In this lecture, we study a relationship between a binary variable,
such as a disease status, and one or more predictors, such as risk
factors of a disease. We extend the linear regression model to binary
outcomes by a modeling approach called **logistic
regression**.

When the outcome \(Y\) of an experiment/observational study is binary (can take only two values, denoted by 0 and 1), we typically want to model how the probability \(p_i\), that the outcome is 1 for individual \(i\), depends on the available knowledge of the values of predictor variables \(X_1,\ldots,X_k\) for individual \(i\). We call this probability “risk” although in general it can be any probability, not necessarily related to a disease risk.

Intuitively, extending our experience from the linear regression model, we would like to have a model for risk like \[p_i = a + b\, x_i + \varepsilon_i,\] from which we could estimate how a change in the predictor value \(x_i\) changes the risk \(p_i\). This has two problems. First, we can’t measure \(p_i\) but we only observe binary outcome \(y_i\) which is either 0 or 1 (e.g. for the patients we set \(y_i=1\) and for the healthy we set \(y_i=0\)). Second, the risk value \(p_i\) is a probability and hence always between 0 and 1, whereas the linear model above doesn’t impose such a restriction to the right hand side of the equation, which could result in inacceptable risk values from the model.

In this lecture, we leave the first problem for R to handle. The
second problem is solved by transforming the outcome value \(p_i\) from the interval (0,1) to the whole
real line by the **log-odds transformation**, where \(p_i\) is replaced by \[ \log\left(\frac{p_i}{1-p_i}\right).\]

```
par(mfrow = c(1,2))
p = seq(0.001, 0.999, 0.001)
y = log( p/(1-p) )
plot(p, y, xlab = "risk p", ylab = "log-odds: log( p/(1-p) )", t = "l", lwd = 1.5)
plot(y, p, xlab = "log-odds: log( p/(1-p) )", ylab = "risk p", t = "l", lwd = 1.5)
```

When \(p_i\) is the probability that
an event of interest happens, then \(1-p_i\) is the probability that the event
does not happen. The **odds** of the event are \(p_i / (1-p_i)\) and they tell how many
times more likely the event is to happen than not to happen. For
example, if risk is 50%, then the odds are 1:1 = 1 and if risk is 2/3
then the odds are 2:1 = 2. Large odds (and also large log-odds)
correspond to very probable events and small odds (and also small
log-odds) correspond to very unlikely events. Mathematically, the
inverse of the log-odds transformation \[
\ell = \log\left( \frac{p}{1-p}\right) \qquad \textrm{ is } \qquad p =
\frac{\exp(\ell)}{1+\exp(\ell)}.\]

By taking the logarithm of odds, we have a quantity that can take any real number as its value. Hence, it is conceptually valid to use a regression model where \[ \log\left(\frac{p_i}{1-p_i}\right) = a + b\,x_i. \] If \(b\) in this model is positive, then, as \(x\) increases, also the odds of the event increases and therefore also the probability of the event increases. If \(b\) is negative, then as \(x\) increases, also the odds of the event and hence also the probability of the event decreases. With the logistic regression model, for each pair of estimates of \(a\) and \(b\), we can do a prediction of risk \(p_i\) for individual \(i\) given his/her value for predictor \(x_i\).

- Risk of a disease is 10%. What are the odds of the disease? And log-odds of the disease? How does odds and log-odds change when risk changes from 10% to (100% - 10%) = 90%?

Odds are \(p/(1-p) = 0.1/(1-0.1) = 0.1/0.9 = 1/9 \approx 0.111\). Log-odds are \(\log(1/9) = -2.197.\) If risk changes to 90%, then odds become \(9/1 = 9\) and log-odds become \(\log(9) = -\log(1/9) = 2.197\). Thus odds change to their inverse value, and log-odds changes the sign.

- Suppose that log-odds of a disease follows a linear model \(a + b\, x\), where \(x\) is measured BMI and parameter values are \(a=-1\) and \(b=0.01\). Is increasing BMI a risk or protective factor for the disease? What is the risk of disease for an individual with bmi = 30? What about bmi = 40?

Since larger log-odds mean larger odds mean larger risk, a positive value of \(b\) means that increasing BMI increases the risk.

```
a = -1
b = 0.01
x = c(30, 40)
logodds = a + b*x
p = exp(logodds)/(1 + exp(logodds))
data.frame(bmi = x, risk = p)
```

```
## bmi risk
## 1 30 0.3318122
## 2 40 0.3543437
```

Let’s use a dataset `Pima.tr`

on 200 women from Pima
population (a Native American population) from `MASS`

package.

```
library(MASS)
y = Pima.tr
str(y)
```

```
## 'data.frame': 200 obs. of 8 variables:
## $ npreg: int 5 7 5 0 0 5 3 1 3 2 ...
## $ glu : int 86 195 77 165 107 97 83 193 142 128 ...
## $ bp : int 68 70 82 76 60 76 58 50 80 78 ...
## $ skin : int 28 33 41 43 25 27 31 16 15 37 ...
## $ bmi : num 30.2 25.1 35.8 47.9 26.4 35.6 34.3 25.9 32.4 43.3 ...
## $ ped : num 0.364 0.163 0.156 0.259 0.133 ...
## $ age : int 24 55 35 26 23 52 25 24 63 31 ...
## $ type : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 1 1 2 ...
```

This data frame contains the following columns:

Variable | Explanation |
---|---|

npreg | number of pregnancies |

glu | plasma glucose concentration in an oral glucose tolerance test |

bp | diastolic blood pressure (mm Hg) |

skin | triceps skin fold thickness (mm) |

bmi | body mass index |

ped | diabetes pedigree function |

age | age in years |

type | Yes or No, for diabetic according to WHO criteria |

Our outcome variable is the binary diabetes status
`type`

.

