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Enregistrement W2895151463 · doi:10.1111/ijcp.13264

An interview with David L. Streiner: Truth teller of statistical concepts in medicine

2018· editorial· en· W2895151463 sur OpenAlexaboutno aff
Leslie Citrome

Notice bibliographique

RevueInternational Journal of Clinical Practice · 2018
Typeeditorial
Langueen
DomaineDecision Sciences
ThématiqueMeta-analysis and systematic reviews
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBachelorBiostatisticsMedicineThe artsPower (physics)Medical educationLibrary sciencePublic healthHistoryLaw

Résumé

récupéré en direct d'OpenAlex

There is too much pressure to publish. Lack of sufficient statistical power remains a significant methodological obstacle. LC: Tell us something about your background. How did you get started in statistics and what are you doing now? DS: I was born in the Bronx, New York and went to City College of New York, graduating in 1963 with a Bachelor of Arts and majoring in Psychology. I went on to study clinical psychology at Syracuse University, ultimately earning a Ph.D. in 1968. The political environment in the US at the time was troubled, and in 1968 we moved as a young family to Canada. I had the fortuitous opportunity to begin work at a nascent medical school in Hamilton, Ontario, and remained at McMaster University for 30 years, employed as professor for about 20 of those years (Department of Psychiatry & Behavioural Neurosciences, and Department of Clinical Epidemiology & Biostatistics), and then retired in 1998. Retirement was brief however, and I went on to join the faculty at the University of Toronto as Professor of Psychiatry and was the founding director of the Applied Research Unit at the Baycrest Centre for Geriatric Care where I was for about 10 years until retiring for the second time. Since then I am back at McMaster University (Professor Emeritus) one day a week and consult at the University of Toronto's Center for Addictions and Mental Health. Throughout my career I have been teaching, writing, and consulting; I have participated in many grant submissions and helped others with statistics and research methodology. I still do that even though I am “retired.” LC: How did you get started providing methodological advice to researchers? DS: I started doing that the after day after I arrived at McMaster. You need to understand that it was a brand new medical school. Because I had taken two graduate courses in statistics I was labelled as the “stats guru.” This began a multi-decade educational process to keep ahead of the questions and I have kept it up since. I started writing commentaries and tutorials for the Canadian Journal of Psychiatry in 1990, inspired by having done peer reviews of poorly-constructed submitted papers. Topics range from using meta-analysis in psychiatric research2 to path analysis.3 The editors of Community Oncology/Journal of Community and Supportive Oncology also invited me to contribute to a series on practical biostatistics, followed by requests from the editors of Chest and the Journal of Clinical Psychopharmacology. In my hands-on work in research I have worked with several disciplines including psychiatry, neurology, family medicine, and pediatrics. I was one of the founding editors of Evidence-Based Mental Health and worked on that journal for 10 years. LC: Of all the commentaries you have written, which are your favorites? DS: Well, the editors of Community Oncology/Journal of Community and Supportive Oncology gave me free rein to use any language as I see fit.4 Another article that comes to mind addresses a common practice (screening) that is done without thinking through the consequences or the issues involved.5 LC: What inspired you to write about P-hacking? DS: I am concerned about problems with lack of a priori hypotheses, multiplicity, and subsequent spurious findings. For example, brain imaging studies may involve only a handful of subjects and yet millions of voxels are being looked at for “findings.” Genome studies also are fraught with chance findings. The articles authored by John Ioannidis further describe some of the issues involved.6, 7 LC: Do you see any improvements in how research is being reported today compared to 10-20 years ago? DS: Sadly, no. There is too much pressure to publish. Lack of sufficient statistical power remains a significant methodological obstacle. I continue to see articles being submitted that have basic errors. LC: Sounds like we have quite a bit of work to be done! What did you think of the animation I sent you of a hapless researcher insisting his/her submission would be welcome because the “P-value is less than 0.05”? It was part of a prior editorial,8 and readers can find it at https://www.youtube.com/watch?v=KBALRk2hjMs. DS: Loved the animation. You must have been sitting in on some of my consultations with researchers! LC: What would readers not normally know about you? DS: About 40 years ago I got into wood working and I am currently the “Master Woodworker” at a train and trolley museum – these old trolley cars need constant repair. Sometimes I even drive them. LC: Thank you so much for your time in answering these questions. DS: It was delightful talking with you. No external funding or writing assistance was utilised in the production of this editorial. In the past 12 months, Leslie Citrome has served as a consultant to: Acadia, Alkermes, Allergan, Indivior, Intra-Cellular Therapeutics, Janssen, Lundbeck, Merck, Neurocrine, Noven, Otsuka, Pfizer, Shire, Sunovion, Takeda, Teva, Vanda. In the past 12 months, Leslie Citrome has served as a speaker for: Acadia, Alkermes, Allergan, Janssen, Lundbeck, Merck, Neurocrine, Otsuka, Pfizer, Shire, Sunovion, Takeda, Teva, Vanda. Other disclosures: stocks (small number of shares of common stock): Bristol-Myers Squibb, Eli Lilly, J & J, Merck, Pfizer purchased >10 years ago; royalties: Wiley (Editor-in-Chief, International Journal of Clinical Practice), UpToDate (reviewer), Springer Healthcare (book).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,281
score de la tête « metaresearch » (Gemma)0,733
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Science ouverte, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,452
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,2810,733
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0080,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0060,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0100,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,748
Tête enseignante GPT0,708
Écart entre enseignants0,040 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission1
Résumé présentoui

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Même revueInternational Journal of Clinical PracticeMême sujetMeta-analysis and systematic reviewsTravaux en français237 207