診断精度研究のメタ分析についてまとめました

1. 䛂デ᩿⢭ᗘ䛾䝯䝍ศᯒ䛃䛾᭩䛝᪉
ᑓಟ኱Ꮫே㛫⛉Ꮫ㒊
ᅜ㔛ឡᙪ
2014/10/25

2. ┠ḟ
• デ᩿⢭ᗘ◊✲䛾䛚䛥䜙䛔
• デ᩿⢭ᗘ䛾䝯䝍ศᯒ䛾ᐇ᪋䛸᭩䛝᪉
①◊✲┠ⓗ䛾≉ᐃ䛸䝥䝻䝖䝁䝹సᡂ
②◊✲䛾ⓗ☜㝖እᇶ‽
③ᩥ⊩䛾᳨⣴
④◊✲䛾㉁䛾ホ౯
⑤◊✲䛾⤫ྜ䛸␗㉁ᛶホ౯
⑥⤖ᯝ䛾ゎ㔘

3. デ᩿⢭ᗘ◊✲䛾䛚䛥䜙䛔
• デ᩿⢭ᗘ◊✲䠖⌧≧䛻䛚䛔䛶᭱䜒⢭ᗘ䛾㧗䛔デ᩿ἲ䜢ཧ↷
ᇶ‽䠄⮳㐺ᇶ‽䠅䛸䛧䛶䚸㛵ᚰ䛾䛒䜛ᣦᶆ᳨ᰝ䛾デ᩿⢭ᗘ䜢
᳨ウ䛩䜛◊✲
ཧ↷ᇶ‽䠄⮳㐺ᇶ‽䠅
⑓Ẽ(᭷) ⑓Ẽ(↓)
ᣦᶆ᳨ᰝ
㝧ᛶ ┿㝧ᛶ(a) ഇ㝧ᛶ(b) 㝧ᛶ䛾⪅䛾ᩘ(a+b)
㝜ᛶ ഇ㝜ᛶ(c) ┿㝜ᛶ(d) 㝜ᛶ䛾⪅䛾ᩘ(c+d)
⑓Ẽ䛾⪅䛾ᩘ(a+c) ⑓Ẽ䛷䛺䛔⪅䛾ᩘ(b+d) ඲ဨ䛾ᩘ(a+b+c+d)
ឤᗘ䠖⑓Ẽ(᭷)䛾୰䛷䚸᳨ᰝ㝧ᛶ
䛾⪅䛾๭ྜ䠙a/(a+c)
≉␗ᗘ䠖⑓Ẽ(↓)䛾୰䛷䚸᳨ᰝ㝜
ᛶ䛾⪅䛾๭ྜ=d/(b+d)
᳨ᰝ
㝧ᛶ
᳨ᰝ
㝜ᛶ
⑓Ẽ
䠄᭷䠅
⑓Ẽ
䠄↓䠅
ឤᗘ ≉␗ᗘ

4. デ᩿⢭ᗘ◊✲䛾䛚䛥䜙䛔
䠘㝧ᛶᑬᗘẚ:
LR+䠚
䠙ឤᗘ/(1-­‐≉␗ᗘ)
→⑓Ẽ(↓)䛻ẚ䜉⑓Ẽ(᭷䠅䛜ఱಸ㝧ᛶ䛻
䛺䜚䜔䛩䛔䛛
䠘㝜ᛶᑬᗘẚ:
LR-­‐䠚
䠙(1-­‐ឤᗘ)/≉␗ᗘ
→⑓Ẽ䠄↓䠅䛻ẚ䜉䛶⑓Ẽ䠄᭷䠅䛜ఱಸ㝜
ᛶ䛻䛺䜚䜔䛩䛔䛛
䠘デ᩿䜸䝑䝈ẚ:
DOR䠚
䠙㝧ᛶᑬᗘẚ/㝜ᛶᑬᗘẚ
᳨ᰝ
㝧ᛶ
⑓Ẽ
䠄᭷䠅
᳨ᰝ
㝧ᛶ
⑓Ẽ
䠄↓䠅
᳨ᰝ
㝜ᛶ
⑓Ẽ
䠄᭷䠅
᳨ᰝ
㝜ᛶ
⑓Ẽ
䠄↓䠅

5. デ᩿⢭ᗘ◊✲䛾䛚䛥䜙䛔
• デ᩿⢭ᗘ◊✲䛿䚸ᇶᮏⓗ䛻䜽䝻䝇䝉䜽䝅䝵䝘䝹◊✲䚹ཧຍ⪅
䛾䝸䜽䝹䞊䝖ἲ䛷Single-­‐Gateᆺ䛸Two-­‐Gateᆺ䛻ศ䛡䜙䜜䜛䚹
䝍䞊䝀䝑䝖≧ἣ䞉⑌ᝈ䛾ྵ䜎䜜䜛㞟ᅋ
䠄㐃⥆䜿䞊䝇䝅䝸䞊䝈䠅
• Two-­‐Gateᆺ◊✲䛿䚸 Gate
Two-­‐Gateᆺ
(䜿䞊䝇䝁䞁䝖䝻䞊䝹ᆺ)
ᣦᶆ᳨ᰝ
ཧ↷ᇶ‽
Single-­‐Gateᆺ
(䝁䝩䞊䝖ᆺ)
䝍䞊䝀䝑䝖⑌
ᝈ䛒䜚
䝍䞊䝀䝑䝖⑌
ᝈ䛺䛧䠄೺ᗣ
ᑐ↷⪅䜒䠅
ᣦᶆ᳨ᰝ
ᣦᶆ᳨ᰝ
Gate
1
1 Gate
2
⑌ᝈ䛒䜚䛸䛺䛧䛜
䛿䛳䛝䜚䛩䜛䛾䛷䚸
デ᩿⢭ᗘ䛜㐣኱ホ
౯䛥䜜䜛䛸䛔䛖䝞䜲
䜰䝇䛜⏕䛨䜛䚹

6. デ᩿⢭ᗘ◊✲䛾䝯䝍ศᯒ䛾ᐇ᪋ᡭ㡰
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011
䚷୍㒊ຍ➹
I • 䝺䝡䝳䞊䛾␲ၥ䛾ᐃᘧ໬
II • 䝥䝻䝖䝁䝹సᡂ䠄㐺᱁䞉㝖እᇶ‽䚸᳨⣴䚸ゎᯒ᪉ἲ䛾Ỵᐃ䠅
III • ໟᣓⓗ䛺ᩥ⊩᳨⣴䛾ᐇ᪋
IV • ◊✲䛾㑅ᢥ
V • 䝺䝡䝳䞊䛻ྵ䜑䜛◊✲䛾㉁䛾ᢈุⓗホ౯
VI • 䝕䞊䝍ᢳฟ
VII • 䝕䞊䝍⤫ྜ
VIII • ⤖ᯝ䛾ゎ㔘
䝍䜲䝖䝹Ⓩ㘓
䝥䝖䝻䝁䝹ฟ∧

7. I.䝺䝡䝳䞊䛾␲ၥ䛾ᐃᘧ໬:◊✲┠ⓗ䜢᫂☜䛻䛩䜛
• PECO䜢౑䛳䛶ᐃᘧ໬䛧䛶䜒Ⰻ䛔䛜PIRATE䛜ศ䛛䜚䜔䛩䛔䚹
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011
Popula'on
䝺䝡䝳䞊䛷㛵ᚰ䛾䛒䜛ẕ㞟ᅋ䛿䛺䛻䛛䠛䛹䛖䛔䛖≧ែ
䛾ᝈ⪅䛛䠛
Index
test
䝺䝡䝳䞊䛷㛵ᚰ䛾䛒䜛ᣦᶆ᳨ᰝ䛿䛺䛻䛛䠛
Reference
test
ᣦᶆ᳨ᰝ䛾᳨ウ䛻⏝䛔䛶䛔䜛ཧ↷᳨ᰝ䛿ఱ䛛䠛ఱ䛜
⌧ᅾ䛾䛸䛣䜝᭱ၿ䛾᳨ᰝ䛛䠛
Accuracy
methods
デ᩿⢭ᗘ䛿䛹䛾䜘䛖䛺ᣦᶆ䜢⏝䛔䛶䛔䜛䛛䠛᥎ዡ䛥䜜
䛶䛔䜛䛾䛿䚸ឤᗘ䚸≉␗ᗘ䚸ᑬᗘẚ䚸ⓗ୰⋡䛺䛹
Test
cut
off
point
䝕䞊䝍䛿䛹䛾䜘䛖䛻஧್໬䛥䜜䛶䛔䜛䛛䠛ᣦᶆ䞉ཧ↷᳨
ᰝ䛻䛚䛡䜛㝜ᛶ䞉㝧ᛶ䜢䛝䜑䜛䜹䝑䝖䜸䝣䜢᫂☜䛻䛩䜛䚹
Expected
test
use
ᣦᶆ᳨ᰝ䛾ᙺ๭䛸䛧䛶ᮇᚅ䛥䜜䜛䛾䛿ఱ䛛䠛ཧ↷᳨ᰝ
䛾௦᭰䠛㏣ຍ᳨ᰝ䠛䝇䜽䝸䞊䝙䞁䜾䠛

