diff --git a/README.md b/README.md
index be3a4fc..09181dd 100644
--- a/README.md
+++ b/README.md
@@ -31,8 +31,9 @@ From the user side, this amounts to creating a dataclass, `SEQopts`, and then fe
 ```python
 import polars as pl
 from pySEQTarget import SEQuential, SEQopts
+from pySEQTarget.data import load_data
 
-data = pl.from_pandas(SEQdata)
+data = load_data("SEQdata")
 options = SEQopts(km_curves = True)
 
 # Initiate the class
@@ -40,17 +41,18 @@ model = SEQuential(data,
                    id_col = "ID",
                    time_col = "time",
                    eligible_col = "eligible",
+                   treatment_col = "tx_init",
+                   outcome_col = "outcome",
                    time_varying_cols = ["N", "L", "P"],
                    fixed_cols = ["sex"],
                    method = "ITT",
-                   options = options)
+                   parameters = options)
 model.expand()  # Construct the nested structure
 model.bootstrap(bootstrap_nboot = 20) # Run 20 bootstrap samples
 model.fit() # Fit the model
 model.survival() # Create survival curves
 model.plot() # Create and show a plot of the survival curves
 model.collect() # Collection of important information
-
 ```
 
 ## Assumptions
@@ -58,4 +60,3 @@ There are several key assumptions in this package -
 1. User provided `time_col` begins at 0 per unique `id_col`, we also assume this column contains only integers and continues by 1 for every time step, e.g. (0, 1, 2, 3, 4, ...) is allowed and (0, 1, 2, 2.5, ...) or (0, 1, 4, 5) are not
     1. Provided `time_col` entries may be out of order at intake as a sort is enforced at expansion.
 2. `eligible_col` and elements of `excused_colnames` are once 1, only 1 (with respect to `time_col`) flag variables.
-