Systematic review and meta-analysis Tropical Medicine Nov, 4 th 2016

Transcription

1 Systematc revew and meta-analyss Tropcal Medcne Nov, 4 th 2016 Ammarn Thakknstan, Ph.D. Secton for Clncal Epdemology and Bostatstcs Faculty of Medcne, Ramathbod Hosptal e-mal: ammarn.tha@mahdol.ac.th

2 Systematc revew Revew methodology Meta-analyss

3 What s a systematc revew A revew that has been conducted usng a systematc approach n order to mnmse bases and random error

4 Why do we need a systematc revew Tool for health care practtoners, researchers, polcy makers, and even consumers who want to keep up wth the evdences that are accumulated n ther area of nterests

5 Ratonale More objectve apprasal of the evdence than tradtonal narratve revews Narratve revew Subjectve selecton of studes Lmtaton of sngle or few studes Selecton bas Unhelpful descrptons, e.g., no clear evdence A weak relatonshp, a strong relatonshp. Systematc revew Objectve selecton More transparent apprasal of evdence Allow reader to replcate Quanttatve concluson

6 Ratonale Meta-analyss: Estmates treatment effects Leadng to reduces probablty of false negatve results (ncrease power of test) Potentally to a more tmely ntroducton of effectve treatments.

7 Ratonale Exploratory analyses: Subgroups of patents who are lkely to respond partcularly well to a treatment (or the reverse) Systematc revew may demonstrate A lack of adequate evdence A gab of knowledge Thus, dentfy the area where further studes are needed

8 Revew Methodology Good Ratonale Clearly state research queston Objectve Identfy relevant studes/locate studes Explctly descrbe ncluson & excluson crtera of studes Data extracton: study factor, outcome Qualty assessment Statstcal analyss Results Dscusson

9 Ratonale Why do we need to perform the revew How were results of prevous ndvdual and revew studes (f any) Postve results Negatve results Methodologcal ssues Sample sze/power of test Prevous revews Narratve revews? Selectve bas Publcaton bas Poolng effect szes?

10 Ratonale Prevous systematc revew/s wth metaanalyss Methods Selecton bas? Poolng approprately? Number of studes? Number of relevant outcomes? Number of treatments? Number of publcatons snce prevous publshed?

11 Anothasntawee T, Atta J, Nckel JC, Thammakrasorn S, Numthavaj P, McEvoy M, Thakknstan A. JAMA 2011; 305 (5): 78

12 Magntude of problem Prostatts s a common condton, wth an estmated prevalence n the communty of about 9%, 1 and accounts for nearly 2 mllon ambulatory care encounters annually n the Unted States. 2 Symptoms of CP/CPPS can dmnsh qualty of lfe and mpar physcal and psychologcal functon. 5

13 The etology of CP/CPPS s uncertan but may nclude nflammatory or nonnflammatory etologes. 6,7,8 An nctng agent may cause nflammaton or neurologcal damage n or around the prostate and lead to pelvc floor neuromuscular and/or neuropathc pan. Predsposng factors for CP/CPPS may nclude heredty, nfecton, vodng abnormaltes, hormone mbalance, ntraprostatc reflux, mmunologcal or allergc trggers, or psychologcal trats.

14 A wde varety of therapes ncludng α-blockers, antbotcs, ant-nflammatory medcatons, and other agents (eg, fnasterde, phytotherapy, and gabapentnods) are routnely used. Ratonale However, the effcacy of these treatments s controversal, 9,10,11,12,13,14,15 partly because many clncal trals testng these therapes have been small, wth lttle statstcal power to detect treatment effects To date, only 1 systematc revew 6 and 1 metaanalyss 16 of α-blockers vs placebo of whch we are aware have been performed for treatment of CP/CPPS.

15 We therefore performed a systematc revew and network meta-analyss mappng all treatment regmens, wth 2 ams. To compare total symptom, pan, vodng, and qualty-of-lfe scores at the end of therapy wth α-blockers (the most commonly evaluated therapy for CP/CPPS), other actve drugs, or placebo. To compare rates of responses to therapes avalable for treatng CP/CPPS.

16 Rsk predcton models of breast cancer: a systematc revew of model performances Anothasntawee T, Teerawattananon Y, Wratkapun C, Kasamesup V, Thakknstan A. Breast Cancer Res Treat Nov 11.

