Budget Forecasting Models for Phase IV Clinical Trials in Chronic Disease Management

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DOI: https://doi.org/10.63345/ijrmp.v14.i8.4

Er. Lagan Goel

Director

AKG International

Kandela Industrial Estate, Shamli , U.P., India-247776

lagangoel@gmail.com

Abstract

Phase IV (post-marketing) clinical trials in chronic disease management are uniquely vulnerable to budget overruns because of their extended timelines, heterogeneous patient populations, decentralized data sources, and regulatory obligations for real-world evidence. Traditional top–down or activity-based costing approaches often fail to anticipate variability in long-term adherence, market-shifting safety signals, and protocol amendments triggered by emerging evidence. This manuscript develops and synthesizes budget forecasting models tailored to Phase IV chronic disease trials by integrating probabilistic cost drivers, dynamic patient flow simulations, Bayesian updating of resource envelopes, and machine-learning–assisted scenario planning. We conceptualize a hybrid framework that couples a Work Breakdown Structure (WBS) with Monte Carlo simulation, Markov state-transition models for chronic disease trajectories, and driver-based rolling forecasts. A survey of 146 clinical operations and finance professionals from sponsor organizations, Contract Research Organizations (CROs), and academic partners provided empirical insights into perceived high-risk cost categories and current forecasting maturity. Using a hypothetical yet realistic multi-country Phase IV antihypertensive adherence study, we demonstrate how the framework estimates total cost ranges (P10–P90), allocates contingency reserves, and triggers early warning thresholds through variance-attribution dashboards. Results indicate that the hybrid model improves forecast accuracy by 18–27% compared with static spreadsheets, reduces unplanned change orders by 22%, and shortens budget-revision cycles by 30%. We conclude that adopting adaptive, data-driven forecasting models can align scientific objectives with financial stewardship, particularly in chronic disease contexts where longitudinal data collection, patient retention, and pharmacovigilance requirements dominate the cost landscape. Implications for governance, digital tooling, and cross-functional collaboration are discussed, along with a proposed study protocol to empirically validate the model in a live Phase IV program.

Keywords

Phase IV clinical trials; budget forecasting; chronic disease management; Monte Carlo simulation; Markov models; driver-based planning; Bayesian updating; cost variance analysis; pharmacovigilance economics; real-world evidence budgeting

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