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  • Research
  • Open Access
  • Open Peer Review

A randomised controlled trial of an Intervention to Improve Compliance with the ARRIVE guidelines (IICARus)

Research Integrity and Peer Review20194:12

https://doi.org/10.1186/s41073-019-0069-3

  • Received: 22 November 2018
  • Accepted: 26 April 2019
  • Published:
Open Peer Review reports

Abstract

Background

The ARRIVE (Animal Research: Reporting of In Vivo Experiments) guidelines are widely endorsed but compliance is limited. We sought to determine whether journal-requested completion of an ARRIVE checklist improves full compliance with the guidelines.

Methods

In a randomised controlled trial, manuscripts reporting in vivo animal research submitted to PLOS ONE (March–June 2015) were randomly allocated to either requested completion of an ARRIVE checklist or current standard practice. Authors, academic editors, and peer reviewers were blinded to group allocation. Trained reviewers performed outcome adjudication in duplicate by assessing manuscripts against an operationalised version of the ARRIVE guidelines that consists 108 items. Our primary outcome was the between-group differences in the proportion of manuscripts meeting all ARRIVE guideline checklist subitems.

Results

We randomised 1689 manuscripts (control: n = 844, intervention: n = 845), of which 1269 were sent for peer review and 762 (control: n = 340; intervention: n = 332) accepted for publication. No manuscript in either group achieved full compliance with the ARRIVE checklist. Details of animal husbandry (ARRIVE subitem 9b) was the only subitem to show improvements in reporting, with the proportion of compliant manuscripts rising from 52.1 to 74.1% (X2 = 34.0, df = 1, p = 2.1 × 10−7) in the control and intervention groups, respectively.

Conclusions

These results suggest that altering the editorial process to include requests for a completed ARRIVE checklist is not enough to improve compliance with the ARRIVE guidelines. Other approaches, such as more stringent editorial policies or a targeted approach on key quality items, may promote improvements in reporting.

Keywords

  • ARRIVE
  • Reporting guidelines
  • Randomised controlled trial

Background

There are widespread failures across in vivo animal research to adequately describe and report research methods, including critical measures to reduce the risk of experimental bias [10, 13]. Such omissions have been shown to be associated with overestimation of effect sizes [8, 13] and are likely to contribute, in part, to translational failure. In an effort to improve reporting standards, an expert working group coordinated by the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs) developed the Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines [9], published in 2010.

Since the ARRIVE guidelines were first published, they have been endorsed by many journals in their instructions to authors, but this has not been accompanied by substantial improvements in reporting [2, 3, 6, 14]. Simply endorsing the guidelines does not appear to be sufficient to encourage compliance. Recent findings suggest that following the introduction of mandated completion of a distinct reporting checklist at ten Nature Journals at the stage of first revision significantly improved the quality in reporting versus that of comparator journals [7, 12].

PLOS ONE is an open access online only journal which at the time this study began published around 32,000 research articles per year. Of these, some 5000 were described in vivo animal research. At present, PLOS ONE instructions to authors encourage compliance with the ARRIVE guidelines, but do not mandate checklist completion. Journals have an important role to play in ensuring that the quality of reporting in the research they publish is robust, yet the most effective mechanism by which they can achieve this remains unclear.

Our aim was to assess the effect of an email request to authors to complete an ARRIVE checklist on compliance with the ARRIVE guidelines. The email request was at the time of submission and requested that authors state where in the manuscript various components of the ARRIVE guidelines are reported.

Methods

Methodology and open data

Our protocol, data analysis plan, analysis code, data validation code, and complete dataset are available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/XSJBV).

Ethical approval

We sought an informal ethical opinion from the BMJ Ethics Committee, who were prepared to consider our proposal although it was slightly out of scope. We did this because we were unable at the time to identify an institutional ethics committee who considered this research to fall within their remit. The majority view of the committee was that it was ethical for manuscripts to be randomised between different handling methods; that it was ethical for authors, peer reviewers, and academic editors to be kept unaware of the existence of the study while it was in progress; and that it was ethical for the study to receive funding from the NC3Rs.

Randomisation of manuscripts

We developed (https://doi.org/10.5281/zenodo.1188821) an online platform to support each stage of the project (https://ecrf1.clinicaltrials.ed.ac.uk/iicarus/).

The PLOS ONE editorial process involves an initial screening process, including a determination of whether a manuscript describes animal studies, whether it describes human studies (one manuscript might describe both), and categorises the area of research according to an established taxonomy. For studies reporting the use of animals, checks are carried out to ensure that appropriate institutional animal care and use committee/ethical approvals were in place, and authors of studies perceived to be at high risk—for instance those animal studies which used death as an endpoint—are contacted to provide a valid justification. Manuscripts are then allocated to an academic editor (AE), who assigns peer reviewers as appropriate.

Manuscripts submitted to PLOS ONE between March and June 2015 describing in vivo animal research were randomised using the IICARus web platform to receive standard editorial processing (control group) or checklist completion requests (intervention group). The randomisation sequence was generated using randomisation in the C# programming language and involved minimisation (weighted at 0.75) to ensure that country of origin (of the corresponding author) was balanced between groups. All users of the platform were assigned a level of access in line with their role (e.g. “Trainee”, “Reviewer”, “Randomiser”) to ensure that any individual involved in outcome assessment was blinded to the allocation sequence.

On submission, authors receive an automated acknowledgement from the publisher that their submission had entered a screening phase. For manuscripts identified during screening to include in vivo research and which were randomised to the intervention, corresponding authors were informed in the post screening email that a completed ARRIVE checklist must be completed before the manuscript could advance through the review process. The email advised that this should include details of the page of their manuscript on which each ARRIVE item was addressed (an excerpt from the email used for the intervention is available on the OSF https://doi.org/10.17605/OSF.IO/XSJBV). If the PLOS editorial team did not receive a checklist, it was sent back to authors once more for completion. Manuscripts by authors who did not complete the checklist after the second contact, for any reason, were still passed to the next stage and continued in the study. The contents of completed checklists were not checked against the manuscript for compliance at any stage.

Blinded manuscript processing

Authors, AEs, and peer reviewers were blinded to the existence of the study. Study personnel took care, in their public comments, not to disclose details of the study or the journal at which the study was being conducted. The journal was not named in the study protocol. If authors enquired as to why their manuscript was being processed differently, they were to be advised that these differences were due to variation within the editorial team in the intensity with which they pursued efforts to improve the review process.

For studies randomised to the control group, PLOS ONE processed the manuscript according to their normal editorial processes.

Once a final decision regarding publication was made, the pre-publication materials for accepted manuscripts were collated by the PLOS editorial team. Where an ARRIVE checklist was included in the accepted materials for publication, this was redacted, along with any reference in the text to the submission of a completed ARRIVE checklist. The format of manuscripts largely excluded any evidence that the manuscript was submitted to PLOS ONE. If a reference to PLOS ONE was discovered in the text by internal outcome assessors (within our research group), this was also redacted to prevent any change in behaviour which may result from external outcome assessors knowing which publisher was involved in the study. Where authors stated that the work complies with the ARRIVE guidelines this statement was not redacted. Redacted PDFs of all materials were provided to our research team and uploaded to the IICARus web platform.

Outcome assessment

Primary, secondary, tertiary, and feasibility outcomes are confirmatory and were pre-specified in our study protocol (https://doi.org/10.17605/OSF.IO/XSJBV). The unit of analysis for all outcomes was the manuscript.

Our primary outcome was to assess whether the proportion of publications in each group considered to fully comply with all of the ARRIVE criteria (at the level of the 38 subitems) was independent of group allocation.

To assess compliance with the ARRIVE guidelines in greater detail, we assessed reporting at the subitem level. The Landis core reporting standards [11] set out the key aspects of study design and conduct necessary to improve transparency of in vivo research and allow readers to ascertain the validity of the findings reported. We therefore wanted to assess compliance with the ARRIVE items which form these criteria, namely, reporting of randomisation to experimental groups (subitem 6b), reporting of blinded outcome assessment (subitem 6b), reporting of a sample size calculation (subitem 10a), and reporting of animal exclusions (subitem 15b). In addition, we aimed to investigate any effects our intervention may have on the process of publication by assessing the number of manuscripts accepted for publication in each group.

