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[分享] [福利] 8本Meta分析英文原版电子书(免费PDF下载)

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本帖最后由 sampson2010 于 2015-3-3 21:18 编辑 ( e1 T# t$ f& [
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Advances in Meta-Analysis# K* V" q+ @3 Y6 x* o
6 `) ~3 }: Z" x) Z& H# L4 v
Author(s):Terri D. Pigott. r0 T. E! w% }1 E( U( I" j; D
Series: Statistics for Social and Behavioral Sciences
" W- M; I: _9 K% V' [% N4 N) [$ FPublisher: Springer) V! ?( g) s' ~
Year: 2012       
) n: y# D1 |( V6 K" ]/ a+ c- OEdition: 2012; s  Y+ D* v' n2 g1 d+ y8 n
Language: English       
" R5 H1 e7 D, F5 ?: a2 VPages: 170
# K# @- E3 _! Y
+ Y& s* B8 X3 k: c+ s
Table of contents :
+ K& K. Q3 |! I, L# d& vCover......Page 1
! x7 {。 X# F: S! MStatistics for Social and Behavioral Sciences......Page 2% \3 c- j9 e0 x/ ?5 A
Advances in Meta-Analysis......Page 4& U2 `, u) N- [* m7 {: z
ISBN 9781461422778......Page 57 b1 I- }! I$ ], n0 f4 x' H
Acknowledgements......Page 8
  c6 Q+ G7 f' M; b4 k& NContents......Page 10( B- v) w; g/ A1 T( I& O; T
1.1 Background......Page 16
) `3 s" p+ Q。 c8 h) k1.2 Planning a Systematic Review......Page 17
: ?4 n' o$ g! u. N( r4 C* o1.4 Interpreting Results from a Meta-analysis......Page 19& J, C% S6 g" r3 M3 X
1.5 What Do Readers Need to Know to Use This Book?......Page 20
# e' _4 J0 @* z/ {& ?5 fReferences......Page 216 u# }4 c5 @& ~" ~
2.2 Introduction to Notation and Basic Meta-analysis......Page 224 g5 v- E* t6 M  ?9 z
2.3 The Random Effects Mean and Variance......Page 23
4 d; I# W7 ^: p。 ~, ]2.4.2 Correlation Coefficient......Page 25
$ n0 t+ w5 I% g% a" ]/ z$ F2.4.3 Log Odds Ratio......Page 26$ V# L4 A1 c# M. ?; k/ {
References......Page 27/ I. F  P/ D: {, m. y# n- V( b  G! t
3.1 Background......Page 28
, s" Q" d( i( W" q3.2 Deciding on Important Moderators of Effect Size......Page 29
9 j) Y- F& v& z1 Q/ X3.3 Choosing Among Fixed, Random and Mixed Effects Modelsƒ......Page 31: M4 ^9 m" U1 X* y: p" o
3.4 Computing the Variance Component in Random and Mixed Models......Page 33
- M; q* g6 X' e5 h4 t# l, Y3.4.1 Example......Page 35
4 x/ |+ ?& p/ G3 W; F+ S3.5 Confounding of Moderators in Effect Size Models......Page 369 [5 r, ~# j& F! l; T$ R
3.5.1 Example......Page 38
; I! w% ]) G' x6 u3.6.1 Example......Page 40
2 X! b。 {; |+ T0 H- W3.7 Interpretation of Moderator Analyses......Page 43% y  n* n& x  u& b
Computing the Variance Component Using SAS......Page 445 d$ I9 m, W% }  [* A
Computing the Variance Component Using R......Page 45
8 w, P8 E2 v6 \4 ~" ~5 WComputing the Fixed Effects Meta-regression Using SAS......Page 46
9 @8 b# |9 m; ~/ s, NReferences......Page 47
6 ~1 X, F+ i8 Q' k# ?% x+ g4 E4.1 Background......Page 50
/ K  }2 D0 ]" Q3 O, A- W4.2 Fundamentals of Power Analysis......Page 52' ^) b( q* l6 y
4.3.1 Z-Test for the Mean Effect Size in the Fixed Effects Modelƒ......Page 54
( O% q) G, v/ G) @. W0 j# H4.3.2 The Power of the Test of the Mean Effect Size in Fixed Effects Models......Page 56
。 C# y: F3 }8 [5 ?; q: m0 @4.3.3 Deciding on Values for Parameters to Compute Power......Page 576 ]' h3 d; ?4 o; t" B0 G  ]
4.3.4 Example: Computing the Power of the Test of the Mean......Page 58
6 d2 Y8 L; o$ r+ c& I, p* P" h4.3.5 Example: Computing the Number of Studies Needed to Detect an Important Fixed Effects Mean......Page 60
  l( @+ Z" Q& I3 W) C+ U( @- J4.3.6 Example: Computing the Detectable Fixed Effects Mean in a Meta-analysis......Page 61- C( C  ~7 `; s0 ]
4.4 Test of the Mean Effect Size in the Random Effects Model......Page 62
0 ~% f' g5 o4 }& w9 h0 |; w4.4.1 The Power of the Test of the Mean Effect Size in Random Effects Models......Page 63. u( N3 y1 x8 |1 x; t) R5 z
4.4.2 Positing a Value for tau2 for Power Computations in the Random Effects Model......Page 64
7 c9 K; Z4 y  s& Z: X  v4.4.3 Example: Estimating the Power of the Random Effects Mean......Page 65+ Z  E6 U. S  X5 E( ?: Y
4.4.4 Example: Computing the Number of Studies Needed to Detect an Important Random Effect Mean......Page 66
  G1 u% W4 @+ w. QExcel......Page 67" Q/ B2 ?5 _" s. K' D2 m, a, }. x
References......Page 681 b* ^; F2 y9 H  C
5.1 Background......Page 704 s/ n: V% X. c; z+ m
5.2.1 The Power of the Test of Homogeneity in a Fixed Effects Model......Page 71
7 H8 I+ B5 O6 R3 ~3 {' U$ B3 E5.2.2 Choosing Values for the Parameters Needed to Compute Power of the Homogeneity Test in Fixed Effects Models......Page 72
# f7 c( x8 j" t* r5.2.3 Example: Estimating the Power of the Test of Homogeneity in Fixed Effects Models......Page 73
, U3 Q" q$ t( }% ?5.3 The Test of the Significance of the Variance Component in Random Effects Models......Page 74
' C5 t/ _。 d% t% U( i& v* _5.3.1 Power of the Test of the Significance of the Variance Component in Random Effects Models......Page 75
: u+ M- V: E) s) W% m" j5.3.2 Choosing Values for the Parameters Needed to Compute the Variance Component in Random Effects Models......Page 76
5 e3 h" A$ c3 U5.3.3 Example: Computing Power for Values of tau2, the Variance Component......Page 77# R' _; s: N8 U( n- ^
SAS......Page 79
5 ~" e* A$ r' BR......Page 80
* Y0 y! a1 l- SReferences......Page 81
' w. X. m7 d: a! {4 N6.1 Background......Page 82' O8 @3 x0 [4 d. L# y: V' f
6.2.2 Power of the Test of Between-Group Homogeneity, QB, in Fixed Effects Models......Page 83' J5 U8 E  r- ~0 Z& v+ ~
6.2.4 Example: Power of the Test of Between-Group Homogeneity in Fixed Effects Models......Page 85, i: [: N0 `3 M% r' n0 b
6.2.5 Power of the Test of Within-Group Homogeneity, QW, in Fixed Effects Models......Page 862 a% ^7 f( p4 P( Y
6.2.6 Choosing Parameters for the Test of QW in Fixed Effects Models......Page 87. r1 O& E3 |/ O3 T' M7 C
