Addressing systematic missing data in the context of causally interpretable meta-analysis
Evidence synthesis involves drawing conclusions from trial samples that may differ from the
target population of interest, and there is often heterogeneity among trials in sample …
target population of interest, and there is often heterogeneity among trials in sample …
Causal interaction trees: Finding subgroups with heterogeneous treatment effects in observational data
We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals
with heterogeneous treatment effects in observational data. The CIT algorithms are …
with heterogeneous treatment effects in observational data. The CIT algorithms are …
Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies
The rich longitudinal individual level data available from electronic health records (EHRs)
can be used to examine treatment effect heterogeneity. However, estimating treatment …
can be used to examine treatment effect heterogeneity. However, estimating treatment …
Tree-based subgroup discovery in electronic health records: Heterogeneity of treatment effects for dtg-containing therapies
The rich longitudinal individual level data available from electronic health records (EHRs)
can be used to examine treatment effect heterogeneity. However, estimating treatment …
can be used to examine treatment effect heterogeneity. However, estimating treatment …
Change surface regression for nonlinear subgroup identification with application to warfarin pharmacogenomics data
P Liu, Y Li, J Li - Biometrics, 2025 - academic.oup.com
Pharmacogenomics stands as a pivotal driver toward personalized medicine, aiming to
optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic …
optimize drug efficacy while minimizing adverse effects by uncovering the impact of genetic …
Marginalization of regression-adjusted treatment effects in indirect comparisons with limited patient-level data
Population adjustment methods such as matching-adjusted indirect comparison (MAIC) are
increasingly used to compare marginal treatment effects when there are cross-trial …
increasingly used to compare marginal treatment effects when there are cross-trial …
[HTML][HTML] Machine learning for precision health economics and outcomes research (P-HEOR): conceptual review of applications and next steps
Y Chen, VV Chirikov, XL Marston, J Yang… - Journal of Health …, 2020 - ncbi.nlm.nih.gov
Precision health economics and outcomes research (P-HEOR) integrates economic and
clinical value assessment by explicitly discovering distinct clinical and health care utilization …
clinical value assessment by explicitly discovering distinct clinical and health care utilization …
Population-adjusted indirect treatment comparisons with limited access to patient-level data
A Remiro Azócar - 2022 - discovery.ucl.ac.uk
Health technology assessment systems base their decision-making on health-economic
evaluations. These require accurate relative treatment effect estimates for specific patient …
evaluations. These require accurate relative treatment effect estimates for specific patient …
[图书][B] Assessing treatment effect heterogeneity: predictive covariate selection and subgroup identification
K Papangelou - 2021 - search.proquest.com
A key objective in an interventional study, such as a randomised clinical trial, is the
evaluation of heterogeneity of treatment effect in the population. This allows us to identify the …
evaluation of heterogeneity of treatment effect in the population. This allows us to identify the …
On Subgroup Identification Via Change Point Detection and Model Average
L Pan - 2023 - search.proquest.com
In medical studies, there is a growing interest in unraveling how the impact of a treatment
varies in relation to an individual's observed covariates. The past decade has witnessed a …
varies in relation to an individual's observed covariates. The past decade has witnessed a …