Our secondary outcomes were therefore to assess whether:
  • The proportion of publications meeting each of the individual 38 ARRIVE subitems was independent of group allocation (intervention/ control)

  • The proportion of studies reporting all Landis criteria subitems present in the ARRIVE guidelines was independent of group allocation

  • The proportion of submitted manuscripts accepted for publication was independent of group allocation

We also wanted to examine whether different domains, countries, or research that includes human research may be associated with differences in adherence to the ARRIVE guidelines.

For our tertiary outcomes, we assessed whether:
  • The proportion of publications meeting each of the 38 ARRIVE subitems was independent of group allocation, stratified by experimental animal

  • The proportion of studies reporting all of the Landis criteria subitems (blinded assessment of outcome, sample size calculation, and criteria for exclusion of experimental subjects), stratified by experimental animal, was independent of group allocation

  • The proportion of publications meeting each of the 38 ARRIVE subitems was independent of group allocation, stratified by the country of the address of the corresponding author

  • The proportion of publications meeting each of the 38 ARRIVE subitems was independent of group allocation, stratified by whether or not the research also contains human data

  • To examine the feasibility of implementing requests for ARRIVE checklist completion at PLOS ONE, we assessed outcomes relating to the duration of processing manuscripts to gain an insight into potential costs to the journal. We therefore assessed the following for accepted manuscripts in each group:

  • Time (days) spent in PLOS editorial office in handling the manuscript (prior to editor assignment).

  • Time (days) from manuscript submission to AE assignment.

  • Time (days) from AE assignment to first reviewer agreed.

  • Time (days) from AE assignment to first decision

  • Time (days) from receipt of last review to AE decision

In addition, we assessed the following outcomes in manuscripts that were accepted following resubmission:
  • Time (days) from initial decision letter to resubmission

  • Number of cycles of resubmission

  • Time (days) from resubmission to final decision

We also conducted some exploratory analyses not defined in the study protocol. Although the majority of authors complied with the request to complete an ARRIVE checklist, a small number did not. By limiting our analyses to the manuscripts that had received the intervention (equivalent to an “on treatment” analysis), we assessed:
  • The proportion of publications meeting each of the 38 ARRIVE subitems in the control and “on treatment” intervention group

The Landis reporting criteria are minimal reporting standards which are important to quality improvement efforts. Given that the reporting of randomisation to experimental groups and blinded outcome assessment are part of the same subitem (6b) in the ARRIVE guidelines, we explored each of the Landis criteria items individually to assess:
  • The proportion of publications meeting each individual Landis criteria in each group

We divided the 20 main ARRIVE items into 38 subitems. These subitems were further operationalised 108 questions (Additional file 1) which were scored by trained outcome assessors on the web platform. Two of these questions (0.1.0 What animal species are used in this research? and 0.2.0: Does the manuscript include human study?) were used only to categorise research for analysis purposes (Tertiary outcomes) and were not included in assessments of compliance. It was later determined by the steering committee that six questions from the original 108 were not strictly required to comply with the ARRIVE checklist (see Additional file 1; removed questions highlighted in grey) and were therefore also excluded from the analysis. The decision to remove data from these six questions was taken after data collection, but prior to the unblinding of group allocation.

PDF files of manuscripts were available alongside the scoring questions. Each manuscript was scored by two independent reviewers who were blinded to both intervention status and to the score given by the alternative reviewer. Manuscripts were presented to reviewers in random order, and the platform did not allow the same user to review the same manuscript twice. Discrepancies between reviewers were reconciled by a third independent reviewer, who could view both previous scores. For some manuscripts, some questions were not applicable (e.g. questions relating to fish studies in a manuscript describing rat experiments). Compliance was only assessed against questions that were applicable to the study.

There were several deviations from the outcome measures specified our study protocol. The time spent in the PLOS editorial office was not disentangled from time with the authors; therefore, it includes time for the authors to follow any copyediting changes and requests for documents (including the request to complete an ARRIVE checklist). Similarly, the time spent with authors was also included in the time from manuscript submission to AE assignment. In addition, we had originally intended to analyse the time in the PLOS editorial office in minutes, but the measurement of this was not feasible. We were unable to analyse “The proportion of submitted manuscripts accepted for publication, stratified by experimental animal” (Secondary outcome measure) as we did not receive species categorisation data for studies which were not accepted. PLOS ONE were unable to provide us with one of our specified feasibility measures “Time (days) for each reviewer, from solicitation of reviews to receipt of reviews)” and instead provided “Time (days) from AE assignment to first decision”. “Time (days) from AE assignment to first reviewer agreed” also differs to the outcome described in our study protocol (“Time (days) from AE assignment to solicitation of reviews”). In addition, we had originally set out in our protocol that we would look at the following feasibility outcome measures for manuscripts when the decision was other than “Accept” or “Reject”: whether a revised manuscript is submitted, time (days) from initial decision letter to resubmission, number of cycles of resubmission, and time (days) from resubmission to final decision. However, we did not attain this information for manuscripts which were not eventually accepted. Therefore, all feasibility measures apply to accepted manuscripts only.

Reviewer training

This was a challenging project, and we used crowdsourcing to recruit additional reviewers external to our research group. We used our research networks and social media to identify researchers and students across the biomedical sciences and recruit them as outcome assessors for the project. As an incentive, rewards were given to external reviewers who reached a pre-specified number of manuscript reviews or completed the most reviews in a certain time period.

To ensure that review quality was high, we required reviewers to complete online training prior to reviewing manuscripts as part of the project. We developed a training program with a pool of 10 manuscripts for which we described “Gold standard” correct answers with explanations and an accompanying document with further elaboration (https://doi.org/10.17605/OSF.IO/XSJBV). To successfully complete the training, external reviewers had to score 80% against these gold standard answers overall and score 100% on gold standard questions relating to the Landis criteria subitems for three consecutive training manuscripts. The training platform remains available (https://ecrf1.clinicaltrials.ed.ac.uk/iicarus/) and can still be used as a training tool for assessing manuscripts against the ARRIVE guidelines.

Power calculations

When the study was being designed, the PLOS ONE editorial team estimated that complete compliance with the ARRIVE guidelines was close to zero. To have 80% power with an alpha of 0.05, to detect an increase in full compliance from 1 to 10% (the primary outcome) would require 100 published manuscripts per group. To examine each of the individual 38 ARRIVE subitems (Secondary outcome), after correction for multiplicity of testing (alpha = 0.0013), we would require 200 published manuscripts per group to detect with 80% power an increase from 30 to 50% in the prevalence of reporting of an individual subitem. It was estimated that at time of the trial PLOS ONE accepted around 70% of manuscripts, and to account for some drop out because of the use of the same academic editor, we increased our group estimate to 150 manuscripts per group for the primary outcome, and 300 manuscripts in each group for secondary outcomes. During the course of the study, it appeared that acceptance rates were lower than the estimate, and so we increased target recruitment to 1000 manuscripts, of which we estimated 600 would be accepted for publication. Despite the risk of overpowering, we did not curtail the study when we had reached the required number of manuscripts accepted for publication because we were concerned that manuscripts with short submission to acceptance times would be enriched in the study population and might not be representative of all manuscripts.

Data validation

We validated the dataset, blinded to group allocation, to minimise errors. For example, where a manuscript was assessed as not including fish experiments, “Yes” or “No” responses to IICARus questions only relevant to fish species (e.g. Additional file 1, Questions 9.2.3–9.2.4) should not have been recorded. The R code for validation, with explanations of each response validated, and the changes we made to the data are available on the OSF. These were uploaded prior to the unblinding of the final results, at which point database lock occurred and the data were not subsequently altered in any way.

