Evidence map›Paper›PMID 38249328›Full record

ArticleTherapeutic advances in medical oncology2024

Handling missing covariates in observational studies: an illustration with the assessment of prognostic factors of survival outcomes in soft-tissue or visceral sarcomas in irradiated fields (SIF).

Noémie Huchet, Nicolas Penel, Sylvie Bonvalot, Juliette Thariat, Françoise Ducimetière, Antoine Giraud, Maud Toulmonde, Axel Le Cesne, Jean-Yves Blay, Carine Bellera

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In one paragraph

Article in Therapeutic advances in medical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 8 institutions in 1 country.

Noémie HuchetINSERM CIC1401, Clinical and Epidemiological Research Unit, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France.
Nicolas PenelDepartment of Medical Oncology, Centre Oscar Lambret, Lille, France.ORCID https://orcid.org/0000-0001-5243-1548
Sylvie BonvalotSurgery Department, Institut Curie, Comprehensive Cancer Center, Paris, France.
Juliette ThariatCentre François Baclesse, Comprehensive Cancer Center, Caen, France.
Françoise DucimetièreDepartment of Medical Oncology, Centre Léon Bérard, Comprehensive Cancer Center, Lyon, France.
Antoine GiraudINSERM CIC1401, Clinical and Epidemiological Research Unit, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France.
Maud ToulmondeDepartment of Medical Oncology, Institut Bergonié, Comprehensive Cancer Center, Bordeaux, France.
Axel Le CesneDepartment of Medical Oncology, Gustave Roussy Cancer Campus, Comprehensive Cancer Center, Villejuif, France.
Jean-Yves BlayDepartment of Medical Oncology, Centre Léon Bérard, Comprehensive Cancer Center, Lyon, France.
Carine BelleraINSERM CIC1401, Clinical and Epidemiological Research Unit, Institut Bergonié, Comprehensive Cancer Center, 229 Cours de l'Argonne, Bordeaux 33076, France.ORCID https://orcid.org/0000-0001-7926-4671
Centre Léon Bérard · FRInserm · FRCentre National de la Recherche Scientifique · FRInstitut Bergonié · FRInstitut Curie · FRInstitut Gustave Roussy · FRUniversité de Bordeaux · FRUniversité de Lille · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Missing covariates are common in observational research and can lead to bias and loss of statistical power. Limited data regarding prognostic factors of survival outcomes of sarcomas in irradiated fields (SIF) are available. Because of the long lag time between irradiation of first cancer and scarcity of SIF, missing data are a critical issue when analyzing long-term outcomes. We assessed prognostic factors of overall (OS), progression-free (PFS), and metastatic-progression-free (MPFS) survivals in SIF using three methods to account for missing covariates. Methods: We relied on the NETSARC French Sarcoma Group database, Cox (OS/PFS), and competitive hazards (MPFS) survival models. Covariates investigated were age, sex, histological subtype, tumor size, depth and grade, metastasis, surgery, surgical resection, surgeon's expertise, imaging, and neo-adjuvant treatment. We first applied multiple imputation (MI): observed data were used to estimate the missing covariate. With the missing-data modality approach, a category missing was created for qualitative variables. With the complete-case (CC) approach, analysis was restricted to patients without missing covariates. Results: CC subjects ( Conclusion: The CC population was not representative of the eligible population, introducing bias, in addition to worst precision. The 'missing-data modality method' results in biased estimates in non-randomized studies, as outcomes may be related to variables with missing values. Appropriate statistical methods for missing covariates, for example, MI, should therefore be considered.

Indexed as

competing risksirradiationsarcomasurvival analysis

Identifiers

PMID38249328
PMCPMC10798078
OpenAlexW4391031110

What OpenQuestion holds

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Registered trials

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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.