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#NightshiftEditor

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Jan R. Boehnke

#ISOQOL

#1/
Excellent talk by Kevin Weinfurt in #Plenary1 discussing multiple types of measures (go also to #Plenary4 for that!), increased use of high-intensity longitudinal data, and generative AI for assessments.

Start reading his work:
rdcu.be/dWXSz
#HRQL #Psychometrics

#2/
Not sure this is what the #ISOQOL_NewInvestigators wanted when asking me to talk about advice for manuscript writing, but this is the slide people talked to me about 👇
#NightshiftEditor #ScientificPublishing

Jan R. Boehnke

#1/ First day of #ISOQOL started with an interesting symposium on progress when optimizing the collection of patient-reported outcomes in clinical trials.

Part of the discussion were desirable changes to the publication system when publishing expert consensus and guidance documents:

1) What does #PeerReview for such a document look like?

2) Static journal publications may not be the best format for such documents.

Both seem solvable, no?
#HTD

#NightshiftEditor#RCT#ScientificPublishing
Jan R. Boehnke

A #ScopingReview on #MissingData reporting in observational studies that use #MultipleImputation for causal effect estimation:
bmcmedresmethodol.biomedcentra

Informative read beyond the particular application.

As a #NightshiftEditor I am still working more on the problem that most studies do not even report their sampling and take it for granted that complete-data only analyses are fine.

As noted before 😉
mastodon.social/deck/@jrboehnk

Jan R. Boehnke

New #AcademicYear - New #introduction

I am interested in health-related quality of life
#HRQL #Psychometrics

I teach #ResearchMethods modules in the #DundeeUni #ProfDoc
#Interdisciplinary #RD62001 #RD62002

I convene our School's #ResearchEthics committee

My terms as academic editor of 'Quality of Life Research' come to an end, and I am mulling over roles and sense in #AcademicPublishing
#NightshiftEditor

There may be some occasional #Deutsch #Svenska #Gaidhlig and local stuff in my feed.

Jan R. Boehnke

Recommendations for conduct and write-up of observational studies from my #NightshiftEditor threads got some attention on #LinkedIn
linkedin.com/feed/update/urn:l

The five suggested papers in brief:

1) STROBE
journals.plos.org/plosmedicine

2) Confounder selection
ncbi.nlm.nih.gov/pmc/articles/

3) DAG tutorial
ncbi.nlm.nih.gov/pmc/articles/

4) Table 2 Fallacy
academic.oup.com/aje/article/1

5) Prediction vs causation
academic.oup.com/ije/article/4

www.linkedin.comJan Rasmus Böhnke on LinkedIn: Jan R. Boehnke (@jrboehnke@mastodon.social)In the past, I promoted resources I used most frequently in my work as Editor or in #PeerReview as part of my #NightshiftEditor series.* Maybe this works here…
#AcademicWriting#Epidemiology#StudyDesign
Jan R. Boehnke

Reflecting that I started using #NightshiftEditor 2+ years ago:
linkedin.com/feed/update/urn:l

Main point of my first post then was that there are tools to help avoiding unwanted references to retracted papers such as the #RetractionWatch data base
retractionwatch.com/retraction

and you can link tools such as @zotero zotero.org/blog/retracted-item
or since last year even #Endnote:
support.clarivate.com/Endnote/

www.linkedin.comJan Rasmus Böhnke on LinkedIn: Jan R. Boehnke @jrboehnke@mastodon.social (@jrboehnke) on XWhen I started the #NighshiftEditor posts on Twitter in 2022, I had been thinking about these for a while. The goal was to post small notices that might be…
#PaperWriting#Retraction#ResearchIntegrity
*
Jan R. Boehnke

Discussing null and negative results, Sarahanne Field (EiC at the Journal of Trial & Error, @smirandafield) describes the conundrum well:
They aren't interested in papers in which “you did a shitty study and you found nothing. We’re interested in stuff that was done methodologically soundly, but still yielded a result that was unexpected”
nature.com/articles/d41586-024