6.2.7 Example: Power of the Test of Within-Group Homogeneity in Fixed Effects Models......Page 88" i& k! j3 s' v9 a6 P: q
6.3.1 Power of Test of Between-Group Homogeneity in the Random Effects Model......Page 89
6 {: q1 c2 k# B$ u6.3.3 Example: Power of the Test of Between-Group Homogeneity in Random Effects Models......Page 91% [; Y- `  S) t+ n& E; {
References......Page 93
+ c0 V) X, t9 ^6 ~* z7.1 Background......Page 94
4 w3 G0 U& A( Q+ c+ J6 ?$ h7.2.1 Identification of Publication Bias......Page 956 D2 b7 @3 c- J& `8 U
7.2.1.1 Example of Funnel Plot......Page 96
. W& C1 o% d1 U* K7.2.2 Assessing the Sensitivity of Results to Publication Bias......Page 976 g! y6 m+ O: X' G9 A4 q
7.3 Missing Effect Sizes in a Meta-analysis......Page 100% n. v6 S( {3 F9 q( ~
7.4 Missing Moderators in Effect Size Models......Page 1010 j: t! C5 T1 z) K' _' w! N
7.5 Theoretical Basis for Missing Data Methods......Page 102
& H2 Q( {  B7 [4 l1 S* v8 ^7.5.1 Multivariate Normality in Meta-analysis......Page 103
, A/ Y! F$ b- v7 o5 c0 l; V7.5.2 Missing Data Mechanisms or Reasons for Missing Data......Page 1047 v$ l6 W# M7 M( [, Y9 N
7.6.1 Complete-Case Analysis......Page 105
; {/ c+ Q。 J3 s( N% M) E7 Z7.6.1.1 Example: Complete-Case Analysis......Page 106  o$ ^9 j2 i/ c$ n$ }, b" w
7.6.2 Available Case Analysis or Pairwise Deletion......Page 107
; {6 c& u3 s6 Q1 D6 y7.6.3 Single Value Imputation with the Complete Case Mean......Page 108
- _2 N* f( H/ s% F, U5 }% F7.6.3.1 Example: Mean Imputation......Page 109
. s6 t4 G+ f9 G7.6.4 Single Value Imputation Using Regression Techniques......Page 110
% o9 }7 o2 V  {8 |( ^7.6.4.1 Example: Regression Imputation......Page 111; e- _( a5 \0 Y* o: ~  t
7.7.1 Maximum-Likelihood Methods for Missing Data Using the EM Algorithm......Page 1121 C4 m; {% F. z; x- @3 k* V
7.7.1.1 Example Using the EM Algorithm......Page 113
- Z" J9 ~! z/ x. g' M7.7.2.1 Generating Multiple Imputations......Page 1142 w; u) }/ c, |4 O5 c9 b# |
7.7.2.3 Combining the Estimates......Page 115
# ]% e, Z9 E, J% v1 M& ~) g& NR Programs......Page 1170 x3 ~! l$ K! G2 V9 y
SAS Proc MI......Page 119
。 c; i6 T  M+ hReferences......Page 121
$ w8 y6 G- ?3 d4 a- p8.1 Background......Page 124/ ?# _/ P+ i+ \
8.2 The Potential for IPD Meta-analysis......Page 125
% p$ p% a. S" @7 b8.3.1 Simple Random Effects Models with Aggregated Data......Page 1275 d! R, X( d! A' d; m
8.3.2.1 Example: Two-Stage Method Using Correlation as the Effect Size......Page 129) G' w- }! t% j: \( X) C
8.4.1 IPD Model for the Standardized Mean Difference......Page 1302 d" t2 k; B4 b. `# j. v
8.4.3 Model for the One-Stage Method with Both IPD and AD......Page 131
. ]5 A+ m8 o1 }7 @; a$ G7 e2 p) G) ~8.5 Effect Size Models with Moderators Using a Mix of IPD and AD......Page 1330 d2 |: @: Q' v' V
8.5.1 Two-Stage Methods for Meta-regression with a Mix of IPD and AD......Page 134/ K* u' o, w7 F+ e
8.5.2 One-Stage Method for Meta-regression with a Mix of IPD and AD......Page 135
0 C' U1 P% F7 p8 K8.5.4 One-Stage Meta-regression with a Mix of IPD and AD......Page 136( q- W: ~& y5 U+ ^: ]
8.5.4.1 Example: One-Stage Method for Meta-regression with Correlationsƒ......Page 137
9 f  I8 K: E7 @- [, m" kSAS Code for Simple Random Effects Model Using the Two-Step Method......Page 1384 F$ {, |/ T% R  v9 m; u) Q, {3 r
Output from Two-Stage Simple Random Effects Model......Page 139: p& O% b" r9 G* j) W& {
SAS Code for Meta-regression Using the Two-Stage Method......Page 140. Q! G" Z5 }7 r4 c2 ^
SAS Code for Simple Random Effects Model Using the One-Stage Model......Page 141. W3 n& v) H/ L: D( A% U0 o4 l
Output from One-Stage Simple Random Effects Model......Page 143' @9 t' Y+ y4 t5 F3 N
Output for Meta-regression Using the One-Step Method......Page 1449 E8 H, a  \- t' |# h
References......Page 1456 n4 J  S7 F8 N+ |
9.1 Background......Page 1487 T& y; J' P5 J; b& A$ y5 V( Z
9.1.1 The Preventive Health Services (2009) Report on Breast Cancer Screening......Page 149" l( G0 a+ |/ L. L: D
9.2.1 Surface Similarity......Page 1502 X% P! _; c- J: g
9.2.2 Ruling Out Irrelevancies......Page 151+ @6 a9 \  C4 x% X; z$ T' J
9.2.3 Making Discriminations......Page 152
, B! ~) X  g- V% T! v- Y- ?6 \8 b: H9.2.5 Causal Explanation......Page 153
% H1 d0 U  }0 c& H% q4 P% p3 J9.3 Suggestions for Generalizing from a Meta-analysis......Page 154/ E6 k# ^# o% f
References......Page 155
( E( T7 \) I: R/ S10.2 Understanding the Research Problem......Page 158
* B4 k( P. c7 R) @/ Z3 _9 l10.3 Having an a Priori Plan for the Meta-analysis......Page 1594 U3 F) k2 J+ q% @. i1 \6 M9 N
10.4 Carefully and Thoroughly Interpret the Results of Meta-analysis......Page 160
) P) D4 Z' Q。 JReferences......Page 1616 v$ a: S: H# ?) W( k
11.1 Sirin (2005) Meta-analysis on the Association Between Measures of Socioeconomic Status and Academic Achievement......Page 162
3 ]( V/ ~: K$ T) x11.2 Hackshaw et al. (1997) Meta-analysis on Exposure to Passive Smoking and Lung Cancer......Page 1645 @7 I/ Q* a6 g2 M; ^' ?  K1 ~! l
11.3 Eagly et al. (2003) Meta-analysis on Gender Differences in Transformational Leadership......Page 166
( c! J, j8 B% P2 b- \3 X) u  m0 dReferences......Page 167
" z6 @/ P0 f, T# _2 kIndex......Page 168
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Advances in Meta-Analysis (2012).pdf (1.03 MB, 下载次数: 621)
。 v2 b' [4 H$ O。 f5 P% e, Z" W, B
, h" d; Z( z3 f( Q6 {
/ B. k/ u4 H% z- k; |4 l6 ~9 BApplied Meta-Analysis for Social Science Research& Q( G$ J+ p7 J
! V" Z* _- L! L$ `1 A
Author(s): Noel A. Card PhD% z* |. [# ?# G4 J
Series: Methodology In The Social Sciences
7 V$ D4 d% v) H4 x- MPublisher: The Guilford Press
+ L  n, g) }6 ?1 y6 q9 e# s* h% WYear: 2011        ) i8 B  r6 ~  y% @3 s
Edition: 1+ j/ o: Z9 s+ ^3 I
Language: English       
0 P9 D0 ?2 Q  ^* ^Pages: 402

