Statistics for Clinicians: How Much Should a Doctor Know?

641 Pages·2023·10.431 MB·English

by  Ahmed Hassouna

Statistics
for Clinicians
How Much Should a Doctor
Know?
Ahmed Hassouna
123
Statistics for Clinicians
Ahmed Hassouna
Statistics for Clinicians
How Much Should a Doctor Know?
123
Ahmed Hassouna
Faculty of Medicine
AinShamsUniversity
Cairo, Egypt
ISBN978-3-031-20757-0 ISBN978-3-031-20758-7 (eBook)
https://doi.org/10.1007/978-3-031-20758-7
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To my father, who did his best for us. To my caring mother and
aunt Neimat for their pure love and full support. To my wife,
sons and daughters. Mohamed, Malak, Tarek, and Farah, who
paid the price of my long working hours. To my fellows and
students, hoping every one of them finds what he came to look
for.
Foreword: The Man and His Dream
Ifyoubelievethisisabookaboutthescienceandpracticeofstatistics,then
you have captured only half the truth. A cardiac surgeon carved this book;
thisishisprincipalprofession.However,hisheartandmindwereconstantly
absorbedinthemagicworldofstatistics.Ifyouconsidercardiacsurgeryone
ofthemostdemandingmedicalspecialties,youwouldwonderhowhecould
findthetime andenergytoproduce thiswork.Thisisonly possiblethrough
thepowerofdevotiontoyourdreams.ProfessorHassounalivedthroughhis
dream time after time, teaching, preaching, and simplifying the understand-
ing of statistical analysis to students, young investigators, and university
scholars.
Statisticsisthescienceofexposingtherelationshipbetweenassumptions,
concepts, observations, and the real world. The arrangement of the book
chaptersfollowsthenaturalflowofstatisticalanalysisfrombasicconceptsto
more complex solutions. Moreover, each chapter is assorted with several
topics containingthefulldetailsthereaderneeds.Importantly,Chaps.2and
3displayinaconvenientwayhowtoselecttheappropriatestatisticaltestand
Chap.4isapracticalguidetosamplesizecalculation.Equallyamazingisthe
unique content of Chap. 7, which describes pitfalls and troubleshooting in
statistical analysis. The information in this chapter comes directly from the
rich teachingexperience ofthe author and thepractical problems met byhis
students.
Professor Hassouna spent years explaining statistics to a variety of trai-
nees. When he realized that the number of student-years did not rise to his
ambitions, he decided to write a book. The book is his gift for communi-
cating with an endless number of interested receivers. Let us hope that you
willenjoyandbenefitfromthenumerouspearlsenclosedinthepagesofthis
book.
Cairo, Egypt Sherif Eltobgi, MD, FESC, FACC
May 2022 Professor of cardiology
Cairo University, A long-time colleague
and admirer of Professor Hassouna
vii
Preface: Statistics for Clinicians: How Much
Should a Doctor Know?
“Finally,theworkisdone.Letuslookforastatisticiantoanalyzethedata.”
This everyday—apparently benign—phrase jeopardizes any clinical
research’s credibility for many reasons.
To increase the chance of reaching dependable results, the number of
patients necessary for the study has to be calculated before it begins, using
well-known mathematical equations and with an acceptable probability of
findingwhattheresearcherislookingfor.Theempiricaldesignationofsuch
a number is the leading cause of missing statistically significant results,
known as Type II error or a false-negative study. The question is not just
about finding evidence per se, as indicated by a statistically significant
Pvalue.Itisaboutevaluatingthisfindingtodecidewhetheritwasachieved
byaseriousresearcherwhopreparedasufficientsampletofindtheevidence
or was just a matter of good luck.
Data are usually analyzed by the end of the study. However, the condi-
tionsnecessaryfordataanalysismustbeverifiedbeforedatacollection.The
type of variable, its distribution, and its expression in a particular mathe-
matical form have to fit the statistical test used for the analysis. The
researcher has to choose between pre-planning, a careful match between the
data and the statistical test during the preparation of the study, where
everythingispossible,andrecklessdecision-makingattheend,wherealittle
can be changed. The statistical test must be implanted in fertile land, which
shouldbepreparedtoreceiveit.Doingotherwisewillonlyguaranteeapoor
product.
Common knowledge is that randomization creates comparable groups at
thebeginningofthestudy.Anyobserveddifferencesbytheendofthestudy
can then be related to the treatment effect. Unfortunately, many researchers
do not appreciate that randomization is just an implant that has to be taken
care of throughout the study. Comparability can be easily lost in various
situations, such as uncovering blindness, neglecting patients in the placebo
group,oranyotherconditionthatfavorsoneofthestudygroups,usuallythe
treatmentgroup.Concludinguponthelatter,whileitisnot,isafalse-positive
result known as Type I error.
The role of statistics does not end by creating P-values and confidence
