amsterdam rehabilitation research center | reade multiple regression analysis analysis of...
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![Page 1: Amsterdam Rehabilitation Research Center | Reade Multiple regression analysis Analysis of confounding and effectmodification Martin van de Esch, PhD](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649ea05503460f94ba3575/html5/thumbnails/1.jpg)
Amsterdam Rehabilitation Research Center | Reade
Multiple regression analysisAnalysis of confounding and effectmodificationMartin van de Esch, PhD
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Literature
Fletcher & Fletcher (2005) Ch. 1, 2Guyatt et al (2008) Ch. A5, B9.1Andy Field Ch. 5 (143-217)
http://www.youtube.com/watch?v=TwwyyA3wIdw
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Content
Checking assumptions (confounding and effect modification)
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Definitions
Bias: A systematic error in the design, recruitment, data collection or analysis that results in a mistaken estimation of the true effect of the exposure and the outcome
Confounding: A situation in which the effect or association between an exposure and outcome is distorted by the presence of another variable. Positive confounding (when the observed association is biased away from the null) and negative confounding (when the observed association is biased toward the null) both occur
Effect modification : a variable that differentially (positively and negatively) modifies the observed effect of a risk factor on disease status. Different groups have different risk estimates when effect modification is present
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Introduction
“Error” in research:•E
ffectmodification (interaction)•T
he combined effect of two or more independent variables on an outcome variable
•Confounding
•Influence on the association between determinant and outcome variable by an independant variable related to the determinant and the outcome variable
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Confounding
determinant(exposure)
confounder
outcome
Association (causal, marker), also in non-exposedAssociatio
n
Association
of our interest
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Effect modification
determinant(exposure)
Effect modifier
outcome
Association (causal, marker), also in non-exposed1
3
2Association
Association
of our interest
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Three conditions for being a confounder of the association between determinant and outcome variable
Appearens of larynx cancer
Alcohol intake(determinant, expositionfactor)
Smoking(confounder, independent factor)
Positive association
Positive association
together
1
2 3
1 = independent determinant2 = association present3 = no causal relationship
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Amsterdam Rehabilitation Research Center | Reademuscle strength Nm/kg
2,52,01,51,0,50,0
wa
lk-t
ime
(1
00
m)
se
c.
220
200
180
160
140
120
100
80
60
40
proprioception
poor
accurate
walktime100m (s)
Muscle strength (Nm/kg)
ProprioceptionO poor
▲ accurate
9
Muscle strengh, activity limitation and proprioception
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Table 2. Results of the regression of functional ability (walking-time, GUG-time‡ and WOMAC-PF) on muscle strength and joint proprioception.
Walking- time GUG WOMAC-PF
Variables** b* (SE)† p-value
b* (SE)† p-value
b* (SE)† p-value
Intercept 91.73 11.91 29.19
Muscle strength -68.13 (8.90)
.000 -13.99 (1.70)
.000 -18.23 (4.37)
.000
Proprioception -1.56 (1.27)
.225 -0.513 (0.24)
.039 0.01 (0.62) .987
Muscle strength *
Proprioception-11.61(3.10)
.000 -3.05 (0.59)
.000 -0.94 (1.51)
.534
R2 =0.54 F=23.23 p<.001
R2 =0.57 F=25.76 p<.001
R2 =0.30 F=8.81 p<.001
•b = unstandardized regression coefficient•** Variables centered around the mean† SE = Standard Error of the Estimate‡ GUG = Get Up and Go test
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Biomechanical model of activity limitations
Dekker et al 2013
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Effectmodification and confounding with a crosstab
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Example
Case-control study: assocation between alcohol-use and myocard infarction
OR = (71 48) / (52 29) = 2.26 (=‘ruwe OR’)
MI control total
alcohol 71 52 123
No alcohol 29 48 77
Total 100 100 200
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95% CI of crude OR
95%-CI of OR:•E
XP(LN(2.26) ± 1.96 (1/71+1/52+1/29+1/48)) indicating 1.3 tot 4.1
Question: Is smoking an effectmodificator of the association between alcohol intake and MI?
Is the association between alcohol intake and MI different between smokers and non-smokers?
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Example: effectmodification (interaction)
How to test?Stratification on variable smoking
Non-smoker smoker
MI control MI control
alcohol 8 17 63 35
no alcohol 22 44 7 4
total 30 61 70 39
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Example: effectmodification
OR non-smoker = (8 44) / (17 22) = 0.94 95% CI = (0.4 - 2.5)
OR smoker = (63 4) / (35 7) = 1.03 95 % CI = (0.3 - 3.8)
No interaction, because OR1 OR2
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Confounding
Question: Is smoking a confounder of the association between alcohol-intake and MI?
Is the effect of alcohol on MI (partly) caused (explained) by smoking?
How to testComparison between the crude association with the
corrected (pooled) association
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Condition for confounding
Smoking is associated with alcoholSmoking is associated with MIOR for stata of the suspected confounder
Non smoker smoker
MI control MI control
alcohol 8 17 63 35
No alcohol 22 44 7 4
total 30 61 70 39
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How do we calculate the pooled association?