`table(y$type)`

```
##
## No Yes
## 132 68
```

Let’s start by seeing how `bmi`

associates to
`type`

using a boxplot and a t-test. Since `type`

is a factor (see above output from `str()`

), we can make
boxplots and the t-test of `bmi`

with respect to the two
levels of the factor by using the notation `bmi ~ type`

.

```
boxplot(bmi ~ type, data = y, xlab = "bmi", ylab = "Diabetes",
horizontal = TRUE, col = c("blue", "red")) #turns boxplot horizontal
```

`t.test(bmi ~ type, data = y)`

```
##
## Welch Two Sample t-test
##
## data: bmi by type
## t = -4.512, df = 171.46, p-value = 1.188e-05
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## -5.224615 -2.044547
## sample estimates:
## mean in group No mean in group Yes
## 31.07424 34.70882
```

There seems to be a difference of about 3.5 kg/m^2, 95%CI (2.0..5.2) between the groups.

If we are given a value of BMI, what would be a risk of diabetes (in
these data)? Let’s use logistic regression. We use `glm( )`

function that fits “generalized linear models”, and we specify logistic
regression by parameter `family = "binomial"`

. This means
that the outcome variable in regression is modeled as having a binomial
distribution, where the success probability may depend on the values of
predictors. For the model formula, the syntax is similar to the linear
model `lm()`

.

**Important**: If you forget to specify
`family = "binomial"`

in `glm( )`

, then the
function fits a linear model, not a logistic model!

```
glm.1 = glm(type ~ bmi, data = y, family = "binomial")
summary(glm.1) #Always check from summary that you have fitted the correct model
```

```
##
## Call:
## glm(formula = type ~ bmi, family = "binomial", data = y)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.11156 0.92806 -4.430 9.41e-06 ***
## bmi 0.10482 0.02738 3.829 0.000129 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 256.41 on 199 degrees of freedom
## Residual deviance: 239.97 on 198 degrees of freedom
## AIC: 243.97
##
## Number of Fisher Scoring iterations: 4
```

Coefficient for `bmi`

is the increase in log-odds of
diabetes per one unit increase in `bmi`

and shows a clear
statistical association with P ~ 1e-4. Let’s demonstrate what this means
in terms of the risk of diabetes by applying `predict( )`

function for a sequence of `bmi`

values. The default output
from `predict( )`

on logistic regression object is in
log-odds but we can ask the prediction directly as probabilities by
specifying `type = "response"`

.

```
bmi.grid = 18:55
data.in = data.frame(bmi = bmi.grid)
y.pred = predict(glm.1, newdata = data.in, type = "response") #type = "response" predicts probabilities
```

Let’s plot these predictions as a function of bmi, and let’s add to
the figure also the bmi distributions of the observed cases and controls
of diabetes using `stripchart()`

.

```
plot(bmi.grid, y.pred, ylim = c(-0.05,1.05), t = "l",
main = "prediction", ylab = "risk of diabetes", xlab = "bmi")
stripchart(y$bmi[y$type == "No"], at = -0.05, add = T, method = "jitter", jitter = 0.02, col = "blue")
stripchart(y$bmi[y$type == "Yes"], at = 1.05, add = T, method = "jitter", jitter = 0.02, col = "red")
```

We drew the predictions up to `bmi = 55`

, but we see that
original data included few observations above 45, and hence we shouldn’t
expect the model to be reliable after that. We see that the risk raises
from about 10% at `bmi < 20`

up to 60% at
`bmi > 40`

. While the relationship in this BMI range is
close to linear, we still see that there is some non-linearity in the
prediction curve, which is a difference from the corresponding linear
regression model.

Is this a good model? Let’s define some measures for how well the
model explains the data. We use **misclassification error**
and **ROC curve** for this purpose.