8. ᪂つ᳨ᰝ䛷⪃䛘䜙䜜䜛ᙺ๭
⌧ᅾ䛾≧ἣ
ẕ㞟ᅋ ẕ㞟ᅋ
䝇䜽䝸䞊䝙
䞁䜾᳨ᰝ
᪤Ꮡ᳨ᰝ
⨨᥮䛘
(Replacement)
䝇䜽䝸䞊䝙
䞁䜾᳨ᰝ ᪂つ᳨ᰝ
᪂つ᳨ᰝ
䝖䝸䜰䞊䝆
(triage)
ẕ㞟ᅋ
᪤Ꮡ᳨ᰝ
• ᳨ウ䛩䜛ᣦᶆ᳨ᰝ䛿⮫ᗋୖ䛾௙஦䛾
ὶ䜜䛾୰䛷䛹䛾ᙺ๭䜢ᢸ䛖䛾䛛䠛
• ᳨ウ䛩䜛ᣦᶆ᳨ᰝ䛿䚸䛹䛾䜘䛖䛻Ⰻ䛔
᳨ᰝ䛺䛾䛛䠛䠄᪩䛔䠛ṇ☜䠛Ᏻ䛔䠛䠅
㏣ຍ
(Add-­‐on)
ẕ㞟ᅋ
䝇䜽䝸䞊䝙
䞁䜾᳨ᰝ
᪤Ꮡ᳨ᰝ
᪂つ᳨ᰝ

9. II.䝥䝻䝖䝁䝹సᡂ䠄㐺᱁䞉㝖እᇶ‽䚸᳨⣴䚸ゎᯒ
᪉ἲ䛾Ỵᐃ䠅
䝁䜽䝷䞁DTA䝺䝡䝳䞊䛾◊✲䝥䝻䝖䝁䝹సᡂ䛻䛚䛡䜛ᚲ㡲グ㍕஦㡯
◊✲
᝟ሗ
◊✲䛾䝍䜲䝖䝹䚸ⴭ⪅᝟ሗ䛺䛹䚸᪥௜᝟ሗ(᭱⤊ᨵᐃ᪥;ᩥ⊩᳨⣴᪥;
ḟ䛾䝇䝔䝑䝥䛻⛣⾜䛩䜛ணᐃ᪥;䝥䝻䝖䝁䝹ึබ㛤᪥䠅
◊✲
⫼ᬒ
ᑐ㇟䛸䛺䜛⑓Ẽ䚸ᣦᶆ᳨ᰝ䚸䜽䝸䝙䜹䝹䝟䝇(஦๓᳨ᰝ䚸ᣦᶆ᳨ᰝ䛾
ᙺ๭䚸௦᳨᭰ᰝ)䚸ྜ⌮ⓗ᰿ᣐ
┠ⓗ ๪ḟⓗ┠ⓗ
᪉ἲ 㐺᱁ᇶ‽䠄ཧຍ⪅䚸ᑐ㇟䛸䛺䜛⑓Ẽ䚸ᣦᶆ᳨ᰝ䚸ཧ↷ᇶ‽䚸◊✲䛾䝍
䜲䝥䠅
ᩥ⊩᳨⣴᪉ἲ䠄㟁Ꮚ䝕䞊䝍䝧䞊䝇᳨⣴ἲ䚸௚䛾䝸䝋䞊䝇䛾฼⏝䠅
䝕䞊䝍཰㞟䛸ศᯒ䠄◊✲䛾㑅ᢥ᪉ἲ䚸䝕䞊䝍䛾ᢳฟ䞉⟶⌮䚸᪉ἲㄽୖ
䛾㉁䛾ホ౯᪉ἲ䚸⤫ィⓗศᯒ䛸䝕䞊䝍⤫ྜ䚸␗㉁ᛶ䛾ㄪᰝ䚸ឤᗘศ
ᯒ䚸ሗ࿌䝞䜲䜰䝇䛾ホ౯䠅
䛭䛾௚ ㅰ㎡䚸ⴭ⪅䛾㈉⊩䚸฼ᐖ㛵ಀ䛾⏦࿌䚸Appendices䠄᳨⣴᪉␎䚸QUADAS䛾ㄪᩚ䠅
“Chapter
4:
Guide
to
the
contents
of
a
Cochrane
DiagnosQc
Test
Accuracy
Protocol.”
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2013.

10. ◊✲䛾㐺᱁䞉㝖እᇶ‽䛾సᡂ
• ௨ୗ䜢≉ᐃ䛧䚸䝥䝻䝖䝁䝹䛻グ㍕䛧䛯ୖ䛷䝺䝡䝳䞊䜢⾜
䛖䚹
① (P)ẕ㞟ᅋ䠖䛣䛾䝺䝡䝳䞊䛷㛵ᚰ䛾䛒䜛ᝈ⪅䛾ẕ㞟ᅋ䛿ఱ䛛䠛
② (I)ᣦᶆ᳨ᰝ䠖䛣䛾䝺䝡䝳䞊䛷ホ౯䛧䛯䛔᳨ᰝ䛿ఱ䛛䠛
③ (R)ཧ↷ᇶ‽:⌧ᅾ౑䛘䜛᭱䜒Ⰻ䛔᳨ᰝ䛿ఱ䛛䠛
④ (A)⢭ᗘ䛾ᣦᶆ:
᳨ᰝ䛾⢭ᗘ䛿䛹䛾䜘䛖䛻 ᐃ䛥䜜䛶䛔䜛䛛䠛
⑤ (T)䜹䝑䝖䜸䝣䝫䜲䞁䝖:᳨ᰝ⤖ᯝ䜢䠎್໬䛩䜛᫬䛾䜹䝑䝖䜸䝣䛿䠛
⑥ (E)ᮇᚅ䛥䜜䜛᳨ᰝ䛾ᙺ๭:ᣦᶆ᳨ᰝ䛾ᮇᚅ䛥䜜䜛ᙺ๭䛿䠛
⑦ ◊✲䛾䝍䜲䝥䠖䝺䝡䝳䞊䛻ྵ䜑䜛デ᩿⢭ᗘ◊✲䛾䝕䝄䜲䞁䛿䛹
䛖䛔䛖䜒䛾䛛䠛
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011

11. 㐺᱁ᇶ‽䛾グ㍕౛
Inclusion
criteria
for
the
primary
studies
were
as
follows:
(i)
parQcipantsᑐ㇟⪅䚸ཧ↷ᇶ‽:
all
cases
must
have
been
diagnosed
by
a
gold
standard
(pathologic
examinaQons
of
biopsied
specimens),
serum
must
have
been
collected
for
anQ-­‐p53
analysis
before
any
treatment,
e.g.
chemotherapy
or
radiotherapy,
and
controls
were
without
other
cancers,
(ii)
index
test䠘ᣦᶆ᳨ᰝ䠚:
studies
evaluated
the
diagnosQc
value
of
s-­‐p53
anQbody
in
esophageal
cancer,
(iii)
outcome䠘⢭ᗘ䛾ᣦᶆ䠚:
studies
reported
the
posiQve
values
of
the
cases
and
controls,
and
the
results
of
an
individual
study
on
diagnosQc
accuracy
can
be
summarized
in
a
2×2
table,
(iv)
study
design䠘◊✲䝕䝄䜲䞁䠚:
No
restricQons
were
made
with
respect
to
study
design
(cross
secQonal,
case
control,
corhort
study)
or
data
collecQon
(prospecQve
or
retrospecQve).
Zhang
et
al.
2012,
Plos
One䠄㣗㐨䛜䜣䛻ᑐ䛩䜛⾑Ύ㻌p53
ᢠయ᳨ᰝ䛾デ᩿⢭ᗘ䠅

12. ᩥ⊩᳨⣴᪉ἲ䛸ゎᯒ᪉ἲ䜢஦๓䛻Ỵ䜑䜛
• ᳨⣴䛩䜛㟁Ꮚ䝕䞊䝍䝧䞊䝇䜔䛭䛾௚䛾᝟ሗ※䚸䛥䜙
䛻᳨⣴䛻⏝䛔䛯᪉␎䛻䛴䛔䛶䜒䝥䝻䝖䝁䝹䛾ẁ㝵䛷
᫂グ䛩䜛䠄᳨⣴᪉␎䛿௜㘓䛸䛧䛶ῧ௜䛩䜛䠅䚹
• ゎᯒ᪉ἲ䛻䛴䛔䛶䜒䚸䝥䝻䝖䝁䝹䛾ẁ㝵䛷Ỵ䜑䛶䛚
䛟䚹≉䛻䚸␗㉁ᛶ䜈䛾ᑐฎ䛸䛧䛶䝃䝤䜾䝹䞊䝥ゎᯒ
䛜⪃䛘䜙䜜䜛ሙྜ䛿䚸ண䜑ᐇ᪋䛩䜛䝃䝤䜾䝹䞊䝥
ゎᯒ䜒Ỵ䜑䛶䛚䛝䚸ᚋ䛷᥈⣴ⓗゎᯒ䜢䛧䛺䛔䚹
“Chapter
4:
Guide
to
the
contents
of
a
Cochrane
DiagnosQc
Test
Accuracy
Protocol.”
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2013.

13. III.ໟᣓⓗ䛺ᩥ⊩᳨⣴䛾ᐇ᪋
• ⣔⤫ⓗ䝺䝡䝳䞊䛿䠈㟁Ꮚ໬䛥䜜䛯䝕䞊䝍䝧䞊䝇䜒䛧䛟
䛿௚䛾䝋䞊䝇䛛䜙ྍ⬟䛺㝈䜚඲䛶䛾䜶䝡䝕䞁䝇䜢ྵ
䜐䜉䛝(≉ᐃ䛾䝕䞊䝍䝧䞊䝇䛻೫䜛䛣䛸䛺䛟ධᡭྍ⬟
䛺඲䛶䛾᝟ሗ䜢㞟䜑䜛䠅
• MEDLINE䛚䜘䜃EMBASE䛾୧䝕䞊䝍䝧䞊䝇䜢฼⏝䛧
䛯ሙྜ䛻䛿ໟᣓᛶ䛾㧗䛔ᩥ⊩᳨⣴䛜ྍ⬟䛸䛺䜛䛯
䜑䚸䠎䛴䛾䝕䞊䝍䝧䞊䝇䛾฼⏝䛜᥎ዡ䛥䜜䜛䚹
• ⅊Ⰽᩥ⊩䜔Ꮫ఩ㄽᩥ䛺䛹䛾᳨⣴䞉཰㞟䚸㟁Ꮚ໬䛥
䜜䛶䛔䛺䛔ᩥ⊩䜢᥈䛩䝝䞁䝗䝃䞊䝏䚸ཧ⪃ᩥ⊩䝸䝇
䝖䜢ཧ↷䛧䛯ᩥ⊩཰㞟䛺䛹䜢⾜䛖䚹
“Chapter
10:
Guidelines
for
conducQng
systemaQc
reviews
of
studies
evaluaQng
the
accuracy
of
diagnosQc
tests”
䛄The
Evidence
Base
of
Clinical
Diagnosis䛅䚷Wiley-­‐Blackwell,
2009
“Chapter
7:
Searching
for
studies.”
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2013.