#AcademicPublishing #NightshiftEditor
HT @cyrilpedia

www.nature.comIlluminating ‘the ugly side of science’: fresh incentives for reporting negative resultsNew data repositories and alternative journals and workshops offer routes for sharing negative results — which could help to solve the reproducibility crisis and give machine learning a boost.
Jan R. Boehnke

Just my usual #NightshiftEditor reminder that when you are currently working on the (secondary) analysis of a data set and thinking of applying some regression modelling, here are some good resources:

#STROBE for reporting
journals.plos.org/plosmedicine

Thinking about confounders
ncbi.nlm.nih.gov/pmc/articles/

Prediction vs causation
academic.oup.com/ije/article/4

And avoiding the #Table2Fallacy
ncbi.nlm.nih.gov/pmc/articles/

journals.plos.orgStrengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and ElaborationIn this explanatory and elaboration document Mattias Egger and colleagues provide the meaning and rationale of each checklist item on the STROBE Statement.
#HRQL
Jan R. Boehnke

Collaborating with the team of [link.springer.com/article/10.1] led to the following letter giving more detail about the proprietary nature of NIH #PROMIS parameters and how to use perturbation to facilitate transparency in measure development
link.springer.com/article/10.1

#HRQL #Psychometrics
#NightshiftEditor

SpringerLinkDevelopment of the PROMIS pediatric stigma and extension to the PROMIS pediatric stigma: skin item banks - Quality of Life ResearchPurpose To develop the PROMIS Pediatric Stigma (PPS) and Skin (PPS-Skin) by constructing a common metric for measuring stigma in children with various conditions, while capturing the unique features of each condition. Methods Data from 860 children, ages 8–17, with a diagnosis of epilepsy, pNF (neurofibromatosis type 1 associated neurofibroma plexform), MD (muscular dystrophy), cancer, or skin conditions recruited from three projects were analyzed. Children with epilepsy, pNF and MD (sample-1) completed the original 18-item Neuro-QoL Stigma, while children with cancer and skin conditions (e.g., atopic dermatitis, psoriasis, and genetic skin disorders; sample-2) completed a 16-item version and 6 additional skin related items. Exploratory factor analysis (EFA) and confirmatory analysis (CFA) were used to evaluate unidimensionality of 24 stigma items. Differential item functioning (DIF) was used to evaluate measurement equivalence on group, gender, age, and conditions. Item response theory model (IRT) was used to construct the final measure. Results Sufficient unidimensionality was supported by both EFA and CFA. No items showed significant DIF indicating stable measurement properties across groups of comparison. All items fit the IRT model and were able to be calibrated together to form the PPS which consists of 18 core items. The PPS-Skin (18 cores items + 6 skin items) was developed by calibrating 6 skin items onto the common metric as the PPS. Conclusions We used IRT techniques to successfully develop the PPS and the PPS-Skin, which share a common metric and account for unique and common concerns related to chronic conditions.
Jan R. Boehnke

As a #PeerReview-er and #NightshiftEditor the software I most frequently recommend is actually FACTOR:
psico.fcep.urv.cat/utilitats/f

Reference: journals.sagepub.com/doi/10.11
Exploratory factor analyses remain a widely used tool in my research area. Most commercial software is really not great at it, and #Rstats may be too steep a learning curve (although I think one rarely regrets it 😅 ).

psico.fcep.urv.catFactor Analysis
#HRQL#Psychometrics#EFA
*
Jan R. Boehnke

Signed. And featured in my #PeerReview seminar yesterday.

I don't think that this is only about #PreRegistration.

One of our #RCTs received this year a 2.5k word review which did not refer to the #registration, statistical analysis plan, protocol, nor submitted appendices 🤦🤷

Also extrapolating from the style of that review:
How much anger and frustration that person could have avoided.

Edit: Link (Google Doc):
docs.google.com/document/d/1Y9

#NightshiftEditor#PublicationEthics#ResearchIntegrity
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