We can predict the class (0 or 1) of each sample based on the model,
for example, by predicting the class to be 1 if the predicted risk >
0.5 and otherwise predicting the class to be 0, and then see which
proportion of the predictions are wrong. This proportion of wrong
predictions is called the missclassification rate. Let’s apply it to our
current data. We can use `predict(glm.1, type = "response")`

to produce risks and then threshold them into 0 or 1 according to
whether the risk is < 0.5 or > 0.5.

```
pred.1 = predict(glm.1, type = "response") #if newdata is not specified, then predictions are given for original data
y.pred.1 = ifelse(pred.1 < 0.5, 0, 1) #if pred.1[i] < 0.5 then sets y.pred.1[i]=0 else sets y.pred.1[i]=1
table(y.pred.1, y$type) #cross-tabulate predictions vs. true type
```

```
##
## y.pred.1 No Yes
## 0 120 57
## 1 12 11
```

We see that missclassification error is \(\frac{57+12}{200} = 0.345\). Two measures often used in medical research are

*sensitivity*, the proportion of true 1s that are correctly labeled as 1s: \(\frac{11}{57+11} = 0.162\) and*specificity*, the proportion of true 0s that are correctly labeled as 0s: \(\frac{120}{120+12} = 0.909\).

The classifier is perfect if and only if it has both sensitivity and specificity equal to 1. Here our model has a very low sensitivity of 16% and a higher specificity of 91%. Low sensitivity means that the model doesn’t really detect well the true diabetes cases.

Let’s add `glu`

to the model. It is expected to predict
strongly diabetes, since it is used to diagnose diabetes.

`boxplot(glu ~ type, data = y, xlab = "glu", ylab = "Diabetes", horizontal = T, col = c("blue","red"))`

`t.test(glu ~ type, data = y)`

```
##
## Welch Two Sample t-test
##
## data: glu by type
## t = -7.3856, df = 121.76, p-value = 2.081e-11
## alternative hypothesis: true difference in means between group No and group Yes is not equal to 0
## 95 percent confidence interval:
## -40.51739 -23.38813
## sample estimates:
## mean in group No mean in group Yes
## 113.1061 145.0588
```

```
glm.2 = glm(type ~ bmi + glu, data = y, family = "binomial")
summary(glm.2)
```

```
##
## Call:
## glm(formula = type ~ bmi + glu, family = "binomial", data = y)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.216106 1.346965 -6.100 1.06e-09 ***
## bmi 0.090016 0.031268 2.879 0.00399 **
## glu 0.035716 0.006311 5.659 1.52e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 256.41 on 199 degrees of freedom
## Residual deviance: 198.47 on 197 degrees of freedom
## AIC: 204.47
##
## Number of Fisher Scoring iterations: 4
```

Let’s see the missclassification rate in the updated model.

```
pred.2 = predict(glm.2, type = "response")
y.pred.2 = ifelse(pred.2 < 0.5, 0, 1)
table(y.pred.2, y$type) #cross-tabulate predictions vs. true type
```

```
##
## y.pred.2 No Yes
## 0 116 31
## 1 16 37
```

Missclassification rate is now \(\frac{31+16}{200} = 0.235\) and has clearly fallen from its earlier value of 0.345 when BMI was alone in the model.

Missclassification rate is an intuitive measure, but it has a downside that it is defined by a particular fixed risk threshold (usually 0.5). We can get more information about a classifier, if we can see its performance throughout all possible risk cutoffs. The ROC curve gives us such information.

In *receiver
operating characteristics* (ROC) curve, we run through all
possible risk thresholds to classify samples into 1s and 0s and, for
each threshold, we compute the true positive rate (= sensitivity, the
proportion of true 1s that are labeled as 1s) against false positive
rate (=1-specificity, the proportion of true 0s that are labeled as 1s).
Let’s draw ROC curves from models 1 and 2 above, and discuss the
curves.

```
p.thresh = seq(-0.001, 1.001, 0.001) #risk thresholds for which ROC is computed
#We make roc.1 and roc.2 matrices here and then fill them in the for-loop below
roc.1 = matrix(NA, ncol = 2, nrow = length(p.thresh)) #will have col1 = FP rate and col2 = TP rate
roc.2 = matrix(NA, ncol = 2, nrow = length(p.thresh))
for(ii in 1:length(p.thresh)){ #goes through all risk thresholds
p = p.thresh[ii] # now p is the current risk threshold
y.pred.1 = ifelse(pred.1 < p, 0, 1) #0-1 predictions based on current risk threshold 'p'
y.pred.2 = ifelse(pred.2 < p, 0, 1)
roc.1[ii,1] = sum( y$type == "No" & y.pred.1 == 1) / sum( y$type == "No" ) #false positive rate
roc.1[ii,2] = sum( y$type == "Yes" & y.pred.1 == 1) / sum( y$type == "Yes") #true positive rate
roc.2[ii,1] = sum( y$type == "No" & y.pred.2 == 1) / sum( y$type == "No" )
roc.2[ii,2] = sum( y$type == "Yes" & y.pred.2 == 1) / sum( y$type == "Yes")
}
plot(NULL, xlim = c(0,1), ylim = c(0,1), main = "ROC",
xlab = "False positive rate", ylab = "True positive rate") #Makes an empty plot
lines(roc.1, col = "skyblue", lwd=1.5) #adds ROC of model 1
lines(roc.2, col = "darkblue", lwd=1.5) #adds ROC of model 2
legend("bottomright", leg = c("bmi", "bmi+glu"), col = c("skyblue", "darkblue"), lwd = 1.5)
abline(a = 0, b = 1, lty = 2) #adds diagonal line Y = X
```