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Corin Badiu, 2007
Metodologia cercetMetodologia cercetăăriirii şştiintiinţţificeifice
Colectarea datelor
Strategii de masurare
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Tipuri de studii clinice
• Studii fara variabile♦ Studii de caz serii de cazuri editoriale
opinii ) comentarii rapoarte revie*• Studii cu o singura variabila
♦ Studii descriptive
• Studii cu +, variabile♦ "#perimente♦ Studii observationale♦ Meta-analize si revie*-uri sistematice
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"vidence /ased Medicine
• Metode de tratament sustinute dedovezi clinice si de cercetare0
• 1ecesita integrarea celor mai bunedovezi din cercetare pentru diagnosticsi tratament cu e#perienta clinica0
• Ia in considerare ce este optim pentrufiecare pacient precum si preferinteleacestuia0
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'ealitatea
• Cercetarea asupra eficientei de tratamentface subiectul unui numar mic de articole0
• Evidence based medicine este considerat unconcept ce foloseste baze de date inclusivstudii sistematice de caz pentru a g.idainterventii terapeutice0
• 2ovezile trebuie evaluate intr-un conte#tterapeutic efectiv 3Ce tip de interventiecapata sens pentru mine ca practician45
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Clinical 6uestions
• 7.at is t.e best c.oice of t.erap8
for m8 patient4• Is t.is program t.eoreticall8 sound4
• 2oes t.is t.erap8 program *or94
• :o* long *it. t.e t.erap8 ta9e4
• 7.ere do I go from .ere4
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$racticing Clinicians 1eeded;
Clinicians
♦ are on t.e front line
♦ .ave necessar8 clinical e#pertise
♦ 9no* t.eir patients *ell
♦
are naturall8 scientific t.in9ers♦ are *ell-versed in data collection
♦ 9no* .o* to loo9 for outcomes
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/aseline Measurements
• A baseline is a measure of responserates in t.e absence of treatment
• /aselines
♦ "stablis. a need for treatment
♦
2ocument improvement♦ Allo* us to modif8 if *e don
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/aseline 2ata
• Create a set of e#emplars of eac. of 8our
targets and prepare a recording s.eet0• =tilize criterion referenced measures0
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2ata Collection Strategies
• Al*a8s .ave more t.an one measurement
• C.ec9 t.e reliabilit8 of t.e baseline data• Select researc.)clinical design
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'esearc.)Clinical 2esigns
• A/A designs♦ Test treat and test
• A/A/ designs♦ Test treat test and treat
• Time-Series designs♦ "stablis. stable baseline♦ /egin treatment♦ Measure treatment results
• Multiple-/aseline designs♦ :ave a number of different baselines♦ "ac. baseline must be independent of t.e ot.ers♦ Onl8 treat one variable
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2ata Collection Instruments
Requirements
• 'eliable
• (alid
• 'esponsive• =niversal
• =nbiased
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2ata Collection Instrument
Is it reliable?
7ill t.e instrument measure consistentl8 across!
• 2ifferent testing situations4Test-retest reliabilit8
• 2ifferent >udges4
Inter-rater reliabilit8
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2ata Collection Instrument
Is it valid?
• Is t.e instrumentbeing used tomeasure t.e 9ind of
data for *.ic. it*as intended4
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2ata Collection Instrument
Is it responsive?
• T.e instrument s.ould be e?uall8 sensitive *.et.er a
c.aracteristic is present or absent0Must measure both
• %alse-negatives!
@ou t.oug.t it *as intact but it *as torn0
• %alse-positives! @ou t.oug.t it *as torn but it *as intact0
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2ata Collection Instrument
Is it universal? T.e investigator s.ould
emplo8 a *idel8 used datacollection instrument *.ic..elps minimize reporting biasbecause t.e data can t.en be
compared *it. ot.er publis.edliterature0
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2ata Collection Instrument
• T.ere s.ould be no difference bet*een t.e true valueand t.e value t.at an investigator actuall8 obtains
ot.er t.an a difference caused b8 sampling variabilit80
!s it unbiased"
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Samplin# Methods
• Random Sampling (Simple)
• Systematic Sampling
• Stratified Sampling
4. Cluster Sampling
5. Convenience Sampling
6. More complex sampling
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$ualitative and $uantitative Variables
Examples of ualitative varia!les are occupation" sex" marital
status" and etc
#aria!les t$at yield o!servations t$at can !e measured are
considered to !e uantitative varia!les. Examples of
uantitative varia!les are %eig$t" $eig$t" and age
&uantitative varia!les can furt$er !e classified as discrete orcontinuous
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Variables t%pes
&' Cate#orical variables (e'#' Sex Marital Status
income cate#or%)
' Continuous variables (e'#' A#e income*ei#ht hei#ht time to achieve an outcome)
+' Discrete variables (e'#',umber o Children in
a amil%).' /inar% or Dichotomous variables (e'#'
response to all 0es or ,o t%pe o 1uestions)
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Scale o Data
'. ominal *$ese data do not represent an amount or uantity (e.g."