14. III.ໟᣓⓗ䛺ᩥ⊩᳨⣴䛾ᐇ᪋
• ᩥ⊩᳨⣴䛿෌⌧ྍ⬟䛺䜘䛖䛻グ㍕䛩䜛ᚲせ
䛜䛒䜛䠄ண䜑䝥䝻䝖䝁䝹䛻グ㍕䛩䜛䠅䚹
• ᳨⣴᪉␎䛻䛿௨ୗ䜢グ㍕䛩䜛
䐟฼⏝䛧䛯䝕䞊䝍䝧䞊䝇䜎䛯䛿᝟ሗ※(䛭䜜䜙
䛜䜹䝞䞊䛩䜛ᩥ⊩䛾ᖺ௦⠊ᅖ)䚸䐠⏝䛔䛯᳨⣴
䝽䞊䝗䜎䛯䛿ᩥ⊩཰㞟䛾᪉ἲ(䝝䞁䝗䝃䞊䝏䛚
䜘䜃䛭䛾௚䛾᳨⣴᪉ἲ)䚸䐡᳨⣴䜢⾜䛳䛯᪥᫬
“Chapter
7:
Searching
for
studies.”
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2013.

15. ᩥ⊩᳨⣴᪉␎䛾グ㍕౛
䠘౑⏝䛧䛯䝕䞊䝍䝧䞊䝇䠚The
following
databases
were
searched
without
the
use
of
Qme
limitaQons:
PubMed,
Ovid,
EMBASE,
the
Cochrane
Library,
the
Chinese
NaQonal
Knowledge
Infrastructure
(CNKI)
and
the
Chinese
Biology
Medicine
disc
(CBMdisc).
䠘᳨⣴᪉␎䠚㻌The
search
strategy
to
idenQfy
all
relevant
arQcles
involved
the
use
of
the
following
key
words:
FIB-­‐4,
aspartate
aminotransferase,
AST,
alanine
amino-­‐
transferase,
ALT,
platelet,
PLT,
hepaQQs
B,
fibrosis
and
cirrhosis.
䠘᳨⣴᪉␎䛾ヲ⣽䛿௜㘓䛻グ㍕䠚
For
example,
File
S1
and
S2
displayed
the
search
strategy
of
Ovid
and
PubMed
respecQvely.
䠘䛭䛾௚䠚AddiQonal
studies
were
idenQfied
via
a
manual
review
of
the
reference
lists
of
idenQfied
studies
and
review
arQcles.
䠘᳨⣴䛧䛯᫬
ᮇ䠚This
literature
search
was
performed
in
November
2013.
Yuanyuan
et
al.
2014,
Plos
One䠄B型肝炎による肝線維化に対するFIB-‐‑‒4 Indexの診断精度度䠅

16. IV.◊✲䛾㑅ᢥ㻌
• ᳨⣴䛧䛯ᩥ⊩䛻䛴䛔䛶䚸(1)䝍䜲䝖䝹䠄䜰䝤䝇䝖䠅
䛻䜘䜛䝇䜽䝸䞊䝙䞁䜾䠈(2)඲ᩥ䛻䜘䜛㐺᱁ᛶ
ホ౯䜢⾜䛔䚸䝺䝡䝳䞊䛻ྵ䜑䜛䛛㑅ᢥ䛩䜛ᚲ
せ䛜䛒䜛䚹
• ◊✲䛾㑅ᢥ㐣⛬䛷䛿䚸䠎ྡ䛾ホ౯⪅䛜㑅ᢥ
䜢⾜䛖䚹䛣䛾㐣⛬䛿䚸➨䠏⪅䛜෌⌧䛷䛝䜛䛠䜙
䛔᫂♧ⓗ䛻⾜䛖䚹
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011

17. IV.◊✲䛾㑅ᢥ㻌
• PRISMAኌ᫂䛾䝣
䝻䞊䝎䜲䜰䜾䝷䝮䜢
౑䛖䛣䛸䛷䚸◊✲䛾
㑅ᢥ㐣⛬䛷䛾ᩥ⊩
ᩘ䜔㝖እ⌮⏤䛺䛹
䜢䜎䛸䜑䜛䚹
Moher
et
al.
(2009).
Preferred
reporQng
items
for
systemaQc
reviews
and
meta-­‐
analyses:
the
PRISMA
statement.
PLoS
Medicine,
6(7),
e1000097.
䝕䞊䝍䝧䞊䝇᳨⣴䛛
䜙ᚓ䜙䜜䛯ᩥ⊩ᩘ
௚䛾䝋䞊䝇䛛䜙ᚓ䜙
䜜䛯㏣ຍⓗ䛺ᩥ⊩ᩘ
㔜」ᩥ⊩๐㝖ᚋ䛾ᩥ⊩ᩘ
㝖እ䛧䛯
ᩥ⊩ᩘ
㝖እ䛧䛯඲ᩥ
ᩥ⊩ᩘ䛸⌮⏤
䝇䜽䝸䞊䝙䞁䜾䛧䛯ᩥ⊩ᩘ
㐺᱁ᛶ䜢ホ౯䛧䛯඲ᩥᩥ⊩
ᩘ
㉁ⓗ⤫ྜ䛻ྵ䜑䛯ᩥ⊩ᩘ
㔞ⓗ⤫ྜ䠄䝯䝍䜰䝘䝸䝅䝇䠅
䛻ྵ䜑䛯ᩥ⊩ᩘ
特
定
吐吆呁䞊
吢呉吇
適
格
性
研
究
双含
叢叀口
叏

18. ◊✲䛾㑅ᢥ㐣
⛬䛾グ㍕౛
䠘ホ౯⪅䠎ྡ䛜⊂❧䛻ホ౯䠚
Two
reviewers
(J
Zhang
and
ZW
Xv)
independently
inspected
the
Qtle
and
abstract
of
each
citaQon
to
idenQfy
those
studies
that
were
likely
to
report
the
diagnosQc
value
of
serum
p53
(s-­‐p53)
anQbody
and
then
obtained
the
full
text.
䠘ホ౯
୙୍⮴᫬䛾ᑐฎ䠚Disagreements
about
study
selecQon
were
resolved
by
consensus.
䠘䝍䜲䝖䝹䛸
䜰䝤䝇䝖䛷㞴䛧䛔ሙྜ䛿඲ᩥ䜢
䝏䜵䝑䜽䠚The
full
text
was
retrieved
for
arQcles
that
could
not
be
excluded
based
on
Qtle
and
abstract
to
determine
inclusion.(㐺
᱁ᛶ䛾ホ౯䜒ྠᵝ䛻ᐇ᪋䠅
Zhang
et
al.
2012,
Plos
One䠄㣗㐨䛜䜣䛻ᑐ䛩䜛⾑Ύ㻌p53
ᢠయ᳨ᰝ䛾デ᩿⢭ᗘ䠅
Figure 1. Flow chart of study selection by using electronic database and other sources.
doi:10.1371/journal.pone.0052896.g001

19. V.䝺䝡䝳䞊䛻ྵ䜑䜛◊✲䛾㉁䛾ᢈุ
ⓗホ౯
• 䠎ྡ䛾◊✲⪅䛜⊂❧䛻䠈㑅䜣䛰ㄽᩥ䛾᪉ἲ
ㄽୖ䛾㉁䜢ホ౯䛩䜛䚹
• デ᩿⢭ᗘ◊✲䛾㉁䛾ホ౯䛻䛿QUADAS䜢⏝
䛔䜛䚹
䠆2014/10/25䛾ẁ㝵䛾䛄Cochrane
Handbook
for
Systema4c
Reviews
of
Diagnos4c
Test
Accuracy
Version
1.0.0.
䛅䛾”
Chapter
9:
Assessing
methodological
quality
”䛷
䛿䚸QUDAS䛾11㡯┠∧䛜᥎ዡ䛥䜜䛶䛔䜛䛜䚸௒ᚋ
QUADAS-­‐2䛻⛣⾜䛩䜛䛸⪃䛘䜙䜜䜛䠄᭱᪂䛾RevMan䛷䛿䛩
䛷䛻QUADAS-­‐2䛜ᐇ⿦䛥䜜䛶䛔䜛䠅䚹
“Chapter
10:
Guidelines
for
conducQng
systemaQc
reviews
of
studies
evaluaQng
the
accuracy
of
diagnosQc
tests”
䛄The
Evidence
Base
of
Clinical
Diagnosis䛅䚷Wiley-­‐Blackwell,
2009
WhiQng
et
al.
(2011).
QUADAS-­‐2:
a
revised
tool
for
the
quality
assessment
of
diagnosQc
accuracy
studies.
Annals
of
Internal
Medicine,
155(8),
529–36.