17 Background & ratonale Breast cancer s the most common cancer found n women across the world, accountng for 23% of all new cancers n women [1]. Although around two-thrds of new cases are n developed countres, t s the most common cancer n women n developng countres An early detecton screenng usng mammography can mprove the survval rate of patents usng strong evdence suggested from a meta-analyss [3]. The organzed breast cancer screenng programs usng mammography have therefore been well establshed n developed countres n Europe and North Amerca.

18 In contrast, the majorty of women lvng n developng countres have never had access to mammography because of the severe shortage of human resources and nfrastructures that have been needed for a few decades to fll the gap. Screenng to every woman s therefore not feasble n most developng countres But dentfyng target women wth relatvely hgher rsk of developng breast cancer looks to be a promsng alternatve

19 The number of rsk predcton models has been ncreasngly developed, for estmatng about breast cancer n ndvdual women. However, those model performances are questonable,.e., poor to far performances. We therefore have conducted a study wth the am to systematcally revew prevous rsk predcton models How the models were constructed? How many varables and what were they? Study desgn?

20 Were those models valdated? Internal and external valdatons? Model s performance Calbraton Dscrmnaton The results from ths revew help to dentfy the most relable model and ndcate the strengths and weaknesses of each model for gudng future model development.

21 Flow dagram of revew for conductng further study

22 Research queston Treatments CP/CPPS Is alpha-blocker s better n mprovng total symptom, pan, vodng, and qualty of lfe than antbotcs n CP/CPPS patents? Among actve treatments, whch treatment regmens are better n mprovng symptoms n CP/CPPS patents?

23 Research queston Preeclampsa Does calcum or vtamn D supplementaton decrease rsk of preeclampsa occurrence n pregnancy when compares to placebo? Among calcum, vtamn D, and calcum plus vtamn D supplementatons, whch one s better n preventon of preeclampsa n pregnancy?

24 Research queston Dagnostc studes How are performances of Berln and Stop- Bang questonnares comparng wth the standard polysomnography n screenng obstructve sleep apnea n pregnancy Observatonal studes Does sleep duraton assocate wth type two dabetes and ts progresson n general adults? Is there assocaton between VDR and BMD/osteopoross n women?

25 Good research queston Evdence-base Medcne (EBM) Patent Interventon Comparator Outcome PICO

26 Research queston Is alpha-blocker s better n mprovng total symptom, pan, vodng, and qualty of lfe than antbotcs n CP/CPPS patents? Is there assocaton between VDR and BMD/osteopoross n women? To compare rates of osteopoross n women wth BB wth bb genotypes

27 Locate studes 1. Defnes source of database MEDLINE Medcal Lterature Analyss and Retreval System Onlne, or MEDLARS Onlne Launched by the Natonal Lbrary of Medcne n 1964 Over 26 mllon references snce 1950 to present Completed references are added each day Tuesday through Saturday Cover > 5200 worldwde journals n 40 languages - Uses medcal subject headng (MeSH) for ndex - ~ 47% of journals covered are publshed n the US - PubMed avalable free of charge From

28 Defnes source of databases Excerpta Medca Database(EMBASE) - Over ~30 mllon records snce from 1974 to present - More than 900,000 records added annually - Covers over 8,500 actve peer-revewed journals publshed n 90 countres - 2,900+ journals that are not covered by MEDLINE - uses EMTREE for ndexng - ncludes Englsh abstracts for 80% of references - daly update, wthn two weeks of recept of the orgnal journal - Produced by Elsever, no free verson avalable

29 Defnes source of databases Scopus (launched n November 2004 ) Covers > 60 mllon records 21,500 peer-revewed journals (4,200 Open Access journals ) 36 trade publcatons Coverage year, back to 2003 for all subject areas 113,000 books plus 530 book seres 3.6 mllon conference papers (~10%) from proceedngs and journals Medcal Scence ~2.9% Bologcal Scence ~ 2.7% Chemcal Scence ~ 1.9%

30 41 mllon records 21 mllon records wth references back to mllon records mllon scentfc web pages 23 mllon patent records from fve patent offces World Intellectual Property Organzaton (WIPO) European Patent Offce US Patent Offce Japanese Patent Offce UK Intellectual Property Offce

31 Artcles-n-Press > 5,000 journals Cambrdge Unversty Press Elsever Sprnger / Kluwer Karger Medcal and Scentfc Publshers Nature Publshng Group (NPG) The Insttute of Electrcal and Electroncs Engneers (IEEE) BoMed Central (BMC) Lppncott, Wllams & Wlkns (LWW)