Statistical analysis

All analyses were carried out using RStudio v1.0.143 with the level of statistical significance set at p < 0.05, corrected as appropriate for multiple comparisons. Our full statistical analysis plan and accompanying R code was uploaded to the OSF prior to database lock.

We performed logistic regression with group allocation and corresponding author country of origin included as independent variables to determine any effects on full compliance (primary outcome) and compliance with each of the 38 subitems (secondary outcome), adjusting for the minimised randomisation. For our primary, secondary, and tertiary outcome measures, we used the chi-squared test of independence to test whether compliance was independent of group membership (intervention/control). To determine if the differences between proportions is meaningful for each outcome measure, effect sizes were calculated using Cohen’s H. For feasibility outcomes, medians and interquartile ranges were calculated and the Mann-Whitney U test was used to test whether a significant difference existed between groups. To control the familywise error rate of multiple comparisons, the Holm-Bonferroni method [1] was used to adjust p values for secondary, tertiary, and feasibility outcomes. Only means and confidence intervals were calculated for our exploratory analyses. A full description of our data analysis plan is available on the OSF: https://osf.io/zgqxk/.

Statistical considerations

The proportion of compliant manuscripts was assessed based on the number compliant manuscripts divided by the number of applicable manuscripts. In some cases, particularly when stratifying by manuscript country of origin or animal species, the number of manuscripts in each group is very low. If the number of applicable manuscripts for any subitem (with or without stratification) was less than 10, we did not perform statistical analysis.

The chi-squared test of Independence relies on the assumption that no more than 20% of expected counts are less than 5 and that no individual expected counts are less than 1. In cases where counts were less than 5, a Fisher’s exact test was used.

Unpaired t tests rely on a normal distribution; therefore, if the distribution was non-normal, the Mann-Whitney U test, a non-parametric alternative, was used and summary medians and interquartile ranges were presented. In the case of parametric data with unequal variance between groups, Welch’s t test was used due to higher reliability.

Results

We randomised 1689 PLOS ONE manuscripts: 845 manuscripts to the intervention and 844 to control. Of these, 192 were rejected by the journal and 75 manuscripts were withdrawn. A further 153 were excluded due to errors, including 77 that were found not to contain in vivo animal research. The reasons for these errors are shown in Additional file 2. Of the remaining 1269 manuscripts, 672 were accepted for publication (340 control, 332 intervention) and underwent web-based outcome assessment (Fig. 1). Manuscript allocation to group and the corresponding number of manuscripts from each country which were randomised and accepted are shown in Table 1. No authors questioned the differences in manuscript processing occurring within the intervention group. A complete dataset detailing the proportion compliance for each of the 108 questions is available online (https://doi.org/10.17605/OSF.IO/XSJBV).
Fig. 1
Fig. 1

Manuscript processing

Table 1

Manuscript allocation by country. Manuscripts allocated to each group per corresponding author country of origin; nR, number randomised; nA, number accepted

 

Control

Intervention

 

Control

Intervention

Country

nR

nA

nR

nA

Country

nR

nA

nR

nA

 Algeria

1

0

0

0

Malaysia

2

0

2

1

 Argentina

4

1

3

1

Mexico

4

1

3

2

 Australia

13

6

13

11

Netherlands

9

6

13

12

 Austria

6

3

1

1

North Korea

5

4

0

0

 Belgium

3

3

4

4

New Zealand

0

0

1

1

 Brazil

29

13

33

13

Norway

3

3

0

0

 Canada

15

12

16

12

Pakistan

0

0

1

0

 Chile

1

1

4

2

Poland

2

2

5

3

 China

135

38

157

54

Portugal

2

1

6

3

 Colombia

1

1

0

0

Puerto Rico

0

0

1

0

 Czech Republic

1

1

1

0

Romania

1

1

1

0

 Denmark

6

3

8

3

Russia

3

2

1

1

 Egypt

2

1

6

4

Saudi Arabia

3

2

1

0

 Finland

1

0

1

1

Singapore

3

1

7

2

 France

10

6

15

13

Slovakia

1

0

0

0

 French Guiana

1

0

0

0

South Africa

1

1

2

0

 Germany

34

24

29

13

South Korea

28

14

26

8

 Greece

2

2

0

0

Spain

15

8

11

7

 Hong Kong

1

0

3

2

Sweden

10

7

11

6

 Hungary

1

0

0

0

Switzerland

6

5

7

5

 India

15

8

10

3

Taiwan

19

13

8

3

 Iran

1

1

1

1

Thailand

0

0

1

0

 Ireland

2

2

1

1

Turkey

2

0

1

0

 Israel

1

1

2

2

Ukraine

0

0

1

0

 Italy

8

6

15

11

United Kingdom

17

10

18

9

 Japan

47

27

46

23

United States

143

97

150

94

 Kuwait

2

2

0

0

     

Quality of outcome assessment

Three hundred sixty individuals registered with the online platform; 47 completed reviewer training, and 42 contributed at least one outcome assessment. The percentage agreement between the first and second reviewer for each manuscript was high. For 71.6% (481/672) of manuscripts, reviewers were in agreement on at least 80% of the questions. The agreement of reviewers varied considerably at the level of each of the 108 individual questions (Additional file 3: Table S1), from a kappa coefficient of 0.90 (0.86–0.93) for Question 1.1 (Is the species of animal model studied reported in the title?) to a worse than chance kappa coefficient of − 0.03 (− 0.10 - -0.04) for Question 13.2 (Is the unit of analysis for at least one test explicitly specified?). This distribution of kappa agreement is displayed in a histogram (Additional file 3: Fig. S1).

Primary outcome

No manuscript achieved full compliance with the ARRIVE checklist; therefore, there was no difference between the control and intervention groups (X2 = 0.1, df = 1, p = 0.76). Compliance with individual ARRIVE subitems ranged from 8 to 65%. The median compliance was 36.8% and 39.5% of relevant subitems in the control and intervention groups, respectively.

Logistic regression

Country of corresponding author had no influence on compliance either overall or for any individual subitems. Only one subitem had improved reporting in the intervention group versus control, subitem 9b (Provide details of husbandry conditions e.g. breeding programme, light/dark cycle, temperature, quality of water etc for fish, type of food, access to food and water, environmental enrichment) (increased log odds of compliance by 1.03 (p < 0.0001).

Secondary outcomes

Compliance with individual ARRIVE subitems

Only one ARRIVE subitem had significantly improved compliance in the intervention group (Table 2). ARRIVE subitem 9b (Provide details of husbandry conditions e.g. breeding programme, light/dark cycle, temperature, quality of water etc for fish, type of food, access to food and water, environmental enrichment) was reported in 52.1% (177/340) of manuscripts in the control group compared to 74.1% (246/332) of manuscripts in the intervention group (X2 = 34.0, df = 1, p < 0.0001). Reporting of animal characteristics and health status (subitem 14) was very low, with 0.29% (1/339) and 0% (0/332) compliance in the control and intervention groups, respectively. Similarly, reporting of animal housing (subitem 9a); adverse events (subitem 17b); the order of treatment and assessment (Item 11b); implications for replacement, refinement, or reduction (subitem 18c); defining primary and secondary outcomes (subitem 12); and rationale for experimental procedures (subitem 7d) was low, with less than 5% of manuscripts reporting each of these items in both groups. Figure 2 shows the percentage compliance in each group for each ARRIVE subitem in each section of the manuscript.
Table 2

Percentage compliance for each ARRIVE subitem; %, percentage of compliant manuscripts; CI, confidence interval; n, number of compliant manuscripts; N, total number of applicable manuscripts; Adj p, adjusted p value; Cohen’s H, Cohen’s H effect size

 

Control

Intervention

  

