Running head: FRAMINGHAM ANALYSIS PROJECT 1

Framingham Analysis Project: Faster Death with Smoking

{Student Name}

COH 602: Biostatistics

Professor Wosu

{Date}

EXAMPLE 2

FRAMINGHAM ANALYSIS PROJECT 2

Abstract

In this analysis I aim to collate statistical facts on cardiovascular disease by examining effects of

smoking on all participants of the Framingham Heart Study and how it affects their level of

blood pressure that could increase their risk of attaining Cardiovascular Disease through their

lifetime. Smokers in this analysis will be compared to non-smokers and both will be analyzed at

death. This project utilizes the “Heart” dataset, which according to Sullivan (2012), is a

longitudinal cohort study that began in 1948 with a cohort enrollment of over five thousand

participants whom were free of cardiovascular disease in the town of Framingham Massachusetts. It

will be used to identify the risk factors for cardiovascular disease such as smoking and blood

pressure.

Commented [KW1]: It is not necessary to write an

abstract.

FRAMINGHAM ANALYSIS PROJECT 3

Framingham Analysis Project: Faster Death with Smoking

Cardiovascular disease (CVD) is a disease of the heart and blood vessels as it restricts the

supply of blood to the brain. They are the number one cause of death globally as more people die

yearly from CVD’s than from any other disease. “An estimated 17.5 million people died from

CVDs in 2012, representing 31% of all global deaths” (WHO, 2014). CVDs are non-

communicable diseases caused by poor lifestyle choices and risk related factors, they can easily

be prevented if these risk factors like heavy tobacco use are addressed. Because these risk factors

are modifiable, it has become a great task and concern for public health prevention management

task force. “The current focus is providing information on the impact of unhealthy lifestyle

choices as risk factors for preventable chronic diseases and encouraging individual responsibility

for one's health” (Koenig, 2014). Education on these risk factors and the need to reduce

consumption of tobacco have aided in the prevention of CVD but a large scale is yet to be done

to prevent tremendous deaths and encourage lifestyle changes that may decrease morbidity.

Analysis introduction

To answer the question on how smoking increases the risk of CVD via increases in

systolic blood pressure, and diastolic blood pressure and weather smokers will die earlier with

cardiovascular disease due to the risk; data from the original Framingham Heart Study Cohort

will be calculated using SAS analysis to run PROC FREQ, PROC MEANS, and PROC

UNIVARIATE to retrieve descriptive and inferential statistics. The data analysis presented will

focus on the represented individuals in this study, their smoking status and the levels of blood

pressure and how it decreases their length of life.

FRAMINGHAM ANALYSIS PROJECT 4

Descriptive Analysis Methods

To produce the total variables based on crude analysis that focuses on the associations

between smoking and cardiovascular disease in the Framingham data set, SAS will be used to

run PROC CONTENTS which is used to view the contents of the data “heart” which is to be

analyzed. The inputted code provides information on the total 5209 observed participants in the

Framingham study and the 17 variables within the data set. To answer the study question, focus

for this study will be on the provided variables on age at death, blood pressure status, cause of

death, and smoking status. To determine the response values of all 5209 observed participants

from the chosen variables, SASHELP.HEART – Framingham Heart Study Data dictionary as

shown below was used to help gather a general response of preferred variables data: Age at

Death (36-93); Smoking Status for each participant categorized as Heavy (16-25), Light (1-5),

Moderate (6-15), Non-smoker, and Very Heavy (>25); Systolic Blood Pressure (82-300);

Diastolic Blood Pressure (50-160).

Data from Framingham Heart Study Data dictionary

Variable Title Variable Label Response Values

AGEATDEATH Age at Death 36-93

SMOKING Number of cigarettes smoked 0-60

SMOKING_STATUS Smoking Status

1 = Heavy (16-25)

2 = Light (1-5)

3 = Moderate (6-15)

4 = Non-smoker

5 = Very Heavy (>25)

DIASTOLIC Diastolic Blood Pressure 50-160

SYSTOLIC Systolic Blood Pressure 82-300

Commented [KW2]: Good job on the tables!

FRAMINGHAM ANALYSIS PROJECT 5

To categorize the variables on smoking status PROC FREQ is used. To retrieve data on

descriptive statistics to analyze numerical data such as: mean, standard deviation, minimum, and

maximum values for all 5,209 participants, PROC UNIVARIATE is used. This helped to analyze

the continuous variables age at death, systolic blood pressure, and diastolic blood pressure.

PROC UNIVARIATE is then used to generate a wider array of summary statistics and PROC

SORT is finally used to stratify the data and answer the research question on how smoking

affects blood pressure and age at death.

