Using marginal structural models to adjust for treatment drop‐in when developing clinical prediction models
Sign Up to like & getrecommendations! Published in 2018 at "Statistics in Medicine"
DOI: 10.1002/sim.7913
Abstract: Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given. However, this is challenging since CPMs are usually derived using data sets where patients received… read more here.
Keywords: clinical prediction; treatment; treatment drop; prediction models ... See more keywords
Risk Factors for Treatment Drop-Out: Implications for Adverse Outcomes When Treating Opioid Use Disorder
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DOI: 10.1080/1533256x.2020.1838859
Abstract: ABSTRACT Pathways to resolve the opioid epidemic focus on enhancing addiction treatment. Dropout rates from outpatient buprenorphine-naloxone (BN) treatment remain as high as 35% to 59%, contributing to poor treatment outcomes. Treatment interventions targeting retention… read more here.
Keywords: treatment drop; treatment; use; risk factors ... See more keywords
Invited Commentary: Treatment Drop-in—Making the Case for Causal Prediction
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DOI: 10.1093/aje/kwab030
Abstract: Abstract Clinical prediction models (CPMs) are often used to guide treatment initiation, with individuals at high risk offered treatment. This implicitly assumes that the probability quoted from a CPM represents the risk to an individual… read more here.
Keywords: treatment; treatment drop; causal; invited commentary ... See more keywords