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Enregistrement W2123190102 · doi:10.1093/ejcts/ezt455

Reply to Yamamoto et al.

2013· letter· en· W2123190102 sur OpenAlexaff
Gail Darling, Eshetu G. Atenafu, Waël C. Hanna

Notice bibliographique

RevueEuropean Journal of Cardio-Thoracic Surgery · 2013
Typeletter
Langueen
DomaineMathematics
ThématiqueAdvanced Causal Inference Techniques
Établissements canadiensPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésPropensity score matchingConfoundingObservational studyCovariateSelection biasMedicineMatching (statistics)Lung cancerSurgeryStatisticsInternal medicinePathologyMathematics

Résumé

récupéré en direct d'OpenAlex

We appreciate the comments by Yamamoto etal. [1] regarding our recent article [2]. We understand the questions regarding our statistical methods with respect to the propensity matching, but in our opinion, the methods used were appropriate in addressing the question of whether video assisted thoracic surgery (VATS) lobectomy is oncologically equivalent to open lobectomy with respect to overall and disease-free survival. By using propensity score matching (PSM), we attempted to reduce the bias due to confounding variables that could be found in an estimate of the open vs VATS lobectomy effect obtained from simply comparing overall survival outcomes among subjects [3]. It is well known that in observational studies, the treatment group often exhibits imbalance on covariates. This imbalance will also be confounded with treatment, and it is difficult to attribute differences in main outcome to the treatment, as the covariates are also believed to influence the outcome. Inability to balance confounders through some mechanism, will show that the treatment groups are not sufficiently overlapping with respect to these confounders and that selection bias may not be resolvable. Therefore, our use of PSM was an attempt to mimic randomization by creating a sample of patients who had open lobectomy, which is comparable on most available covariates to a sample of subjects who had VATS lobectomy. In particular, in our study we were interested in matching patients who were similar except for the operative approach so that we could compare the lung cancer survival for each approach. Among the five covariates we used, histology was known preoperatively because of a preoperative biopsy. We used pathological stages as we were interested in survival, and pathological stage is more accurate than clinical stage. For example, a patient may be clinical T1 N0 but pathological stage T1 N1 or T1 N2, which would give them a significantly different prognosis. Shadish et al. [4] argue that PSM requires large samples, overlap between treatment and control groups must be substantial, and hidden bias may remain after matching, because the procedure only controls for observed variables (to the extent that they are perfectly measured), to be more accurate, as one of the disadvantages. Hence, our work was focused on investigating any difference in survival outcome after balancing the impact of the covariates including pathological T and N, which was known to predict oncological prognosis. The other point mentioned as an option was the use of simple multivariable analysis as adjustment. We agree that one can proceed in that direction if the research question directs to that. However, in our case, as our research interest was to see the impact of operative approach, it was advantageous to have patients comparable in most aspects. Liem et al. [5] further explained the advantages of propensity score-stratified vs traditional multivariable-adjusted modelling. One of the advantages of the PSM model is that it does not need to be parsimonious and is easy to understand because it is not the focus of the study other than balancing the covariates included in the model. In conclusion, we believe that PSM allowed us to compare two groups of patients who were essentially identical in terms of key variables that are known to affect survival after lung cancer surgery with the exception of the operative approach. If clinical staging had been used rather than pathological staging, we would have undoubtedly compared apples and oranges. Thus, the only significant difference between the two groups of patients in our study that may have influenced their overall and disease-free survivals was whether their lung cancer was resected by open lobectomy or VATS lobectomy. A propensity-matched analysis is therefore appropriate for minimizing selection bias and allowing us to compare two very similar groups. Although a randomized study would be ideal to address this question, lack of equipoise on the part of the public and surgeons likely precludes completion of such a study.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,009
score de la tête « metaresearch » (Gemma)0,070
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,051
Score d'incertitude au seuil0,045

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0090,070
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0020,001
Études des sciences et des technologies0,0030,004
Communication savante0,0050,009
Science ouverte0,0050,004
Intégrité de la recherche0,0510,056
Charge utile insuffisante (le modèle a refusé de juger)0,0090,011

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 Opus0,152
Tête enseignante GPT0,389
Écart entre enseignants0,237 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2013
Routes d'admission1
Résumé présentoui

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