arXiv subjects

Jeroen P Jansen

Publications and source records attributed to Jeroen P Jansen.

4 recordsLinked to original sources

arXiv2026

Multilevel network meta-regression for multistate models: Population-adjusted joint synthesis of progression and survival data from individual and aggregate evidence

In network meta-analysis (NMA) of oncology time-to-event outcomes, there is growing recognition that progression-free survival (PFS) and overall survival (OS) are better synthesized jointly than in separate analyses. The multistate NMA of Jansen et al. addresses this with a tri-state (stable, progressed, dead) Markov model fitted to aggregate survival curves, but it cannot adjust for imbalance in effect-modifying covariates across trials, so its relative effects may be biased or not relevant for the target population of interest. Multilevel network meta-regression (ML-NMR) resolves covariate imbalance by integrating an individual-level model over the aggregate covariate distribution, coherently combining individual participant data (IPD) and aggregate data (AgD); however, it has been developed only for single-endpoint outcomes. This paper introduces multilevel network meta-regression for multistate models (ML-NMR-MS), which embeds the ML-NMR integrated IPD-plus-AgD likelihood inside an illness--death multistate model. For IPD studies the full multistate likelihood over linked transitions is used; for AgD studies covariate-marginal state-occupancy probabilities are computed by quasi-Monte-Carlo integration of the occupancies implied by the transition intensities over the reconstructed covariate distribution, and entered through a conditional-survival likelihood. This yields population-adjusted joint PFS/OS treatment effects and can directly parameterize a state-transition economic model. On an illustrative oncology network, ML-NMR-MS resolves a treatment's joint effect into its component transitions, separating an effect on progression from an effect on post-progression survival, and produces conditional and marginal effects. A simulation study with fully known truth confirms unbiased recovery and near-nominal coverage under correct specification.

stat.ME↗

arXiv2025

Augmented two-stage estimation for treatment crossover in oncology trials: Leveraging external data for improved precision

Randomized controlled trials (RCTs) in oncology often allow control group participants to crossover to experimental treatments, a practice that, while often ethically necessary, complicates the accurate estimation of long-term treatment effects. When crossover rates are high or sample sizes are limited, commonly used methods for crossover adjustment (such as the rank-preserving structural failure time model, inverse probability of censoring weights, and two-stage estimation (TSE)) may produce imprecise estimates. Real-world data (RWD) can be used to develop an external control arm for the RCT, although this approach ignores evidence from trial subjects who did not crossover and ignores evidence from the data obtained prior to crossover for those subjects who did. This paper introduces ''augmented two-stage estimation'' (ATSE), a method that combines data from non-switching participants in a RCT with an external dataset, forming a ''hybrid non-switching arm''. With a simulation study, we evaluate the ATSE method's performance compared to TSE crossover adjustment and an external control arm approach. Results indicate that performance is dependent on scenario characteristics, but when unconfounded external data are available, ATSE may result in less bias and improved precision compared to TSE and external control arm approaches. When external data are affected by unmeasured confounding, ATSE becomes prone to bias, but to a lesser extent compared to an external control arm approach.

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arXiv2023

Estimating marginal treatment effects from observational studies and indirect treatment comparisons: When are standardization-based methods preferable to those based on propensity score weighting?

In light of newly developed standardization methods, we evaluate, via simulation study, how propensity score weighting and standardization -based approaches compare for obtaining estimates of the marginal odds ratio and the marginal hazard ratio. Specifically, we consider how the two approaches compare in two different scenarios: (1) in a single observational study, and (2) in an anchored indirect treatment comparison (ITC) of randomized controlled trials. We present the material in such a way so that the matching-adjusted indirect comparison (MAIC) and the (novel) simulated treatment comparison (STC) methods in the ITC setting may be viewed as analogous to the propensity score weighting and standardization methods in the single observational study setting. Our results suggest that current recommendations for conducting ITCs can be improved and underscore the importance of adjusting for purely prognostic factors.

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arXiv2021

hesim: Health Economic Simulation Modeling and Decision Analysis

Health economic models simulate the costs and effects of health technologies for use in health technology assessment (HTA) to inform efficient use of scarce resources. Models have historically been developed using spreadsheet software (e.g., Microsoft Excel) and while use of R is growing, general purpose modeling software is still limited. hesim is an R package that helps fill this gap by facilitating parameterization, simulation, and analysis of economic models in an integrated manner. Supported model types include cohort discrete time state transition models (cDTSTMs), individual continuous time state transition models (iCTSTMs), and partitioned survival models (PSMs), encompassing Markov (time-homogeneous and time-inhomogeneous) and semi-Markov processes. A modular design based on R6 and S3 classes allows users to combine separate submodels for disease progression, costs, and utility in a flexible way. Probabilistic sensitivity analysis (PSA) is used to propagate uncertainty in model parameters to model outputs. Simulation code is written in C++ so complex simulations such as those combining PSA and individual simulation can be run much more quickly than previously possible. Decision analysis within a cost-effectiveness framework is performed using simulated costs and quality-adjusted life years (QALYs) from a PSA.

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