企業でのRWDを用いた製販後DB調査計画
弘 新太郎
ファイザー株式会社

Acknowledge
Yoichi Ii (Pfizer)
Kazuya Fujita (Pfizer)
Maki Okada (Pfizer)
Yoshiomi Nakazuru (Pfizer)
Yosuke Fujii (Pfizer)

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Contents
What are Real World Data (RWD)?
Planning of DB study
Validation Study for Outcomes2

What are Real World Data (RWD)?
What are RWD and where do they come from?
Real world data are the data relating to patient health status and/or thedelivery of health care routinely collected from a variety of sources. RWD
can come from a number of sources, for example:• Electronic health records (EHRs)• Claims and billing activities• Product and disease registries• Patient-generated data including in home-use settings• Data gathered from other sources that can inform on health status,
such as mobile devices
引用：
https://www.fda.gov/ScienceResearch/SpecialTopics/RealWorldEvidence/default.htm
3What is happening now?
Real world data (RWD) and real world evidence (RWE) are playing an
increasing role in health care decisions.
• FDA uses RWD and RWE to monitor post market safety and adverse
events and to make regulatory decisions.
• The health care community is using these data to support coveragedecisions and to develop guidelines and decision support tools for use
in clinical practice.
• Medical product developers are using RWD and RWE to support
clinical trial designs (e.g., large simple trials, pragmatic clinical trials)
and observational studies to generate innovative, new treatment
approaches.
The 21st Century Cures Act, passed in 2016, places additional focus on
the use of these types of data to support regulatory decision making.
引用：https://www.fda.gov/ScienceResearch/SpecialTopics/RealWorldEvidence/default.htm
4Key Drivers in Japan

平成30年3月15日
第４回 臨床開発環境整備推進会議 資料3-2よりhttps://www.mhlw.go.jp/stf/shingi2/0000197750.html

5Key Drivers in Japan

平成30年3月15日
第４回 臨床開発環境整備推進会議 資料3-2よりhttps://www.mhlw.go.jp/stf/shingi2/0000197750.html

6Databases
EHR(Electronic Health Record)

Claims / Administrative

MID-NET

MDV

リアルワールドデータ
株式会社

JMDC

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Planning of DB study
Points to consider for feasibility assessment of DB study
CohortTarget patients are in the hospitals which is source of DB
Target drug will be used in the hospitals which is source of DB

Hospitals

Number ofPatients

MID-NETMDVJMDC*23 hospitals

DPC hospitals

Clinics and Hospitals(Medical institutionsare disclosed)(Medical institutions
are NOT disclosed)

Visited by Insured person(Medical institutions are NOTdisclosed)

4 million patients(Feb 2018)

24 million patients(Sep 2018)

5.6 million insured persons(Jun 2018)* JMDC has also DPC hospital based DB with 5.5 million patients, separated byinsurance based DB8Planning of DB study
Points to consider for feasibility assessment of DB studyOutcome
Target outcome is able to be detected by EHR or claims data
The outcome event is occurred in out of hospitals and recovered
before the next visit of hospital• If a patient doesn’t receive any medical care for the outcome,there would be no record in claims data
• Ex) falling down, mild fever
Limitation of traceability
MID-NETMDV

JMDC*

Hospital type Hospitals

DPC hospitals

Clinics and Hospitals

Data source

EHR, Claims data and Lab

Claims data and Lab(partial hospitals)

Claims data

TraceabilityWithin institution

Within institution

Across institutions* JMDC has also DPC hospital based DB with nd 5.5 million patients, separated byinsurance based DB9

Planning of DB study
Points to consider for feasibility assessment of DB study
Study DesignObjective• Comparison between exposure and non-exposure?
Cohort Design• To investigate risk factors impacted on the outcomes?
Nested Case Control Design

Cohort Design• Define non-exposure cohort• It can be identified in the feasibility assessment phaseNested Case Control Design• Define risk factors to be investigated• The risk factors can be defined within information of DB
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Cohort designTime• What is confounder?
• Patient Matching
Outcome Event

TargetPopulation

Baseline
Information(Gender, Age,Comorbidity,
the othermedicaltreatment,etc.)

Exposed group
No Event

Not Exposed
group

Outcome Event
No Event• Timing of the index date
• Which comparator drugs?
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Nested Case Control design
Time
Exposed group

TargetPopulation

Baseline
Information(Gender, Age,Comorbidity,
the other
medicaltreatment,etc.)

Not Exposedgroup

Exposed group

Not Exposedgroup• Timing of the index date for timematching of controls• Which risk factors?

Cases
Patients withOutcome EventControls
Patients without
Outcome Event12

Accuracy and Precision
Accuracy: small systematic error
 Systematic Error = Bias
Selection bias
Information bias
Precise(smallConfoundingrandomerrors)

Accurate(small systematic error)

××
×××
×

Inaccurate(large systematic error)

××
×××
×Precision Random Error
Can Manage due tolarge number ofsample size

×
Imprecise(largerandomerrors)

×× ×××× ×××××13

Validation Study for Outcomes Validation study for Outcome definition
日本でのDB研究の信頼性向上のためには必要な課題
日本の利用可能なDBで，どのようなアウトカム（定義）にどんなバイアスが入っているのかを調べる
GPSP下での製造販売後DB調査ではアウトカム定義に対するValidation studyが求められる
DB調査自体よりValidation studyの方が大変！？

業界全体で協力して，エビデンスを高める努力が必要です。
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What is Validation study
DBに提供されているデータex)診療報酬請求データ

バリデーション研究の参考にできる資料：
日本における傷病名を中心とするレセプト
情報から得られる指標のバリデーションに関するタスクフォース報告書
http://www.jspe.jp/committee/020/0271_1/

１．対象のアウトカムが発現した症例を特定病院からアウトカム発現を確認するデー
タを提供してもらうex) 電子カルテ，診断画像など
２．対象症例が対象のアウトカムが発現しているか調査
調査方法は様々： 判定委員会を設置，疾患レジストリーと比較，独立した医師で判定するなど３．陽性的中率（Positive Predictive Value:陽性的中率(PPV) =PPV）を算出

真にアウトカム発現していた例数
DBから特定されたアウトカム発現例数
15How to conduct Validation study 医療機関との共同研究で実施可能
 DBの匿名加工前の情報源（例：レセプト）と真のアウトカ
ムかを判断する情報源（カルテ等）を照合できるのは医療機関の医師しかできない。

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Challenges in Validation study Target population• Target patients from whole DB or• limit to the patients who have the outcome and the target disease Outcome definition• Need experts who have deeply understanding actual practice as well as
clinical/medical knowledge Site selection
 Representativeness• Need to know difference/similarity of patients background amonghospitals (clinical sites)• Hospital type (management organization), Number of Beds
 Feasibility• Need to collaborate with medical doctors with high specialty for theoutcome disease17

How to conduct Validation study 大規模コホートを利用したValidation studyの実施可能性
レセプト情報を有する大規模コホート研究との共同研究

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How to conduct Validation study

引用：
http://www.jspe.jp/committee/020/0271_1/

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How to conduct Validation study
引用：
http://www.jspe.jp/committee/020/0271_1/

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How to conduct Validation study
現在日本で進行中の大規模コホート

引用：
http://www.jspe.jp/committee/020/0271_1/

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