* S' l; r$ ]6 k# B+ f% _* O2 D# I) {4 q2 D3 P* h
Contents:
# z+ o5 u9 O) {Part 1. The Blueprint: Planning and Preparing a Meta-Analytic Review. 1. An Introduction to Meta-Analysis. 1.1 The Need for Research Synthesis in the Social Sciences. 1.2 Basic Terminology. 1.3 A Brief History of Meta-Analysis. 1.4 The Scientific Process of Research Synthesis. 1.5 An Overview of the Book. 1.6 Practical Matters: A Note on Software and Information Management. 1.7 Summary. 1.8 Recommended Readings. 2. Questions that Can and Questions that Cannot be Answered Through Meta-Analysis. 2.1 Identifying Goals and Research Questions for Meta-Analysis. 2.2 The Limits of Primary Research and the Limits of Meta-Analytic Synthesis. 2.3 Critiques of Meta-Analysis: When Are They Valid and When Are They Not? 2.4 Practical Matters: The Reciprocal Relation between Planning and Conducting a Meta-Analysis. 2.5 Summary. 2.6 Recommended Readings. 3. Searching the Literature. 3.1 Developing and Articulating a Sampling Frame. 3.2 Inclusion and Exclusion Criteria. 3.3 Finding Relevant Literature. 3.4 Reality Checking: Is My Search Adequate? 3.5 Practical Matters: Beginning a Meta-Analytic Database. 3.6 Summary. 3.7 Recommended Readings. Part 2. The Building Blocks: Coding Individual Studies. 4. Coding Study Characteristics. 4.1 Identifying Interesting Moderators. 4.2 Coding Study "Quality". 4.3 Evaluating Coding Decisions. 4.4 Practical Matters: Creating an Organized Protocol for Coding. 4.5 Summary. 4.6 Recommended Readings. 5. Basic Effect Size Computation. 5.1 The Common Metrics: Correlation, Standardized Mean Difference, and Odds Ratio. 5.2 Computing r from Commonly Reported Results. 5.3 Computing g from Commonly Reported Results. 5.4 Computing o from Commonly Reported Results. 5.5 Comparisons among r, g, and o. 5.6 Practical Matters: Using Effect Size Calculators and Meta-Analysis Programs. 5.7 Summary. 5.8 Recommended Readings. 6. Corrections to Effect Sizes. 6.1 The Controversy of Correction. 6.2 Artifact Corrections to Consider. 6.3 Practical Matters: When (and How) to Correct: Conceptual, Methodological, and Disciplinary Considerations. 6.4 Summary. 6.5 Recommended Readings. 7. Advanced and Unique Effect Size Computation. 7.1 Describing Single Variables. 7.2 When the Metric is Meaningful: Raw Difference Scores. 7.3 Regression Coefficients and Similar Multivariate Effect Sizes. 7.4 Miscellaneous Effect Sizes. 7.5 Practical Matters: The Opportunities and Challenges of Meta-Analyzing Unique Effect Sizes. 7.6 Summary. 7.7 Recommended Readings. Part 3. Putting the Pieces Together: Combining and Comparing Effect Sizes. 8. Basic Computations: Computing Mean Effect Size and Heterogeneity Around this Mean. 8.1 The Logic of Weighting. 8.2 Measures of Central Tendency in Effect Sizes. 8.3 Inferential Testing and Confidence Intervals of Average Effect Sizes. 8.4 Evaluating Heterogeneity Among Effect Sizes. 8.5 Practical Matters: Nonindependence Among Effect Sizes. 8.6 Summary. 8.7 Recommended Readings. 9. Explaining Heterogeneity Among Effect Sizes: Moderator Analysis. 9.1 Categorical Moderators. 9.2 Continuous Moderators. 9.3 A General Multiple Regression Framework for Moderation. 9.4 An Alternative SEM Approach. 9.5 Practical Matters: The Limits of Interpreting Moderators in Meta-Analysis. 9.6 Summary. 9.7 Recommended Readings. 10. Fixed-, Ramdom-, and Mixed-Effects Models. 10.1 Differences Among Models. 10.2 Analyses of Random-Effects Models. 10.3 Mixed-Effects Models. 10.4 A Structural Equation Modeling Approach to Random- and Mixed-Effects Models. 10.5 Practical Matters: Which Model Should I Use? 10.6 Summary. 10.7 Recommended Readings. 11. Publication Bias. 11.1 The Problem of Publication Bias. 11.2 Managing Publication Bias. 11.3 Practical Matters: What Impact Do Sampling Biases Have on Meta-Analytic Conclusions? 11.4 Summary. 11.5 Recommended Readings. 12. Multivariate Meta-Analytic Models. 12.1 Meta-Analysis to Obtain Sufficient Statistics. 12.2 Two Approaches to Multivariate Meta-Analysis. 12.3 Practical Matters: The Interplay between Meta-Analytic Models and Theory. 12.4 Summary. 12.5 Recommended Readings. IV. The Final Product: Reporting Meta-Analytic Results. 13. Writing Meta-Analytic Results. 13.1 Dimensions of Literature Reviews, Revisited. 13.2 What to Report and Where to Report it. 13.3 Using Figures and Tables in Reporting Meta-Analyses. 13.4 Practical Matters: Avoiding Common Problems in Reporting Results of Meta-Analyses. 13.5 Summary. 13.6 Recommended Readings.
3 g& }5 g$ [0 j
8 A1 n# ?9 m$ n, I. | Applied Meta-Analysis for Social Science Research (2011).rar (5.31 MB, 下载次数: 373) / \* X( u  J0 }  J2 p" C

