Useful package to help download and merge data from NHANES medical database. Can clean/filter data, do statistical comparison analysis, and perform multivariate logistic regression.
Example downloading and cleaning data:
from nhutils.generate import create_dataset
from nhutils.clean import Scrubber
# define parameters of interest for new dataset
years_of_interest = ['2011-2012', '2013-2014', '2015-2016', '2017-2018']
var_names = ['SEQN', 'RIDAGEYR', 'RIAGENDR', 'DMDEDUC2', 'DIQ080', 'SMQ020']
# download and create dataset
df = create_dataset(vars=var_names, years=years_of_interest)
# clean data by using Scrubber class
scrub = Scrubber(df)
scrub.minus_one('RIAGENDR')
scrub.remove_7_and_9('DMDEDUC2')
scrub.convert_to_binary(['DIQ080', 'SMQ020'])
# save cleansed data to csv
df.to_csv('dataset.csv', index=False)Example of comparison anaylsis:
import pandas as pd
from nhutils.stats import compare_stats
# import data
data = pd.read_excel('dataset.xlsx')
# define groups to be compared
dr = data[data['DIQ080'] == 1]
diab = data[(data['DIQ010'] == 1) & (data['DIQ080'] == 0)]
# call compare_stats function and provide function parameters
results = compare_stats(group1=dr,
group1_label='DR',
group2=diab,
group2_label='Diabetes',
numerical_vars=['RIDAGEYR', 'URDACT', 'BMXBMI'],
categorical_vars=['RIAGENDR', 'RIDRETH1', 'DMDEDUC2'],
output_excel_filename='DRvsDiabetes.xlsx',
welchs_t_test=True,
decimal_places=4)After running above code block, results would look like this:
| variable | DR | Diabetes | p-value |
|---|---|---|---|
| RIDAGEYR | 62.9549 (12.424) | 61.2482 (13.9428) | 0.0022 |
| URDACT | 313.9478 (969.5149) | 136.8675 (613.404) | 0 |
| BMXBMI | 32.3415 (8.2252) | 32.3103 (7.5454) | 0.9321 |
| RIAGENDR | 0.0823 | ||
| RIAGENDR = 0 | 55.3383 | 51.4528 | |
| RIAGENDR = 1 | 44.6617 | 48.5472 | |
| RIDRETH1 | 0.1905 | ||
| RIDRETH1 = 1 | 16.391 | 16.6667 | |
| RIDRETH1 = 2 | 12.0301 | 9.9677 | |
| RIDRETH1 = 3 | 28.8722 | 32.6069 | |
| RIDRETH1 = 4 | 26.6165 | 26.7958 | |
| RIDRETH1 = 5 | 16.0902 | 13.9629 | |
| DMDEDUC2 | 0.0727 | ||
| DMDEDUC2 = 1.0 | 18.6747 | 15.91 | |
| DMDEDUC2 = 2.0 | 16.5663 | 14.9284 | |
| DMDEDUC2 = 3.0 | 23.7952 | 22.4949 | |
| DMDEDUC2 = 4.0 | 26.3554 | 28.3436 | |
| DMDEDUC2 = 5.0 | 14.6084 | 18.3231 |
Example of multivariate logistic regression:
import pandas as pd
from nhutils.stats import log_reg
# import data
df = pd.read_excel('dataset.xlsx')
# define group to do logistic regression on
diabetes = df[df['DIQ010'] == 1]
# call log_reg function and provide function parameters
results = log_reg(data=diabetes,
dependent_var='depression',
independent_numerical_vars=['RIDAGEYR', 'BMXBMI'],
independent_categorical_vars=['DIQ080', 'RIAGENDR', 'RIDRETH1', 'DMDEDUC2'],
output_excel_filename='log_reg_results.xlsx')After running above code block, results would look like this:
| OR | p-value | 2.5% | 97.5% | |
|---|---|---|---|---|
| const | 0.072711 | 4.85E-10 | 0.03185 | 0.165991 |
| RIDAGEYR | 0.990237 | 0.027291 | 0.981649 | 0.998901 |
| BMXBMI | 1.043185 | 8.88E-10 | 1.029174 | 1.057386 |
| DIQ080=1.0 | 1.414383 | 0.007342 | 1.097718 | 1.822398 |
| RIAGENDR=1 | 1.738442 | 1.11E-06 | 1.391635 | 2.171678 |
| RIDRETH1=2 | 1.748111 | 0.005543 | 1.178047 | 2.594034 |
| RIDRETH1=3 | 1.606838 | 0.008162 | 1.130741 | 2.283394 |
| RIDRETH1=4 | 1.020704 | 0.912587 | 0.707948 | 1.471628 |
| RIDRETH1=5 | 1.090866 | 0.707666 | 0.692392 | 1.718663 |
| DMDEDUC2=2.0 | 0.947286 | 0.76531 | 0.663837 | 1.351762 |
| DMDEDUC2=3.0 | 0.522351 | 0.000372 | 0.365311 | 0.746899 |
| DMDEDUC2=4.0 | 0.518183 | 0.000192 | 0.366803 | 0.732039 |
| DMDEDUC2=5.0 | 0.276303 | 3.57E-08 | 0.174871 | 0.436569 |