Evidence map›Paper›PMID 38623615›Full record

ArticleStatistical methods in medical research2024

Estimating dynamic treatment regimes for ordinal outcomes with household interference: Application in household smoking cessation.

Cong Jiang, Mary Thompson, Michael Wallace

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Article in Statistical methods in medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Cong JiangFaculty of Pharmacy, Université de Montréal, Montreal, Canada.ORCID 0000-0002-4486-5423
Mary ThompsonDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
Michael WallaceDepartment of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.ORCID 0000-0002-5763-5723

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The focus of precision medicine is on decision support, often in the form of dynamic treatment regimes, which are sequences of decision rules. At each decision point, the decision rules determine the next treatment according to the patient's baseline characteristics, the information on treatments and responses accrued by that point, and the patient's current health status, including symptom severity and other measures. However, dynamic treatment regime estimation with ordinal outcomes is rarely studied, and rarer still in the context of interference - where one patient's treatment may affect another's outcome. In this paper, we introduce the weighted proportional odds model: a regression based, approximate doubly-robust approach to single-stage dynamic treatment regime estimation for ordinal outcomes. This method also accounts for the possibility of interference between individuals sharing a household through the use of covariate balancing weights derived from joint propensity scores. Examining different types of balancing weights, we verify the approximate double robustness of weighted proportional odds model with our adjusted weights via simulation studies. We further extend weighted proportional odds model to multi-stage dynamic treatment regime estimation with household interference, namely dynamic weighted proportional odds model. Lastly, we demonstrate our proposed methodology in the analysis of longitudinal survey data from the Population Assessment of Tobacco and Health study, which motivates this work. Furthermore, considering interference, we provide optimal treatment strategies for households to achieve smoking cessation of the pair in the household.

Indexed as

Smoking CessationFamily CharacteristicsHumansModels, StatisticalPrecision MedicinePropensity Scoredouble robustnessDynamic treatment regimeshousehold interferenceordinal outcomesweighted proportional odds models

Identifiers

PMID38623615
PMCPMC11334379

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