Descriptive Analysis Results

Table 1: Smoking status, Frequency and Percentage

Smoking Status Frequency Percent

Non-smoker 2501 48.35 %

Light (1-5) 579 11.19 %

Moderate (6-15) 576 11.13 %

Heavy (16-25) 1046 20.22 %

Very Heavy (>25) 471 9.10 %

Table 1 shows that most participants in the Framingham study are non-smokers with a

high percentage of 48.35%, second are the heavy smokers at 20.22%, light smokers at 11.19%,

moderate smokers at 11.13%, and lastly very heavy smokers at 9.10%. The derived data shows

that there are other risk factors besides smoking that increases the risks of developing

cardiovascular disease which is already known from the Framingham Heart Study.

FRAMINGHAM ANALYSIS PROJECT 6

Table 2: Descriptive Statistics of Continuous Variables

Variable N Mean STDEV Median Mode Minimum Maximum

Age At Death 1991 70.54 10.56 71.00 68.00 36 93

Diastolic Pressure 5209 85.36 12.97 84.00 80.00 50 160

Systolic Pressure 5209 136.91 23.74 132.00 120.00 82 300

Table 2 contains information on the descriptive statistics From the derived data, the mean

and median for the variables age at death, systolic pressure and diastolic pressure are relatively

close in range to one another with values ranging no more than 5 units apart. Age at death has a

mean of 70.54 and 71.00 median, systolic pressure variable with mean of 136.91 and 132.00

median, and lastly the diastolic pressure variable has a mean of 85.36 and 84.00 median. Further

representation is shown in Graphs 1-3.

Table 3. Further Summary Statistics for Framingham Heart Study Participants

Variable Range IQR* Skewness Distrib. Pos./Neg./Norm.

Age at Death 57.0 16 -0.32 Normal

Diastolic Pressure 110.0 16 0.88 Normal

Systolic Pressure 218.0 28 1.49 Normal

*IQR=Interquartile Range

FRAMINGHAM ANALYSIS PROJECT 7

Graph 1: Normal distribution and Histogram for Age at Death

Graph 2: Normal Distribution and Histogram for Diastolic Pressures

FRAMINGHAM ANALYSIS PROJECT 8

Graph 3: Normal Distribution and Histogram for Systolic Pressures

In Graph 1-3 we observe the Skewness and the direction of asymmetry for each distribution.

Table 3 also aids in understanding the graphs skewness and distribution. Although all the graphs

have a normal distribution due to the mean and median being similar, Graph 1 for Age at Death

shows a normal distribution that is skewed to the left with a skewness of -0.32. while Diastolic

Pressure in graph 2 has a normal distribution and table 3 column 3 line 2 has a skewness of 0.88 .

Lastly Systolic Pressure in graph 3 has a normal distribution and a skewness od1.49 in graph 2

and 3 respectively has a positive skewness.

FRAMINGHAM ANALYSIS PROJECT 9

Table 4: Mean of Age at Death, Diastolic and Systolic Pressure categorized by smoking

Variable Non-Smoker

Light

(1-5)

Moderate

(6-15)

Heavy

(16-25)

Very

Heavy

(>25)

Age at Death 73.76 70.52 68.59 68.02 65.41

Diastolic Pressure 86.91 83.78 82.61 83.85 85.67

Systolic Pressure 140.38 134.14 131.71 133.36 136.00

Table 4 shows a breakdown of mean values for age at death, diastolic pressure and

systolic pressure by smoking status computed using PROC SORT and PROC MEANS to

produce exact statistics in relation to smoking status and the provided variables. To compare the

results of smokers vs non-smokers it is observed that the diastolic pressure mean in non-smokers

is 86.91 and in very heavy smokers it is 85.67, and systolic pressure mean in non-smoker is

140.38 and in very heavy smoker is 136. These numbers become lower as we go from non-

smoker to smoker but we can see that there is no major change in number from smoker to non-

smoker which then counteract our expected effect that smoking will cause a negative change in a

participant’s blood pressure status. Age at death on the other hand shows a gradual decrease in

mean from non-smoker at 73.76 compared to a very heavy smoker with the mean of 65.41; this

shows that a person can die at a younger age when they introduce smoking which might support

the research question on how smoking can affect the length of life.

Inferential Statistics Methods

Statistical evidence using a hypotheses approach will be used to test the research question

and know if participants who have cardiovascular disease and are heavy smokers when

compared to non-smokers, light, heavy and moderate smokers have a faster rate of death at an

FRAMINGHAM ANALYSIS PROJECT 10

early age. Because the data contains one continuous variable and one categorical variable with

more than two categories, a hypotheses test using Analysis of variance (ANOVA) will be used.

According to Sullivan (2012), the ANOVA technique applies when there are more than two

independent comparison groups, it is used to compare the means of the comparison groups and is

conducted using a five-step approach. Under ANOVA the one-factor approach will be used to

compare the means of different variables of the factor representing different smoking levels.

