## Description

## Background and Data

Heart disease is the annual leading cause of death worldwide, accounting for more than 25% of deaths in

2016 (World Health Organization 2018).

It is also a significant economic burden for the healthcare system

with Nichols et al. (2010) estimating that heart disease and other cardiovascular diseases cost an average of

roughly USD $19,000 per patient, according to a study in the United States over the period of 2000-2005.

Early detection of heart disease (along with many other diseases) is important in terms of reducing both

mortality and costs to the healthcare system.

We will examine data on 4,240 participants in the Framingham Heart Study (Boston University and the

National Heart, Lung, & Blood Institute 2020), an ongoing study that began in 1948 and has been instrumental

in the identification of a number of risk factors for heart disease and other cardiovascular diseases.

The data

are available in the file Framingham Heart Study.xlsx, which can be read into R using the code below but

with the path changed to point to the location of the file on your computer.

A full list of variables contained

in the dataset and descriptions of these variables is also provided, both here and in the Excel file.

# Load the “readxl” package to read in data from an Excel file.

library(readxl)

# Read in the heart disease dataset.

hd <- read_xlsx(“~/Documents/Dropbox/Courses/DATA303/Data/Framingham Heart Study.xlsx”,

sheet = “Data”, na = “NA”)

Table 1: Variables and their descriptions for data contained in the

file Framingham Heart Study.xlsx.

Variable Description

SEX Sex of the individual (0 = “Female”, 1 = “Male”).

AGE Age (in years) of the individual at the time of the health exam.

EDUC Highest level of education of the individual (1 = “Some high school”, 2 = “High school or

Graduate Equivalency Diploma”, 3 = “Some university or vocational school”, 4 =

“University”).

SMOKER Indicator of whether or not the individual is a current smoker (0 = “No”, 1 = “Yes”).

CIG Average number of cigarettes that the individual smokes each day.

BP_MED Indicator of whether or not the individual is on blood pressure medication (0 = “No”, 1 =

“Yes”).

STROKE Indicator of whether or not the individual previously had a stroke (0 = “No”, 1 = “Yes”).

HYPER Indicator of whether or not the individual was hypertensive (0 = “No”, 1 = “Yes”).

DIAB Indicator of whether or not the individual is diabetic (0 = “No”, 1 = “Yes”).

CHOL Total cholesterol level (in mg/dL).

SBP Systolic blood pressure (in mmHg).

DBP Diastolic blood pressure (in mmHg).

BMI Body mass index.

HR Resting heart rate (in beats per minute)

GLUC Glucose level (in mg/dL)

HD_RISK Indicator of whether the individual has 10-year risk of future coronary heart disease (0 =

“No”, 1 = “Yes”)

Our focus will be on 10-year risk of coronary heart disease (CHD). Ten-year risk of CHD is a predicted risk

(i.e., a probability ranging between 0 and 1) of developing CHD within the next 10 years. Although this is

not an observed outcome but rather an estimated value, 10-year risk of CHD is a well-established measure in

the medical community.

We will consider a binary version of this variable which indicates whether or not a

person would be considered as at risk of developing CHD within the next 10 years.

## Assignment Questions

## 1. Missing data and variable recode: (10 marks)

Although our objective will be to consider inferential and predictive models for 10-year risk of CHD, we

will first ensure that we understand aspects of the underlying data as well as create a new variable

that may prove useful in producing comparisons of 10-year risk of CHD for medically-meaningful blood

pressure ranges. (In practice, we would want to examine each relevant variable to identify extreme

observations and be sure that there are not any erroneous values. As this dataset has already been

cleaned, we will not do so for this assignment.)

a. (2 marks) First, perform an analysis of the level of missing data for each variable. For only

those variables for which there are missing data, produce a table of the form shown below, where

VARIABLE_i is the name of the variable with missing data, ni

is the count for number of missing

observations for that variable, and pi

is the proportion (to 5dp) of missing observations for that

variable. Which variable has the highest level of missing data?