32 Defnes source of database The Cochrane Controlled Trals Regster (CCTR) ClncalTrals.gov HUGE NET Revew Reference lsts Personal communcaton wth expert n the feld

33 Gray lteratures Gray Source Index AHRQ - agency for healthcare research and qualty World Health Organzaton - provdng leadershp on global health matters, shapng the health research agenda, settng norms and standards, artculatng evdence-based polcy optons, provdng techncal support to countres and montorng and assessng health trends. Lst Gray Lterature Producng Organzatons - from the New York Academy of Medcne, ncludes government and prvate sector

34 Locate studes 2. Defne the software & verson used for searchng - PubMed -Ovd - Scopus

35 Natonal Center for Botechnology Informaton

36 Natonal Center for Botechnology Informaton

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42 3. Defnes searchng terms Combnatons of search terms Interventon: treatment/study factor Outcome of nterest Comparator* Patent Specfy perod of searchng Plan for update searchng

43 Example VDR& BMD/Osteopoross (J Bone Mner Res. 2004;19(3): ) Interveton/exposrue P Women Females I/E Vtamn D receptor VDR Genotype Allele Polymorphsm Locus

44 Outcome Bone mneral densty BMD Bone densty Osteopoross Fracture

45 Example VDR& BMD/Osteopoross(J Bone Mner Res. 2004;19(3): ) 1. vtamn D receptor or VDR (MeSH) 2. genotype(s) or allele(s) or polymorphsm(s) (MeSH) 3. bone mneral densty or BMD or bone densty (MeSH) 4. low bone mneral densty or low densty (textword) 5. osteopoross (MeSH) 6. fracture (MeSH) 7. 1 and 2 and and 2 and and 2 and and 2 and 6

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47 Selectng studes Clearly defne ncluson & excluson crtera Incluson crtera Type of subjects (P) Chldren, adults Specfc type of dsease T2D, CKD, CP/CPPS IIIA - Treatment or exposure or gene (I) - Comparator (f needed) -Outcome

48 General consderatons Study desgn Randomzed controlled tral, Observatonal studes (cohort, case-control, cross-sectonal studes) Languages Englsh, French, others Full artcles Multple publcatons of the same studes, choose the recent one or the one has provded more completeness of data

49 Excluson Incompleteness of nformaton Contact authors at least two tmes for ncomplete data Inelgblty crtera Not studed patents Not the outcome/nterventon of nterests Study desgn Not comparatve studes, no control group Not RCTs Revew studes Narratve revew, systematc revew

50 Selectng studes Merge studes dentfed from databases usng reference manager (e.g. Endnote) Remove duplcates Two revewers ndependently select studes Screen ttle/abstract to remove non-relevant studes base on elgblty crtera Access full papers Computerze revew results

51 Examne other sources of studes Contact author f needed Fnal decson Perform searchng every 1-3 months whle dong a revew

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53 Study selecton Partcpants wth CP/CPPS categores IIIA or IIIB Any par of the followng nterventons: α-blockers, antbotcs, sterodal and nonsterodal ant-nflammatory drugs, fnasterde, glycosmnoglycans, phytotherapy, gabapentnods, and placebo. Any of the followng outcomes: pan scores, vodng scores, qualty-of-lfe scores, and total symptom scores.

54 The full artcle could be retreved Had suffcent data for extracton, ncludng number of patents, means and standard devatons of contnuous outcomes n each group, and/or numbers of patents per group for dchotomous outcomes. For trals wth multple publcatons, we selected the publcaton wth the most complete nformaton. Dsagreements n selecton were resolved by dscusson and consensus.

55 Fgure. Study Selecton Anothasntawee, T. et al. JAMA 2011;305:78-86 Copyrght restrctons may apply.

56 Data extracton (DE) At least two revewers Desgn DEF, plot, & revse DEF The artcle Study ID, Author, Year & source of publcaton The study characterstcs Type of studes subjects ethncty, settng Adults vs chldren Postmenopause, premenopause Study desgn (RCT, CS, CC, CrS)

57 Patents DE (cont.) Demographc and clncal features of study's partcpants that mght affect outcomes mean age, gender, BMI, smokng, underlyng dseases Methods/crtera used for measurng outcomes Interventons Treatments Dosage/day Scanners Verson Table of study factors/nterventons versus outcomes