20. QUADAS-­‐2
䝇䝔䝑䝥1
• 䝺䝡䝳䞊䜽䜶䝇䝏䝵䞁䛾᫂☜໬(᝿ᐃẕ㞟ᅋ䚸ᣦᶆ᳨ᰝ䚸ཧ
↷ᇶ‽䜔ᑐ㇟䛸䛺䜛⑕≧䛾᫂☜䛺ᐃ⩏)
䝇䝔䝑䝥2
• 䝺䝡䝳䞊䛾┠ⓗ䛻≉໬䛧䛶ㄪᩚ䛩䜛(┠ⓗ䛻ἢ䛳䛶䚸㡯┠䛾
㏣ຍ䞉๐㝖䜔᥇Ⅼ᪉ᘧ䛻䛴䛔䛶ㄪᩚ䜢⾜䛖)
䝇䝔䝑䝥3
• 䝣䝻䞊䝎䜲䜰䜾䝷䝮䛾సᡂ(୍ḟ◊✲䛜䛹䛾䜘䛖䛻ཧຍ⪅䜢
㞟䜑䚸ᣦᶆ᳨ᰝ䛚䜘䜃ཧ↷ᇶ‽䜢᪋⾜䞉ゎ㔘䛧䛯䛾䛛䝣
䝻䞊䝎䜲䜰䜾䝷䝮䜢సᡂ䛩䜛)
• 䝞䜲䜰䝇䛚䜘䜃㐺ᛂྍ⬟ᛶ䛾ホ౯(QUADAS-­‐2䜢⏝䛔䛶ྛ୍
ḟ◊✲䛾䝞䜲䜰䝇䛚䜘䜃㐺⏝ྍ⬟ᛶ䛻䛴䛔䛶ホ౯䜢⾜䛖)
• 䝇䝔䝑䝥䠎䛻䛚䛔䛶䚸᪤Ꮡ䛾QUADAS-­‐2㡯┠䛻ಟṇ䜢
ຍ䛘䛯ሙྜ䛿䝥䝻䝖䝁䝹䛻グ㍕䛩䜛䚹
䝇䝔䝑䝥4

21. QUADAS-­‐2
• QUADAS-­‐2䛿䚸䠐㡿ᇦ䛻ศ䛡䜙䜜䚸඲11㡯┠䛛䜙ᵓᡂ䛥䜜䜛䚹
ྛ㡯┠䛻䛴䛔䛶䚸䛂䛿䛔/䛔䛔䛘/୙᫂䛃䛷ᅇ⟅䛧䚸ྛ㡿ᇦ䛤䛸䛻
䝞䜲䜰䝇䛾ྍ⬟ᛶ(䝸䝇䜽:ప/㧗/୙᫂)䛸䝺䝡䝳䞊䜽䜶䝇䝏䝵䞁䜈
䛾㐺⏝ྍ⬟ᛶ䠄ྜ⮴䛧䛺䛔ᠱᛕ:ప/㧗/୙᫂䠅䜒ᅇ⟅䛩䜛䚹
㡿ᇦ QUADAS-­‐2䛾㡯┠
ཧຍ⪅㑅
ᢥ
䞉ཧຍ⪅䛿㐃⥆䛒䜛䛔䛿䝷䞁䝎䝮䛻䝃䞁䝥䝸䞁䜾䛥䜜䛯䛛
䞉䜿䞊䝇䞉䝁䞁䝖䝻䞊䝹ᆺ◊✲䛷䛿䛺䛔䛛䚹
䞉୙㐺ษ䛺䝕䞊䝍䛾㝖እ䜢⾜䛳䛶䛔䛺䛔䛛䚹
ᣦᶆ᳨ᰝ 䞉ᣦᶆ᳨ᰝ䛾⤖ᯝ䛿ཧ↷ᇶ‽䛾⤖ᯝ䜢▱䜙䛺䛔≧ែ䛷ゎ㔘䛥䜜䛯䛛䚹
䞉㜈್䛜⏝䛔䜙䜜䛯ሙྜ䚸䛭䛾㜈್䛿஦๓䛻ᐃ⩏䛥䜜䛶䛔䛯䛛䚹
ཧ↷ᇶ‽ 䞉ཧ↷ᇶ‽䛿䝍䞊䝀䝑䝖⑕≧䜢ṇ䛧䛟ศ㢮䛧䛶䛔䜛䛸௬ᐃ䛥䜜䜛䛛䚹
䞉ཧ↷ᇶ‽䛾⤖ᯝ䛿ᣦᶆ᳨ᰝ䛾⤖ᯝ䜢▱䜙䛺䛔≧ែ䛷ゎ㔘䛥䜜䛯䛛䚹
䝣䝻䞊䛸䝍
䜲䝭䞁䜾
䞉ᣦᶆ᳨ᰝཬ䜃ཧ↷ᇶ‽䛾㛫䛻㐺ษ䛺ᮇ㛫䛜Ꮡᅾ䛧䛯䛛䚹
䞉඲䛶䛾ཧຍ⪅䛻ᑐ䛧ཧ↷ᇶ‽䜢᪋⾜䛧䛯䛛䚹
䞉඲䛶䛾ཧຍ⪅䛜ྠ୍䛾ཧ↷ᇶ‽䛷ศ㢮䛥䜜䛯䛛䚹
䞉඲䛶䛾ཧຍ⪅䛜ゎᯒ䛻ྵ䜎䜜䛶䛔䜛

22. Zhu[29], 2012, China 159 (71%) 42 (18,62) unclear METAVIR $15 mm unclear
Ucar[18], 2013, Turkey 73 (64%) 42.81612.86 unclear METAVIR unclear Yes
Gong[21], 2013, China 41 (73%) 50.8610.3 unclear METAVIR unclear unclear
Wang[20], 2013, China 231 (68%) 34.169.8 ,1d Scheuer .15 mm Yes
Ji[17], 2011, China 313 (69%) 35.6611.2 1d METAVIR 20 mm unclear
Bas¸ar[25], 2013, Turkey 76 (55%) unclear ,1d METAVIR .10 mm Yes
Bonnard[19], 2010, France 59 (68%) 3569 0.5–10 m METAVIR 2166 mm Yes
Erdogan[11], 2013, Turkey 221 (63%) 43.68612.56 #1d Ishak unclear Yes
Wu[30], 2010, China 78 (85%) 32.6612.3 unclear METAVIR .15 mm unclear
Mallet[16], 2009, France 138 (71%) 42615 ,1d METAVIR 17.666.8 unclear
Seto[24], 2011, China 237 (68%) 38.2 (18,63) same time Ishak $15 mm Yes
Zhu[27], 2011, China 175 (78%) 36.569.4 #7d METAVIR .15 mm Yes
Liu[23], 2012, China 114 (80%) 38.32611.36 same time METAVIR 15,20 mm unclear
Wang[26], 2013, China 149 (93%) 37 (30,42) #2d Scheuer .10 mm Yes
Xun[28], 2013, China 197 (76%) 31 (21–45) same time Scheuer .15 mm unclear
Zhang[32], 2009, China 86 (60%) 39 (16–64) ,1d METAVIR 15,20 mm unclear
Zhang[22], 2012, China 361 (62%) 36611 #7d Scheuer unclear unclear
Zhang[31], 2010, China 212 (88%) 3167 1day Scheuer 20 mm Yes
グ㍕౛䠖QUADS-­‐2䜢⏝䛔䛯୍ḟ◊✲䛾㉁
䛾ホ౯䠄䝥䝻䝖䝁䝹䛸ㄽᩥグ㍕౛䠅
䝥䝻䝖䝁䝹䛷䛾㉁䛾ホ౯䛻㛵䛩䜛グ㍕౛䠖Two
review
authors
(JFC,
MC)
will
independently
assess
the
methodological
quality
of
each
study
using
a
four
domain
tool
adapted
from
QUADAS-­‐2
(WhiQng
2011a).
ኚ᭦⟠ᡤ䜒ㄝ᫂We
tailored
the
quality
assessment
tool
to
our
review
quesQon.
Cohen
et
al.,
(2013)
Cochrane
Protocol䚷
䠄Ꮚ䛹䜒䛾ဗ㢌⅖䛻䛚䛡䜛A⩌䝺䞁䝃⌫
Literature and search strategy
QUADAS-­‐2䛾せ⣙䛾グ㍕౛䠚
⳦䜢᳨ฟ䛩䜛ᛴ㏿ᢠయ᳨ᰝ䛾⢭ᗘ䠅
←⾲䛷せ⣙
Mohamed
et
al.,
CMAJ,
2014
ᅗ䛷せ⣙→
Yuanyuan
et
al.,
Plos
one,
2014
The following databases were searched without the use of time
limitations: PubMed, Ovid, EMBASE, the Cochrane Library, the
Chinese National Knowledge Infrastructure (CNKI) and the
Chinese Biology Medicine disc (CBMdisc). The search strategy to
identify all relevant articles involved the use of the following key
words: FIB-4, aspartate aminotransferase, AST, alanine amino-transferase,
ALT, platelet, PLT, hepatitis B, fibrosis and cirrhosis.
For example, File S1 and S2 displayed the search strategy of Ovid
and PubMed respectively. Additional studies were identified via a
manual review of the reference lists of identified studies and review
articles. This literature search was performed in November 2013.
Inclusion criteria
Studies were deemed eligible if they met the following inclusion
criteria: 1) the study evaluated the performance of the FIB-4 index
for the diagnosis of fibrosis in mono-HBV-infected patients before
antiviral therapy. Studies including patients with other causes of
liver disease were included if data of HBV-infected patients could
be extracted. 2) Liver biopsy was used as the reference standard for
assessing fibrosis. METAVIR [8] or comparable staging systems
doi:10.1371/journal.pone.0105728.t001

