Lecture 11 Multicollinearity BMTRY 701 Biostatistical Methods II

Multicollinearity Introduction  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?

Easy answers?  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

Multicollinearity  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

No Multicollinearity Example: Mouse experiment MouseDose ADose BDietTumor size

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

Dose of Drug A on Tumor > reg.a <- lm(Tumor.size ~ Dose.A, data=data) > summary(reg.a) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) * Dose.A Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 10 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 1 and 10 DF, p-value: >

Dose of Drug B on Tumor > reg.b <- lm(Tumor.size ~ Dose.B, data=data) > summary(reg.b) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) *** Dose.B ** --- Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 10.4 on 10 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 1 and 10 DF, p-value: >

Diet on Tumor > reg.diet <- lm(Tumor.size ~ Diet, data=data) > summary(reg.diet) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) ** Diet Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 10 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 1 and 10 DF, p-value:

All in the model together > reg.all <- lm(Tumor.size ~ Dose.A + Dose.B + Diet, data=data) > summary(reg.all) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) e-05 *** Dose.A Dose.B *** Diet * --- Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: on 8 degrees of freedom Multiple R-Squared: , Adjusted R-squared: F-statistic: on 3 and 8 DF, p-value:

Correlation matrix of predictors and outcome > cor(data[,-1]) Dose.A Dose.B Diet Tumor.size Dose.A Dose.B Diet Tumor.size >

Result  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

The other extreme: perfect collinearity MouseDose ADose CDietTumor size

The model has infinitely many solutions  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”

Effects of Multicollinearity  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

Effects of Multicollinearity  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

Implications in inference  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

Implications in inference  multicollinearity tends to inflate the standard errors on the regression coefficients  when multicollinearity is present, you will see the 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.

Implications in Inference  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.

So, which is the ‘correct’ variable?  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.

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

SENIC

> 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 AGE INFRISK CULT XRAY BEDS MEDSCHL REGION CENSUS NURSE FACS logLOS nurse ms >

Let’s try an example with serious multicollinearity  To anticipate multicollinearity, ALWAYS good to look at scatterplots and correlation matrices of potential covariates  What covariates would give rise to a good example?