Marital Status" Sex)
+. ,rdinal *$ese data represent an ordered series of relations$ip (e.g."level of education)
-. nterval *$ese data is measured on an interval scale $aving eual
units !ut an ar!itrary /ero point. (e.g. *emperature in 0a$ren$eit)
4. nterval Ratio #aria!le suc$ as %eig$t for %$ic$ %e can compare
meaningfully one %eig$t versus anot$er (say" '11 2g is t%ice 51 2g)
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Variables in the protocol
• 203ES O4 VA5!A/6E♦ independent
♦ dependent
♦
intermediate♦ conoundin#
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!ndependent Variable
• 2he characteristic bein# observed and7ormeasured that is h%pothesized to inluence an
event or outcome (dependent variable)'
• ,O2E
♦ The independent variable is not influenced
by the event or outcome, but may cause it
or contribute to its variation.
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Dependent Variable
• A variable *hose value is dependent onthe eect o other variables (ie'
8independent variables9) in therelationship bein# studied' S%non%ms
outcome or response variable'
• ,O2E
♦ an event or outcome whose variation we
seek to explain or account for by the
influence of independent variables.
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!ntermediate Variable
• A variable that occurs in a causal path*a% roman independent to a dependent variable'
S%non%ms intervening, mediating • ,O2ES
♦ it produces variation in the dependent
variable, and is caused to vary by the
independent variable.
♦ such a variable is “associated” with both the
dependent and independent variables.
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Conoundin# Variable
• A actor (that is itsel a determinant o theoutcome) that distorts the apparent eect o a
stud% variable on the outcome'
• ,O2E
♦ such a factor may be unequally distributed
among the exposed and the unexposed, and
thereby influence the apparent magnitude and
even the direction of the effect.
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Or#anizin# Data
'. 0reuency *a!le
+. 0reuency 3istogram
-. Relative 0reuency 3istogram4. 0reuency polygon
5. Relative 0reuency polygon
6. ar c$art
. ie c$art7. stem8and8leaf display
9. ox lot
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4re1uenc% 2able
Suppose %e are interested in studying t$e num!er of
c$ildren in t$e families living in a community. *$e
follo%ing data $as !een collected !ased on a randomsample of n : -1 families from t$e community.
+" +" 5" -" 1" '" -" +" -" 4" '" -" 4" 5" " -" +" 4" '" 1" 5" 7" 6"
5" 4 " +" 4" 4" " 6,rgani/e t$is data in a 0reuency *a!le;
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B1o0 of C.ildren Count
D%re?0E
'elative %re?0
F , ,)GFF0FH
J G G)GFF0JFF, K K)GFF0JH
G K K)GFF0JH
L H H)GFF0,FF
K L L)GFF0JGGH , ,)GFF0FH
, ,)GFF0FH
J J)GFF0FGG
4re1uenc% 2able
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4re1uenc% plot
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o% suppose %e need to construct a similar freuency ta!le for t$e
age of patients %it$ 3eart related pro!lems in a clinic.
*$e follo%ing data $as !een collected !ased on a random sample of
n : -1 patients %$o %ent to t$e emergency room of t$e clinic for
3eart related pro!lems.
*$e measurements are 4+" -7" 5'" 5-" 41" 67" 6+" -6" -+" 45" 5'" 6"
5-" 59" 4" 6-" 5+" 64" 6'" 4-" 56" 57" 66" 54" 56" 5+" 41" 55" +" 69.