23. VI.
䝕䞊䝍ᢳฟ
• ᳨⣴䛧䛯ᩥ⊩䛛䜙ᚲせ᝟ሗ䜢ᢤ䛝ฟ䛩సᴗ䛻
䛚䛔䛶䝭䝇䛜㉳䛣䜛ྍ⬟ᛶ䛜䛒䜛䚹௨ୗ䛾䜘䛖
䛺ᡭẁ䜢ㅮ䛨䜛ᚲせ䛜䛒䜛
①ᶆ‽໬䛧䛯䝕䞊䝍ᢳฟἲ䛾฼⏝
②䝺䝡䝳䞊䛾㛤ጞ๓䛻ᢳฟ䛾䝟䜲䝻䝑䝖䝔䝇䝖
③䝕䞊䝍ᢳฟ䛾カ⦎䛸ホ౯
④ಶ䚻䛾◊✲䛛䜙䛾䝕䞊䝍ᢳฟ䛿䠎ྡ䛷ᐇ᪋
• ᝟ሗ䛜㊊䜚䛺䛔ሙྜ䛿䚸ⴭ⪅䛻ၥ䛔ྜ䜟䛫䜛䚹
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011

24. 䝕䞊䝍ᢳฟ᪉ἲ䛾グ㍕౛
䠘ホ౯⪅䠎ྡ䛜⊂❧䛻ᢳฟTwo
invesQgators
(Zi
Chen
and
Hong-­‐bing
Liu)
extracted
the
following
data
from
independently
the
selected
studies:
ᢳฟ䛧䛯᝟ሗ10ಶ(1)
year
of
publicaQon;
(2)
locaQon
of
the
study;
(3)
number
of
tumor
Qssue
or
cytology
specimens;
(4)
IHC
methodology䠄௨
ୗ┬␎䠅.
䠘᝿ᐃ䛥䜜䜛␗㉁ᛶ䛾䛯䜑䛻䛥䜙䛻ᢳฟ䛧䛯᝟ሗ䠚In
addiQon,
for
an
accurate
evaluaQon
of
heterogeneity,
the
following
characterisQcs
of
study
design
were
retrieved:
(1)
whether
the
study
was
double-­‐blind
regarding
the
results
of
the
immunohistochemical
method
and
the
results
of
the
molecule-­‐based
analysis䠄௨ୗ┬␎䠅.䠘ホ౯୙୍⮴᫬䛾ᑐฎ
䠚Disagreements
were
resolved
by
discussion
between
Zi
Chen
and
Hong-­‐bing
Liu.
Chen
et
al.
2014,
Plos
One䠄㠀ᑠ⣽⬊ᛶ⫵䛜䜣䛻䛚䛡䜛EGFR㑇ఏᏊኚ␗᳨ᰝ䛾デ᩿⢭ᗘ䠅

25. VII.
䝕䞊䝍⤫ྜ
• 䝯䝍ศᯒ䛾䠒䛴䛾䝇䝔䝑䝥(Deville
et
al.,
2002,
BMC
Med
Res
Methodol,
2,
9)
1 • ಶ䚻䛾◊✲⤖ᯝ䜢࿊♧
2 • ␗㉁ᛶ䛾᭷↓䜢᳨ウ
3 • 㜈್ຠᯝ䛾᭷↓䜢᳨ウ
4 • ␗㉁ᛶ䜈䛾ᑐฎ
5 • 㔞ⓗ⤫ྜ䛜㐺ษ䛺䜙䚸౑⏝䛩䜛䝰䝕䝹䜢Ỵᐃ
6 • 㔞ⓗ⤫ྜ

26. ①ಶ䚻䛾◊✲⤖ᯝ䜢࿊♧:forest
plot
• ୍ḟ◊✲䛾ⴭ⪅ྡ䚸Ⓨหᖺ䚸┿㝧ᛶ䜔ഇ㝧ᛶ䛺䛹䛾4䛴
䛾ᣦᶆ䚸ឤᗘ䞉≉␗ᗘ䛺䛹䛻䛴䛔䛶グ㍕䛧䚸ឤᗘ䛸≉␗
ᗘ䛸䛭䛾ಙ㢗༊㛫䠄䜒䛧䛟䛿ᶆ‽ㄗᕪ䠅䜢䝥䝻䝑䝖䛧䛯䜒䛾䚹
• forest
plot䛻䛿ឤᗘ䛸≉␗ᗘ䛾䠎䛴䛾ᣦᶆ䛜䝥䝻䝑䝖䛥䜜
䜛䛣䛸䛛䜙䚸coupled
forest
plot䛸䛔䛖ゝ䛔᪉䜢䛩䜛䛣䛸䜒
䛒䜛䚹
Meta-Analysis of the FIB-4 Index
Li
et
al.,
(2014),
PloS
One

27. ①ಶ䚻䛾◊✲⤖ᯝ䜢࿊♧:
sROC
plot
• ROCᖹ㠃䛻୍ḟ◊✲䛾ឤᗘ䞉≉␗ᗘ䜢䝥䝻䝑䝖䛧䛯䜒
䛾(RevMan䛾ሙྜ䚸ᅄゅ䛷䝥䝻䝑䝖䛥䜜䚸⦪䛾㛗䛥䛜ឤᗘ䛾⢭ᗘ䚸ᶓ䛾
㛗䛥䛜≉␗ᗘ䛾⢭ᗘ䜢⾲䛧䚸኱䛝䛔䜋䛹⢭ᗘ䛜㧗䛔䛣䛸䜢⾲䛩䠅
• ⤫ィ䝰䝕䝹䛻ᇶ䛵䛔䛯sROC᭤⥺
䜢グ㍕䛩䜛䛣䛸䜒ከ䛔䚹
• ᳨ᰝ㛫ẚ㍑䜢⾜䛖ሙྜ䚸ᙧ䜔Ⰽ䛷
䛭䜜䛮䜜䛾᳨ᰝ䛾ឤᗘ䞉≉␗ᗘ䜢
䝥䝻䝑䝖䛧䚸ྠ୍䛾◊✲䛛䜙ᚓ䜙䜜
䛯◊✲䜢⥺䛷⤖䜣䛰Linked
ROC
plot䜢౑⏝䛩䜛䛣䛸䜒䛒䜛䚹
“
Chapter
10:
Analysing
and
PresenQng
Results.”
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2010.

28. ②␗㉁ᛶ䛾᭷↓䜢᳨ウ
• ୍ḟ◊✲䛾⤖ᯝ䛿䚸ᑐ㇟㞟ᅋ䚸᳨ᰝ䛾ᐇ᪋᪉ἲ䚸᳨
ᰝ⤖ᯝ䛾ゎ㔘䚸ཧ↷ᇶ‽䛾✀㢮䚸᪉ἲୖ䛾䝞䜲䜰䝇
䛾㐪䛔䜔㜈್䛾㐪䛔䛻䜘䛳䛶␗䛺䜛䚹
→デ᩿⢭ᗘ◊✲䛾䝯䝍ศᯒ䛷䛿䚸␗㉁ᛶ䛜⏕䛨䜔䛩䛔
(Willis
Quigley,
2011,
BMC
Medical
Research
Methodology䠖236䛾デ᩿⢭ᗘ䛾
䝯䝍ศᯒ䜢ㄪᰝ䛧䛯⤖ᯝ䚸70%䛾◊✲䛷␗㉁ᛶ䛜ሗ࿌)
• ␗㉁ᛶ䛿䚸forest
plot䛻䜘䛳䛶どぬⓗ䛻☜ㄆ䛷䛝䜛䛜䚸
䜹䝑䝖䜸䝣䝫䜲䞁䝖䛾㐪䛔䛻䜘䛳䛶デ᩿⢭ᗘ䛜␗䛺䜛㜈
್ຠᯝ䛾᳨ウ䛜䛷䛝䛺䛔䛾䛷sROC
plot䜒ᚲせ
• ௓ධ◊✲䛷౑⏝䛥䜜䜛I2䛿㜈್ຠᯝ䜢⪃៖䛧䛶䛺䛔䛾
䛷䚸䝁䜽䝷䞁DTA䛷䛿㠀᥎ዡ
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011

29. ③㜈್ຠᯝ䛾᭷↓䜢᳨ウ
• ྑᅗ䛾䜘䛖䛻䚸㜈್ຠᯝ䛜
䛒䜛ሙྜ䚸ᖹᆒ್䛿ㄗ䛳䛯
⤖ㄽ䜢ᑟ䛟䚹
• 㜈್ຠᯝ䛾᳨ウ᪉ἲ
①୍ḟ◊✲䛾ឤᗘ䛸≉␗ᗘ
㛫䛾Spearman
䛾㡰఩┦㛵ಀ
ᩘ䜢⟬ฟ䛩䜛(Devillé
et
al.,
2002,
BMC
medical
research
methodology)
→ᙉ䛔㈇䛾┦㛵䛜䛒䜛䛸䚸㜈
್ຠᯝ䛒䜚
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
◊✲2
◊✲1
◊✲3
ᖹᆒ
0
0.2
0.4
0.6
0.8
1
ឤᗘ
≉␗ᗘ
Gatsonis
Paliwal,
2006䛾౛