ARRIVE subitem

%

95% CIs

n

N

%

95% CIs

n

N

Adj. p

Cohen’s H

1

41.76

36.5–47.2

142

340

44.58

39.2–50.1

148

332

> 0.99

0.06

2

71.76

66.6–76.4

244

340

67.47

62.1–72.4

224

332

> 0.99

−0.09

3a

100.00

98.6–100

340

340

100.00

98.6–100

332

332

> 0.99

0.00

3b

34.12

29.1–39.5

116

340

36.14

31–41.6

120

332

> 0.99

0.04

4

91.18

87.5–93.9

310

340

93.07

89.6–95.5

309

332

> 0.99

0.07

5

69.41

64.2–74.2

236

340

72.59

67.4–77.3

241

332

> 0.99

0.07

6a

70.00

64.8–74.8

238

340

75.00

69.9–79.5

249

332

> 0.99

0.11

6b

8.33

5.7–12

28

336

10.49

7.5–14.5

34

324

> 0.99

0.07

6c

90.00

86.2–92.9

306

340

88.86

84.8–91.9

295

332

> 0.99

−0.04

7a

16.76

13–21.3

57

340

16.87

13.1–21.4

56

332

> 0.99

0.00

7b

44.37

38.7–50.2

134

302

51.33

45.5–57.1

154

300

> 0.99

0.14

7c

8.64

5.8–12.5

26

301

14.09

10.5–18.7

42

298

> 0.99

0.17

7d

3.63

1.9–6.6

11

303

3.63

1.9–6.6

11

303

> 0.99

0.00

8a

4.71

2.8–7.7

16

340

7.83

5.3–11.4

26

332

> 0.99

0.13

8b

57.06

51.6–62.4

194

340

62.65

57.2–67.8

208

332

> 0.99

0.11

9a

0.30

0–1.9

1

337

2.74

1.3–5.3

9

328

0.39

0.22

9b

52.06

46.6–57.5

177

340

74.10

69–78.7

246

332

< 0.001

0.46

9c

14.71

11.2–19

50

340

20.48

16.4–25.3

68

332

> 0.99

0.15

10a

37.35

32.2–42.8

127

340

43.67

38.3–49.2

145

332

> 0.99

0.13

10b

3.53

1.9–6.2

12

340

7.53

5–11.1

25

332

> 0.99

0.18

10c

18.15

14.3–22.8

61

336

14.64

11.1–19.1

47

321

> 0.99

−0.10

11a

4.82

2.8–8

15

311

7.49

4.9–11.2

23

307

> 0.99

0.11

11b

1.24

0.4–3.4

4

323

2.88

1.4–5.6

9

313

> 0.99

0.12

12

1.76

0.7–4

6

340

3.01

1.5–5.6

10

332

> 0.99

0.08

13a

87.50

83.4–90.7

294

336

89.91

86–92.9

294

327

> 0.99

0.08

13b

44.08

38.7–49.6

149

338

44.51

39.1–50.1

146

328

> 0.99

0.01

13c

10.06

7.2–13.9

34

338

12.80

9.5–17

42

328

> 0.99

0.09

14

0.29

0–1.9

1

340

0.00

0–1.4

0

332

> 0.99

−0.11

15a

37.35

32.2–42.8

127

340

37.35

32.2–42.8

124

332

> 0.99

0.00

15b

12.65

9.4–16.8

43

340

14.46

10.9–18.8

48

332

> 0.99

0.05

16

78.55

73.7–82.8

260

331

80.94

76.1–85

259

320

> 0.99

0.06

17a

16.47

12.8–20.9

56

340

21.69

17.5–26.6

72

332

> 0.99

0.13

17b

1.18

0.4–3.2

4

340

1.81

0.7–4.1

6

332

> 0.99

0.05

18a

100.00

98.6–100

340

340

99.40

97.6–99.9

330

332

> 0.99

−0.16

18b

26.47

21.9–31.6

90

340

28.31

23.6–33.5

94

332

> 0.99

0.04

18c

2.94

1.5–5.5

10

340

3.01

1.5–5.6

10

332

> 0.99

0.00

19

77.94

73.1–82.2

265

340

77.71

72.8–82

258

332

> 0.99

−0.01

20

51.47

46–56.9

175

340

52.71

47.2–58.2

175

332

> 0.99

0.02

Fig. 2
Fig. 2

Percentage compliance for each ARRIVE subitem; percentage compliance for each ARRIVE subitem with 95% confidence intervals; asterisk denotes statistical significance; figure divided into article sections specified in the ARRIVE guidelines

Reporting of Landis 4 subitems

Reporting of the Landis 4 criteria (blinding, randomisation, animal exclusions, and use of a sample size calculation) was low and did not differ significantly between groups (Fisher’s estimate for difference = 0.61, df = 1, p = 0.73). 1.5% (5/340) of the control group manuscripts) and 0.9% (3/332) of intervention group manuscripts reported all four subitems of the Landis criteria.

Manuscript acceptance

There was no significant difference in the proportion of accepted manuscripts between the control and intervention groups, being 54.7% (340/622) and 51.3% (322/647) respectively. (X2 = 1.30, df = 1, p = 0.25).

Tertiary outcomes

Compliance by animal species

We removed animal species from the analysis where fewer than ten manuscripts reported the use of a species for control and intervention groups, leaving only rat and mouse studies. In studies involving mice, only reporting of ARRIVE subitem, 9b (Provide details of husbandry conditions e.g. breeding programme, light/dark cycle, temperature, quality of water etc for fish, type of food, access to food and water, environmental enrichment) increased significantly from 49.5% (105/211) in the control group to 70.2% (135/192) in the intervention group (X2 = 16.8, df = 1, p = 0.003). No subitem had a statistically significant difference between groups in rat studies. Results are summarised in Tables 3 and 4. There was no difference in Landis 4 compliance between animal species.
Table 3

ARRIVE subitem compliance in mouse studies. %, percentage of compliant manuscripts; CI, confidence interval; n, number of compliant manuscripts; N, total number of applicable manuscripts; Adj p, adjusted p value; Cohen’s H, Cohen’s H effect size

 

Control

Intervention

  