Table 2: Frequency and proportion of missing values for variables

with missing data.

Variable VARIABLE_1 VARIABLE_2 . . . VARIABLE_k

Frequency (n) n1 n2 . . . nk

Proportion (p) p1 p2 . . . pk

b. (3 marks) Create a new data frame called hd.complete, which only keeps people/observations

that have no missing data. In total, what proportion (to 5dp) of people have been removed from

the original dataset to produce this final data frame?

c. (3 marks) Add a variable to the data frame hd.complete called SBP_CAT, which converts systolic

blood pressure (SBP) from a numeric variable to a categorical variable according to the blood

pressure ranges specified by Madell and Cherney (2018). (See references listed at the end of the

assignment.)

For the purposes of coding SBP_CAT, you can assume that the values for each blood

pressure category go to just below that of the next category, as our dataset does not consist of

blood pressures that are rounded to the nearest whole number. This means that, for instance, the

systolic blood pressure range of 120 – 129 should in fact be interpreted as 120 – < 130. This should

produce five levels (i.e., blood pressure ranges) for SBP_CAT. (Note that the final level corresponds

to systolic blood pressure above 180 mmHg.) Produce a table for SBP_CAT which shows how many

observations fall into each blood pressure range.

d. (2 marks) Explain when we would expect that using the categorical variable SBP_CAT rather

than the numeric variable SBP would lead to a better fit for a regression model (whether logistic

regression, linear regression, or Poisson regression).

## 2. Inferential analysis: (25 marks)

Now we will focus on 10-year risk of CHD and look at the role that blood pressure may play in whether

or not someone is considered to be at risk of developing CHD within the next 10 years.

a. (3 marks) We will first consider a logistic regression model of 10-year risk of CHD (HD_RISK) on

systolic blood pressure (SBP) and diastolic blood pressure (DBP).

Previous research suggests that

the following variables are potential confounders for the true relationship between blood pressure

and 10-year risk of CHD and should also be included in the logistic regression model:

• sex of the individual (SEX)

• age of the individual (AGE)

• highest level of education of the individual (EDUC)

• average number of cigarettes smoked per day (CIG)

• total cholesterol level (CHOL)

• body mass index (BMI)

• glucose level (GLUC)

For this logistic regression model, calculate the variance inflation factors for predictors (to 3dp) to

determine whether or not there is evidence of significant multicollinearity among the predictors

in the model. If so, comment on which predictor(s) should be removed, and use this model for

subsequent parts of this question.

b. (3 marks) Using your model from part (a), produce a table of logistic regression model output

and write out the estimated logistic regression equation using the form

log

pb

1 − pb

= βb0 + βb1X1 + · · · + βbkXk,

where you clearly define the variables X1, X2, . . . , Xk and replace βb0, βb1, . . . , βbk with their

estimated values (to 4dp).

c. (6 marks) Carry out Wald tests for the coefficients for

• systolic blood pressure and

• diastolic blood pressure.

For each coefficient, clearly state

i. the hypotheses you are testing,

ii. the value of the test statistic,

iii. the p-value, and

iv. your conclusion in terms of whether the “effect” of the predictor on the response is statistically

significant.

d. (3 marks) For any significant Wald tests in part (c), provide a precise interpetation of what the

estimated coefficient suggests about the “effect” of the predictor on the response, and calculate a

corresponding 95% confidence interval (to 3dp) for the estimated “effect”.

e. (4 marks) A 2015 study by Wu et al. (2015) found that

“cardiovascular and expanded-cardiovascular mortality risks were lowest when systolic

blood pressures were 120 to 129 mm Hg, and increased significantly when systolic blood

pressures (SBPs) were ≥ 160 mm Hg. . . .”

Although Wu et al. (2015) considered different ranges of systolic blood pressures (< 120, 120-–129,

130-–139, 140-–149, 150—159, ≥ 160 mmHg) than Madell and Cherney (2018), we will use those

specified by Madell and Cherney (2018) in investigating whether ranges of blood pressures may

differ in terms of associated 10-year risk of CHD.

