» Articles » PMID: 32510004

Improving the Efficiency of Clinical Trials by Standardizing Processes for Investigator Initiated Trials

Overview
Date 2020 Jun 9
PMID 32510004
Citations 2
Authors
Affiliations
Soon will be listed here.
Abstract

Early phase clinical trials are the first step in testing new medications and therapeutics developed by clinical and biomedical investigators. These trials aim to find a safe dose of a newly targeted drug (phase I) or find out more about the side effects and early signals of treatment efficacy (phase II). In a research institute, many biomedical investigators in oncology are encouraged to initiate such trials early in their careers as part of developing their research portfolio. These investigator-initiated trials (IITs) are funded internally by the University of Kansas Cancer Center or partially funded by pharmaceutical companies. As financial, administrative, and practical considerations play an essential role in the successful completion of IITs, it is imperative to efficiently allocate resources to plan, design, and execute these studies within the allotted time. This manuscript describes monitoring tools and processes to improve the efficiency, cost-effectivness, and reliability of IITs. The contributions of this team to processes such as: participant recruitment, feasibility analysis, clinical trial design, accrual monitoring, data management, interim analysis support, and final analysis and reporting are described in detail. This manuscript elucidates how, through the aid of technology and dedicated personnel support, the efficiency of IIT-related processes can be improved. Early results of these initiatives look promising, and the Biostatistics and Informatics team intends to continue fostering innovative methodologies to enhance cancer research by improving the efficiency of IITs.

Citing Articles

The Survival and Financial Benefit of Investigator-Initiated Trials Conducted by Korean Cancer Study Group.

Kim B, Maeng C, Keam B, Im Y, Ro J, Jung K Cancer Res Treat. 2024; 57(1):39-46.

PMID: 38993093 PMC: 11729315. DOI: 10.4143/crt.2024.421.


Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Shi X, Mudaranthakam D, Wick J, Streeter D, Thompson J, Streeter N Contemp Clin Trials Commun. 2024; 38:101281.

PMID: 38419809 PMC: 10900093. DOI: 10.1016/j.conctc.2024.101281.


Publication of clinical trials on medicinal products: follow-up on trials authorized in Hungary.

Sandor-Bajusz K, Kraut A, Baasan O, Marovics G, Berenyi K, Lohner S Trials. 2022; 23(1):330.

PMID: 35449017 PMC: 9022244. DOI: 10.1186/s13063-022-06268-y.

References
1.
Gajewski B, Simon S, Carlson S . Predicting accrual in clinical trials with Bayesian posterior predictive distributions. Stat Med. 2007; 27(13):2328-40. DOI: 10.1002/sim.3128. View

2.
Jiang Y, Simon S, Mayo M, Gajewski B . Modeling and validating Bayesian accrual models on clinical data and simulations using adaptive priors. Stat Med. 2014; 34(4):613-29. PMC: 4314351. DOI: 10.1002/sim.6359. View

3.
Mudaranthakam D, Thompson J, Hu J, Pei D, Chintala S, Park M . A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials. JAMIA Open. 2018; 1(2):166-171. PMC: 6241508. DOI: 10.1093/jamiaopen/ooy023. View

4.
Liu J, Wick J, Mudaranthakam D, Jiang Y, Mayo M, Gajewski B . Accrual Prediction Program: A web-based clinical trials tool for monitoring and predicting accrual for early-phase cancer studies. Clin Trials. 2019; 16(6):657-664. PMC: 6904514. DOI: 10.1177/1740774519871474. View