vbz-drug-persistency 0.1.2

Retention-based TRx projection using NBRx and VBZ S(t)

vbz-drug-persistency

A Python package that estimates treatment persistency (retention) and produces a Total TRx fit (and optional projection) using:

• New Patients per period (input column: NBRx)
• Total Prescriptions (TRx) per period (input column: TRx)
• a Weibull-based retention curve (“VBZ S(t)”)

This tool is designed for longitudinal brand analytics where total prescriptions (new + refills) depend on how long patients remain on therapy.

Terminology note: The input column is named NBRx for compatibility with common datasets, but in this package it represents new patients / new starts, not “new prescriptions.”

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What the package does

Given a time series of:

• NBRx = New Patients (New Starts) each period
• TRx = Total Prescriptions each period

IMPORTANT: This should be provided from the launch of the drug. Not at any given time.

the package fits a retention model and returns:

1. Retention curve:

   • (S(t)) = probability a patient is still on therapy at age (t) periods
   • plus dropout summaries (optional)

2. TRx fitted series:

   • (\widehat{TRx}_t) generated from new patient cohorts + retention

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Model (VBZ Retention)

Retention Curve (Weibull Survival)

The retention curve is modeled as:

$$S(t) = \exp\left(-\left(\frac{t}{\alpha}\right)^\beta\right)$$

Where:

• $t$: Patient "age" in periods since start ($0, 1, 2, \dots$)
• $\alpha > 0$: Scale parameter
• $\beta > 0$: Shape parameter

From New Patients to Total TRx

Each month's total retained "active cohort mass" ($A_t$) is computed as:

$$A_t = \sum_{i=0}^{t} \text{NBRx}_i \cdot S(t-i)$$

Total prescriptions are modeled as:

$$\widehat{TRx}_t = k \cdot A_t$$

Where:

• $k > 0$: A fitted multiplier that maps retained patient mass to prescription volume (captures refills and average fill behavior at an aggregate level).

Fitting

The package fits $\alpha$, $\beta$, and $k$ by minimizing the squared error between observed TRx and fitted $\widehat{TRx}$.

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Input Format

CSV or Excel

The package accepts .csv or .xlsx input with two columns only:

Column	Meaning
NBRx	New Patients (New Starts) per period
TRx	Total Prescriptions per period

Notes:

• One row per period (typically monthly).
• Chronological order (oldest → newest).
• Values must be non-negative.
• Data MUST be right from launch (no missing early history).

Example CSV:

NBRx,TRx
120,120
135,205
150,295
...

Installation

pip install vbz-drug-persistency

Quick Start (CLI)

You can run the model directly from the command line.

1. Standard Run:

persistency run --input input.csv --months-forward 0 --output results.xlsx

This writes an Excel file with the following sheets:

• Inputs_Clean: The processed input data.
• Fit_Params: The fitted values for $\alpha$, $\beta$, and $k$.
• Retention_S(t): The retention curve values over time.
• TRx_Fit_Forecast: Contains Actual vs Fitted data.

2. With Projection: To project future values, specify the number of months forward:

persistency run --input input.csv --months-forward 12 --output results.xlsx

Jupyter Notebook Example

Below is an example of how to run the model, view tables, and plot results within a Python script or Jupyter Notebook.

import pandas as pd
import matplotlib.pyplot as plt

from persistency.io import load_input
from persistency.fit import fit_weibull_and_scale, predict_trx
from persistency.forecast import build_retention_table

# 1. Load Data
df = load_input("input.csv")
nbrx = df["nbrx"].to_numpy()
trx  = df["trx"].to_numpy()

# 2. Fit Model
# max_lag defines the historical window used for fitting
fit = fit_weibull_and_scale(nbrx, trx, max_lag=36)
trx_hat = predict_trx(nbrx, fit.alpha, fit.beta, fit.k, max_lag=36)

# 3. Create Fit Table
fit_table = pd.DataFrame({
    "t": df["t"],
    "new_patients": df["nbrx"],
    "trx_actual": df["trx"],
    "trx_fitted": trx_hat
})

# 4. Create Retention Table
ret = build_retention_table(fit.alpha, fit.beta, horizon=36)
ret["dropout_cum"] = 1.0 - ret["S_t"]

# 5. Display Results
print(f"Fitted Parameters: {fit}")
display(fit_table.tail(12))
display(ret.head(12))

# 6. Plot Retention Curve
plt.figure(figsize=(10, 5))
plt.plot(ret["age_months"], ret["S_t"], linewidth=2)
plt.xlabel("Age (months)")
plt.ylabel("Retention S(t)")
plt.title("Retention Curve (VBZ S(t))")
plt.grid(True, alpha=0.3)
plt.show()

# 7. Plot Actual vs Fitted TRx
plt.figure(figsize=(10, 5))
plt.plot(fit_table["t"], fit_table["trx_actual"], label="Actual", marker='o')
plt.plot(fit_table["t"], fit_table["trx_fitted"], label="Fitted", linestyle="--")
plt.xlabel("Time (t)")
plt.ylabel("TRx")
plt.title("TRx: Actual vs Fitted")
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

Model Details

Retention Curve (Weibull Survival)

The retention curve describes the expected fraction of patients retained at age $t$. It is modeled as:

$$S(t) = \exp\left(-\left(\frac{t}{\alpha}\right)^\beta\right)$$

Where:

• $t$: Patient "age" in periods since start ($0, 1, 2, \dots$)
• $\alpha > 0$: Scale parameter
• $\beta > 0$: Shape parameter

From New Patients to Total TRx

Each month's total retained "active cohort mass" ($A_t$) is computed as the sum of all previous cohorts' remaining patients:

$$A_t = \sum_{i=0}^{t} \text{NBRx}_i \cdot S(t-i)$$

Total prescriptions are modeled as:

$$\widehat{TRx}_t = k \cdot A_t$$

Where:

• $k > 0$: A fitted multiplier that maps retained patient mass to prescription volume (captures refills and average fill behavior at an aggregate level).

Interpretation Tips

• Cumulative Dropout: Calculated as $1 - S(t)$.
• Month-to-month Dropout: Calculated as $S(t-1) - S(t)$.
• Scaling Factor $k$:

  Important: $k$ is not a direct "refills per patient" parameter; it is an aggregate mapping factor linking patient mass to prescription units.

Limitations

• Aggregate Scope: This is an aggregate-level model; it does not represent individual patient behavior.
• Data Requirements: Parameter stability improves with longer time series (12+ periods recommended).
• Forecasting Dependencies: Forecasts depend on assumptions for future new patients (NBRx) if projection is enabled.

Disclaimer

This package is for analytics and modeling purposes only and does not provide medical advice.