Fit the same model as before, but replace SBP with SBP_CAT.

i. Produce a table of logistic regression model output for this model.

ii. Based strictly on p-values, comment on what conclusions you would make for Wald tests

based on coefficients for SBP_CAT. (Note that you do not need to state hypotheses or values

for test statistics. You simply need to use the p-values to explain what these results mean

about comparisons of systolic blood pressure ranges.)

iii. Do your results agree with the findings of Wu et al. (2015)?

f. (3 marks) Does the model that uses SBP_CAT (i.e., the model fit in part (e)) provide a better fit

than the model that uses SBP (i.e., the model from part (a))?

g. (3 marks) Finally, for the best model of the two you fit (in parts (a) and (e)), perform a

Hosmer-Lemeshow test for g = 10, 20, and 30 groups, and comment on what these suggest about

the goodness-of-fit of this model to the 10-year risk of CHD data.

## 3. Statistical learning: (15 marks)

Now we perform an exploratory analysis to try to identify the best set of predictors in predicting 10-year

risk of CHD. Consider as predictors all variables other than the new variable that you constructed in

Question 1 (SBP_CAT).

a. (4 marks) Find the optimal models identified by forward and backward selection algorithms.

Report the predictors included in these optimal models. If these models are different, highlight

how they differ, and explain why forward and backward selection algorithms may not arrive at the

same optimal model.

b. (5 marks) Find the optimal models identified by best subset selection using AIC and BIC as

selection criteria. Report the predictors included in these optimal models. If these models are

different, highlight how they differ, and explain why the criteria of AIC and BIC may lead to

different “best” models. If these models differ from those identified as “best” by forward and

backward selection, explain why that may be the case.

c. (6 marks) Although it would be most appropriate to consider all possible combinations of the

15 predictor variables for a cross-validation routine to select a model based on maximising the

accuracy or maximising area under the receiver operating characteristic curve (AUC), it is not

feasible to do so on home computers in a reasonable amount of time.

Consequently, use the

predictors identified by best subset selection according to the criterion of minimising AIC from

part (b). (If unable to perform the required subset selection in part (b), make note of that here

and use the predictors in the optimal model identified by backward selection in part (a).)

For this

set of predictors, use 20 repetitions of 10-fold cross-validation to identify the optimal model(s)

identified according to the criteria of

i. maximising accuracy and

ii. maximising AUC.

If the optimal model(s) identified according to these criteria are different, highlight how they differ,

and explain why the criteria of maximising accuracy and maximising AUC may lead to different

“best” models. If these models differ from those identified as “best” in parts (a) and (b), explain

why this may be the case.

Assignment total: 50 marks

References

Boston University and the National Heart, Lung, & Blood Institute. 2020. “The Framingham Heart Study.”

https://framinghamheartstudy.org/.

Madell, R., and K. Cherney. 2018. “Blood Pressure Readings Explained.” Healthline. https://www.healthli

ne.com/health/high-blood-pressure-hypertension/blood-pressure-reading-explained.

Nichols, G. A., T. J. Bell, K. L. Pedula, and M. O’Keeffe-Rosetti. 2010. “Medical Care Costs Among Patients

with Established Cardiovascular Disease.” The American Journal of Managed Care 16 (3): e86–93.

World Health Organization. 2018. “The Top 10 Causes of Death.” https://www.who.int/news-room/factsheets/detail/the-top-10-causes-of-death.

Wu, C.-Y., H.-Y. Hu, Y.-J. Chou, N. Huang, Y.-C. Chou, and C.-P. Li. 2015. “High Blood Pressure and

All-Cause and Cardiovascular Disease Mortalities in Community-Dwelling Older Adults.” Medicine 94

(47): e2160. https://doi.org/10.1097/MD.0000000000002160.