Causal effects of time-dependent treatments in older patients with non-small cell lung cancer.
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.
Type
Journal articleSubject
Age FactorsAged
Aged, 80 and over
Female
Humans
Lung Neoplasms
Male
Medicare
Neoplasm Staging
Registries
United States
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https://hdl.handle.net/10161/14817Published Version (Please cite this version)
10.1371/journal.pone.0121406Publication Info
Akushevich, Igor; Arbeev, Konstantin; Kravchenko, Julia; & Berry, Mark (2015). Causal effects of time-dependent treatments in older patients with non-small cell
lung cancer. PLoS One, 10(4). pp. e0121406. 10.1371/journal.pone.0121406. Retrieved from https://hdl.handle.net/10161/14817.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Igor Akushevich
Research Professor in the Social Science Research Institute
Konstantin Arbeev
Associate Research Professor in the Social Science Research Institute
Konstantin G. Arbeev received the M.S. degree in Applied Mathematics from Moscow State
University (branch in Ulyanovsk, Russia) in 1995 and the Ph.D. degree in Mathematics
and Physics (specialization in Theoretical Foundations of Mathematical Modeling, Numerical
Methods and Programming) from Ulyanovsk State University (Russia) in 1999. He was
a post-doctoral fellow in Max Planck Institute for Demographic Research in Rostock
(Germany) before moving to Duke University in 2004 to work as a Resea
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