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Causal effects of time-dependent treatments in older patients with non-small cell lung cancer.

dc.contributor.author Akushevich, Igor
dc.contributor.author Arbeev, Konstantin
dc.contributor.author Kravchenko, J
dc.contributor.author Berry, Mark Francis
dc.coverage.spatial United States
dc.date.accessioned 2017-06-05T19:13:48Z
dc.date.available 2017-06-05T19:13:48Z
dc.date.issued 2015
dc.identifier https://www.ncbi.nlm.nih.gov/pubmed/25849715
dc.identifier PONE-D-14-43833
dc.identifier.uri http://hdl.handle.net/10161/14817
dc.description.abstract BACKGROUND: Treatment selection for elderly patients with lung cancer must balance the benefits of curative/life-prolonging therapy and the risks of increased mortality due to comorbidities. Lung cancer trials generally exclude patients with comorbidities and current treatment guidelines do not specifically consider comorbidities, so treatment decisions are usually made on subjective individual-case basis. METHODS: Impacts of surgery, radiation, and chemotherapy mono-treatment as well as combined chemo/radiation on one-year overall survival (compared to no-treatment) are studied for stage-specific lung cancer in 65+ y.o. patients. Methods of causal inference such as propensity score with inverse probability weighting (IPW) for time-independent and marginal structural model (MSM) for time-dependent treatments are applied to SEER-Medicare data considering the presence of comorbid diseases. RESULTS: 122,822 patients with stage I (26.8%), II (4.5%), IIIa (11.5%), IIIb (19.9%), and IV (37.4%) lung cancer were selected. Younger age, smaller tumor size, and fewer baseline comorbidities predict better survival. Impacts of radio- and chemotherapy increased and impact of surgery decreased with more advanced cancer stages. The effects of all therapies became weaker after adjustment for selection bias, however, the changes in the effects were minor likely due to the weak selection bias or incompleteness of the list of predictors that impacted treatment choice. MSM provides more realistic estimates of treatment effects than the IPW approach for time-independent treatment. CONCLUSIONS: Causal inference methods provide substantive results on treatment choice and survival of older lung cancer patients with realistic expectations of potential benefits of specific treatments. Applications of these models to specific subsets of patients can aid in the development of practical guidelines that help optimize lung cancer treatment based on individual patient characteristics.
dc.language eng
dc.relation.ispartof PLoS One
dc.relation.isversionof 10.1371/journal.pone.0121406
dc.subject Age Factors
dc.subject Aged
dc.subject Aged, 80 and over
dc.subject Female
dc.subject Humans
dc.subject Lung Neoplasms
dc.subject Male
dc.subject Medicare
dc.subject Neoplasm Staging
dc.subject Registries
dc.subject United States
dc.title Causal effects of time-dependent treatments in older patients with non-small cell lung cancer.
dc.type Journal article
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/25849715
pubs.begin-page e0121406
pubs.issue 4
pubs.organisational-group Center for Population Health & Aging
pubs.organisational-group Clinical Science Departments
pubs.organisational-group Duke
pubs.organisational-group Duke Population Research Institute
pubs.organisational-group Institutes and Provost's Academic Units
pubs.organisational-group Physics
pubs.organisational-group Sanford School of Public Policy
pubs.organisational-group School of Medicine
pubs.organisational-group Social Science Research Institute
pubs.organisational-group Staff
pubs.organisational-group Surgery
pubs.organisational-group Surgery, Surgical Sciences
pubs.organisational-group Trinity College of Arts & Sciences
pubs.organisational-group University Institutes and Centers
pubs.publication-status Published online
pubs.volume 10
dc.identifier.eissn 1932-6203


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