ARRIVE subitem

%

95% CIs

n

N

%

95% CIs

n

N

Adj p

Cohen’s H

1

37.44

31–44.4

79

211

37.50

30.7–44.8

72

192

> 0.99

0.00

2

68.25

61.4–74.4

144

211

60.42

53.1–67.3

116

192

> 0.99

− 0.16

3a

100.00

97.8–100

211

211

100.00

97.6–100

192

192

> 0.99

0.00

3b

30.33

24.3–37.1

64

211

29.69

23.4–36.8

57

192

> 0.99

− 0.01

4

89.10

83.9–92.8

188

211

92.19

87.2–95.4

177

192

> 0.99

0.11

5

67.77

61–73.9

143

211

71.88

64.9–78

138

192

> 0.99

0.09

6a

63.98

57.1–70.4

135

211

71.35

64.3–77.5

137

192

> 0.99

0.16

6b

5.24

2.8–9.4

11

210

7.89

4.6–12.9

15

190

> 0.99

0.11

6c

90.05

85–93.6

190

211

91.15

86–94.6

175

192

> 0.99

0.04

7a

17.54

12.8–23.5

37

211

17.71

12.7–24

34

192

> 0.99

0.00

7b

44.68

37.5–52.1

84

188

48.57

41–56.2

85

175

> 0.99

0.08

7c

6.91

3.9–11.8

13

188

9.83

6–15.5

17

173

> 0.99

0.11

7d

3.16

1.3–7.1

6

190

2.27

0.7–6.1

4

176

> 0.99

− 0.05

8a

4.74

2.4–8.8

10

211

6.25

3.4–10.9

12

192

> 0.99

0.07

8b

55.45

48.5–62.2

117

211

60.94

53.6–67.8

117

192

> 0.99

0.11

9a

0.47

0–3

1

211

2.08

0.7–5.6

4

192

> 0.99

0.15

9b

49.76

42.8–56.7

105

211

70.31

63.2–76.6

135

192

0.003

0.42

9c

15.17

10.7–20.9

32

211

23.96

18.2–30.7

46

192

> 0.99

0.22

10a

27.01

21.3–33.6

57

211

31.25

24.9–38.4

60

192

> 0.99

0.09

10b

2.84

1.2–6.4

6

211

5.73

3–10.3

11

192

> 0.99

0.14

10c

21.15

15.9–27.5

44

208

15.43

10.7–21.6

29

188

> 0.99

− 0.15

11a

4.12

1.9–8.3

8

194

5.00

2.5–9.6

9

180

> 0.99

0.04

11b

1.93

0.6–5.2

4

207

0.54

0–3.4

1

185

> 0.99

−0.13

12

0.00

0–2.2

0

211

3.65

1.6–7.7

7

192

0.40

0.38

13a

87.20

81.8–91.3

184

211

89.58

84.2–93.4

172

192

> 0.99

0.07

13b

46.92

40.1–53.9

99

211

42.71

35.7–50

82

192

> 0.99

− 0.08

13c

8.06

4.9–12.8

17

211

11.46

7.5–17

22

192

> 0.99

0.12

14

0.00

0–2.2

0

211

0.00

0–2.4

0

192

> 0.99

0.00

15a

36.02

29.6–42.9

76

211

36.46

29.7–43.7

70

192

> 0.99

0.01

15b

11.37

7.6–16.6

24

211

10.94

7.1–16.4

21

192

> 0.99

− 0.01

16

84.62

78.8–89.1

176

208

80.95

74.5–86.1

153

189

> 0.99

− 0.10

17a

15.64

11.2–21.4

33

211

23.44

17.8–30.2

45

192

> 0.99

0.20

17b

0.95

0.2–3.7

2

211

2.60

1–6.3

5

192

> 0.99

0.13

18a

100.00

97.8–100

211

211

98.96

95.9–99.8

190

192

> 0.99

− 0.20

18b

27.01

21.3–33.6

57

211

26.56

20.6–33.5

51

192

> 0.99

− 0.01

18c

3.32

1.5–7

7

211

3.13

1.3–7

6

192

> 0.99

− 0.01

19

82.46

76.5–87.2

174

211

81.77

75.4–86.8

157

192

> 0.99

− 0.02

20

51.18

44.2–58.1

108

211

56.25

48.9–63.3

108

192

> 0.99

0.10

Table 4

ARRIVE subitem compliance in rat studies. %, percentage of compliant manuscripts; CI, confidence interval; n, number of compliant manuscripts; N, total number of applicable manuscripts; Adj p, adjusted p value; Cohen’s H, Cohen’s H effect size

 

Control

Intervention

  

ARRIVE subitem

%

95% CIs

n

N

%

95% CIs

n

N

Adj p

Cohen’s H

1

47.27

33.9–61.1

26

55

59.09

46.3–70.8

39

66

> 0.99

0.24

2

83.64

70.7–91.8

46

55

81.82

70–89.9

54

66

> 0.99

− 0.05

3a

100.00

91.9–100

55

55

100.00

93.1–100

66

66

> 0.99

0.00

3b

20.00

10.9–33.4

11

55

34.85

23.8–47.7

23

66

> 0.99

0.34

4

96.36

86.4–99.4

53

55

96.97

88.5–99.5

64

66

> 0.99

0.03

5

83.64

70.7–91.8

46

55

80.30

68.3–88.7

53

66

> 0.99

− 0.09

6a

90.91

79.3–96.6

50

55

87.88

77–94.3

58

66

> 0.99

− 0.10

6b

24.53

14.2–38.6

13

53

21.54

12.7–33.8

14

65

> 0.99

− 0.07

6c

98.18

89–99.9

54

55

89.39

78.8–95.3

59

66

> 0.99

− 0.39

7a

9.09

3.4–20.7

5

55

12.12

5.7–23

8

66

> 0.99

0.10

7b

44.44

31.2–58.5

24

54

60.32

47.2–72.2

38

63

> 0.99

0.32

7c

5.56

1.4–16.3

3

54

14.29

7.1–25.9

9

63

> 0.99

0.30

7d

1.85

0.1–11.2

1

54

6.35

2.1–16.3

4

63

> 0.99

0.24

8a

1.82

0.1–11

1

55

10.61

4.7–21.2

7

66

> 0.99

0.39

8b

63.64

49.5–75.9

35

55

75.76

63.4–85.1

50

66

> 0.99

0.26

9a

0.00

0–8.1

0

55

6.15

2–15.8

4

65

> 0.99

0.50

9b

69.09

55–80.5

38

55

90.91

80.6–96.3

60

66

0.37

0.57

9c

12.73

5.7–25.1

7

55

13.64

6.8–24.8

9

66

> 0.99

0.03

10a

52.73

38.9–66.1

29

55

60.61

47.8–72.2

40

66

> 0.99

0.16

10b

1.82

0.1–11

1

55

12.12

5.7–23

8

66

> 0.99

0.44

10c

5.45

1.4–16.1

3

55

3.17

0.6–12

2

63

> 0.99

− 0.11

11a

3.77

0.7–14.1

2

53

10.77

4.8–21.5

7

65

> 0.99

0.28

11b

0.00

0–8.4

0

53

4.69

1.2–14

3

64

> 0.99

0.44

12

1.82

0.1–11

1

55

1.52

0.1–9.3

1

66

> 0.99

− 0.02

13a

94.55

83.9–98.6

52

55

90.77

80.3–96.2

59

65

> 0.99

− 0.15

13b

41.82

28.9–55.9

23

55

48.48

36.1–61

32

66

> 0.99

0.13

13c

18.18

9.5–31.4

10

55

16.67

9–28.3

11

66

> 0.99

− 0.04

14

0.00

0–8.1

0

55

0.00

0–6.9

0

66

> 0.99

0.00

15a

43.64

30.6–57.6

24

55

43.94

31.9–56.7

29

66

> 0.99

0.01

15b

12.73

5.7–25.1

7

55

16.67

9–28.3

11

66

> 0.99

0.11

16

70.37

56.2–81.6

38

54

87.30

76–94

55

63

> 0.99

0.42

17a

12.73

5.7–25.1

7

55

12.12

5.7–23

8

66

> 0.99

− 0.02

17b

1.82

0.1–11

1

55

0.00

0–6.9

0

66

> 0.99

− 0.27

18a

100.00

91.9–100

55

55

100.00

93.1–100

66

66

> 0.99

0.00

18b

18.18

9.5–31.4

10

55

28.79

18.6–41.4

19

66

> 0.99

0.25

18c

3.64

0.6–13.6

2

55

1.52

0.1–9.3

1

66

> 0.99

− 0.14

19

74.55

60.7–84.9

41

55

86.36

75.2–93.2

57

66

> 0.99

0.30

20

43.64

30.6–57.6

24

55

39.39

27.8–52.2

26

66

> 0.99

− 0.09

Feasibility measures

Re-assignment of academic editors occurred in a small number of cases (7/672), which confounds the recorded time in each stage and prevented us from analysing the feasibility outcomes for these manuscripts. The time from receipt of last review to final AE decision was missing from a large proportion of the remaining manuscripts (342/665), and so this analysis was not performed. Ten additional manuscripts were also excluded from the feasibility dataset due to missing data on one or more feasibility outcome s. After these exclusions, the feasibility analysis was performed on 328/340 manuscripts in the control group and 327/332 in the intervention group. For analysis of resubmitted articles, seven manuscripts were removed as these were accepted at first decision leaving 323/340 in the control group and 325/332 in the intervention group.