) r& V4 P7 c2 Y  N6 j" f3 l$ uMeta analysis : a guide to calibrating and combining statistical evidence0 B. z( \  ?6 P0 r. x, G# y8 g
3 ~* i* @- j4 b0 }3 M- i
Author(s): Elena Kulinskaya, Stephan Morgenthaler, Robert G. Staudte
" D2 ?& n4 C+ U0 l。 W: }Series: Wiley Series in Probability and Statistics
, j# x' q/ R5 J0 G2 l- qPublisher: Wiley7 ^, p$ p0 L- }6 r: `* \9 a
Year: 2008
' w. D! e8 b  X" ~/ nLanguage: English        7 ]; b9 [% _) `  c: T+ Q  `
Pages: 272  M, }- x/ w/ w7 r( m2 @7 }
$ s* h! v* Z2 i% x# M/ J& N
Contents:
3 g7 g( M. D& [; k$ ~/ q: l* h
Preface.
5 R( c( h: S。 B" iPart I The Methods.
5 X0 Q) B& J* R' Y! C# ?4 n, {1 What can the reader expect from this book?0 Z1 e  W, t" w, I0 |
1.1 A calibration scale for evidence.
" W( K$ e% c$ z* [9 h% i1.2 The efficacy of glass ionomer versus resin sealants for prevention of caries.
! F& w6 a6 g8 Q% E/ Z% a1 Y7 G/ e1.3 Measures of effect size for two populations.; v! e* k& S5 q0 g) [, m
1.4 Summary." E0 J( ~) y* B
2 Independent measurements with known precision.  \2 M/ t% Y  v3 Y) g4 E% B
2.1 Evidence for one-sided alternatives.5 G% B/ S9 n6 w" ^& f
2.2 Evidence for two-sided alternatives.
( s: \$ p( V# Y; V3 l9 w  h4 G2.3 Examples.+ W: e! m4 q, ]$ R$ O2 ?
3 Independent measurements with unknown precision.
" ]4 f; }& d, L. @9 s' j( B* E& I6 V3.1 Effects and standardized effects.
。 ~$ z# `' D+ E( x9 S! w3.2 Paired comparisons.! I  W) X8 j! p
3.3 Examples.
" w8 I, j1 F5 A2 H; q* Q4 Comparing treatment to control./ d: `: U0 ~/ A$ M) y' t$ J
4.1 Equal unknown precision.
; W( z/ L' A  _4.2 Differing unknown precision.$ J8 D0 L% ~) M. c4 v7 F
4.3 Examples.
/ U* h7 R" ]" a5 Comparing K treatments.$ Z  U( y; }) H6 {& M5 f  [
5.1 Methodology.( ]+ K1 d! X( ]$ v/ r
5.2 Examples.9 W  }" a& ~, `
6 Evaluating risks.
- t& K( F$ [& `' t" m6.1 Methodology.
# F9 L" `+ A+ ?$ i0 S3 G6.2 Examples.
# M% e) Z4 s2 l5 k* b; Y7 Comparing risks.
; X$ h$ ^3 M/ M1 d- o/ h1 q7.1 Methodology.
$ Q% ^; c4 [& A7 o7.2 Examples.
, s! f4 ?6 a0 B。 C8 Evaluating Poisson rates.- w% r9 K; l) f4 }
8.1 Methodology.& b$ D. T( n- D8 p/ m, @
8.2 Example.
8 R6 H2 }! m; N" T5 r+ K0 E3 s9 Comparing Poisson rates.7 w/ O0 y$ i) G# ]  X% y
9.1 Methodology.8 N  r6 k3 b% N# ^
9.2 Example.
3 ?8 `' A$ k* F. C10 Goodness-of-fit testing.4 C5 P$ c7 v  v8 F
10.1 Methodology.( C9 `  j, r2 E4 u! @2 n( i
10.2 Example.
! k% ]9 m) N$ m11 Evidence for heterogeneity of effects and transformed effects.
$ B4 v# i8 @" q4 M11.1 Methodology.
1 z% N8 J/ e7 X' S6 i3 C8 U11.2 Examples.
) L" ]" g. R+ B3 L9 m$ q' d# I12 Combining evidence: fixed standardized effects model.2 C2 L$ c, l; E" B1 B
12.1 Methodology.3 `- D9 H2 _5 T1 R! H: _6 M$ X
12.2 Examples.% v, Z$ ~3 ~' v, r' b
13 Combining evidence: random standardized effects mode.
% b( u$ @0 J8 I13.1 Methodology.
& W. P1 Y2 ^. u+ e* |* h13.2 Example..! d+ j8 H! J! m6 [8 X- s7 B8 t
14 Meta-regression.
& t) X' z0 r" [  g; G+ o( m14.1 Methodology.
3 I  ~6 I* ^1 W: s& S- u14.2 Commonly encountered situations.7 K# h, a4 W, H. A6 ]+ \1 k
14.3 Examples.
$ }3 f, B5 n+ W7 U15 Accounting for publication bias.
* A" @  \+ t! o  ?- o6 {* `( ]% j15.1 The downside of publishing.) m: L2 U! I6 o( c8 q7 z
15.2 Examples.
" {! E! y; d0 d2 a5 y0 L% [: h& nPart II The Theory.
* R4 w# w1 G5 c$ K$ c# p16 Calibrating evidence in a test.
5 d2 ]# X+ A& N! @: p16.1 Evidence for one-sided alternatives.% N1 p/ c" K7 m4 G; }( k
16.2 Random p-value behavior.
! W# W' x; `$ ~; T) \2 F' G16.3 Publication bias.
6 p1 u& X2 G7 D; ]: G" w1 w6 C16.4 Comparison with a Bayesian calibration.
6 I+ m( Q  o6 D+ I  J6 Z: l16.5 Summary.4 ~" @5 H( [1 @9 t/ F4 {
17 The basics of variance stabilizing transformations.& Z/ U4 P( c  Z, `; J0 L# m
17.1 Standardizing the sample mean., m, {  S$ j. P  R: ~9 a
17.2 Variance stabilizing transformations.