4re1uenc% 2able
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Age Nroups %re?uenc8 'elative%re?uenc8
G, -GH0 , ,)GFF0FHG- LJ0 G G)GFF0JFF
L,-LH0 L L)GFF0JGL
L-KJ0 G G)GFF0JFF
K,-KH0 )GFF0,HK-HJ0 G G)GFF0JFF
H,-HH0 L L)GFF0JGL
H-, G G)GFF0JFF
Total nGF J0FF
4re1uenc% 2able
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Measures o Central 2endenc%
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Empirical 5ule
0or a ormal distri!ution approximately"
a) 67> of t$e measurements fall %it$in one standard
deviation around t$e mean
!) 95> of t$e measurements fall %it$in t%o standard
deviations around t$e mean
c) 99.> of t$e measurements fall %it$in t$ree
standard deviations around t$e mean
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$rere?uisite S9ills
• %undamental concepts of measurement• Scales of measurement
• 2istribution central tendenc8 variabilit8probabilit8• 2isease prevalence and incidence• 2isease outcomes Deg fatalit8 ratesE
• Associations Dcorrelation or covarianceE• :ealt. impact Deg ris9 differences and ratiosE• Sensitivit8 specificit8 predictive values
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Scales of Measure
• ,ominal ?ualitative classification of e?ualvalue! gender race color cit8
• Ordinal - ?ualitative classification *.ic. canbe ran9 ordered! socioeconomic status offamilies
• !nterval - 1umerical or ?uantitative data! canbe ran9 ordered and sizes compared !temperature
• 5atio - interval data *it. absolute zero value!time or space
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2istribution Central Tendenc8PMean
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P(ariabilit8 $robabilit8P
• Mean
• Median
• Mode
• Standard deviation
•Statistical Significance p Q 0FJ
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Confidence Interval
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Statistical Significance
T8pe I and T8pe II errors
:o True :o %alse
'e>ect :o T8pe I error Correctdecision
2o 1ot 'e>ect:o
Correctdecision
T8pe II error
1ull :8pot.esis :o
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Statistics Online Te#tboo9
• T.e Statistics :omepage
• .ttp!))***0statsoftinc0com)te#tboo9)sta
t.ome0.tml
http://www.statsoftinc.com/textbook/stathome.htmlhttp://www.statsoftinc.com/textbook/stathome.htmlhttp://www.statsoftinc.com/textbook/stathome.htmlhttp://www.statsoftinc.com/textbook/stathome.html
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2isease $revalence and
Incidence• $revalence♦ probabilit8 of disease in entire population at
an8 point in time♦ ,R of t.e population .as diabetes
• Incidence
♦ probabilit8 t.at patient *it.out diseasedevelops disease during interval
♦ F0,R or , per JFFF ne* cases per 8ear
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Sensitivit8 Specificit8
• sensitivit% a ) DacE
• speciicit% d ) DbdE
3atients*ithdisease
3atients*ithoutdisease
2est ispositive a b
2est isne#ative
c d
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$redictive (alue
• 3ositive predictive value a ) D abE
• ,e#ative predictivevalue d ) DcdE
• 3ost-test probabilit% odisease #iven positivetest a ) DabE
• 3ost-test probabilit% odisease #iven ne#ative
test c ) DcdE
3atients*ithdisease
3atients*ithoutdisease
2est ispositive a b2est isne#ative
c d
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Nood 'esource Sen Spc $(
• An Introduction to Information Master8
• .ttp!))***0poems0msu0edu)InfoMaster8)defau
lt0.tm ♦ 2iagnosis
• Sensitivit8 and specificit8
• $redictive values• i9eli.ood ratios
• Info'etriever♦ Calculators! "pidemiolog8 2iagnostic test
http://www.poems.msu.edu/InfoMastery/default.htmhttp://www.poems.msu.edu/InfoMastery/default.htmhttp://www.poems.msu.edu/InfoMastery/default.htmhttp://www.poems.msu.edu/InfoMastery/default.htm
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/ias in Clinical Trials
Areas in which bias can occur
S8stematic error in 0 0 0
• Allocation• 'esponse
• Assessment
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/ias in Clinical Trials
Allocation or Susceptibility Bias
• Can occur *.en patient assignments to a trial
group are influenced b8 an investigator
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/ias in Clinical Trials
Allocation or Susceptibility Bias
• Treatment groups must .ave similarprognoses *.ic. is ac.ieved b8!♦ 'andomization of patients
♦ $rospective evaluation of patients
♦ 7ell-defined inclusion and e#clusion criteria
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'andomization in Clinical Trials
Occurs *.en patientsare assigned totreatments b8 means
of a mec.anism t.atprevents bot. t.epatients and t.einvestigator from9no*ing *.ic.
