Unit-Based Model Costing in Statistical Programming

Unit-Based Model Costing in Statistical Programming

Unit-Based Model Costing in statistical programming refers to a pricing structure where costs are determined based on the number of units analyzed, processed, or stored in a statistical model. This approach is common in clinical research, data analysis, and cloud-based statistical computing platforms.

1. Definition of Unit-Based Costing

In statistical programming, unit-based costing means: ✅ The cost is determined per unit of analysis (e.g., per patient, per observation, per simulation run). ✅ Common in SAS, R, Python, and cloud-based services (AWS, Databricks, etc.). ✅ Helps optimize computational resources and manage project budgets efficiently.

2. Factors Influencing Unit-Based Costing

Number of Observations

• Cost increases as the number of subjects, records, or time points grows.
• Example: In a clinical trial, cost per patient record in a statistical model.

Computational Complexity

• Simple regression models cost less than Bayesian models, Machine Learning, or Mixed Effects models.

Software & Licensing Costs

• SAS: Pricing is often based on data volume and computational workload.
• R/Python: Free, but cloud-based execution (e.g., AWS, Google Cloud) incurs costs based on CPU/memory usage.

Storage & Data Handling

• Large datasets may increase database storage costs (e.g., using SAS Viya, Snowflake, or SQL).

Parallel Processing & Cloud Usage

• If models run in parallel (e.g., Monte Carlo simulations, bootstrapping), computing costs scale with workload.

3. Example: Costing in Clinical Research

Let’s say you're conducting a survival analysis in a Phase III clinical trial using SAS:

• Per-Patient Cost: $5 per patient for statistical processing.
• Total Patients: 10,000
• Total Cost: 10,000 × $5 = $50,000

If you move this to AWS (SAS Viya on Cloud), the cost may vary based on: Instance type (CPU, RAM) Data storage (GB per month) Runtime per analysis

4. Optimizing Unit-Based Costs

🔹 Use efficient data structures (e.g., data.table in R, PROC SQL in SAS) 🔹 Leverage cloud cost calculators (AWS, Azure, SAS Viya) Reduce unnecessary iterations in statistical modeling

Unit-Based Costing for Statistical Programming in Clinical Research (Datasets & TFLs)

In clinical research and statistical programming, costing for unit-based models depends on:

• Datasets: Cost is based on the volume of data processed (number of patients, observations, or variables).
• TFLs (Tables, Listings, and Figures): Cost is calculated per output generated, considering complexity, programming effort, and validation needs.

1. Costing for Datasets in Statistical Programming

Datasets are typically charged based on: ✅ Number of Patients (Subjects) → Per-patient cost ✅ Number of Observations (Records per Patient) → Cost scales with data points ✅ Number of Variables (Columns in Dataset) → More variables increase processing time ✅ Data Cleaning & Standardization Effort → Raw vs. CDISC (SDTM, ADaM) ✅ Software & Infrastructure Costs → SAS, R, Python, or cloud-based execution

Note: Costs increase for larger, more complex datasets (e.g., genetics, imaging data).

2. Costing for TFLs (Tables, Listings, and Figures)

TFL development cost depends on: ✅ Number of Outputs → Per Table/Listing/Figure cost ✅ Complexity of Analysis → Simple summary tables vs. advanced modeling ✅ Statistical Programming Effort → Macro development, automation, validation ✅ QC & Validation Needs → Double programming, independent reviewer effort

3. Software & Infrastructure Costing

Software

Pricing Model

SAS (On-Premise)

License-based (Fixed Cost)

SAS Viya (Cloud)

Pay-per-use (AWS, Azure)

R / Python

Free, but cloud execution incurs cost

Data Storage (AWS, Snowflake)

Pay-per-GB

4. Total Project Cost Estimate

If a project involves 1,000 subjects, 3 datasets (Raw, SDTM, ADaM), and 100 TFLs, the estimated cost could be:

• Dataset Preparation: $45,000
• TFL Programming: $22,000
• Validation & QC: $10,000
• Software Costs (SAS Viya Cloud, Data Storage, etc.): $8,000

💡 Estimated Total Cost: $85,000 for statistical programming.

How to Optimize Costs?

✅ Automate TFL generation using macros (reducing manual effort) ✅ Optimize dataset processing using efficient SAS procedures ✅ Use cloud resources wisely (on-demand vs. reserved instances) ✅ Leverage reusable ADaM datasets for multiple TFLs

Would you like a detailed cost breakdown for a specific project (e.g., Phase II or III trials)? 🚀. Do connect with me for more Information.