6 a( c) h  v9 P17.3 Poisson model example.7 @. K$ H5 Z3 r+ ]) V
17.4 Two-sided evidence from one-sided evidence.- Y5 y# O* X/ y! t3 y7 B
17.5 Summary.$ Y1 Y% Z+ b4 e7 [! Q1 C- _
18 One-sample binomial tests.. A) f2 s+ k2 B8 L. _4 A$ X3 j
18.1 Variance stabilizing the risk estimator.
9 B% @+ p% r7 f4 C! M( m0 M, g18.2 Confidence intervals for p.2 f, `0 q0 A% r1 U- d; T
18.3 Relative risk and odds ratio.
! z% g, f2 e! Z5 V& O18.4 Confidence intervals for small risks p.+ J* E# l) ^0 @* R( o4 ?: T
18.5 Summary.' U3 R3 O% ]" S5 t- V7 u3 k; y) b
19 Two-sample binomial tests.1 L  M2 Y+ R1 I3 Q! ]" Z& ?
19.1 Evidence for a positive effect.
/ ^0 m, y& U9 d) j; h19.2 Confidence intervals for effect sizes.
( v4 ~; b+ ~' A) T2 {4 T# z19.3 Estimating the risk difference.
- E  f1 D8 U, i' C& F: o6 U& p19.4 Relative risk and odds ratio.+ n& b( L0 J# S( F3 W4 y7 O0 T; f
19.5 Recurrent urinary tract infections.
7 M2 h, K+ t2 P19.6 Summary.
# K7 _3 V6 X4 K20 Defining evidence in t-statistics.8 Z# b/ {: X) B
20.1 Example.
5 E. q1 U- N9 @& o8 s0 ^& O6 N20.2 Evidence in the Student t-statistic.
) i& K7 C4 |: T( N  w9 B" ^& x20.3 The Key Inferential Function for Student’s model.7 ]; r7 L! p" V; W# ?5 w  i
20.4 Corrected evidence.
, q# m+ U$ k8 C1 c( {0 k9 K20.5 A confidence interval for the standardized effect.
0 \7 B7 [0 p8 m+ G9 U  Z+ l20.6 Comparing evidence in t- and z-tests.2 W4 T5 j* \9 n$ A
20.7 Summary.( S, g9 ]% Y8 Y: V5 F
21 Two-sample comparisons.
3 D2 ^/ T8 G& F21.1 Drop in systolic blood pressure.
& L' N' d" X0 V" C" X( p8 G, A21.2 Defining the standardized effect.5 R$ @$ ~/ n* c
21.3 Evidence in the Welch statistic.
8 ^9 ~" b* M。 O* a& r: I7 C; W21.4 Confidence intervals for d.
' h& g- H+ \1 F& l" Z1 i21.5 Summary.# f8 p8 Z; K2 U8 f5 x' c
22 Evidence in the chi-squared statistic.2 ?# {# b5 r5 L& a
22.1 The noncentral chi-squared distribution.0 _5 n, U9 |* L1 K
22.2 A vst for the noncentral chi-squared statistic.+ b& a0 E4 Q# {
22.3 Simulation studies.1 N9 k8 r& o, G0 b$ L* |
22.4 Choosing the sample size.7 i. J4 O% f# B( H: I0 {
22.5 Evidence for l > l0., G+ m( g) _0 F. @3 G" L
22.6 Summary.& f' F; G1 E$ e6 T
23 Evidence in F-tests.! ?! i% l- C9 d
23.1 Variance stabilizing transformations for the noncentral F.; ~5 v8 O# }% t& X' s; I& J
23.2 The evidence distribution.! p+ N) b. F1 a  `) P0 z
23.3 The Key Inferential Function.
, ?) d3 b7 F6 Z。 q23.4 The random effects model.* z$ E; d9 h# P" T6 p) o! F, D' `
23.5 Summary.) M) x/ }7 r0 i, F/ {* g' b2 w( L
24 Evidence in Cochran’s Q for heterogeneity of effects.( U& Z2 O" p6 O6 ~8 T
24.1 Cochran’s Q: the fixed effects model.
; z! C2 T. ?1 P24.2 Simulation studies.$ u8 }) F, e5 {6 }+ \% N6 ^% g
24.3 Cochran’s Q: the random effects model.
。 z! |# k7 ^; b& R3 U  w0 h24.4 Summary.
/ D8 u% u$ B6 c1 S5 a* S' E  Q25 Combining evidence from K studies.
5 c0 B5 C! E2 t% h0 D# t9 X25.1 Background and preliminary steps.
  }  h4 g. U! f; x. m( `25.2 Fixed standardized effects.
  N# x/ |# W$ g- |4 S25.3 Random transformed effects.  L  {2 K- n$ G0 x* a! p1 ~8 k
25.4 Example: drop in systolic blood pressure.2 \0 w5 j* m2 @, {% O
25.5 Summary.
1 o1 e/ u. k% e4 k5 [2 k; R26 Correcting for publication bias.9 a  m  z& J5 p1 E. f* p
26.1 Publication bias.8 g8 ]$ y% a  i
26.2 The truncated normal distribution.
  O# \, j; \8 `2 `- C; c26.3 Bias correction based on censoring.. n- I; a6 p4 ?9 ^% t4 C2 X
26.4 Summary.6 O1 ~. f0 U+ s7 s  n+ D
27 Large-sample properties of variance stabilizing transformations./ {/ X1 b, `/ x9 x* A* b) P- s7 }( Y
27.1 Existence of the variance stabilizing transformation.! l! S9 |& p  D- D% I: N7 O4 E' Q
27.2 Tests and effect sizes.
- \$ d# C/ Y& a( o  o( @6 @0 E27.3 Power and efficiency.! Y  e3 g/ B& h) Z/ D
27.4 Summary.
9 b; ], ?# K! Z; p5 Q  Z  GReferences.
  B# t( q. @9 r) q4 QIndex.0 L) K9 o, [* d9 u1 `- z/ H* w0 ~

