Measures¶
Below is documentation for each of the implemented calculation functionalities available for a pandas DataFrame
Measures¶
RiskRatio([reference, alpha]) |
Estimate of Risk Ratio with a (1-alpha)*100% Confidence interval from a pandas DataFrame. |
RiskDifference([reference, alpha]) |
Estimate of Risk Difference with a (1-alpha)*100% Confidence interval from a pandas DataFrame. |
NNT([reference, alpha]) |
Estimates of Number Needed to Treat. |
OddsRatio([reference, alpha]) |
Estimates of Odds Ratio with a (1-alpha)*100% Confidence interval. |
IncidenceRateRatio([reference, alpha]) |
Estimates of Incidence Rate Ratio with a (1-alpha)*100% Confidence interval. |
IncidenceRateDifference([reference, alpha]) |
Estimates of Incidence Rate Difference with a (1-alpha)*100% Confidence interval. |
interaction_contrast(df, exposure, outcome, …) |
Calculate the Interaction Contrast (IC) using a pandas dataframe and statsmodels to fit a linear binomial regression. |
interaction_contrast_ratio(df, exposure, …) |
Calculate the Interaction Contrast Ratio (ICR) using a pandas dataframe, and conducts either log binomial or logistic regression through statsmodels. |
Diagnostics¶
Sensitivity([alpha]) |
Generates the sensitivity and (1-alpha)% confidence interval, comparing test results to disease status from pandas dataframe |
Specificity([alpha]) |
Generates the sensitivity and (1-alpha)% confidence interval, comparing test results to disease status from pandas dataframe |
Diagnostics([alpha]) |
Generates the Sensitivity, Specificity, and the corresponding (1-alpha)% confidence intervals, comparing test results to disease status from pandas DataFrame |
Others¶
spline(df, var[, n_knots, knots, term, …]) |
Creates spline dummy variables based on either user specified knot locations or automatically determines knot locations based on percentiles. |
create_spline_transform(array[, n_knots, …]) |
Creates spline dummy variables based on either user specified knot locations or automatically determines knot locations based on percentiles. |
table1_generator(df, cols, variable_type[, …]) |
Code to automatically generate a descriptive table of your study population (often referred to as a Table 1). |