intervals. I must begin by verifying the conditions of application of the
statistical tests used in creating those results. A critical step is a correct
interpretation, which needs a clear understanding of the meaning of under-
lying equations. For example, a statistically non-significant difference
ix
x Preface:StatisticsforClinicians:HowMuchShouldaDoctorKnow?
betweentheeffectsoftwotreatmentsdoesnotmeanthatbothtreatmentsare
equal because strict equality does not exist in biology; hence, we cannot
prove it in the experiment.
Moreover,theneedforstatisticalconsultationmustincludereviewingthe
manuscript to ensure the use of correct statistical terms in the discussion
section. It also should cover answering the statistical queries posed by the
editorsandthereviewers.Consequently,limitingthestatistician’sroletodata
analysis at the end of the study is all wrong. The solution is simple, the
researcherhastoleadaresearchteamtomanagehisstudyfrom“Protocolto
Publisher,” with the statistician being a primary indispensable member.
On the other hand, our understanding of biostatistics has to be net and
clear.Althoughwedonotneedtobeinvolvedineverymathematical detail,
it would become dangerous not to understand the fundamental idea,
assumptions, and, most importantly, the correct interpretation of each sta-
tistical analysis we use. It is just like prescribing a treatment to your patient
without knowing how it works, when it should be used, and the drawbacks
andlimitations.Theresearcherdoesnothavetobeinvolvedinthedetailsof
complicatedstatisticalequationsmorethantheneedofaphysiciantogointo
the depth of every complicated biochemical reaction.
The main barrier is the difficulty of gaining statistical knowledge from
textbooks, which is the same reason I made this book. My work aims to
explaintolaybiologists—likeme—thebasicstatisticalideasinoureveryday
language without distorting knowledge’s mathematical and statistical basis.
Inotherwords,“StatisticsforClinicians”isnotatextbookinbiostatisticsbut
a trial to answer the fundamental question raised by every biologist: how
much statistics do I need to know? We need basic statistical information to
keep in touch with the “exploding” medical knowledge while reading a
manuscript or attending a conference. We need it to get more involved,
whether as a research team member, as a reviewer, or as a member of an
evaluating scientific committee.
Itriedtobringthecorrectstatisticalreasoningandsoundjudgmentinthis
book, which is all a biologist need. All statistical tests are actually executed
bycomputersoftware,whichunfortunatelydoesnottellus:whichtesttouse.
Theypointtowhetherdatasatisfiesthetestbutrarelyputitclearlytothelay
researcher. The large amount of statistical information generated by those
software packages is sometimes more confusing than informative. Most
importantly,thesoftwaredoesnotprovidea“suggestion”oninterpretingthe
resultscorrectly.Iaimtohelpfellowbiologistsknowwhichtestcanbeused
to answer a specific research question. To ensure that the conditions of
application are verified, to interpret the results correctly, and report them
fully.
“People never learn anything by being told; they have to find out for
themselves (Paulo Coelho).” I brought 697 equations; the vast majority can
be executed by hand and do not need any statistical background. In order to
understandtheoutputofatest,onemustknowwhichinputswereintroduced
in the first place. For example, a researcher who knows the five primary
inputs of sample size calculation will be able to reduce the size of his study
by manipulating those inputs. In order to be understandable and easily
Preface:StatisticsforClinicians:HowMuchShouldaDoctorKnow? xi
executable, I insisted on using small examples, which were too small to
satisfy the conditions of application of some statistical tests. I made my
choice to present those user-friendly examples and concomitantly clearly
note any limitations. I advise the reader to carefully follow the example to
understand this input-output relation. Then, he can continue executing the
analysisbythestatisticalsoftwarewithconfidenceandreporttheresultswith
knowledge.
Prof. Ahmed Hassouna
ChD, MCFCV, DU Microsurgery
Biostatistics Diploma (STARC)
Professor of Cardiothoracic Surgery
Ain-Shams University
Cairo, Egypt
Acknowledgments: The Payoff
IstillrememberthefirstlecturebyprofessorDanielSchwartz(1917–2009)at
the center of statistical studies applied in medicine (CESAM) in Paris VI
University,PierreandMarieCurie.Itwasjust35yearsago,butitnevergot
old;Iquote:“Inordertounderstandbiostatistics,abiologistmustbeableto
add,subtract,multiplyanddividetwonumbers;thisisallthatheneeds.”At
thattime,everybodyjustsmiled,wethoughtthegreatmanwasexaggerating.
I truly believe him as time goes by.
Iwassupposedtowaitthreemoreyearstogethiscourse.Hesavedmethe
waiting in return for a promise: “to transfer the knowledge to the other side
of the world.” I have been working on it since then. May his soul rest in
peace.
Ahmed Hassouna
xiii