/ Z9 a* s, `' {0 Z/ D Meta analysis a guide to calibrating and combining statistical evidence (2008).pdf (2.33 MB, 下载次数: 365)
! @# l# k- [; {& x$ K" }, j8 t! |1 {- ]+ a4 D
Meta-analysis and combining information in genetics and genomics# h; J8 l  k' n

$ w5 E: l; E  M7 SAuthor(s): Rudy Guerra, Darlene Renee Goldstein
' b1 {。 I; S/ i0 k6 S/ A0 ]2 g! b; u; ESeries: Chapman and Hall/CRC mathematical & computational biology series0 b: v- }, j5 W+ P/ B
Publisher:        CRC Press        City:        Boca Raton4 g. A# o/ {  ^, u$ u" t1 M
Year: 2010        * l* f( f. j$ ]5 D+ }6 _
Language: English       
6 m: W3 O, f! D/ p& PPages: 335: k+ Y5 f! N* h! n
, M4 g5 s+ A0 U7 _: [
Table of contents :

* o* |0 T5 _/ d2 v8 |( A# j7 PPublished Titles......Page 48 q7 X& J$ [# V
Dedication......Page 81 f; A- B" A+ ~. m; J: B( r8 H
Contents......Page 10
# E" t9 k2 @% O0 ?6 ~+ B2 A: b2 `1 hContributors......Page 16
, F  e; E/ r0 b6 [Preface......Page 221 \/ o/ g8 e* Y& `! V2 [
Part 0. Introductory Material......Page 261 L6 ?. v: @  @8 O
CHAPTER 1: A brief introduction to meta-analysis,genetics and genomics......Page 28) h2 x: @6 f" K  j9 N# ^5 d* m
Part I. Similar Data Types I: Genotype Data......Page 46* m" R" h9 u0 o9 y7 q& g
CHAPTER 2: Combining information across genome-wide linkage scans......Page 486 l# d* F7 v' K- [# P+ l0 j& H
CHAPTER 3: Genome search meta-analysis (GSMA):a nonparametric method formeta-analysis of genome-wide linkage studies......Page 58
, {. z, W! _4 iCHAPTER 4: Heterogeneity in meta-analysis of quantitative trait linkage studies......Page 74
3 M1 |# h7 u  B; pCHAPTER 5: An empirical Bayesian framework for QTL genome-wide scans......Page 928 _+ [5 z: ]" Y( p+ Z5 P: Y
Part II. Similar Data Types II: Gene Expression Data......Page 106# v8 r! L) f  K. `4 \4 ~
CHAPTER 6: Composite hypothesis testing: anapproach built on intersection-uniontests and Bayesian posterior probabilities......Page 1086 U1 }/ b- }7 Z% x
CHAPTER 7: Frequentist and Bayesian error pooling methods for enhancing statistical powerin small sample microarray data analysis......Page 1204 x, R! g( t! M
CHAPTER 8: Significance testing for small microarray experiments......Page 1389 q& N' J/ W7 e1 L! i& ?
CHAPTER 9: Comparison of meta-analysis tocombined analysis of a replicated microarray study......Page 160
" ?  ]! @' H9 V* v0 h# G2 W。 k6 VCHAPTER 10: Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays......Page 1824 T8 O; G$ P; @' U9 e! {5 D2 [" J  L
CHAPTER 11: Gene ontology-based meta-analysis ofgenome-scale experiments......Page 200
5 Y% ?6 T3 u4 VPart III. Combining Different DataTypes......Page 224
' P, O9 Q( U8 JCHAPTER 12: Combining genomic data in human studies......Page 226
  N" s6 n。 k5 \8 m7 S! tCHAPTER 13: An overview of statistical approachesfor expression trait loci mapping......Page 238- m- q3 O, y  _% B! o8 i
CHAPTER 14: Incorporating GO annotation information in expression trait loci mapping......Page 250
" d( w* {$ f4 D! l) ^( v8 h: k- ACHAPTER 15: A misclassification model for inferring transcriptional regulatory networks......Page 268
. t- Z6 W8 j5 p; {8 x8 V7 RCHAPTER 16: Data integration for the study of protein interactions......Page 284
" P: \9 ?6 D3 t! M1 S  c0 UCHAPTER 17: Gene trees, species trees, and species networks......Page 3003 l' e( N+ ~3 D
References......Page 3206 O3 q& U; g% J7 C! B) @
Back cover......Page 3549 W6 r+ I/ k6 N9 h4 Y6 h
8 X8 N* m4 l) u- ]
Meta-analysis and combining information in genetics and genomics (2010).pdf (6.29 MB, 下载次数: 316) 4 L' z: T) d$ G. a0 i' j9 Y' G
1 k* y9 Q3 P* j- q8 K
Meta-Analysis in Medicine and Health Policy
; n8 l5 m1 Z6 T9 Y* s3 R: | 1 e, A0 g/ |; B: K
Author(s): Donald A. Berry, Dalene K. Stangl
1 j3 w* w: `* V  t: QPublisher: CRC Press
" Q: C% G# ?, A: wYear: 2000        5 O+ R' [4 [& [
Edition: 11 D  V5 A& D2 W2 F# x6 ^
Language: English        ; z8 J! c! d3 \: {" |* N
Pages: 418/ y+ W0 T: c) z2 d. U
' q/ Q) _: g& l4 K% `6 e$ A' R' K
Table of contents :