Inferential Statistics Results

1. Set up hypotheses and determine the level of Significance

u1=non-smoker, u2=light, u3=moderate, u4=heavy, u5= very heavy

H0: u1=u2=u3=u4 =u5

H1: Means are not all equal

a=0.05

2. Select the appropriate test statistic

F= MSB/MSE

3. Set up the decision rule

To determine the critical value of F, we need degrees of freedom:

df1= k-1 => 5-1=4

df2=N-k => 1971-4=1967

Using the critical value table at the end of this paper derived from textbook appendix

With a=0.05, df1=4, and df2=1967, we will reject H0 if F> 2.46

4. Compute the test statistic

F=47.21

5. Conclusion

Reject H0 because 47.21 is > 2.46 We have statistically significant evidence at a=0.05 to show

that the mean age at death for non-smokers, light smokers, moderate smokers, heavy

smokers, and very heavy smokers are not all equal.

Using PROC GLM I was able to determine the level of significance which is a=0.05, the

SAS data also helped to set up the decision rule providing the sample number (N) which I was

then able to calculate using the appendix table 4 from the textbook by Sullivan (2012) receiving

a 2.46 critical value. To compute the test statistic F, the information was derived from the SAS

results for the F value data. With this I was able to reject the null hypothesis because F> 2.46

k)/(N)XΣΣ(X

1)/(k)XX(ΣnF

2

j

2

jj=

Commented [KW3]: Excellent work explaining what

the subscripts represent.

Commented [KW4]: Shows how you can do symbols if

you do not know how to use the “Symbols” option in

Microsoft Word.

Commented [KW5]: Formula was copied and pasted

from lecture PowerPoints or some other course

material, which is perfectly acceptable and resourceful.

FRAMINGHAM ANALYSIS PROJECT 11

which helped to prove that the mean age at death for non-smokers, light smokers, moderate

smokers, heavy smokers, and very heavy smokers are not all equal.

Conclusion

This paper shows the cardiovascular participant data of the Framingham Heart Study

cohort by smoking status. As shown in table 4, smoking shortens the duration of life as we

observed the decrease in death at age between different levels of smokers and non-smoker. The

statistics proves that overtime; non-smokers can live longer with cardiovascular disease as the

mean age of death for non-smokers is higher than that of smokers’. To relate this back to public

health, this research can help provide support for anti-smoking education to help reduce diseases

caused by lifestyle choices like smoking; It is important for those individuals to learn that life

expectancy will increase if an individual stops smoking.

Commented [KW6]: The major issue with this part is

that after ANOVA result is to reject the null hypothesis,

there is no further discussion of the Tukey post-hoc

multiple comparisons test, which tells us specifically

which groups have means that are not equal. This part

is necessary, and you can see how it’s supposed to be

done in the third example paper.

FRAMINGHAM ANALYSIS PROJECT 12

References

Koenig, P. (2014, October, 10). Chronic disease as a result of poor lifestyle choices. Retrieved

August 21, 2016, from https://www.eastporthealth.org/articles/detail.php?Chronic-

Disease-As-a-Result-of-Poor-Lifestyle-Choices-6

Lisa, S. (2012). Essentials of biostatistics in public health. 2nd Edition. Sudbury, MA: Jones

&Bartlett Learning.

WHO. (May 2014). Cardiovascular diseases (CVDs). Retrieved August 21, 2016, from

http://www.who.int/mediacentre/factsheets/fs317/en/

FRAMINGHAM ANALYSIS PROJECT 13

Appendix

SAS Codes

Descriptive Analysis Codes For Categorical Variable

title "Sashelp.heart --- Framingham Heart Study";

PROC CONTENTS DATA = sashelp.heart;

RUN;

PROC FREQ DATA =sashelp.heart;

tables Status;

tables smoking;

run;

Descriptive Analysis Codes for Continuous Variables

PROC UNIVARIATE DATA = sashelp.heart;

VAR ageatdeath diastolic systolic ;

RUN;

PROC UNIVARIATE DATA = sashelp.heart;

VAR diastolic systolic ageatdeath;

HISTOGRAM / NORMAL;

RUN;

PROC SORT DATA = sashelp.heart OUT = temp;

BY smoking;

RUN;

PROC UNIVARIATE DATA = temp;

VAR ageatdeath diastolic systolic diastolic systolic;

BY smoking_status;

RUN;

PROC MEANS DATA = temp2;

VAR AgeAtDeath Smoking Systolic Diastolic;

BY smoking;

RUN;

Inferential Analysis Codes

PROC GLM DATA = sashelp.heart;

CLASS smoking_status;

MODEL ageatdeath = smoking_status;

MEANS smoking_status / TUKEY;

RUN;