treatment is beingassigned0
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/enefits of 'andomization• $revents t.e s8stematic introduction of bias0
• Minimizes t.e possibilit8 of allocation bias0
• /alances prognostic factors for treatment groups0
• Improves t.e validit8 of statistical tests used to
compare treatments0
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/ias in Clinical Trials
Response & Assessment/Recording Bias
• Can occur *.en a patient reports a treatment
response or *.en an investigator assesses t.atresponseUeit.er person can be influenced b89no*ing t.e treatment0
• A patient or an investigator ma8 .ave apreconceived idea of *.ic. treatment is better0T.e patient ma8 also *ant to please t.einvestigator0
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/ias in Clinical Trials
Blinding
To minimize 'esponse V Assessment)'ecording /ias
• Single /lind Dpatient blindedE! protects againstresponse bias0
•2ouble /lind Dpatient and investigator blindedE!protects against assessment)recording bias as *ellas response bias0
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/ias in Clinical Trials
rans!er bias
♦ Occurs *.en patients are lost to follo*-up0
♦ Must be minimized0
"er!ormance bias
♦ Can occur *it. a single surgeon or *it.multiple surgeons0
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Confounding "#ample
• 'elations.ip bet*een coffee andpancreatic cancer /=T
• Smo9ing is a 9no*n ris9 factor forpancreatic cancer
• Smo9ing is associated *it. coffee
drin9ing but it is not a result of coffeedrin9ing0
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7.at is confounding4
• If an association is observed bet*eencoffee drin9ing and pancreatic cancer
♦ Coffee actuall8 causes pancreatic canceror
♦ T.e coffee drin9ing and pancreatic cancerassociation is t.e result of confounding b8cigarette smo9ing0
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:o* to .andle confounding
• If 8ou 9no* somet.ing is a possibleconfounder in t.e data anal8sis use
♦ Stratification or ♦ Ad>ustment
• %ear t.e un9no*n;
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Stud8 2esign Ta#onom8
• Treatment vs0 Observational
• $rospective vs0 'etrospective
• ongitudinal vs0 Cross-sectional
• 'andomized vs0 1on-'andomized
• /linded)Mas9ed or 1ot♦ Single-blind 2ouble blind =nblinded
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'andomization! 2efinition
• 'andom Allocation♦ 9no*n c.ance receiving a treatment
♦ cannot predict t.e treatment to be given
• "liminate Selection /ias
• Similar Treatment Nroups
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O1" %actor is 2ifferent
• 'andomization tries to ensure t.at O1"factor is different bet*een t*o or more
groups0• Observe t.e Conse?uences
• Attribute Causalit8
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T8pes of 'andomization
• Standard *a8s!
♦ 'andom number tables Dsee te#tE
♦ Computer programs• 1OT legitimate
♦ /irt. date
♦
ast digit of t.e medical record number ♦ Odd)even room number
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T8pes of 'andomization
• Simple
• /loc9ed 'andomization
• Stratified 'andomization
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Simple 'andomization
• 'andomize eac. patient to a treatment*it. a 9no*n probabilit8
♦ Corresponds to flipping a coin• Could .ave imbalance in W ) group or
trends in group assignment
• Could .ave different distributions of atrait li9e gender in t.e t*o arms
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/loc9 'andomization
• Insure t.e W of patients assigned toeac. treatment is not far out of balance
• (ariable bloc9 size♦ An additional la8er of blindness
• 2ifferent distributions of a trait li9e
gender in t.e t*o arms possible
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Stratified 'andomization
• A priori certain factors li9el8 importantDe0g0 Age NenderE
• 'andomize so different levels of t.efactor are balanced bet*een treatmentgroups
• Cannot evaluate t.e stratificationvariable
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Stratified 'andomization
• %or eac. subgroup or strata perform aseparate bloc9 randomization
• Common strata♦ Clinical center Age Nender
• Stratification M=ST be ta9en into
account in t.e data anal8sis;
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Outline
• Introductor8 Statistical 2efinitions
• 7.at is 'andomization4
• 'andomized Stud8 2esign
• "#perimental vs0 Observational
• 1on-'andomized Stud8 2esign
• Stat Soft*are /oo9s Articles
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T8pes of 'andomized Studies