30. ③㜈್ຠᯝ䛾᭷↓䜢᳨ウ
• 㜈್ຠᯝ䛾᳨ウ᪉ἲ
②SROC᭤⥺䜢ᥥ䛔䛯sROC
plot䛛䜙㜈್ຠᯝ䜢᳨ウ䛩
䜛䛣䛸䜒䛷䛝䜛䚹
→ឤᗘ䞉≉␗ᗘ䛾䝥䝻䝑䝖䛜
SROC᭤⥺䛻䝣䜱䝑䝖䛧䛶䛔䜛
ሙྜ䛿㜈್ຠᯝ䛜⏕䛨䛶䛔
䜛䛸⪃䛘䜙䜜䜛䚹
◊✲2
◊✲1
㜈್ຠᯝ䛻䜘
䜛䜀䜙䛴䛝
◊✲3
◊✲4
◊✲5
㜈್ຠᯝ௨እ䛾せᅉ䛻䜘䜛䜀䜙䛴䛝
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.2
0.4
0.6
0.8
ឤᗘ
≉␗ᗘ
• 㜈್ຠᯝ䠙SROC᭤⥺䛻ἢ䛳䛯୍ḟ◊✲㛫䛾䜀䜙䛴䛝
• 㜈್ຠᯝ௨እ䛾せᅉ䠙SROC᭤⥺䛛䜙㊥㞳䛜㐲䛟䛺䜛䜘䛖
䛺䜀䜙䛴䛝

31. ④␗㉁ᛶ䜈䛾ᑐฎ
• ᭱ึ䛻䚸␗㉁ᛶ䛾ཎᅉ䜢୍ḟ◊✲䜎䛷ᡠ䛳䛶ㄪ䜉䜛
①␗㉁ᛶ䛾ཎᅉ䛜᫂䜙䛛䛺ሙྜ䛿䚸㐺᱁䞉㝖እᇶ‽
䛾ኚ᭦or䝃䝤䜾䝹䞊䝥ゎᯒ䠄᥈⣴ⓗ䛺᳨ウ䛿㑊䛡䜛䚹
㐺᱁䞉㝖እᇶ‽䛾ኚ᭦䛿䝕䞊䝍ゎᯒ๓䛻ᐇ᪋䛧䚸䛭䛾
⌮⏤䜒ሗ࿌䛩䜛䚹䝃䝤䜾䝹䞊䝥ゎᯒ䛿䝥䝻䝖䝁䝹䛾ẁ
㝵䛷グ㍕䛧䛶䛚䛟䚹䠅
②␗㉁ᛶ䜒⪃៖䛷䛝䜛䝷䞁䝎䝮ຠᯝ䝰䝕䝹䛻䜘䜛ゎᯒ
③␗㉁ᛶ䛜ᙉ䛔ሙྜ䛿䚸䝕䞊䝍⤫ྜ䜢⾜䜟䛺䛔䠃㉁
ⓗ⤫ྜ䜢ᐇ᪋
“Chapter4:
Planning
a
systemaQc
review
of
diagnosQc
test
accuracy
evidence”,
䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅,
LippincoZ
Williams
Wilkins,
2011
“Chapter
10:
Guidelines
for
conducQng
systemaQc
reviews
of
studies
evaluaQng
the
accuracy
of
diagnosQc
tests”
䛄The
Evidence
Base
of
Clinical
Diagnosis䛅䚷Wiley-­‐Blackwell,
2009

32. ␗㉁ᛶ䛸㜈್ຠᯝ䛾᳨ウ䛸ᑐฎ䛾グ㍕౛
• 㜈್ຠᯝ䜢⪃䛘䜛䛸I2䛿᥎ዡ䛥䜜䛺䛔䛜䚸⌧≧䛾ከ䛟䛾䝯䝍ศᯒ䛷
䛿⏝䛔䜙䜜䛶䛔䜛䠄䝷䞁䝎䝮ຠᯝ䝰䝕䝹䜢⏝䛔䛶␗㉁ᛶ䛾ᣦᶆ䜒
ฟ䛫䜛䛜䚸I2䛾䜘䛖䛺ศ䛛䜚䜔䛩䛔ゎ㔘䛜䛷䛝䛺䛔䠅
In
addiQon
to
visual
assessment
with
the
use
of
the
forest
plots,
we
formally
quanQfied
the
extent
of
heterogeneity
by
calculaQng
the
inconsistency
index
(I2
staQsQcs)
.
StaQsQcally
significant
heterogeneity
was
considered
present
at
I2=50%.
Yuanyuan
et
al.
2014,
Plos
One䠄B型肝炎による肝線維化に対するFIB-‐‑‒4 Indexの診断精度度䠅
䠘㜈್ຠᯝ䛾᳨ウ䠚3-­‐tesQng
of
the
presence
of
cut-­‐off
threshold
effects.
䠘␎䠚We
can
test
for
the
presence
of
a
cut-­‐
off
point
effect
between
studies
by
calculaQng
a
Spearman
correlaQon
coefficient
between
sensiQvity
and
specificity
of
all
included
studies.
䠘␗㉁ᛶ䜈䛾ᑐฎ䠚4-­‐dealing
with
heterogeneity.
Subgroup
analysis
and
meta
regression
could
be
conducted
to
detect
the
heterogeneity
between
studies.
Zhang
et
al.
2012,
Plos
One䠄㣗㐨䛜䜣䛻ᑐ䛩䜛⾑Ύ㻌p53
ᢠయ᳨ᰝ䛾デ᩿⢭ᗘ䠅

33. ⑤㔞ⓗ⤫ྜ䛜㐺ษ䛺䜙䚸౑⏝䛩䜛䝰䝕䝹䜢Ỵᐃ䛧
䛯ୖ䛷䚸⑥㔞ⓗ⤫ྜ䜢ᐇ᪋
• ␗㉁ᛶ䛾᭷↓䛸㜈್ຠᯝ䛾᭷↓䛛䜙䚸㐺ษ䛺䝰䝕䝹㑅
ᢥ䜢⾜䛖䚹デ᩿⢭ᗘ䛾䝯䝍ศᯒ䛷䛿䚸I2䛾䜘䛖䛺౽฼䛺ᣦ
ᶆ䛜䛺䛔䛜䚸ᇶᮏⓗ䛻䛿␗㉁ᛶ䛿㧗䛔䛸⪃䛘䜛䚹どぬⓗ
䜰䝉䝇䝯䞁䝖䜢⾜䛖䛸䛸䜒䛻䚸䝷䞁䝎䝮ຠᯝ䝰䝕䝹䛸஦๓䛻
ᐃ䜑䛯䝃䝤䜾䝹䞊䝥ศᯒ䜢ᇶᮏ䛸䛩䜛䚹
␗㉁ᛶ
䛺䛔䠄ྠ㉁䠅 䛒䜛䠄␗㉁䠅
閾
値
効
果
䛺䛔
䞉ᅛᐃຠᯝ䝰䝕䝹(Simple
pooling䚸
඲䛶䛾◊✲䜢䠍䛴䛾2×2⾲䛻䜎䛸
䜑䛶せ⣙)
①䝷䞁䝎䝮ຠᯝ䝰䝕䝹䠄Bivariate䝰
䝕䝹䜔HSROC䝰䝕䝹䠅
②䝃䝤䜾䝹䞊䝥ศᯒ
③䝯䝍ศᯒ䛧䛺䛔
䛒䜛
䞉ᅛᐃຠᯝ䝰䝕䝹(Moses-­‐
LiZenbergἲ)䜢⏝䛔䛯SROC᭤⥺
“Chapter
10:
Guidelines
for
conducQng
systemaQc
reviews
of
studies
evaluaQng
the
accuracy
of
diagnosQc
tests”
䛄The
Evidence
Base
of
Clinical
Diagnosis䛅䚷Wiley-­‐Blackwell,
2009

34. Moses-­‐LiZenbergἲ䛻䜘䜛SROC᭤⥺㻌
• 㜈್ຠᯝ䜢⪃៖䛧䛯䝕䞊䝍⤫ྜ䛻䛚䛔䛶᭷⏝䛺ᅛ
ᐃຠᯝ䝰䝕䝹䠄␗㉁ᛶ䛾ᣦᶆ䛜䛺䛔,
せ⣙᥎ᐃ್䞉
95%ಙ㢗༊㛫䞉᭤⥺ୗ㠃✚䛜ṇ☜䛷䛺䛔䛺䛹䛾㝈
⏺䜒䠅
①Moses-­‐LiZenbergἲ䛷䛿䚸ಶ䚻䛾୍ḟ◊✲䛛䜙D䛸
S䜢⟬ฟ䛩䜛䚹
D
=
logit(ឤᗘ)
–
logit(1-­‐≉␗ᗘ)
=log(デ᩿䜸䝑䝈ẚ)
S
=
logit(ឤᗘ)
+
logit(1-­‐≉␗ᗘ)
=(┿㝧ᛶ䠆ഇ㝧ᛶ)/
(┿㝜ᛶ䠆ഇ㝜ᛶ)≠㜈್䛾௦⌮ⓗ䛺ᣦᶆ
Chapter
10:
Analysing
and
PresenQng
Results.
䛄Cochrane
Handbook
for
Systema4c
Reviews
of
Diagnos4c
Test
Accuracy
Version
1.0.䛅
The
Cochrane
CollaboraQon,
2010

35. Moses-­‐LiZenbergἲ䛻䜘䜛SROC᭤⥺㻌
②ồ䜑䛯D䛸S䛸௨ୗ䛾⥺ᙧᅇᖐ䝰䝕䝹
䛛䜙α䛸β䜢᥎ᐃ䛩䜛䠄D
䜢⏝䛔䛯ศᩓ㏫
ᩘ㔜䜏௜䛡ἲ䜢౑䛖஦䜒䠅䚹
䚷䚷D = α+βS +ㄗᕪ㻌
③௨ୗ䛾ᘧ䚸α䠄ษ∦䚸DOR䛾ᑐᩘ䛾ᖹ
ᆒ䠅䛸β䠄ഴ䛝䠅䜢⏝䛔䛶䚸≉␗ᗘ䛛䜙ឤ
ᗘ䜢ồ䜑䜛஦䛜䛷䛝䜛䛾䛷䚸sORC
plot
䛻SROC᭤⥺䜢ᘬ䛟஦䛜䛷䛝䜛
Chapter
10:
Analysing
and
PresenQng
Results.
1
0.8
0.6
0.4
0.2
䛄Cochrane
Handbook
for
Systema4c
Reviews
of
Diagnos4c
Test
Accuracy
Version
1.0.䛅
The
Cochrane
CollaboraQon,
2010
E(Sensitivity) =
1
1+ exp −
α +(1+β ) logit(1− Specificity)
1−β
# $
%
'
0
0
0.5
1

