Evidence map›Paper›PMID 29945673›Full record

Trial reportTrials2018

Exploring and adjusting for potential learning effects in ROLARR: a randomised controlled trial comparing robotic-assisted vs. standard laparoscopic surgery for rectal cancer resection.

Neil Corrigan, Helen Marshall, Julie Croft, Joanne Copeland, David Jayne, Julia Brown

Open access · goldAbstract readComparative StudyMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Trials, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
60citing papers in PubMed, 8 pooled it
4.2field-weighted citation impact, top 4% of its field
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

60 citing papers in PubMed, 8 syntheses or guidelines pooled it, 118 citations in OpenAlex.

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  20. [Robotic rectal surgery].Chirurgie (Heidelberg, Germany) · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Neil CorriganClinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, LS2 9JT, UK. n.corrigan@leeds.ac.uk.ORCID http://orcid.org/0000-0002-1424-9830
Helen MarshallClinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, LS2 9JT, UK.
Julie CroftClinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, LS2 9JT, UK.
Joanne CopelandClinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, LS2 9JT, UK.
David JayneDepartment of Academic Surgery, Leeds Institute of Biological and Clinical Sciences, Clinical Sciences Building, University of Leeds, St. James's University Hospital, Leeds, LS9 7TF, UK.
Julia BrownClinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, LS2 9JT, UK.
University of Leeds · GB

Funding

Efficacy and Mechanism Evaluation Programme EME project ref. number: 08/52/01
6 · The paper itself

Abstract

backgroundCommonly in surgical randomised controlled trials (RCT) the experimental treatment is a relatively new technique which the surgeons may still be learning, while the control is a well-established standard. This can lead to biased comparisons between treatments. In this paper we discuss the implementation of approaches for addressing this issue in the ROLARR trial, and points of consideration for future surgical trials.

methodsROLARR was an international, randomised, parallel-group trial comparing robotic vs. laparoscopic surgery for the curative treatment of rectal cancer. The primary endpoint was conversion to open surgery (binary). A surgeon inclusion criterion mandating a minimum level of experience in each technique was incorporated. Additionally, surgeon self-reported data were collected periodically throughout the trial to capture the level of experience of every participating surgeon. Multi-level logistic regression adjusting for operating surgeon as a random effect is used to estimate the odds ratio for conversion to open surgery between the treatment groups. We present and contrast the results from the primary analysis, which did not account for learning effects, and a sensitivity analysis which did.

resultsThe primary analysis yields an estimated odds ratio (robotic/laparoscopic) of 0.614 (95% CI 0.311, 1.211; p = 0.16), providing insufficient evidence to conclude superiority of robotic surgery compared to laparoscopic in terms of the risk of conversion to open. The sensitivity analysis reveals that while participating surgeons in ROLARR were expert at laparoscopic surgery, some, if not all, were still learning robotic surgery. The treatment-effect odds ratio decreases by a factor of 0.341 (95% CI 0.121, 0.960; p = 0.042) per unit increase in log-number of previous robotic operations performed by the operating surgeon. The odds ratio for a patient whose operating surgeon has the mean experience level in ROLARR - 152.46 previous laparoscopic, 67.93 previous robotic operations - is 0.40 (95% CI 0.168, 0.953; p = 0.039).

conclusionsIn this paper we have demonstrated the implementation of approaches for accounting for learning in a practical example of a surgery RCT analysis. The results demonstrate the value of implementing such approaches, since we have shown that without them the ROLARR analysis would indeed have been confounded by the learning effects.

trial registrationInternational Standard Randomised Controlled Trial Number (ISRCTN) registry, ID: ISRCTN80500123. Registered on 27 May 2010.

Indexed as

Clinical CompetenceLaparoscopyLearning CurveRobotic Surgical ProceduresConversion to Open SurgeryDigestive System Surgical ProceduresFemaleHumansMalePostoperative ComplicationsRectal NeoplasmsRisk FactorsTime FactorsTreatment OutcomeLearning curveLearning effectsRandomised controlled trialsSurgery

Identifiers

PMID29945673
PMCPMC6020359
OpenAlexW2811512259

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.