Evidence map›Paper›PMID 33292682›Full record

ArticlePilot and feasibility studies2020

Development and validation of a follow-up methodology for a randomised controlled trial, utilising routine clinical data as an alternative to traditional designs: a pilot study to assess the feasibility of use for the BladderPath trial.

Harriet P Mintz, Amandeep Dosanjh, Helen M Parsons, Ana Hughes, Alicia Jakeman, Ann M Pope, Richard T Bryan, BladderPath trial management group, Nicholas D James, Prashant Patel

Abstract read
In one paragraph

Article in Pilot and feasibility studies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Trial
  2. Image directed redesign of bladder cancer treatment pathways: the BladderPath RCT.Health technology assessment (Winchester, England) · 2024
    Trial
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Harriet P MintzWarwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Amandeep DosanjhUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, B15 2GW, UK.
Helen M ParsonsWarwick Medical School, University of Warwick, Coventry, CV4 7AL, UK.
Ana HughesInstitute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Alicia JakemanUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, B15 2GW, UK.
Ann M PopeInstitute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Richard T BryanInstitute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
BladderPath trial management group
Nicholas D JamesThe Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK.
Prashant PatelUniversity Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, B15 2GW, UK. P.Patel@bham.ac.uk.ORCID http://orcid.org/0000-0002-2882-5990

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBladder cancer outcomes have not changed significantly in 30 years; the BladderPath trial (Image Directed Redesign of Bladder Cancer Treatment Pathway, ISRCTN35296862) proposes to evaluate a modified pathway for diagnosis and treatment ensuring appropriate pathways are undertaken earlier to improve outcomes. We are piloting a novel data collection technique based on routine National Health Service (NHS) data, with no traditional patient-Health Care Professional contact after recruitment, where trial data are traditionally collected on case report forms. Data will be collected from routine administrative sources and validated via data queries to sites. We report here the feasibility and pre-trial methodological development and validation of the schema proposed for BladderPath.

methodsLocally treated patient cohorts were utilised for routine data validation (hospital interactions data (HID) and administrative radiotherapy department data (RTD)). Single site events of interest were algorithmically extracted from the 2008-2018 HID and validated against reference datasets to determine detection sensitivity. Survival analysis was performed using RTD and HID data. Hazard ratios and survival statistics were calculated estimating treatment effects and further validating and assessing the scope of routine data.

resultsOverall, 829/1042 (sensitivity 0.80) events of interest were identified in the HID, with varying levels of sensitivity; identifying, 202/206 (sensitivity 0.98; PPV 0.96) surgical events but only 391/568 (sensitivity 0.69; PPV 0.95) radiotherapy regimens. An overall temporal quality improvement trend was present: detecting 41/117 events (35%) in 2011 to 104/109 (95%) in 2017 (all event types). Using the RTD, 5-year survival rates were 43% (95% CI 25-59%) in the chemoradiotherapy group and 30% (95% CI 23-36%) in the radiotherapy group; using the HID, the 5-year radical cystectomy survival rate was 57% (95% CI 50-63%).

conclusionsRoutine data are a feasible method for trial data collection. As long as events of interest are pre-validated, very high sensitivities for trial conduct can be achieved and further improved with targeted data queries. Outcomes can also be produced comparable to clinical trial and national dataset results. Given the real-time, obligatory nature of the HID, which forms the Hospital Episode Statistics (HES) data, alongside other datasets, we believe routine data extraction and validation is a robust way of rapidly collecting datasets for trials.

Indexed as

AdministrativeBladder cancerBladderPathDataEventsFollow-upHospital episode statistics (HES)OutcomesRandomised controlled trial (RCT)Routine

Identifiers

PMID33292682
PMCPMC7599120

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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.