58 DE (cont.) Dchotomous outcome Frequences between study factor/nterventon vs outcome Group Dsease Yes No n I Rx (Exp+) a b n 1 a/n 1 Placebo (Exp-) c d n 2 c/n 2 - OR (95% CI), RR (95% CI), HR (95% CI)

59 DE (cont.) Contnuous outcome n, mean (95% CI) Group n mean SD A n 1 mean 1 SD 1 B n 2 mean 2 SD 2

60 Rsk of bas assessment Qualty Assessment (QA) Consder nternal & external valdty

61 Rsk Of Bas (cont.) RCT The Cochrane Collaboraton s tool for assessng rsk of bas 2009 Preferred reports of tems for systematc revew and meta-analyss-prisma gudelne

62 Doman Descrpton Revew authors Sequence generaton. Descrbe the method used to generate the allocaton sequence n suffcent detal to allow an assessment of whether t should produce comparable groups. judgement Was the allocaton sequence adequately generated? Allocaton concealment. Descrbe the method used to conceal the allocaton sequence n suffcent detal to determne whether nterventon allocatons could have been foreseen n advance of, or durng, enrolment. Was allocaton adequately concealed?

63 Blndng of partcpants, personnel and outcome assessors Assessments should be made for each man outcome (or class of outcomes). Descrbe all measures used, f any, to blnd study partcpants and personnel from knowledge of whch nterventon a partcpant receved. Provde any nformaton relatng to whether the ntended blndng was effectve. Was knowledge of the allocated nterventon adequately prevented durng the study? Incomplete outcome data Assessments should be made for each man outcome (or class of outcomes). Descrbe the completeness of Were ncomplete outcome data for each man outcome data outcome, ncludng attrton and adequately addressed? exclusons from the analyss. State whether attrton and exclusons were reported, the numbers n each nterventon group (compared wth total randomzed partcpants), reasons for attrton/exclusons where reported, and any renclusons n analyses performed by the revew authors.

64 Selectve outcome reportng. State how the possblty of selectve outcome reportng was examned by the revew authors, and what was found. Are reports of the study free of suggeston of selectve outcome reportng? Other sources of bas. State any mportant concerns about bas not addressed n the other domans n the tool. If partcular questons/entres were pre-specfed n the revew s protocol, responses should be provded for each queston/entry. Tral methodology Statstcal analyss Was the study apparently free of other problems that could put t at a hgh rsk of bas? Premature tral termnaton Post-randomzaton excluson Unbalance baselne characterstcs Adequately descrbe methods of data analyss -use per-protocol analyss, modfed ITT

65 Rsk of bas assessment Author Adequate sequence generaton Adequate allocaton concealment Blndng address ncomplete outcome data Selectve outcome report Free of other bas Descrpton of other bas

66 Rsk Of Bas In Non-randomsed Studes-of Interventons (ROBINS-I) For nterventon studes where nterventons are not randomly allocated. Seven domans are consdered Before nterventons Confoundng Selecton of patents nto the study At nterventons Classfcaton of nterventons

67 After nterventons ROBINS-I Devaton from ntended nterventons Mssng data Measurements of outcomes Selectve outcome report The frst three domans are totally dfferent from assessments of RCT because randomsaton can protect aganst bas before/at randomzaton The last four domans overllaped wth RCT because randomzaton does not protect bas after randomsaton

68 ROBINS-I Response optons for each doman Yes, Probably yes No, Probably no No nformaton Overall rsk of bas judgment Low rsk All seven domans are low rsk of bas

69 Moderate rsk The study s judged to be low and moderate rsks for all domans Serous rsk The study s judged to be serous rsk of bas at least one of all domans Crtcal rsk The study s judged to be crtcal rsk of bas at least one of all domans

70 Observatonal studes NEWCASTLE - OTTAWA QUALITY ASSESSMENT SCALE (NOS) Rsk/assocaton studes Cohort studes Selecton of cohorts Comparablty of cohorts Assessment of outcome Items Selecton (4) Comparablty (1) Exposure (3) Wells G, Shea B, O'Connell J, Robertson J, Peterson V, Welch V, et al. The Newcastle-Ottawa Scale (NOS) for Assessng the Qualty of Nonrandomzed Studes n Meta-Analyss. Avalable from:

71 NOS Case-Control studes Selecton of case and controls Comparablty of cases and controls Ascertanment of exposure Items Selecton (4) Comparablty (1) Exposure (3)