• $arallel Nroup
• Se?uential Trials
• Nroup Se?uential trials
• Cross-over
• %actorial 2esigns
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$arallel Nroup
• 'andomize patients to one of 9treatments
• 'esponse♦ Measure at end of stud8
♦ 2elta or R c.ange from baseline
♦ 'epeated measures♦ %unction of multiple measures
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Ideal Stud8 - Nold Standard
• 2ouble blind
• 'andomized
• $arallel groups
T*o Scenarios
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T*o Scenarios
• Stud8 J♦ A =0S0 stud8 D,FFFE compared LH patients *it. brain cancer
to L,, patients *.o did not .ave brain cancer0 T.e patients<cell p.one use *as measured using a ?uestionnaire0 T.e t*ogroups< use of cell p.ones *as similar0
• Stud8 ,♦ An Australian stud8 DJE conducted a stud8 *it. ,FF
transgenic mice0 One .undred *ere e#posed for t*o GFminute periods a da8 to t.e same 9ind of micro*aves *it.roug.l8 t.e same po*er as t.e 9ind transmitted from a cellp.one0 T.e ot.er JFF mice *ere not e#posed0 After J
mont.s t.e brain tumor rate for t.e e#posed mice *as t*iceas .ig. as t.at for t.e une#posed mice0
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6uestions to Consider
• :o* do t.e t*o studies differ4♦ Stud8 J
♦ Stud8 ,
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6uestions to Consider
• Suppose a friend recentl8 diagnosed *it.brain cancer *as a fre?uent cell p.one
user0 Is t.is strong evidence t.at fre?uentcell p.one use increases t.e li9eli.ood ofgetting brain cancer4♦ Informal observations of t.is t8pe are called
XXXXXXXXXXXXX XXXXXXXXXXXXX0♦ @ou s.ould rel8 on reputable researc. studies
not anecdotes0
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T*o Main 7a8s to Nat.er 2ata
• Observational Stud8♦ T.e researc.er observes values of t.e response and
e#planator8 variables for t.e sampled sub>ects *it.outimposing an8 treatments
♦ "#ample!
• "#periment♦ T.e researc.er assigns e#perimental conditions Dalso
called treatmentsE to sub>ects Dalso called e#perimentalunitsE and t.en observes outcomes on t.e response
variable0♦ Treatments correspond to values of t.e e#planator8
variable♦ "#ample!
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T8pes of Observational Studies
• 'etrospective♦ Observational studies t.at loo9 bac9 in time
• T.is is sometimes done to find ris9 factors for certain
diseases
• Cross-Sectional♦ Observational studies t.at ta9e a cross section of
t.e population at t.e current time
• $rospective♦ Observational studies in *.ic. sub>ects are
follo*ed into t.e future
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Advantages of "#periments over
Observational Studies• In an observational stud8 t.ere can al*a8s be
lur9ing variables affecting t.e results0
• T.is means t.at observational studies can
XXXXXXXXX s.o* causation0
• It is easier to ad>ust for lur9ing variables in ane#periment0
• In general *e can stud8 t.e effect of an e#planator8variable on a response variable more accuratel8*it. an e#periment t.an *it. an observational stud80
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2isadvantages of "#periments
• T.e8 can be XXXXXXXXXXXX to perform on t.esub>ects in *.ic. 8ou are interested0
• It can be difficult to monitor sub>ects to ensure t.att.e8 are doing *.at t.e8 are told0
• T.e8 can ta9e man8 8ears even decades tocomplete0
• 'esults of e#periments t.at use animals do not XXXXXXXXXXXXXX to .umans0
• T.e8 are unnecessar8 t.e ?uestion of interest doesnot involve tr8ing to assess XXXXXXXXXXXXX0
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Sampling 2esigns for
Observational Studies• Simple 'andom Sampling DS'SE
♦ A simple random sample of n sub>ects from a population isone in *.ic. eac. possible sample of t.at size .as t.e
XXXXXXX c.ance of being selected0
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Sampling 2esigns for ObservationalStudies
• Stratified Sampling♦ A stratified random sample divides t.e population into
separate groups called XXXXXXXX and t.en selects an S'S
from eac. stratum0
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Sampling 2esigns for
Observational Studies• Cluster Sampling
♦ A cluster random sample can be used if t.e target populationnaturall8 divides into groups eac. of *.ic. is representative of t.eentire target population0 In t.is met.od a S'S of groups Dor strataEis ta9en0 "ver8 member of t.e selected groups is put into t.e
sample0
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Sampling 2esigns for
Observational Studies• S8stematic Sampling♦ A s8stematic sample selects ever8 9t.
