Evidence map›Paper›PMID 38699379›Full record

ArticleResearch square2024

Evaluating the impact of delayed study startup on accrual in cancer studies.

Isuru Ratnayake, Anh-Tuan Do, Daniel Gajewski, Sam Pepper, Oluwatobiloba Ige, Natalie Streeter, Tara L Lin, Matthew McGuirk, Byron Gajewski, Dinesh Pal Mudaranthakam

Abstract readPreprint
In one paragraph

Article in Research square, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Isuru RatnayakeDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.ORCID 0000-0001-9596-781X
Anh-Tuan DoThe University of Kansas Cancer Center- Accelerated Cancer Education (ACE) Summer Intern 2023, Piper High School, Kansas City, KS, USA.
Daniel GajewskiDepartment of Biostatistics & Data Science Summer Intern 2023, Rockhurst High School, Kansas City, MO, USA.
Sam PepperDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.
Oluwatobiloba IgeDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.
Natalie StreeterThe University of Kansas Cancer Center, Kansas City, KS, USA.
Tara L LinThe University of Kansas Cancer Center, Kansas City, KS, USA.
Matthew McGuirkDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.
Byron GajewskiDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.
Dinesh Pal MudaranthakamDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, USA.

Funding

Transgenic & Gene-Targeting Shared ResourceP30CA168524 · NCI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI ROY A. JENSEN · 2012 to 2026
$40.1M
NCI NIH HHS P30 CA168524
6 · The paper itself

Abstract

Background: Drug development in cancer medicine depends on high-quality clinical trials, but these require large investments of time to design, operationalize, and complete; for oncology drugs, this can take 8-10 years. Long timelines are expensive and delay innovative therapies from reaching patients. Delays often arise from study startup, a process that can take 6 months or more. We assessed how study-specific factors affected the study startup duration and the resulting overall success of the study. Method: Data from The University of Kansas Cancer Center (KUCC) were used to analyze studies initiated from 2018 to 2022. Accrual percentage was computed based on the number of enrolled participants and the desired enrollment goal. Accrual success was determined by comparing the percentage of enrollments to predetermined threshold values (50%, 70%, or 90%). Results: Studies that achieve or surpass the 70% activation threshold typically exhibit a median activation time of 140.5 days. In contrast, studies that fall short of the accrual goal tend to have a median activation time of 187 days, demonstrating the shorter median activation times associated with successful studies. Wilcoxon rank-sum test conducted for the study phase (W=13607, p-value=0.001) indicates that late-phase projects took longer to activate compared to early-stage projects. We also conducted the study with 50% and 90% accrual thresholds; our findings remained consistent. Conclusions: Longer activation times are linked to reduced project success, and early-phase studies tend to have higher success than late-phase studies. Therefore, by reducing impediments to the approval process, we can facilitate quicker approvals, increasing the success of studies regardless of phase.

Indexed as

clinical trialsoncology studiesstudy activationstudy startuptrial accrual

Identifiers

PMID38699379
PMCPMC11065059

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.