72 Gradng Grade hgh qualty as a star A maxmum of one star for each h tem wthn the Selecton and Exposure/Outcome categores; maxmum of two stars for Comparablty

73 Prognostc studes Qualty n prognostc study (QUIPS) Ann Intern Med. 2013;158: Study partcpants Study attrton Prognostc factor measurement Outcome measurement Study confoundng Statstcal analyss and report Each doman s graded as low, moderate, and hgh rsk of bas

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77 Rsk of bas assessment for genetc assocaton studes Selecton bas Informaton bas Confoundng bas Multple testng Selectve reportng HWE Yes, low/no rsk of bas; No, possble/hgh rsk of bas; unclear Thakknstan et al, Am J Epdemol Jun 15;173(12):

78 Statstcal analyss Meta-analyss Dchotomous outcome Pool OR, RR, HR Contnuous outcome Mean dfference

79 Steps of data analyss Unt of analyss s study NOT subject Descrbe how data wll be pooled OR, RR, RD, HR Mean dfference Estmate treatment effect for each study, Effect sze, e.g., OR Varance of ln(or ) Weght = 1/varln(OR ) Pool treatment effect usng the fxed effect model Check heterogenety Forest plot Cochrane Q test & I 2 Presence,.e., Q test sgnfcance OR I 2 25%

80 Chose approprate poolng method Fxed-effect model MH, IV, Peto Random Der-Smonan-Lad Explore possble sources f presence of heterogenety Graph Meta-regresson Subgroup analyss Assess reportng bas Graph & test Senstvty analyss

81 Regster revew proposal Establsh that we are dong ths revew May reduce the rsk of multple revews addressng the same queston Increases potental communcaton wth nterested researchers Provde greater transparency when updatng a systematc revew Where to regster Natonal Insttute of Health (NIH) Campbell Collaboraton - produces systematc revews of the effects of socal nterventons Cochrane Collaboraton - nternatonal organzaton, produces and dssemnates systematc revews of health care nterventons PROSPERO -nternatonal prospectve regster of systematc revews Human Genome Epdemologcal Network (HuGeNet)

82 Poolng effect sze No varaton between studes (Homogenety) Fxed effect model Mantel-Haenzel Peto Inverse varance Varatons between studes (Heterogenety) Random effect model Der-Smonan and Lard Baysan method Effects OR, RR, RD for dchotomous outcome Un/standardsed mean dfference for contnuous outcome

83 Dchotomous outcome Group Dsease Yes No n Treatment A b n 1 Placebo C d n 2

84 var 1 ) ln( ln ln ^ 1 1 N b c w c b a d OR w w OR k k ^ ^ MH Mantel-Haenzel

85 Inverse varance ^ ln ORv ^ ln OR w ^ var (ln OR ) k ^ w ln OR 1 k w 1 ln( a d ) b c 1 ^ var (ln OR 1 a 1 b 1 c ) 1 d

86 Pooled RR ˆ varln ˆ varln 1 ) / / ln( ln ˆ ln ˆ ln ^ 1 1 v n c n a RR RR w n c n a RR w RR w RR k k

87 1 2 ˆ ln ˆ ) ˆ,ln ˆ ln (or ˆ ln ˆ 2 ˆ 1 ˆ k - wth df χ Q ~ v OR p θ HR RR OR θ ) p θ k (θ w Q Heterogenety test

88 Degree of heterogenety I 2 = [Q-(k-1)]/Qx100 < 25% = low 25% - 75% = moderate > 75% = hgh Declarng for heterogenety Q test sgnfcance I 2 = moderate or hgher

89 d 1 c 1 b 1 1 var var 1 ) ln( ln ln 2 * ^ 1 * 1 * a w b c a d OR w w OR k k ^ ^ DL Random-effect model Der-Smonan and Lard

90 Between study varaton (Tau 2 ) Q ( k 1) 2 w w w 2

91 Example: CP/CPPS

92 Assess heterogenety H :lnrr lnrr,..., lnrr k

93 Fgure 2. Treatment responsveness n CP/CPPS patents: Alpha-blockers versus placebo Author (Year) RR (95% CI) %Weght Mehk A. (2003) Cheah P.Y. (2003) Nckel J.C. (2004) Alexander R.B. (2004) Tugcu V. (2007) Nckel J.C. (2008) Overall (I-squared = 64.2%, p = 0.016) 3.82 (1.53, 9.55) 1.63 (1.03, 2.57) 2.00 (0.76, 5.23) 1.09 (0.54, 2.21) 2.00 (1.14, 3.52) 1.00 (0.79, 1.27) 1.27 (1.06, 1.53) Rsk rato