According to the Mantel-Haenszel method:Notation:
Stratum i Cases Non-cases Total
Exposure + ai bi m1i
Exposure - ci di m2i
Total n1i n2i ti
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Mantel-Haenszel OR
Mantel-Haenszel Odds Ratio
i
ii
i
ii
MH
tc
b
td
a
OR
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Example confounding
In our example:
ORMI = 0.97
1097
359122
17
1094
639144
8
MIOR
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Example confounding
Summary
ORcrude = 2.26ORpooled= 0.97
Confounding, beacuse ORcrude ORpooled
We present the pooled ORAlmost 100(1-a)%-CI for ORMI (don’t remember the formula). In
the example:ORMI = 0.97 (0.4 - 2.1)
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Summary effectmodification and confounding
•Effectmodification (interaction)
•The combined effect of two or more independent predictor variables on an outcome variable.
•Confounding
•Influence on the association between determinant and outcome variable by an independant variable related to the determinant and the outcome variable
Conclusion: there is no average association, crude association is not present for an individual subject within the study population.
In publication: present two associations (one OR for smokers and one OR for non-smokers)
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Summary effectmodification and confounding
Confounding: The association between determinant and outcome is influenced, moderated by a third variable Confounder is related to determinant and outcome.
Non smoker smoker MI control MI control alcohol 8 17 63 35 non alcohol
22 44 7 4
total 30 61 70 39
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summary effectmodification and confounding
Compare crude assocoation with corrected associationWhen these is a difference (>10%): confounding is assumed!
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Example 2
Question: Is gender an interactor (moderator) of the association between alcohol inake and MI?
Is there a difference between male and female in the assocation between alcohol intake and MI?
male female
MI control MI control
alcohol 38 34 33 18
No alcohol 20 43 9 5
total 58 77 42 23
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Example 2
OR male = (38 43) / (34 20) = 2.40
95 % BI: (1.2 - 4.9)
OR female = (33 5) / (18 9) = 1.0295% BI: (0.3 - 3.5)
Modification because OR1 OR2Presentation of stratum specific OR's:“The" OR does not exist
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Example 2
Question: is gender a confounder?
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Summary examples
Smoking is a confounder which can be corrected by stratified analyses
Gender is an effect modificator (moderator): modification will be studied and the influence of the interactor will be presented in each stratum
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Stratified analyses
Confounding and modification can be studied by splitting the data into strata: stratified analyses
Aim stratified analyses:
1. increasing “feeling” with data
2. studying effect modification
3. Reduction of confounding
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General procedure
1. Calculation of "crude" association and ratio’s (OR/RR/RV)
2. Stratify always for one variable and calculate the specific measure
3. Compare the measure of each stratum with each other: Strong differences – moderationNo differences – no moderation
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4. calculate the total/ composite measurecompare crude and composite measures
- When "crude" measure composite measure: no confoundingPresent "crude" measure and CI
- When "crude" measure composite measure: Confounding and present the composite measure + CI
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"Beyond stratified analysis”
In case of more then one potential confounder or interactor; what to do?
Multipele regression analysis
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Variables in the Equation
,767 ,326 5,529 1 ,019 2,154
-1,386 ,250 30,748 1 ,000 ,250
GROEP
Constant
Step1
a
B S.E. Wald df Sig. Exp(B)
Variable(s) entered on step 1: GROEP.a.
Variables in the Equation
,531 ,340 2,442 1 ,118 1,701
-1,168 ,396 8,695 1 ,003 ,311
-,904 ,287 9,922 1 ,002 ,405
GROEP
LEEFTIJD
Constant
Step1
a
B S.E. Wald df Sig. Exp(B)
Variable(s) entered on step 1: GROEP, LEEFTIJD.a.
Confounding in (logistic) regression analysis
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Confounding in case of (logistic) regression analysis
In regression analyses more than one confounder is possible: how to act?
•Step wise or other ways of input in the regression model: depending on type of analysis (association or prediction)
•Type of analysis is based on the hypothesis
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Variables in the Equation
,442 ,388 1,295 1 ,255 1,556
-1,350 ,563 5,740 1 ,017 ,259
,369 ,789 ,219 1 ,640 1,446
-,847 ,309 7,538 1 ,006 ,429
GROEP
LEEFTIJD
INTERACT
Constant
Step1
a
B S.E. Wald df Sig. Exp(B)
Variable(s) entered on step 1: GROEP, LEEFTIJD, INTERACT.a.
Effectmodification in case of logistic regression-analysis
Interact= group x age
Group is dichotomized or ordinal
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Confounding in regression analysis
Confounding: adding a variable to the regression model – does B coefficient change with > 10%?
Statistical approach •
confoundes are known from literature, from correlation analses or confounder analyses
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Confounding in regression analysis
•Present the model without and with confounders
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Effectmodification and regression-analysis
Effect modification: adding the interaction variable to the regression model
Is the addition of the interaction significant? In the presence of a significant interaction: present
crude model and model with interaction. Explain what the interaction means (use graphs)
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Coefficientsa
-7,200 ,550 -13,084 ,000
-2,800 ,778 -,248 -3,598 ,000
(Constant)
groep
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: VERSCHILa.
Coefficientsa
-6,300 1,292 -4,877 ,000
-2,800 ,779 -,248 -3,594 ,000
-,600 ,779 -,053 -,770 ,442
(Constant)
groep
geslacht
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: VERSCHILa.
Example: confounding by linear regression analysis
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Coefficientsa
-10,200 1,696 -6,013 ,000
5,000 2,399 ,442 2,084 ,038
2,000 1,073 ,177 1,864 ,064
-5,200 1,517 -,763 -3,427 ,001
(Constant)
groep
geslacht
INTERACT
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: VERSCHILa.
Effecmodification by linear regression analysis: example
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Questions?
42