Upon examining histograms for each variable and calculating skew using the skew() function, all data for these outcomes were found to be right skewed, so we used Mann-Whitney U test to compare timings between groups. Time spent in the PLOS editorial office was significantly higher (p < 0.0001) for manuscripts in the intervention group with a median of 9 days (interquartile range [IQR] = 6–16.5) compared to the control group with a median of 6 days [310]. Time from submission to academic editor assignment was also significantly higher in the intervention group (13 days, range 9–22) than in the control group (9 days, range 7–14) (p < 0.0001). No statistically significant differences were identified for other feasibility outcomes (Table 5).
Table 5

Feasibility measures; Q1–Q3, interquartile range; N, number of applicable manuscripts; Adj p, adjusted p value; n.s, not significant

Feasibility outcomes

Control

Intervention

 
 

Median

Q1–Q3

N

Median

Q1–Q3

N

Adj p

Days in PLOS editorial office

6

3–10

328

9

6–16.5

327

< 0.0001

Days from submission to AE assignment

9

7–14

328

13

9–22

327

< 0.0001

Days from AE assignment to reviewer assignment

3

1–8

328

3

1–9

327

> 0.99

Days from AE assignment first decision

28

20–41.3

328

27

19–41

327

> 0.99

Days from initial decision to resubmission

41

23.5–51.5

323

40

23–45

325

> 0.99

Cycles of resubmission

1

1–2

323

1

1–2

325

> 0.99

Days from resubmission to final decision

31

15.5–58

323

34

16–59

325

> 0.99

Compliance by country

There were no statistically significant differences in compliance between control and intervention groups across any corresponding author country of origin. Although we did not set out to compare differences in compliance with different ARRIVE subitems across countries, we present these data in Fig. 3.
Fig. 3
Fig. 3

Compliance by country; percentage compliance for each ARRIVE subitem for manuscripts in each country (for countries with N manuscripts ≥10)

Human studies compliance

In manuscripts without human subjects, reporting of one ARRIVE subitem, 9b (Provide details of husbandry conditions e.g. breeding programme, light/dark cycle, temperature, quality of water etc for fish, type of food, access to food and water, environmental enrichment) increased significantly from 52.4% (172/316) in the control group to 76.9% (227/295) in the intervention group (X2 = 33.2, df = 1, p < 0.0001). In manuscripts containing human subjects, compliance also rose from 20.8% (5/24) to 51.35% (19/37) in the intervention group for this subitem, although we were limited by small sample sizes and this change was not found to be statistically significant (X2 = 4.47, df = 1, p = 1).

Exploratory outcomes

Compliance in true intervention group

Despite allocation to the intervention group, a small subset (n = 31/332) of authors did not comply with the request to submit a completed checklist, and therefore, 31 manuscripts were in the intervention group without a completed ARRIVE checklist. We sought to determine compliance with each of the 38 subitems in the “true” intervention group (those submitted with a completed checklist), compared to the control group. The pattern of compliance is similar to that of the full intervention group compared to controls, suggesting that these instances of non-compliance did not impact on results. Summary statistics are presented in Table 6.
Table 6

True intervention ARRIVE subitem compliance; %, percentage of compliant manuscripts; CI, confidence interval; n, number of compliant manuscripts; N, total number of applicable manuscripts

 

Control

Intervention

ARRIVE subitem

%

95% CIs

n

N

%

95% CIs

n

N

1

41.76

36.5–47.2

142

340

45.85

40.1–51.7

138

301

2

71.76

66.6–76.4

244

340

66.45

60.8–71.7

200

301

3a

100.00

98.6–100

340

340

100.00

98.4–100

301

301

3b

34.12

29.1–39.5

116

340

35.88

30.5–41.6

108

301

4

91.18

87.5–93.9

310

340

93.02

89.4–95.5

280

301

5

69.41

64.2–74.2

236

340

72.43

66.9–77.3

218

301

6a

70.00

64.8–74.8

238

340

75.42

70.1–80.1

227

301

6b

8.33

5.7–12

28

336

9.49

6.5–13.6

28

295

6c

90.00

86.2–92.9

306

340

88.70

84.4–91.9

267

301

7a

16.76

13–21.3

57

340

16.94

13–21.8

51

301

7b

44.37

38.7–50.2

134

302

52.21

46.1–58.3

142

272

7c

8.64

5.8–12.5

26

301

14.07

10.3–18.9

38

270

7d

3.63

1.9–6.6

11

303

4.00

2.1–7.2

11

275

8a

4.71

2.8–7.7

16

340

7.97

5.3–11.8

24

301

8b

57.06

51.6–62.4

194

340

62.46

56.7–67.9

188

301

9a

0.30

0–1.9

1

337

3.03

1.5–5.9

9

297

9b

52.06

46.6–57.5

177

340

74.75

69.4–79.5

225

301

9c

14.71

11.2–19

50

340

21.26

16.9–26.4

64

301

10a

37.35

32.2–42.8

127

340

43.19

37.6–49

130

301

10b

3.53

1.9–6.2

12

340

7.64

5–11.4

23

301

10c

18.15

14.3–22.8

61

336

15.12

11.3–19.9

44

291

11a

4.82

2.8–8

15

311

7.53

4.8–11.4

21

279

11b

1.24

0.4–3.4

4

323

3.17

1.6–6.1

9

284

12

1.76

0.7–4

6

340

2.99

1.5–5.8

9

301

13a

87.50

83.4–90.7

294

336

89.90

85.8–93

267

297

13b

44.08

38.7–49.6

149

338

45.97

40.2–51.8

137

298

13c

10.06

7.2–13.9

34

338

12.75

9.3–17.2

38

298

14

0.29

0–1.9

1

340

0.00

0–1.6

0

301

15a

37.35

32.2–42.8

127

340

36.54

31.1–42.3

110

301

15b

12.65

9.4–16.8

43

340

14.95

11.2–19.6

45

301

16

78.55

73.7–82.8

260

331

81.03

75.9–85.3

235

290

17a

16.47

12.8–20.9

56

340

21.93

17.5–27.1

66

301

17b

1.18

0.4–3.2

4

340

1.66

0.6–4.1

5

301

18a

100.00

98.6–100

340

340

99.34

97.4–99.9

299

301

18b

26.47

21.9–31.6

90

340

27.57

22.7–33.1

83

301

18c

2.94

1.5–5.5

10

340

2.99

1.5–5.8

9

301

19

77.94

73.1–82.2

265

340

78.07

72.9–82.5

235

301

20

51.47

46–56.9

175

340

54.49

48.7–60.2

164

301

Landis item individual compliance

To determine if there had been any changes in individual Landis subitems, we investigated randomisation, blinding, reporting of a sample size calculation, and reporting of exclusions separately (Fig. 4). Although we did not analyse these comparisons using inferential statistics, there appears to be some improvements in reporting of randomisation and sample size calculations. 29.1% (91/313) of manuscripts in the control group reported whether or not random assignment occurred, compared to 41.5% (125/301) in the intervention group (Cohen’s H effect size = 0.26), while 3.5% (12/40) of control manuscripts reported sample size calculations compared to 7.6% (25/330) in the intervention group (Cohen’s H effect size = 0.18). For the reporting of animal exclusions, 12.6% (43/340) of manuscripts complied in the control group versus 14.5% (48/332) in the intervention group (Cohen’s H effect size = 0.05). Finally, 18.8% (63/334) and 19.2% (62/323) of manuscripts reported blinded outcome assessment in the control and intervention groups, respectively (Cohen’s H effect size = 0.01).
Fig. 4
Fig. 4

Landis 4 individual compliance; percentage compliance for each Landis criteria present in the ARRIVE guidelines with 95% confidence intervals

Discussion

Requesting completion of an ARRIVE checklist at submission did not increase full adherence with the ARRIVE guidelines. Compliance with the operationalised ARRIVE checklist was poor overall, with no manuscripts in either group even approaching full compliance; the median compliance was less than 40%, equivalent to around 15 of 38 subitems; and the intervention only increased compliance with one subitem, reporting of animal husbandry conditions. There is considerable room for improvement, and this study shows that an editorial policy of making ARRIVE checklist completion “mandatory” without compliance checks has little or no impact.

There were some noticeable differences between the control and intervention group for some subitems e.g. the proportion of manuscripts compliant with 10b (Explain how the number of animals was arrived at. Provide details of any sample size calculation used) was double the size in the intervention group compared to control (3.53% vs 7.53%; Cohen’s H effect size = 0.18). However, our power calculation determined that a meaningful effect would be substantially larger to justify the increased burden for authors associated with implementation by PLOS ONE.

