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Lecture 11 MulticollinearityPowerPoint Presentation

Lecture 11 Multicollinearity

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Lecture 11 Multicollinearity

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Lecture 11Multicollinearity

BMTRY 701Biostatistical Methods II

- Some common questions we ask in MLR
- what is the relative importance of the effects of the different covariates?
- what is the magnitude of effect of a given covariate on the response?
- can any covariate be dropped from the model because it has little effect or no effect on the outcome?
- should any covariates not yet included in the model be considered for possible inclusion?

- If the candidate covariates are uncorrelated with one another: yes, these are simple questions
- If the candidate covariates are correlated with one another: no, these are not easy.
- Most commonly:
- observational studies have correlated covariates
- we need to adjust for these when assessing relationships
- “adjusting” for confounders

- Experimental designs?
- less problematic
- patients are randomized in common designs
- no confounding exists because factors are ‘balanced’ across arms

- Also called “intercorrelation”
- refers to the situation when the covariates are related to each other and to the outcome of interest
- like confounding, but a statistical terminology for it because of the effects it has on regression modeling

- Interested in seeing which factors influence tumor size in mice
- Notice that the experiment is perfectly balanced.
- What does that mean?

> reg.a <- lm(Tumor.size ~ Dose.A, data=data)

> summary(reg.a)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 32.50000 12.29041 2.644 0.0246 *

Dose.A -0.05250 0.05689 -0.923 0.3779

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 16.09 on 10 degrees of freedom

Multiple R-Squared: 0.07847, Adjusted R-squared: -0.01368

F-statistic: 0.8515 on 1 and 10 DF, p-value: 0.3779

>

> reg.b <- lm(Tumor.size ~ Dose.B, data=data)

> summary(reg.b)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 58.0000 9.4956 6.108 0.000114 ***

Dose.B -0.9600 0.2402 -3.996 0.002533 **

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 10.4 on 10 degrees of freedom

Multiple R-Squared: 0.6149, Adjusted R-squared: 0.5764

F-statistic: 15.97 on 1 and 10 DF, p-value: 0.002533

>

> reg.diet <- lm(Tumor.size ~ Diet, data=data)

> summary(reg.diet)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 28.000 6.296 4.448 0.00124 **

Diet -12.000 8.903 -1.348 0.20745

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 15.42 on 10 degrees of freedom

Multiple R-Squared: 0.1537, Adjusted R-squared: 0.06911

F-statistic: 1.817 on 1 and 10 DF, p-value: 0.2075

> reg.all <- lm(Tumor.size ~ Dose.A + Dose.B + Diet, data=data)

> summary(reg.all)

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 74.50000 8.72108 8.543 2.71e-05 ***

Dose.A -0.05250 0.02591 -2.027 0.077264 .

Dose.B -0.96000 0.16921 -5.673 0.000469 ***

Diet -12.00000 4.23035 -2.837 0.021925 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.327 on 8 degrees of freedom

Multiple R-Squared: 0.8472, Adjusted R-squared: 0.7898

F-statistic: 14.78 on 3 and 8 DF, p-value: 0.001258

> cor(data[,-1])

Dose.A Dose.B Diet Tumor.size

Dose.A 1.0000000 0.0000000 0.0000000 -0.2801245

Dose.B 0.0000000 1.0000000 0.0000000 -0.7841853

Diet 0.0000000 0.0000000 1.0000000 -0.3920927

Tumor.size -0.2801245 -0.7841853 -0.3920927 1.0000000

>

- For perfectly balanced designs, adjusting does not affect the coefficients
- However, it can affect the significance
- Why?
- residual sum of squares is affected
- if you explain more of the variance in the outcome, less is left to chance/error
- when you adjust for another related factor, you will likely improve the significance

- Too much flexibility
- What happens?
- The fitting algorithm usually gives you some indication of this
- will not fit the model and gives an error
- drops one of the predictors

- “perfectly collinear” = “perfect confounding”

- Most common result
- two covariates are independently associated with Y in simple linear regression models
- in MLR model with both covariates, one or both is insignificant
- the magnitude of the regression coefficients is attenuated
- why?
- recall the adjusted variable plot
- if the two are related, removing the systematic part of one from Y may leave too little left to explain