0 Q- w0 l. V. a& L9 NSeries Introduction......Page 6( a& w+ ]/ {. W. A2 Z3 N* H) d
Preface......Page 8
/ \。 v: s; @' v。 W! ^Contents......Page 10
# f3 _5 Y% ~- `* LContributors......Page 14$ ^1 d, [+ k& v5 c& B% w  x* @/ J
Meta-analysis: Past and Present Challenges......Page 18  c* c, t' i* J3 o7 a
Meta-analysis of Heterogeneously Reported Study Results: A Bayesian Approach......Page 46
* c0 Q1 \  s: }+ r* OMeta-analysis versus Large Trials: Resolving the Controversy......Page 82. w% q5 z3 ]/ t
A Bayesian Meta-analysis of Randomized Mega-trials for the Choice of Thrombolytic Agents in Acute Myocardial Infarction......Page 100
: r8 m# Z8 c1 T- b/ B1 PCombining Studies with Continuous and Dichotomous Responses: A Latent-Variables Approach......Page 122
1 M! _; [7 F, ]* G4 B! uComputer-modeling and Graphical Strategies for Meta-analysis......Page 144" y* B: O" F) w: W
Meta-analysis for 2 x 2 Tables with Multiple Treatment Groups......Page 196
2 m" @; M2 }7 m. z! L6 t2 u. r' aA Bayesian Meta-analysis of the Relationship between Duration of Estrogen Exposure and Occurrence of Endometrial Cancer......Page 208/ ?9 t' C: G: d" p- c
Modeling and Implementation Issues in Bayesian Meta-analysis......Page 222
1 z$ E2 g/ }& o: r: uMeta-analysis of Population Pharmacokinetic Data......Page 248; y( {& }  A# E
Meta-analysis of Individual-patient Survival Data Using Random-effect Models......Page 272" R$ q5 q) i, s
Adjustment for Publication Bias and Quality Bias in Bayesian Meta-analysis......Page 294
" [5 K+ z- u* R6 v0 }3 b% \" ?  |, rMeta-analysis of Clinical Trials: Opportunities and Limitations......Page 322- I7 V1 ?/ ~* i
Research Synthesis for Public Health Policy: Experience of the Institute of Medicine......Page 338/ S+ K- k6 C1 |! K  ?
Meta-analysis in Practice: A Critical Review of Available Software......Page 3769 U8 f: q* q' F* r, I, X4 y
Index......Page 408
. ], m) m6 p# r0 g/ ~0 X7 }) K6 B
' t" G。 f# v9 @6 ^ Meta-Analysis in Medicine and Health Policy (2000).part1.rar (7 MB, 下载次数: 269)
' c) d; }, C8 E Meta-Analysis in Medicine and Health Policy (2000).part2.rar (6.38 MB, 下载次数: 257) ' z  y. \; @8 X( f( l1 Y

0 N/ ]# ?1 D% cMeta-Analysis of Controlled Clinical Trials (Statistics in Practice)) M1 d) q3 X  F# O0 p5 F' j! e- N) e: v

) K3 [; X: ^  ?: K9 _; U. yAuthor(s): Anne Whitehead, u5 G8 O  B6 e, e( s9 b
Publisher: Wiley7 q+ H8 H4 I- X  o: A6 H' c
Year: 2002       
3 x4 s8 G2 u7 l' H3 ZEdition: 1
" d) v6 @8 J; u& B- s7 E% ULanguage: English       
  {9 p6 B- Y  d- L* z# ]Pages: 352
" R0 W: @: ?' r( O。 t1 J8 Q( N' w8 S' x) [2 x) F2 A
Table of Contents
" J4 b% N# U7 o5 [
Introduction
' X! e1 i" }: d$ J3 ~Protocol development
' s! y' m9 u" H) S$ T4 W# xEstimating the treatment difference in an individual trial8 s) Y" ?  f& W
Combining estimates of a treatment difference across trials
' t+ s$ }- T5 }- }& ]& V, dMeta-analysis using individual patient data3 ]4 R5 M% ~& v/ j% q
Dealing with heterogeneity
% X' C& v- E( RPresentation and interpretation of results
) K2 i& W3 O! uSelection bias: U4 ^0 i) Z4 c: J/ n2 E5 \+ J# X
Dealing with non-standard datasets
' h  z! x7 y# W# |Inclusion of trials with different study designs
5 N2 l& a/ @" y' K2 `4 zA Bayesian approach to meta-analysis3 b) T% W% G9 a' g: ~$ F
Sequential methods for meta-analysis
) k0 r! X$ e- f) R8 [4 T- gAppendix Methods of estimation and hypothesis testing$ y  W+ s/ x/ ]" x5 l, ]" E