36. 㝵ᒙⓗ䝰䝕䝹
• ␗㉁ᛶ䛜䛒䜛ሙྜ䛿䚸䝷䞁䝎䝮ຠᯝ䝰䝕䝹䜢⏝䛔䛶
デ᩿⢭ᗘ䛾᥎ᐃ䜢⾜䛖ᚲせ䛜䛒䜛䚹
• デ᩿⢭ᗘ䛾䝷䞁䝎䝮ຠᯝ䝰䝕䝹䛸䛧䛶䛿䚸
①Bivariate
䝰䝕䝹(Reitsma
et
al.,
2005,
Journal
of
clinical
epidemiology)䚸②Hierarchical
SROC䝰䝕䝹(HSROC,
RuZer
Gatsonis,
2001,
StaQsQcs
in
Medicine)䛺䛹
䛾㝵ᒙⓗ䝰䝕䝹䛜䛒䜛䚹
→◊✲ෆኚືᛶ䛸◊✲㛫ኚືᛶ䛻䛴䛔䛶䚸Ỉ‽䜢ศ
䛡䛶ゎᯒ䜢⾜䛖䠄㝵ᒙⓗ䝰䝕䝹䠅
䠆R䜔SAS䛷䜒ゎᯒ䛷䛝䜛䛜䚸䜘䜚ᰂ㌾䛺䝰䝕䝸䞁䜾䜢
䛩䜛ሙྜ䛿䚸WinBUGS䜔Stan䜢౑⏝䛩䜛䚹

37. HSROC䝰䝕䝹
RuZer
Gatsonis,
2001,
StaQsQcs
in
Medicine
• Moses-Littenberg ἲ䛸㻌ྠ䛨䜘䛖䛻㻌α䛸β䜢᥎ᐃ䛩䜛䛾
䛻ຍ䛘䛶, 㜈್θ䜒᥎ᐃ䛷䛝䜛䚹
• 䝺䝧䝹䠍䠖䛒䜛◊✲i䛻䛚䛡䜛㞟ᅋj䠄1=⑓Ẽ䛒䜚, 2=
⑓Ẽ䛺䛧䠅䛾㝧ᛶᩘ䛜䝙㡯ศᕸ䛻ᚑ䛖
yij ~ Binomial(nij, πij)
䠆䛒䜛◊✲i䛻䛚䛡䜛㞟ᅋj䠄1=⑓Ẽ䛒䜚, 2=⑓Ẽ䛺䛧䠅䛾㝧ᛶᩘ(yij)䚸ேᩘ
(nij)䚸㝧ᛶ☜⋡㻌(πij)
logit(πij)=(θi+αi*disij)exp(-β*disij)
*disij(⑓Ẽ䛺䛧䛷-0.5䚸⑓Ẽ䛷0.5)䚹θi䛿䚸䝔䝇䝖㜈್䛾䛯䜑䛾䝷䞁䝎䝮ຠᯝ䚹
αi䛿䛭䜜䛮䜜䛾デ᩿⢭ᗘ䛾䝷䞁䝎䝮ຠᯝ䚹β䛿䚸䝇䜿䞊䝹䝟䝷䝯䞊䝍䛷䛒䜚䚸
ᅛᐃຠᯝ䛻䛺䜛䚹

38. HSROC䝰䝕䝹
RuZer
Gatsonis,
2001,
StaQsQcs
in
Medicine
• 䝺䝧䝹㻌2䠖䝔䝇䝖㜈್䠄θi䠅䛸⢭ᗘ(αi)䛾䝷䞁䝎䝮ຠᯝ
䛿䚸ṇつศᕸ䛻ᚑ䛖䛸䛥䜜䜛䠄䛹䛱䜙䜒ศᩓ䛛䜙␗㉁
ᛶ䜒᳨ウ䛷䛝䜛䠅䚹
θi
2 ( ) αi
~ Normal Θ,σθ
2 ( )
~ Normal Α,σα
• ᅛᐃຠᯝβ䚸䝷䞁䝎䝮ຠᯝΘ䛸A䛛䜙䚸௨ୗ䛾ᘧ䜢⏝
䛔䛶SROC᭤⥺䜢ᥥฟ䛷䛝䜛䚹㻌
1
Sensitivity =
) #$
1+ exp −(Αe−0.5β +logit (1−specificity)e−β
%

39. Bivariate䝰䝕䝹
Reitsma
et
al.,
2005,
Journal
of
Clinical
Epidemiology
• SROC䝰䝕䝹䜔HSROC䝰䝕䝹䛸䛿␗䛺䜚䚸ឤᗘ䛸≉␗
ᗘ䛾ᖹᆒ್䜔ศᩓ䚸ឤᗘ䛸≉␗ᗘ㛫䛾┦㛵䜢┤᥋
᥎ᐃ䛩䜛㝵ᒙⓗ䝰䝕䝹䚹
• 䝺䝧䝹䠍䠖ṇデᩘ䛾◊✲ෆኚື䛜䝙㡯ศᕸ䛻ᚑ䛖䛸
䝰䝕䝸䞁䜾
yij ~ Binomial(nij, πij)
䠆䛒䜛◊✲i䛻䛚䛡䜛⑓Ẽ䛾᭷↓䛻ᇶ䛵䛟㞟ᅋj䠄A=⑓Ẽ䛒䜚,
B=⑓Ẽ䛺䛧䠅䛻䛚䛡䜛ṇデᩘ(yij)䚸㞟ᅋ䛾ேᩘ(nij)䚸ṇデ⋡(πij)
• ṇデᩘ䛛䜙ឤᗘ䛸≉␗ᗘ䜢ồ䜑䝻䝆䝑䝖ኚ᥮䛩䜛
μAi=logit(ឤᗘi)=logit(yiA/niA)
μBi=logit(≉␗ᗘi)㻌䠙logit(yiB/niB)

40. Bivariate䝰䝕䝹
Reitsma
et
al.,
2005,
Journal
of
Clinical
Epidemiology
• 䝺䝧䝹䠎䠖logitኚ᥮䛧䛯ឤᗘ䛸≉␗ᗘ䛾◊✲
㛫ኚື䛜ṇつศᕸ䛻ᚑ䛖䛸䝰䝕䝸䞁䜾
μAi
μBi
!
##
$
%
~ Normal
## ## $
$
μA
μB
!
,Σ
%
!
%
withΣ
σ 2 σ A
AB
σ σ 2
AB B
!
##
$
%
◊✲i䛻䛚䛡䜛logit
ኚ᥮䛧䛯ឤᗘ䠄μAi䠅
䛸≉␗ᗘ䠄μBi䠅
logitኚ᥮䛧䛯ឤ
ᗘ䛸≉␗ᗘ䛾
ᖹᆒ್
logitኚ᥮䛧䛯ឤᗘ䛸
≉␗ᗘ䛾ศᩓ䠄␗㉁
ᛶ䛾ᣦᶆ䛻䜒䛺䜛䠅
logitኚ᥮䛧䛯ឤ
ᗘ䞉≉␗ᗘ㛫䛾
ඹศᩓ
䛺䛚䚸┦㛵䜢௬ᐃ䜢䛧䛺䛔䝰䝕䝹䛾᫬䚸HSROC䝰䝕
䝹䛸Bivariate䝰䝕䝹䛿䚸⤫ィᏛⓗ䛻ྠ➼䛻䛺䜛
䠄Harbord
et
al.,
2007,
BiostaQsQcs䠅

41. グ㍕౛䠖ゎᯒἲ䛾グ㍕
For
meta-­‐analyses,
a
bivariate
random
effects
model
was
used
to
calculate
summary
esQmates
of
sensiQvity,
specificity,
posiQve
likelihood
raQo
(PLR)
and
negaQve
likelihood
raQo
(NLR),
and
to
fit
a
hierarchical
summary
receiver-­‐operaQng
characterisQc
(HSROC)
curve.
These
models
take
into
account
potenQal
threshold
effects
and
the
correlaQon
between
sensiQvity
and
specificity.
They
also
allow
addiQon
of
covariates
for
invesQgaQon
of
potenQal
sources
of
heterogeneity,
thus
are
standard
methods
recommended
for
meta-­‐analyses
of
diagnosQc
tests.
Yuanyuan
et
al.
2014,
Plos
One䠄B型肝炎による肝線維化に対するFIB-‐‑‒4 Indexの診断精度度䠅
To
summarise
test
accuracy
data
across
studies,
we
fiZed
hierarchical
summary
receiver
operaQng
characterisQc
(HSROC)
models
䠄౑⏝䝋䝣
䝖䛺䛹䠅.
The
HSROC
model
accounts
for
between
study
variability
through
the
inclusion
of
random
effects
that
allow
for
heterogeneity
in
threshold
and
accuracy.
Ritchie
et
al.,
2014,
Cochrane
Database
Syst
Rev䠄䜰䝹䝒䝝䜲䝬䞊⑓䛸䛭䛾௚䛾㍍ᗘㄆ▱⑕
デ᩿䛻䛚䛡䜛⾑₢䛸⬻⬨㧊ᾮ䜰䝭䝻䜲䝗䝧䞊䝍䠅