- Other situations
- Neither is significant alone, but they are both significant together (somewhat rare)
- Both are significant alone and both retain signficance in the model
- The regression coefficient for one of the covariates may change direction
- Magnitude of coefficient may increase (in absolute value)

- It is usually hard to predict exactly what will happen when both are in the model

- the interpretation of a regression coefficient measuring the change in the expected value of Y when the covariate is increased while all other are held constant is not quite applicable
- It may be conceptually feasible to think of ‘holding all constant’
- but, practically, it may not be possible if the covariates are related.
- Example: amount of rainfall and hours of sunshine

- multicollinearity tends to inflate the standard errors on the regression coefficients
- when multicollinearity is present, you will see that coefficient of partial determination will have little increase with the addition of the collinear covariate
- Predictions tend to be relatively unaffected for better or worse when a highly collinear covariate is added to the model.

- Recall the interpretation of the t-statistics in MLR
- The represent the significance of a variable, adjusting for all else in the model
- If two covariates are highly correlated, then both are likely to end up insignificant
- Marginal nature of t-tests!
- ANOVA can be more useful due to conditional nature of tables.

- Almost impossible to tell
- Usually, people choose the one that is ‘more’ significant.
- but that does not mean it is the correct choice
- it could be the correct choice
- it could be the one that is less associated
- why might it be less associated?
- measurement issues

- the correct ‘culprit’ could be a variable that is related to the ones in the model but not in the model itself.

- Let’s look at our classic example of logLOS
- What variables are associated with logLOS?
- What variables have the potential to create multicollinearity?

> data <- read.csv("senicfull.csv")

> data$logLOS <- log(data$LOS)

> data$nurse2 <- data$NURSE^2

> data$ms <- ifelse(data$MEDSCHL==2,0,data$MEDSCHL)

>

> data.cor <- data[,-1]

> round(cor(data.cor),2)

LOS AGE INFRISK CULT XRAY BEDS MEDSCHL REGION CENSUS NURSE FACS logLOS nurse2 ms

LOS 1.00 0.19 0.53 0.33 0.38 0.41 -0.30 -0.49 0.47 0.34 0.36 0.98 0.25 0.30

AGE 0.19 1.00 0.00 -0.23 -0.02 -0.06 0.15 -0.02 -0.05 -0.08 -0.04 0.17 -0.04 -0.15

INFRISK 0.53 0.00 1.00 0.56 0.45 0.36 -0.23 -0.19 0.38 0.39 0.41 0.55 0.26 0.23

CULT 0.33 -0.23 0.56 1.00 0.42 0.14 -0.24 -0.31 0.14 0.20 0.19 0.35 0.15 0.24

XRAY 0.38 -0.02 0.45 0.42 1.00 0.05 -0.09 -0.30 0.06 0.08 0.11 0.39 0.04 0.09

BEDS 0.41 -0.06 0.36 0.14 0.05 1.00 -0.59 -0.11 0.98 0.92 0.79 0.42 0.86 0.59

MEDSCHL -0.30 0.15 -0.23 -0.24 -0.09 -0.59 1.00 0.10 -0.61 -0.59 -0.52 -0.32 -0.56 -1.00

REGION -0.49 -0.02 -0.19 -0.31 -0.30 -0.11 0.10 1.00 -0.15 -0.11 -0.21 -0.52 -0.07 -0.10

CENSUS 0.47 -0.05 0.38 0.14 0.06 0.98 -0.61 -0.15 1.00 0.91 0.78 0.48 0.84 0.61

NURSE 0.34 -0.08 0.39 0.20 0.08 0.92 -0.59 -0.11 0.91 1.00 0.78 0.37 0.95 0.59

FACS 0.36 -0.04 0.41 0.19 0.11 0.79 -0.52 -0.21 0.78 0.78 1.00 0.38 0.66 0.52

logLOS 0.98 0.17 0.55 0.35 0.39 0.42 -0.32 -0.52 0.48 0.37 0.38 1.00 0.28 0.32

nurse2 0.25 -0.04 0.26 0.15 0.04 0.86 -0.56 -0.07 0.84 0.95 0.66 0.28 1.00 0.56

ms 0.30 -0.15 0.23 0.24 0.09 0.59 -1.00 -0.10 0.61 0.59 0.52 0.32 0.56 1.00

>

- To anticipate multicollinearity, ALWAYS good to look at scatterplots and correlation matrices of potential covariates
- What covariates would give rise to a good example?