When to stop follow-up (censoring)

• First occurrence of outcome

• Loss to follow-up or death

• Switch in drug exposure

• End of study period (specific date) / end of data capture

18 Schneeweiss et al. Ann Intern Med 2019;170(6):398.

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19 Ou et al. Br J Clin Pharmacol 2021

Research

question

To compare the short-term risk of cardiovascular events associated with the use

of tramadol (exposed) compared with codeine (active comparator)

CohortPeople without cancer (≥18 years) who initiated either tramadol or codeine with a

12-month washout period (new user design)

Entry (t0) Date of initiation after ≥12 months of continuous history in the CPRD

Censoring

Event (hospitalisation/death due to myocardial infarction), death, end of CPRD

registration, cancer diagnosis, end of study period (31 March 2017), maximum

follow-up (30 days)

AnalysisTime to event (Cox proportional hazards) adjusted for high dimensional

propensity score

*CPRD = Clinical Practice Research Datalink

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Time-dependent classification of exposure

Exposed (A=1) Unexposed (A=0)

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Conceptual considerations for drug exposure measurement

Link exposure measurement to the study question:

• Short- vs long-term use, single vs chronic use, prevalent vs new use

• Biological mechanism (pharmacology) of the drug

Need to account for changes in exposure status

Induction period: time required for the treatment to biologically affect the

outcome

Latency period: time required between outcome onset and diagnosis

**It can be difficult to differentiate between the induction and latency periods and

they are often combined

23 Billioti de Gage et al. BMJ 2012;345:e6231.

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Drug exposure definitions

Lee and Pickard. Chapter 4: Exposure definition and measurement. In:

Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide. 2013

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Exposed

Exposed?

Last

dispensing/

prescription

Extending drug exposure period?

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May be appropriate if:

• A drug effect is known to continue after stopping use

• Drug supply is likely not exhausted

• Drug was stopped because of early symptoms related to the outcome

What if I get it wrong (misclassification)?

• Erroneous attribution of an event to non-exposure (if patient was

actually exposed) will increase incidence in non-users and bias the

comparison towards no association

Extending drug exposure period?

27 Gamble et al. Value in Health 2012;15:191.

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What if I am unsure?

Define exposure classification from the outset based on sound

reasoning (clinical and empirical)

Don’t forget the pharmacology in pharmacoepidemiology

Undertake sensitivity analyses

To account for

residual drug effects

To account for informative

discontinuation or

continued use

To account for

induction period and

reverse causation

Initial Current Recent Past

29 Margulis et al. Eur J Clin Pharmacol 2018;74:193.

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Measures of frequency and risk

1. Incidence proportion (cumulative incidence): number of

new cases in the population over a defined period of time

• Denominator = Total population at risk

2. Incidence density (incidence rate): number of new cases

per unit of time

• Denominator = Sum of the time that each individual is followed

until the event, death, or lost to follow-up (person-time)

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Incidence measurement

PY = person-years

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Comparing incidence – relative measures

Incidence risk ratio (“relative risk”) (RR)

RR =Incidence proportion in exposed

Incidence proportion in unexposed

Incidence rate ratio (IRR)

IRR =Incidence density in exposed

Incidence density in unexposed

Hazard ratio (HR) (for time-to-event analyses)

HR =Hazard in exposed

Hazard in unexposed

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Comparing incidence – absolute measures

Absolute risk difference: difference in two incidence proportions

or rates

Risk difference RD = Incidence in exposed − Incidence in unexposed

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Statistical analysis

Start with descriptive/unadjusted comparisons

Need to adjust for confounding (e.g. multivariable regression,

propensity scores, IPTW)

Can use intent-to-treat analysis, or incorporate time-varying

exposure/confounding

Adjusted analysis: use regression modelling (logistic, linear,

Poisson)• For time-to-event analyses: Cox proportional hazards regression

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No. events No. people

No. person-

years

Incidence rate

(95% CI)

Adjusted HR

(95% CI)

Codeine 646 913,966 74,544 8.7 (8.0-9.4) 1.00 (Ref)

Tramadol 106 123,390 10,051 10.5 (8.7-12.8) 1.00 (0.81-1.24)

Table. Association between tramadol vs codeine and the risk of myocardial infarction

Ou et al. Br J Clin Pharmacol 2021

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Cohort study design

Advantages Disadvantages

Timing is clear (i.e. exposure

precedes outcome)

Large sample size needed for rare

outcomes

Can calculate incidence Long follow-up may be required for

outcomes with long latency

Can study multiple outcomes Loss to follow-up can introduce bias

More intuitive Can be costly

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Any questions?

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cohort study design and estimating drug exposures

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