Evaluating the Impact of BMI on Cholesterol

Project page: https://rachlllg.github.io/project/2023-Evaluating_the_impact_of_BMI_on_Cholesterol/

Background & Requirements:

This was the final project for the Statistics for Data Science class in my Masters in Data Science program, a collaborative effort involving me and three other classmates. To retain confidentiality of the assignment, listed below are some key requirements which the project was built upon.

1. The research question must be specific and should clearly state an X (a design feature of a product that could be modified in the production process) and a Y (a metric of success).

• 'Product' and 'success' are loosely defined and goes beyond the scope of physical goods and profits.
• The goal of the project is to formulate an explanatory study, therefore, the design feature must be modifiable and the causal relationship from feature to success must be clear. An example of un-modifiable feature would be weather (not within human control), and an example of unclear causal relationship would be education and wealth (do higher education lead to higher wealth or do higher wealth provide for higher education?).

2. The dataset must be publicly available and be relevant to the research question.

• The dataset must be large enough to split the data into an exploration set and a confirmation set, with at least 200 observations in the exploration set (usually around 30% of the entire dataset).
• The data must be cross-sectional (not have multiple measurements for a single unit of observation). An example of non-cross-sectional data would be time series.
• The outcome variable (Y) must be metric. An example of non-metric outcome variable is the Likert Scale.
• The data should attempt to include (at least a proxy) omitted variables that would hinder the model result.

Dataset:

The dataset was sourced from National Health and Nutrition Examination Survey (NHANES), 2005-2006.

Source: https://www.icpsr.umich.edu/web/ICPSR/studies/25504/datadocumentation

The zip file in the Dataset folder contains the entire original dataset downloaded from the ICPSR Institute for Social Research, University of Michigan. Each participant is identifiable via the unique SEQN in each sub-dataset.

Raw Data:

Below listed are the sub-datasets we extracted from the complete dataset for our analysis.

The raw sub-datasets can be found in the 'Dataset > Raw' folder. And the user guides for each sub-dataset can be found in the 'Dataset > User_guides' foler.

Dataset	Variable Name	Variable Description	User guide page number
DS13 Examination: Body Measurements	SEQN	Respondent sequence number	10/129
	BMXBMI	Body Mass Index (kg/m**2)	15/129
DS129 Laboratory: Total Cholesterol	SEQN	Respondent sequence number	8/65
	LBXTC	Total cholesterol (mg/dL)	8/65
DS111 Laboratory: HDL Cholesterol	SEQN	Respondent sequence number	10/67
	LBDHDD	Direct HDL-Cholesterol (mg/dL)	10/67
DS110 Laboratory: Glycohemoglobin	SEQN	Respondent sequence number	9/66
	LBXGH	Glycohemoglobin (%)*	9/66
DS001 DS1 Demographics	SEQN	Respondent sequence number	8/25
	RIAGENDR	Gender of the sample person	9/25
	RIDAGEYR	Age in years of the sample person	10/25
DS242 Questionnaire: Smoking - Cigarette Use	SEQN	Respondent sequence number	10/68
	SMQ020	Smoked at least 100 cigarettes in life	10/68
	SMD641	# of days smoke cigarette in past 30 days	15/68
	SMD650	# of cigarettes smoked/day on days that you smoked	16/68

* Glycohemoglobin is a lab test that measures the average level of blood sugar in an individual. It is the primary test used to diagnose diabetes.

Clean Data:

A series of data cleaning were performed in data_cleaning.rmd file. Below are the cleaned dataset output from the data_cleaning.rmd file.

File name	Variable Name	Variable Description	Variable Nature
main.csv	TC/HDL-C	Ratio of total to HDL-Cholesterol	Primary Y variable
	BMI	Body Mass Index (kg/m**2)	Primary X variable
	Glycohemoglobin	Glycohemoglobin (%)	Supplemental X variable
	Age	Age (years)	Supplemental X variable
	Gender	Gender (Female/Male)	Supplemental X variable

Of the 6360 respondents who had valid measurements for BMI, Cholesterol, and Glycohemoglobin, only around 20% (1248) answered they had smoked at least 100 cigarettes in life and provided responses for the frequency of cigarette smoking. We were unable to determine the smoking habit of the other 80%.

Consider the response on frequency of smoking could be inaccurate as respondents could feel a social pressure to under-report their frequency of smoking, we believed we should not narrow down our main dataset to only include the 1248 respondents who answered the smoking habit question. Therefore, we treated the sample of 1248 respondents as a separate dataset from the main dataset of the 6360 respondents and analyzed the effect of smoking on Cholesterol separately.

File name	Variable Name	Variable Description	Variable Nature
smoking.csv	TC/HDL-C	Ratio of total to HDL-Cholesterol	Primary Y variable
	BMI	Body Mass Index (kg/m**2)	Primary X variable
	Glycohemoglobin	Glycohemoglobin (%)	Supplemental X variable
	Age	Age (years)	Supplemental X variable
	Gender	Gender (Female/Male)	Supplemental X variable
	num_cigarettes	# of cigarettes in a year	Supplemental X variable

Each output file was split to explore (30% of the output file) and confirm (70% of the output file). All data analysis were performed on explore dataset while the final results were generated from confirm dataset.