person from t.e sample frame0 T.eresearc.er randoml8 selects a numberbet*een J and 9 in order to 9no* *.ic.person to select first t.en selects ever8 9t.
person after t.is0
f
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Advantages of t.e (arious
Sampling 2esigns• Simple 'andom Sampling DS'SE
♦ It is t.e easiest most *idespread form of sampling0
♦ "ac. sub>ect .as an XXXXXXX c.ance to be in t.esample0
♦ T.e sample enables us to determine .o* li9el8 it ist.at descriptive statistics Dli9e t.e sample meanEfall close to corresponding values for *.ic. *e*ould li9e to ma9e inference Dli9e t.e populationmeanE0
Ad f . ( i
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Advantages of t.e (arious
Sampling 2esigns• Stratified Sampling
♦ It ensures t.at t.ere are enoug. XXXXXXXXX in
eac. group t.at 8ou *ant to compare0
• Cluster Sampling♦ It does not re?uire a sampling frame of sub>ects0
♦
It is less XXXXXXXXXXX to implement0
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/ias in Sampling
• A sampling met.od is XXXXXXXXX if ♦ T.e sample tends to favor some parts of t.e
population over ot.ers0
♦ In ot.er *ords t.e results from t.e sample are notrepresentative of t.e population0
• Obviousl8 XXXXXXXXXX samples are our goal0
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" l f $ S l t. t
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"#amples of $oor Samples t.at
'esult in /ias• Convenience Samples
• (oluntar8 'esponse Samples
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"lements of a Nood "#periment
• Control group♦ Nives us somet.ing to compare against♦ "nables us to control t.e XXXXXXXXXX XXXXXXX
• T.e placebo effect occurs *.en patients seem to improve
regardless of t.e treatment t.e8 receive0• 'andomization
♦ "liminates XXXXXX t.at can result *.en researc.ers assigntreatments to t.e sub>ects
♦ /alances t.e group on variables t.at 8ou 9no* affect t.e
response♦ /alances t.e group on XXXXXXXXX variables t.at ma8 beun9no*n to 8ou
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"#ample
• To investigate *.et.er antidepressants .elp smo9ers to ?uit smo9ingone stud8 used L, men and *omen *.o *ere J or older and .adsmo9ed JK cigarettes or more per da8 in t.e previous 8ear0 T.e8 *ereall .ig.l8 motivated to ?uit and in good .ealt.0 T.e8 *ere assigned toone of t*o groups! one group too9 an antidepressant called Y8ban
*.ile t.e ot.er group did not ta9e an8t.ing0 At t.e end of a 8ear t.estud8 observed *.et.er eac. sub>ect .ad successfull8 abstained fromsmo9ing0
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2id @ou Zno*4
• Observational studies can also .ave control groups0♦ T.ese are called ______-________ studies0♦ T.e cases are people *.o .ave a certain disease or
condition and t.e controls are people *.o do not .ave t.edisease0
♦ T.eir purpose is to see if one of t.e e#planator8 variables isrelated to t.e disease0
♦ XXXXXXXXX from t.e beginning of t.ese notes is an e#ampleof a case-control stud80
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Important $oints
• T8pes of studies!♦ Observational studies and e#periments♦ "#periments control for lur9ing variables
• Sampling designs!♦
S'S stratified random samples and cluster samples♦ S'S is t.e preferred met.od• $otential sources of bias!
♦ =ndercoverage♦ 'esponse bias♦ 1onresponse bias♦
Convenience sampling♦ (oluntar8 response sampling
• "lements of good e#periments!♦ Control group randomization and blinding
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Important $oints
• If a group is underrepresented in t.e sample *e cannotma9e inference about it0
• 7e must be careful *.en interpreting t.e results ofobservational studies0
• %or comparison of several treatments to be valid 8ou mustappl8 all treatments to similar groups of e#perimental units0
• Interesting ?uestions are usuall8 prett8 toug. to ans*er0 T.isis due in part to t.e fact t.at no single e#periment orobservational stud8 can determine causation0
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Stop and *$in?;;;Stop and *$in?;;;
•Write the study!
•Describe & classify the
variables.
•Instruents for easure
•Bias
•"re#are to analy$e data!