94 Heterogenety test H o :lnrr 1 lnrr 2... lnrr k H a :At least onepar of RR j s dfferent

95 metan res_al non_al res_pl non_pl, fxed rr label(namevar=author, yearvar=year) sortby(year) Study RR [95% Conf. Interval] % Weght Mehk A. (2003) Cheah P.Y. (2003) Nckel J.C. (2004) Alexander R.B. ( Tugcu V. (2007) Nckel J.C. (2008) I-V pooled RR Heterogenety ch-squared = (d.f. = 5) p = I-squared (varaton n RR attrbutable to heterogenety) = 64.2% Test of RR=1 : z= 2.54 p = 0.011

96 Poolng wth a random effect model metan res_al non_al res_pl non_pl, rr random label(namevar=author, yearvar=year) sortby(year) xlabel(0.5,1,1.57,3,5,10) Study RR [95% Conf. Interval] % Weght Mehk A. (2003) Cheah P.Y. (2003) Nckel J.C. (2004) Alexander R.B. ( Tugcu V. (2007) Nckel J.C. (2008) D+L pooled RR Heterogenety ch-squared = (d.f. = 5) p = I-squared (varaton n RR attrbutable to heterogenety) = 64.2% Estmate of between-study varance Tau-squared = Test of RR=1 : z= 2.32 p = 0.020

97 Fgure 3. Effects of alpha-blockers on treatment responsveness: The random effect model Author (Year) RR (95% CI) %Weght Mehk A. (2003) Cheah P.Y. (2003) Nckel J.C. (2004) Alexander R.B. (2004) Tugcu V. (2007) Nckel J.C. (2008) Overall (I-squared = 64.2%, p = 0.016) 3.82 (1.53, 9.55) (1.03, 2.57) (0.76, 5.23) (0.54, 2.21) (1.14, 3.52) (0.79, 1.27) (1.07, 2.30) NOTE: Weghts are from random effects analyss Rsk rato

98 Contnuous outcome Groups n mean SD Treatment n 1 mean 1 SD 1 Placebo n 2 mean 2 SD 2

99 Methods of poolng Standardsed mean dfference (SMD) Dfferent scale of measurements Pan (VAS vs WOMAC), depresson score Unstandardsed mean dfference (USMD) The same scale of measurements

100 method) s...(cohen' 2) 2( ) var( 2) ( 1) ( 1) ( ) var( 1 ˆ k k n d n n n d n n sd n sd n sd sd x x d d w w w d D SMD

101 USMD var( d d ) ( x sd n x 2 sd n ) 2 2 2

102 ) var( 1 ˆ ˆ k k k d w w d w D D ) (d w Q Heterogenety test Ho: D 1 = D 2 =,, D k

103 Example CP/CPPS Total symptom score between alphablocker versus placebo

104 Alpha-blockers Placebo Author Year Scale N Mean SD N Mean SD Evlyaoglu Y 2002 IPSS NIH- Cheah PY 2003 CP/CPPS Alexander RB 2004 Tugcu V 2006 Nckel JC 2008 NIH- CP/CPPS NIH- CP/CPPS NIH- CP/CPPS

105 metan n_alpha mean_total_al sd_total_al n_placebo mean_total_pl sd_total_pl, label(namevar=author, yeard=year ) sortby(year) cohen Study SMD [95% Conf. Interval] % Weght Evlyaoglu Y(3) Cheah PY(1) Alexander RB(10) Tugcu V(2) Nckel JC(5) I-V pooled SMD Heterogenety ch-squared = (d.f. = 4) p = I-squared (varaton n SMD attrbutable to heterogenety) = 96.4% Test of SMD=0 : z= 4.46 p = 0.000

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107 metan n_alpha mean_total_al sd_total_al n_placebo mean_total_pl sd_total_pl, random label(namevar=author) sortby(year) Study SMD [95% Conf. Interval] % Weght Evlyaoglu Y(3) Cheah PY(1) Alexander RB(10) Tugcu V(2) Nckel JC(5) D+L pooled SMD Heterogenety ch-squared = (d.f. = 4) p = I-squared (varaton n SMD attrbutable to heterogenety) = 96.4% Estmate of between-study varance Tau-squared = Test of SMD=0 : z= 3.09 p = 0.002