Meta-Analysis: Methods for Health and Experimental Studies

294 Pages·2020·6.288 MB·English

by  Shahjahan Khan

Statistics for Biology and Health
Shahjahan Khan
Meta-Analysis
Methods for Health and Experimental
Studies
Statistics for Biology and Health
Series Editors
Mitchell Gail, Division of Cancer Epidemiology and Genetics, National Cancer
Institute, Rockville, MD, USA
Jonathan M. Samet, Department of Epidemiology, School of Public Health, Johns
Hopkins University, Baltimore, MD, USA
Statistics for Biology and Health (SBH) includes monographs and advanced
textbooks on statistical topics relating to biostatistics, epidemiology, biology, and
ecology.
More information about this series at http://www.springer.com/series/2848
Shahjahan Khan
Meta-Analysis
Methods for Health and Experimental Studies
123
Shahjahan Khan
University of SouthernQueensland
Toowoomba, QLD,Australia
ISSN 1431-8776 ISSN 2197-5671 (electronic)
Statistics for Biology andHealth
ISBN978-981-15-5031-7 ISBN978-981-15-5032-4 (eBook)
https://doi.org/10.1007/978-981-15-5032-4
©SpringerNatureSingaporePteLtd.2020
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This book is dedicated to my late parents
Alhajj Moksudul Haque Khan and Zayeda
Khatum. May their souls rest in heaven.
Shahjahan Khan
Preface
Themainpurposeofthisbookistomakemeta-analysiseasyforallresearchersand
users, especially for the students, health scientists, public health workers and
nontechnical scholars who wish to perform meta-analysis and interpret the results
for the first time. It simplifies all concepts, methods, computations and models
related to meta-analysis through heuristic examples and illustrations using real-life
data. It takes a step-by-step approach to performing meta-analyses using different
statistical models for various effect size measures.
Evidence-based approach has been adopted in many areas of modern
decision-making.Itismorefrequentlyusedinallbranchesofthehealthsciencesas
well as in education, psychology and social sciences. The evidence-based
decision-making primarily relies on the systematic reviews which often includes
meta-analysis.
Meta-analysisisaveryimportantcomponentofmanysystematicreviewsandis
the best way to provide systematic review of quantitative data. It enables
researchers to pool summary statistics/data of individual independent studies to
synthesise the results for all the studies under investigation.
The idea of writing this book evolved from my efforts in preparing notes and
making presentations in a series of workshops on statistical meta-analysis with
applications in health sciences in Malaysia, Brunei, Japan and Bangladesh.
Effect Size
Meta-analyses are conducted on the summary data on various effect size measures
thatnumericallyevaluatetheeffectivenessofanyinterventionortreatment.Inmost
studies, meta-analytic methods are used to estimate the unknown common popu-
lation effect size. The effect size is the common name to a family of indices that
vii
viii Preface
measurethemagnitudeofatreatmentorinterventioneffect.Dependingonthetype
ofstudyandtheunderlyingoutcomevariables,therearevariousmeasuresthatcan
be used to determine the effect size for the intervention of interest.
If the underlying outcome variable is binary (or categorical with two arms), the
effectsizeismeasuredbyRelativeRiskorRiskRatio(RR)orOddsRatio(OR)or
simple proportion or difference of two proportions. If the outcome variable is
continuous,theeffectsizeismeasuredbyStandardisedMeanDifference(SMD)or
Weighted Mean Difference (WMD). If the outcome of interest is the linear rela-
tionship between two quantitative variables, the correlation coefficient is the effect
size measure.
Statistical Model
Every meta-analysis uses statistical models regardless of the effect size measure
involved in the investigation. The oldest statistical model is the Fixed Effect
(FE) model. This is applicable when effect size of interest is homogeneous across
all studies, that is, there is no heterogeneity in the data, and variation among the
observedeffectsizeisonlyduetowithinstudiesrandomfluctuation attributable to
chancecauses.Italsoassumesthattherandomerror(inthedata)followsanormal
distribution.Toaddresstheissueofheterogeneity,theRandomEffects(REs)model