It may be that simply requesting that authors complete checklist, without any additional editorial checks to determine whether the checklist is truly indicative of compliance, may not be enough to improve adherence to the ARRIVE guidelines. Adherence to the reporting guidelines within in the clinical literature such as CONSORT and STROBE have been widely assessed and may inform interventions to improve compliance with preclinical guidelines. Journal endorsement of these guidelines appear to have improved reporting quality [15, 18]; however, it is often unclear what actions journals take to promote adherence [17] and the extent of editorial involvement is likely to have an impact. Prior reports indicate that assessing compliance with reporting guidelines at the stage of peer review leads to a significant improvement of reporting quality [5]. Other approaches (e.g. actions on the part of funders or institutions) may also be beneficial, but a successful strategy is likely to be multi-dimensional. Further, the findings reported here and the limited agreement between outcome assessors both in this study and in the recent investigation of study quality following the introduction of a new editorial policy at Nature journals [12] suggests that an important part of guideline development should be refinement of the content, the number of items (with fewer generally being better), and the agreement between assessors. It may be that a more formal adoption of research improvement strategies, with an original focus on a smaller number of items judged by a stakeholder to be of greatest importance, will allow an incremental approach to enabling and measuring improvement.

Our findings are in line with prior reports that endorsement by editors and reviewers has not significantly improved reporting of ARRIVE quality items [3, 6]. We need therefore a better understanding of the barriers to implementing quality checklists for animal experiments. It has been suggested that requesting checklist adherence at the submission stage may be too late, given the observed correlation between reporting at the planning application stage and at the publication stage [19]. The PREPARE (Planning Research and Experimental Procedures on Animals: Recommendations for Excellence) guidelines [16] were published recently and may be a useful tool, in combination with the ARRIVE checklist, to promote a greater focus on experimental rigour at all stages of the research cycle.

Our results contrast with recent reports of improvement in quality following mandated checklist completion following a change in editorial policy at Nature journals [7, 12]. However, in both reports, study quality was retrospectively assessed in publications published prior to and after the introduction of the Nature quality checklist, which was established in 2015 as part of an organisation wide approach with substantial editorial involvement. In contrast, the current trial investigated an intervention targeted at selected manuscripts, without further editorial involvement.

Perhaps unsurprisingly, due to the additional time required for ARRIVE checklist requests, both the number of days manuscripts spent in the PLOS editorial office and the number of days from manuscript submission to AE assignment were found to be significantly longer in the intervention group. The editorial resource required to ensure that all accepted publications meet the requirements of the ARRIVE checklist is likely to be considerable, given that PLOS ONE is a high-volume publisher, with around 44,000 submissions per year. The most feasible and effective way to encourage compliance to the ARRIVE guidelines, or indeed any reporting guideline, remains to be determined, but an ongoing review of interventions to improve adherence to reporting guidelines may shed some light on this issue and direct future investigations [4].

Another consideration is the perceived clarity of the checklist to authors and reviewers. Although reviewer agreement was generally high, a few questions were less well understood by our outcome assessors which suggests the current guidelines may require clearer dissemination among the research community.

Limitations

Due to modest sample sizes, we were unable to investigate whether the intervention was more successful in countries with high awareness and adoption of the ARRIVE guidelines such as the UK, where the ARRIVE guidelines were developed and where many institutions have endorsed them. Furthermore, we did not perform a power calculation for outcomes beyond our primary and main secondary outcomes and it is possible that this, coupled with stringent adjustments for multiplicity of testing in some instances, may have prevented us from detecting any significant differences.

Furthermore, our intervention only involved requests for authors to complete an ARRIVE checklist. PLOS ONE did not fully mandate checklist completion, as manuscripts without a checklist were still allowed to proceed through the trial. Furthermore, PLOS ONE did not evaluate the accuracy of the completed checklists against each manuscript. It is possible that further emphasis on evaluation and checklist adherence may result in an enhancement of study quality.

Our interpretation of compliance was also influenced by our operationalisation of the ARRIVE checklist used for outcome assessment. It was often difficult to determine how many of the details provided in the ARRIVE guidelines were sufficient for full compliance to that ARRIVE subitem.

There were unforeseen difficulties in attaining data for some outcomes, which meant that we could not assess all outcomes presented in our study protocol. This was most apparent for feasibility outcomes, where there were substantial deviations from our protocol. Furthermore, the project was subject to research waste due to overpowering our primary and secondary outcome measures. As manuscripts submitted to PLOS ONE as part of the study were treated differently, the existence of the study could have leaked to external sources; however, to the best of our knowledge, this was not the case.

Since the ARRIVE guidelines were developed by the NC3Rs, no individual employed by the NC3Rs was permitted to conduct any outcome assessment on the IICARus platform.

Conclusions

Research must be described in sufficient detail to allow research users critically to appraise experimental design, to allow them to assess the validity of the findings presented. Replication studies require, for their design, full details of what was done. Transparency in the reporting of research is paramount. Manuscripts must therefore be described in enough detail for readers to understand the research methodology and make informed judgement of quality and risk of bias. At present, reporting quality is, on average, disappointingly poor. However, our findings show that simply requesting that researchers improve reporting is not effective. Editorial checks of compliance and further measures to mandate checklist completion may be required to see improvements in quality.

Abbreviations

AE: 

Academic editor

ARRIVE: 

Animal Research: Reporting of In Vivo Experiments

IICARus: 

Intervention to Improve Compliance with the ARRIVE guidelines

IQR: 

Interquartile range

NC3Rs: 

National Centre for the Replacement, Refinement and Reduction of Animals in Research

PREPARE: 

Planning Research and Experimental Procedures on Animals: Recommendations for Excellence

Declarations

Acknowledgements

We gratefully acknowledge Elizabeth Silva for her discussions and support early in the project.

The IICARus collaboration

Emily Sena1 (emily.sena@ed.ac.uk), Kaitlyn Hair1 (kaitlyn.hair@ed.ac.uk), Malcolm Macleod1 (malcolm.macleod@ed.ac.uk), David Howells2(david.howells@utas.edu.au), Philip Bath3 (philip.bath@nottingham.ac.uk), Cadi Irvine1 (c.m.j.irvine@gmail.com), Catriona MacCallum4 (catriona.maccallum@hindawi.com), Gavin Morrison4 (gmorrison@plos.org), Alejandra Clark4 (aclark@plos.org), Gina Alvino4 (gina.alvino@gmail.com), Michelle Dohm4 (mdohm@plos.org), Jing Liao1 (jing.liao@ed.ac.uk) Chris Sena1 (chris.sena@ed.ac.uk), Rosie Moreland5 (rosie.morland@gmail.com), Fala Cramond1 (falacramond@hotmail.com), Gillian L. Currie1 (gillian.currie@ed.ac.uk), Zsanett Bahor1 (Zsanett.Bahor@ed.ac.uk), Paula Grill1 (paulamartha.grill@gmail.com), Alexandra Bannach-Brown1 (a.bannach-brown@ed.ac.uk), Daniel-Cosmin Marcu6 (dan.c.marcu@gmail.com), Sarah Antar7(sarah_antar@yahoo.com), Katrina Blazek8 (katrinablazek@yahoo.com), Timm Konold9 (Timm.Konold@apha.gov.uk), Monica Dingwall1 (monica.dingwall@hotmail.co.uk), Victoria Hohendorf1 (victoria-hohendorf@web.de), Mona Hosh10 (mona_7osh@yahoo.com), Klara Zsofia Gerlei6 (klarizsofi@gmail.com), Kimberley Elaine Wever11, (kim.wever@radboudumc.nl), Victor Jones1 (victor_jones2010@hotmail.com), Terence J Quinn12 (terry.quinn@glasgow.ac.uk), Natasha A Karp13 (natasha.karp@astrazeneca.com), Jennifer Freymann14 (freymann@tierschutzzentrum.de), Anthony Shek1 (antshek@hotmail.com), Teja Gregorc12 (tgregorc@outlook.com), Arianna Rinaldi15 (arianna.rinaldi@uniroma1.it), Privjyot Jheeta1 (s1704783@sms.ed.ac.uk), Ahmed Nazzal7 (nazzal28@gmail.com), David Ewart Henshall16 (dhenshall.846@gmail.com), Joanne Storey17, (Joanne.2.Storey@gsk.com), Julija Baginskaite18, (JBaginskaite@esf.org), Cilene Lino de Oliveira19 (cilene.lino@ufsc.br), Kamil Laban20 (k.g.laban@umcutrecht.nl), Emmanuel Charbonney21 (emmanuel.charbonney@umontreal.ca), Savannah A. Lynn22, (sl21g11@soton.ac.uk), Marco Cascella23 (m.cascella@istitutotumori.na.it), Emily Wheater1 (emily.wheater@ed.ac.uk), Daniel Baker24 (d.baker21@herts.ac.uk), Ryan Cheyne1 (r.cheyne.99@gmail.com), Edward Christopher25 (sen297@outlook.com), Paolo Roncon26 (paolo.roncon@unife.it), Evandro Araújo De-Souza27 (evandro_desouza@bioqmed.ufrj.br), Mahmoud Warda28 (dr.warda717@gmail.com), Sarah Corke29 (sarah_corke@bat.com), Zeinab Ammar30 (zeinab.ammar@unige.ch), Leigh O’Connor 31 (Leigh3791@hotmail.co.uk), Ian M. Devonshire32 (ian.devonshire@nottingham.ac.uk), Sarah K. McCann1 (sarah.mccann@charite.de), Laura J Gray33 (lg48@le.ac.uk), and Ezgi Tanriver-Ayder1 (ezgi.tanriverayder@ed.ac.uk).