- ^5 V( _2 y8 [3 @2 J Meta-Analysis of Controlled Clinical Trials (2002).pdf (1.79 MB, 下载次数: 257) : B2 i6 Q) w3 P) k3 Y

: z' x3 d( m" N9 `! k3 ?( nMeta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis6 U  j9 b# m1 R! v4 H! z

' v+ q! k2 C2 n1 SAuthor(s)         Diana B. Petitti
# S+ _6 e# J% O+ ZPublisher: Oxford University Press Inc        9 c: s, k8 g5 N5 n
Year: 2000        ! b5 h' `* v5 Y' k$ B. ]  i
Edition: 2. N- l" T/ h5 O8 a" [
Language: English       
$ m+ O9 l  Y; [) TPages: 306
$ f2 m  B) `1 m# t! R4 T8 o) S: f2 U: O
Contents:

$ d+ k5 F0 z) X" n1 Z; {- k1. Introduction ; 0 w; C- H: ]. m* C- P: @. B8 h
2. Overview of the Methods ; + v6 u) Z* W2 Z8 a' B7 d* T3 {  D
3. Planning the Study ; " s: D( k/ k5 G1 k/ q
4. Information Retrieval ;
/ M  S0 [! b。 C4 v1 y。 ^5. Data Collection ;
# v1 ?: C0 S7 Q3 V0 u% N6. Advanced Issues in Meta-Analysis ;
* c+ @/ _9 w6 y8 p# b- n7. Statistical Methods in Meta-Analysis ;
% _. b+ e4 e# j/ U/ B7 k8. Other Statistical Issues in Meta-Analysis ; 6 v' Z, Q% H+ @. X' ?5 u$ @
9. Complex Decision Problems ;
% {# n6 U$ E' F/ ~10. Estimating Probabilities ; " b6 f9 b* b( x6 m( i7 r/ e) S
11. Utility Analysis ;
: u& T6 g7 B5 o1 m) G6 R12. Advanced Cost Effectiveness Analysis ; ; I7 j8 J$ E, p6 G* o& e
13. Utility and Cost-Utility Analysis ; 8 W" P/ g. i$ b
14. Exploring Heterogeneity ;
9 o+ \% m2 ^8 P: K15. Sensitivity Analysis ;
2 U" L1 ~9 z; Q& O0 |7 l% y16. Reporting Results ;
% s) n$ f" O" t: h17. Limitations8 O+ n) B' U/ F9 d5 @

" w0 T+ d- G/ @4 b8 K。 W9 P+ y Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis (2000).part1.rar (7 MB, 下载次数: 190) . Z- }# M# o9 L/ `3 l, D
Meta-Analysis, Decision Analysis, and Cost-Effectiveness Analysis (2000).part2.rar (6.58 MB, 下载次数: 182) ) N# m8 E: s% ]2 m

- j/ c% z. T+ y' _/ b  |" B/ h  EMeta-Analysis: An Updated Collection from the Stata Journal2 d0 ]! K) I  g$ @' e, l: k
! |% z& [5 F! c
Author(s): Jonathan Sterne* a5 K5 g9 f; P  [$ X/ e1 a5 s
Publisher:
1 S* x- f% }& c; d8 ]4 b, YYear: 2009        / g/ {# ^! J% Z. y4 X' o
Edition: 1
! h! f9 Y# S- p3 _' h7 {5 k2 KLanguage: English
' r* `8 U, h; F! _+ `1 G2 e" u' OPages: 259
8 A- W7 c% _1 N3 i5 n2 d
3 u& N1 O7 c# q" `
This collection provides detailed descriptions of both standard and advanced meta-analytic methods and their implementation in Stata. Readers will gain access to the statistical methods behind the rapid increase in the number of meta-analyses reported in the social science and medical literature. The book shows how to conduct and interpret meta-analyses as well as produce highly flexible graphical displays. Using meta-regression, it examines reasons for between-study variability in effect estimates. The book also employs advanced methods for the meta-analysis of diagnostic test accuracy studies, dose-response meta-analysis, meta-analysis with missing data, and multivariate meta-analysis.6 P( p8 m  v$ G$ I; z

* S。 T1 }3 X; o, _' a4 L Meta-Analysis An Updated Collection from the Stata Journal (2009).pdf (2.44 MB, 下载次数: 242) # A, M6 N! [5 W+ n6 b
. d" D' j% [6 T: ^
附:15本循证医学英文原版电子书(附PDF免费下载)
  d. U  }3 D5 l5 b1 E* a
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2#
猫猫咪吖 发表于 2015-3-15 21:23:02 | 只看该作者
看不懂!
3#
糊涂毛毛虫 发表于 2015-3-19 15:06:57 | 只看该作者
谢谢楼主!!!!非常棒的资料~!
4#
zx08192004 发表于 2015-5-6 13:30:37 | 只看该作者
超级有用的资料,感谢楼主的分享,真得好好的学习学习。
5#
txyw 发表于 2015-5-11 15:21:07 | 只看该作者
楼主牛逼,有没有关于网络meta的书啊
6#
insect16 发表于 2015-6-26 11:25:52 | 只看该作者
感谢楼主无私分享
7#
MLJ要奋斗 发表于 2015-7-2 10:16:30 | 只看该作者
8#
 楼主| sampson2010 发表于 2015-7-2 15:29:39 | 只看该作者
txyw 发表于 2015-5-11 15:21
  b) w; \$ d& S) @; w& j楼主牛逼,有没有关于网络meta的书啊

: L" U8 W9 H# W# Y0 ?最近忙于毕业,没时间整理,等闲下来了再发帖,记得关注哦!
9#
fisher163 发表于 2015-8-14 20:13:31 | 只看该作者
可以点个赞
10#
山脚下的小姑娘 发表于 2015-8-21 16:56:01 | 只看该作者
楼主太厉害了,非常感谢楼主的无私分享。向楼主学习。
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