42. グ㍕౛䠖ゎᯒ⤖ᯝ䛾グ㍕
䠘ゎᯒ䛻ྵ䜑䛯◊✲䛻䛴䛔䛶䠚
Twelve
studies,
including
1,908
paQents
(male:
71%;
average
age:
37.1
years;
average
prevalence
57.4%)
were
used
in
our
meta-­‐
analysis
for
tesQng
the
diagnosQc
accuracy
of
the
FIB-­‐4
index
for
predicQng
significant
fibrosis䠘␎䠚.
HSROC䛾⤖ᯝThe
area
under
the
HSROC
was
0.78
(95%
CI
=
0.74–
0.81,
ྑᅗ).
Bivariate䝰䝕
䝹䛻䜘䜛せ⣙ឤᗘ䞉≉␗ᗘThe
summary
sensiQvity
and
specificity
were
0.71
(95%
CI
=
0.64–0.77)
and
0.73
(95%
CI
=
0.67–0.78),
respecQvely.
Yuanyuan
et
al.
2014,
Plos
One䠄B型肝炎による肝線維化に対するFIB-‐‑‒4 Indexの診断精度度䠅

43. VIII.
⤖ᯝ䛾ゎ㔘
1 • ୺䛺⤖ᯝ䛾せ⣙
2 • 䝺䝡䝳䞊䛾㛗ᡤ䛚䜘䜃▷ᡤ
3 • 䝺䝡䝳䞊䜽䜶䝇䝏䝵䞁䜈䛾㐺⏝ྍ⬟ᛶ
4 • ⮫ᗋⓗព⩏
5 • ௒ᚋ䛾◊✲䜈䛾♧၀
“Chapter
11:
InterpreQng
results
and
drawing
conclusions”
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2013.

44. ୺䛺⤖ᯝ䛾せ⣙
䠄䝁䜽䝷䞁䝺䝡䝳䞊䛾ሙྜ䚸Summary
of
Finding
(SoF)䜢グ㍕)
Figure 5: What is the diagnostic accuracy of the Platelia© Aspergillus test for invasive aspergillosis
for different cut-off values? (Leeflang 2008)
Patients/population Immunocompromised patients, mostly haematology patients
Prior testing Varied, mostly physical examination and history (fever, neutropenia)
Settings Mostly inpatients in haematology or cancer departments
Index test Platelia© Aspergillus test, a sandwich ELISA for galactomannan, an Aspergillus
antigen
Importance Depends on the time-gain the test may provide
Reference standard Gold standard would have been autopsy, but this is virtually never done. Actual
reference used: clinical and microbiological criteria
Studies Cross-sectional studies including an equally suspected patient sample (case-control
studies) were excluded. Studies had to report cut-off values that were
used (n = 29). Each study can be present in more than one subgroup.
Test /
Subgroup
Summary
accuracy
(95% CI)
No. of
participants
(studies)
Prevalence
Median
(range)
Cut-off 0.5 Sensitivity
0.79 (0.61-
0.93)
Specificity
0.82 (0.71-
0.92)
901 (7)
9.9%
(0.8-34%)
“Chapter
11:
InterpreQng
results
and
drawing
conclusions”
Implications Quality and Comments
With a prevalence of 10%,
10 out of 100 patients will
develop IA. Of these, 2 will
be missed by the Platelia
test (21% of 10), but will be
tested again. Of the 90
patients without IA, 15 will
be unnecessarily referred
for CT scanning.
䛄Cochrane
Handbook
for
SystemaQc
Reviews
of
DiagnosQc
Test
Accuracy
Version
1.0.0.䛅
The
Cochrane
CollaboraQon,
2013.
Low numbers of diseased
patients per study (1 to
20).
These studies contained a
representative spectrum.
Uninterpretable results
and withdrawals poorly
reported.
Cut-off 1.0 Sensitivity
0.71 (0.61-
0.81)
1744
(12)
12%
(0.8-44%)
Of the 10 in 100 patients
developing IA, 3 will be
missed. Of the 90 patients
Low numbers of diseased
patients per study (1 to
34).

45. 1
• ୺䛺⤖ᯝ䛾せ⣙
2
• 䝺䝡䝳䞊䛾㛗ᡤ䛚
䜘䜃▷ᡤ
3
• 䝺䝡䝳䞊䜽䜶䝇䝏䝵
䞁䜈䛾㐺⏝ྍ⬟ᛶ
4
• ⮫ᗋⓗព⩏
5
• ௒ᚋ䛾◊✲䜈䛾♧
၀
①୍ḟ◊✲䛾㉁(QUADS-­‐2䛻䜘䜛ホ౯)䚸
②䝺䝡䝳䞊㐣⛬䛾㉁䠄䝺䝡䝳䞊䛾ྛ㐣⛬䛻
䛚䛡䜛㝈⏺Ⅼ䛾ホ౯䠅䛻䛴䛔䛶ホ౯䛧䚸䝺
䝡䝳䞊䛾㛗ᡤ䛸▷ᡤ䜢䜎䛸䜑䜛䚹
QUADAS-­‐2䛾㐺⏝ྍ⬟ᛶ䛾㡯┠䜢ཧ↷䛧䛺
䛜䜙䚸䝯䝍ศᯒ⤖ᯝ䜢䛹䜜䛰䛡䝺䝡䝳䞊䜽䜶
䝇䝏㻌䝵䞁䛻㐺⏝䛩䜛䛣䛸䛜䛷䛝䜛䛛⪃ᐹ
䠄཰㞟䛥䜜䛯୍ḟ◊✲䛿,
䝺䝡䝳䞊䜽䜶䝇
䝏㻌䝵䞁䛻ᛂ䛨䛯ẕ㞟ᅋ䜔◊✲䝕䝄䜲䞁䠛䠅
䜽䝸䝙䜹䝹䝟䝇䛻䛚䛡䜛ᣦᶆ᳨ᰝ䛾఩⨨䛵
䛡䜔ពᅗ䛧䛯ᙺ๭(⨨䛝᥮䛘䚸䝖䝸䜰䞊䝆䚸
㏣ຍ)䜢䛹䛾䛟䜙䛔ᯝ䛯䛧䛶䛔䜛䛾䛛,
ᣦᶆ
᳨ᰝ䛜㝧ᛶ䞉㝜ᛶ䜢♧䛧䛯ሙྜ䛾⤖ᯝ䛻䛴
䛔䛶䜒⪃៖䛧䛶䚸⮫ᗋⓗព⩏䜢᭩䛟
デ᩿䛾⢭ᗘ௨እ䛻ᚲせ䛺㏣ຍ䛾◊✲䛻䛴
䛔䛶ලయⓗ䛺◊✲䝕䝄䜲䞁䜔᪉ἲ䛻䛴䛔㻌
䛶䜒グ㍕䛩䜛䚹௒ᅇ䛾䝯䝍ศᯒ䛷䛿୙༑ศ
䛺ሙྜ䚸ሗ࿌䛾㉁䛾㧗䛔◊✲䛺䛹ලయⓗ
䛻䛹䛾䜘䛖䛺◊✲䛜ᚲせ䛛᭩䛟

46. ཧ⪃ᩥ⊩
ᩍ⛉᭩
• 䛄Synthesizing
Evidence
of
DiagnosQc
Accuracy䛅(White,
S,
LippincoZ
Williams
Wilkins,
2011)
• 䛄Handbook
for
DTA
Reviews
䛅hZp://
srdta.cochrane.org/handbook-­‐dta-­‐reviews
• 䛄The
Evidence
Base
of
Clinical
Diagnosis:
Theory
and
Methods
of
DiagnosQc
Research䠄䠎∧䠅䛅(KnoZnerus䜙,
BMJ
Books,
2011)
QUADAS-­‐2
• WhiQng
et
al.
(2011).
QUADAS-­‐2:
a
revised
tool
for
the
quality
assessment
of
diagnosQc
accuracy
studies.
Annals
of
Internal
Medicine,
155(8),
529–36.

47. ཧ⪃ᩥ⊩
ゎᯒἲ䛻㛵䛩䜛ㄽᩥ
• Moses
et
al.
(1993).
Combining
independent
studies
of
a
diagnosQc
test
into
a
summary
ROC
curve:
data-­‐analyQc
approaches
and
some
addiQonal
consideraQons.
StaQsQcs
in
Medicine,
12(14),
1293–316.
• Reitsma
et
al.
(2005).
Bivariate
analysis
of
sensiQvity
and
specificity
produces
informaQve
summary
measures
in
diagnosQc
reviews.
Journal
of
Clinical
Epidemiology,
58(10),
982–90.
• RuZer
Gatsonis
(2001).
A
hierarchical
regression
approach
to
meta-­‐analysis
of
diagnosQc
test
accuracy
evaluaQons.
StaQsQcs
in
Medicine,
20(19),
2865–84.
• Harbord
et
al.(2007).
A
unificaQon
of
models
for
meta-­‐analysis
of
diagnosQc
accuracy
studies.
BiostaQsQcs,
8(2),
239–51.
• Harbord
et
al.(2008).
An
empirical
comparison
of
methods
for
meta-­‐analysis
of
diagnosQc
accuracy
showed
hierarchical
models
are
necessary.
Journal
of
Clinical
Epidemiology,
61(11),
1095–
103.

48. ㅰ㎡
• ᮏⓎ⾲䛿䚸◊✲ᐊ䛾ᆏᮏḟ㑻䛥䜣䚸㕥ᮌᙬኟ䛥䜣䚸
᮴ྲྀᜨኴ䛥䜣䚸᫬⏣᳚Ꮚ䛥䜣䛾༠ຊ䛻䜘䛳䛶ᙧ䛻䛺
䜚䜎䛧䛯䚹῝䛟ឤㅰ䛔䛯䛧䜎䛩䚹