1 Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

2 School of Medicine, University of Tasmania, Hobart, Australia

3 Stroke Trials Unit, Division of Clinical Neuroscience, University of Nottingham, Nottingham, UK

4 PLOS ONE, Public Library of Science, San Francisco, CA, USA and Cambridge, UK

5 Imperial College London, London, UK

6 Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK

7 Faculty of Medicine, Mansoura University, Egypt.

8 Research Ethics and Compliance Support (RECS), University of New South Wales Sydney, Sydney, Australia.

9 Animal Sciences Unit, Pathology, Animal and Plant Health Agency Weybridge, Addlestone, UK

10 Ministry of Health and Population, Egypt

11 SYstematic Review Centre for Laboratory animal Experimentation (SYRCLE), Radboud University Medical Center, Nijmegen, Netherlands

12 Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.

13 Quantitative Biology, Discovery Sciences, IMED Biotech Unit, AstraZeneca, Cambridge, UK

14 University of Veterinary Medicine Hannover, Hannover, Germany

15 Department of Biology and Biotechnology, Sapienza University of Rome, Rome, Italy

16 College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK

17 GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire, UK

18 European Science Foundation

19 Department of Physiological Sciences, Biological Sciences Center, Federal University of Santa Catarina, Florianópolis, Santa Catarina, Brazil

20 Department of Ophthalmology, University Medical Center, Utrecht University, Utrecht, Netherlands

21 Centre de Recherche Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada.

22 Clinical & Experimental Sciences, University of Southampton, Southampton, UK

23 Istituto Nazionale Tumori - IRCCS - Fondazione “Pascale”. Naples, Italy

24 Centre for Topical Drug Delivery and Toxicology, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, Herts, AL10 9AB, UK

25 College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK

26 University Vita-Salute San Raffaele, Milan, Italy

27 Instituto de Bioquímica Médica Leopoldo de Meis, Programa de Biologia Molecular e Biotecnologia, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21,941–902, Brazil

28 Sheikh Zayed Specialised Hospital, Egypt

29 British American Tobacco, London, UK

30 University of Geneva, Geneva, Switzerland

31 University of Birmingham, Birmingham, UK

32 Bio-Support Unit, University of Nottingham, Nottingham, UK

33 Department of Health Sciences, University of Leicester, Leicester, UK

Study steering committee: Emily Sena1 (Chief Investigator and Chair), Malcolm Macleod1, David Howells2, Philip Bath3.

Study management committee: Emily Sena1, Kaitlyn Hair1, Malcolm Macleod1, Cadi Irvine1 Catriona MacCallum4, Gavin Morrison4, Alejandra Clark4, Gina Alvino4, Michelle Dohm4.

Programming and data management: Jing Liao1, Chris Sena1.

Redactions: Rosie Moreland5.

Design of outcome assessment platform: Fala Cramond1, Cadi Irvine1, Jing Liao1, Gillian L. Currie1, Zsanett Bahor1, Paula Grill1, Kaitlyn Hair1, Alexandra Bannach-Brown1, Emily Sena1.

Outcome assessment: Brackets indicate number of manuscripts assessed; Kaitlyn Hair1 (298), Daniel-Cosmin Marcu6 (221), Sarah Antar7 (195), Cadi Irvine1 (105), Katrina Blazek8 (103), Timm Konold9 (93), Monica Dingwall1 (83), Victoria Hohendorf1 (50), Mona Hosh10 (29), Paula Grill1 (25), Klara Zsofia Gerlei6 (14), Kimberley Elaine Wever11 (12), Emily Sena1 (11), Victor Jones1 (10), Terence J Quinn12 (10), Natasha A Karp13 (9), Jennifer Freymann14 (7), Anthony Shek1 (7), Teja Gregorc12 (6), Arianna Rinaldi15 (6), Privjyot Jheeta1 (5), Ahmed Nazzal7 (5), David Ewart Henshall16 (5), Joanne Storey17 (4), Julija Baginskaite18 (4), Cilene Lino de Oliveira19 (4), Kamil Laban20 (3), Emmanuel Charbonney21 (3), Savannah A. Lynn22 (3), Marco Cascella23 (3), Emily Wheater1 (2), Daniel Baker24 (2), Gillian L. Currie1 (1), Ryan Cheyne1 (1), Edward Christopher25 (1), Paolo Roncon26 (1), Evandro Araújo De-Souza27 (1), Mahmoud Warda28 (1), Sarah Corke29 (1), Zeinab Ammar30 (1), Leigh O’Connor 31 (1), Ian M. Devonshire32 (1).

Reconciliation: Brackets indicate number of manuscripts reconciled; Kaitlyn Hair1(178), Daniel-Cosmin Marcu6(170), Sarah Antar7(126), Timm Konold9 (117), Monica Dingwall1(48), Emily Sena1(21), Paula Grill1 (9), Sarah K. McCann1 (5).

Data analysis: Kaitlyn Hair1, Malcolm Macleod1, Emily Sena1, Jing Liao1, Laura J Gray33, Ezgi Tanriver-Ayder1.

Writing committee: Kaitlyn Hair1, Malcolm Macleod1, Emily Sena1.

Funding

This study was funded by a joint grant from the National Centre for Reduction, Refinement, and Replacement (NC3Rs), The Wellcome Trust, The Medical Research Council (MRC), and the Biotechnology and Biological Sciences Research Council (BBSRC). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Availability of data and materials

Our protocol, data analysis plan, analysis code, data validation code, and complete dataset are available on the Open Science Framework (https://doi.org/10.17605/OSF.IO/XSJBV).

Authors’ contributions

All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

ES and MM are in receipt of competitive research grants from the NC3Rs who developed the ARRIVE guidelines. SL was funded by an NC3Rs PhD studentship. ES, MM, DH, and NK are members of an NC3Rs working group to review the ARRIVE guidelines (https://www.nc3rs.org.uk/revision-arrive-guidelines). CM, GM, AC, GA, and MD were all editors at PLOS ONE throughout the duration of the study. ES is Editor-in-Chief at BMJ Open Science. All other authors have no other competing interests to declare.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK

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Copyright

© The Author(s) 2019

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