Evidence map›Paper›PMID 38811511›Full record

ArticleCancer causes & control : CCC2024

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017.

Emily K Roberts, Lingfeng Luo, Alison M Mondul, Mousumi Banerjee, Christine M Veenstra, Angela B Mariotto, Matthew J Schipper, Kevin He, Jeremy M G Taylor, Andrew F Brouwer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancer causes & control : CCC, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

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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.

Emily K RobertsDepartment of Biostatistics, University of Iowa, Iowa City, Iowa, USA. Emily-roberts-1@uiowa.edu.ORCID http://orcid.org/0000-0002-5838-9691
Lingfeng LuoDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Alison M MondulDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA.
Mousumi BanerjeeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Christine M VeenstraDepartment of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
Angela B MariottoDivision of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, Maryland, USA.
Matthew J SchipperDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Kevin HeDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Jeremy M G TaylorDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Andrew F BrouwerDepartment of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Modeling Precision Interventions for Prostate Cancer ControlU01CA253915 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI ETZIONI, RUTH D, TSODIKOV, ALEXANDER · 2020 to 2025
$6.5M
NCI NIH HHS U01 CA253915
6 · The paper itself

Abstract

purposeSurveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period.

methodsUsing SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks.

resultsOur comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival.

conclusionThese findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Indexed as

NeoplasmsSEER ProgramAdultAgedFemaleHumansMaleMiddle AgedProportional Hazards ModelsSurvival RateTime FactorsUnited StatesCancerCancer survivorsHazard modelRisk factorsSurvival analysis

Identifiers

PMID38811511
PMCPMC11461102

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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.