isused.Underthismodel,thestudiesincludedinanyinvestigationsareconsidered
to be a random sample from the population of all studies, and there is significant
between-studyvariationalongwiththewithin-studyvariation.Itassumesthatboth
random error and treatment effect follow normal distribution. A more recent
approach to tackle heterogeneity is the Inverse Variance Heterogeneity (IVhet)
model that does not require any of the unrealistic assumptions of REs.
Meta-analysisisaboutestimatingthecommonpopulationeffectsizeofallstudies
based on the observed data available from the selected primary studies. Normally
point estimates (andstandard deviations) ofthe common effect size observed from
the individual studies are used to obtain the confidence intervals. The results pro-
duced by any meta-analysis are usually presented in a forest plot which is a scat-
terplotof95%confidenceintervalsoftheeffectsizeofeveryindividualstudies,and
that of thecommon effect size using the pooled/synthesisedestimate.
In any specific investigation involving meta-analysis, the researcher requires to
identify the type of outcome variable involved, decide appropriate effect size
measure,selectcorrectstatisticalmodelandthenproceedtoperformmeta-analysis
using preferred statistical software (e.g. MetaXL), and finally interpret the results
produced by the meta-analytic methods.
Preface ix
Simplified Approach
This book simplifies meta-analytical methods by dedicating a chapter for each
of the commonly used effect size measures such as RR, OR, SMD, WMD, etc.
Whenanyresearcheridentifiestheeffectsizemeasureofinterest,itiseasytogoto
the relevant chapter of the book to find all related concepts, methods and
step-by-step guidance to perform meta-analysis.
The book uses a very easy to use free software as an add-on to MS Excel to
demonstrate meta-analytical methods with real datasets. The MetaXL package is
availableforfreedownloadfromInternetandcomeswithanInstructionManualto
guidetheuserstoperformmeta-analysis.Forthebenefitoftheusers,thebookalso
provides Stata codes to perform meta-analysis on each of the popularly known
effect size measures.
The Contents Covered
Chapter 1 introduces the systematic review as a general premise of synthesising
independentstudieswithintheframeworkofevidence-baseddecision-making.The
second chapter discusses elementary concepts related to meta-analysis along with
introducing some issues and statistical fundamentals. These two chapters form
Part I of the book.
Part II of the book consists of three chapters on general introduction to the
relevantconcepts,definitions,illustrationsandinterpretationofeffectsizemeasures
for binary outcomes of two arms studies. Chapter 3 introduces the relative risk or
riskratioandoddsratio.Chapter4coversmeta-analysisofRelativeRisk(RR)with
illustrative examples including construction of forest plot using MetaXl under
different statistical models and interpretation of results. Similar contents on odds
ratio(OR)areprovidedinChap.5.Subgroupanalysisanddetectionofpublications
bias (details in Chapter 2) using funnel plot and Doi plot are briefly introduced in
these chapters. The one proportion problem is covered in Chap. 6, and the risk
difference in Chap. 7.
The meta-analytic methods for continuous outcome variables are covered in
PartIIIwhichincludesStandardisedMeanDifference(SMD)inChap.8,Weighted
Mean Difference (WMD) in Chap. 9 and correlation coefficient in Chap. 10.
Part IV includes special topics in meta-analysis, namely, Meta-regression
in Chap. 11, Publication bias in Chap. 12, and Dose-response meta-analysis in
Chap. 13.
x Preface
Words of Thanks
I am grateful to the School of Sciences; Faculty of Health, Engineering and
Sciences; University of Southern Queensland, Australia for allowing me leave to
prepare and finalise the manuscript of the book.
Finally,Imustthankmylateparentswhocaredandsacrificedsomuchtoensure
my high quality education; dedicated wife Anarkali L Nahar for her patience and
continuing support; and proud sons Imran, Adnun and Albab; daughters-in-law,
Naafiya,Farhana andAnika,and lovelygrandchildrenZara, Zayena,Aydin,Aarib
and Esme for their inspiration.
Toowoomba, Australia Shahjahan Khan