arXiv subjects

Maryam Farhadizadeh

Publications and source records attributed to Maryam Farhadizadeh.

4 recordsLinked to original sources

arXiv2026

Testing similarity of competing risks models by comparing transition probabilities

Assessing whether patient populations exhibit comparable event dynamics is important for evaluating treatment equivalence, pooling cohorts and comparing clinical pathways. Existing similarity tests for competing risks models measure distances between transition intensities, which describe instantaneous event rates. In biomedical applications, similarity may be more naturally formulated through transition probabilities, which quantify cumulative event risks over a clinically relevant horizon. Assuming constant cause-specific transition intensities, we develop a framework for testing similarity based on a maximum-type distance between vectors of transition-probability functions. We propose a constrained parametric bootstrap test and establish asymptotic level control and consistency under administrative and independent exponential random right censoring. The constant-intensity formulation is motivated by small-data settings in which few events are observed and nonparametric estimators may be unstable. Simulations across sample sizes, censoring mechanisms and degrees of dissimilarity show that the proposed test can attain larger finite-sample rejection probabilities than an intensity-based benchmark under comparable alternatives. An application to routine prostate cancer data illustrates how the procedure identifies the smallest examined margin for which similarity of 90-day readmission-probability functions can be established under the fitted model. The method provides an interpretable and practically implementable basis for similarity assessment in parametric competing risks models.

stat.ME↗

arXiv2026

Challenges and proposed solutions in modeling multimodal medical data: A systematic review

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications.

cs.LG↗

arXiv2026

Typical Healthcare Pathways as a Basis for Admixture Modeling of Patient Trajectories

Background: Understanding whether patients follow similar or distinct patterns of care is important for characterizing clinical practice, identifying patient subgroups, and supporting quality improvement. However, routine healthcare trajectories are difficult to compare directly because patients may differ in their diagnostic workup, treatment sequencing, timing of clinical events, and documentation practices. Despite this variation, trajectories often contain recurring patterns at the cohort level. Methods: To address this challenge, we present a framework that explicitly separates cohort-level typical pathway identification from patient-level inference. At the cohort level, we derive an interpretable representation of care processes using a rule-based algorithm to identify typical healthcare pathways, resulting in a compact pathway graph. These pathways are then modeled as Markov chains and used as structured components in an admixture model, allowing each patient to be represented as a probabilistic mixture of typical pathways rather than being assigned to a single pathway component. The resulting admixture weights provide a compact representation of patient trajectories for subgroup characterization. We further assess the stability of the identified pathways and inferred admixture representations across multiple train-test splits. Results: Across train-test splits, the framework demonstrated consistent pathway structures and patient-level mixture patterns. Applied to routine care data from prostate cancer patients undergoing radical prostatectomy, the framework identified interpretable care patterns and supported the identification of patient subgroups with similar clinical event patterns. Conclusion: Overall, the proposed framework provides an interpretable and stable approach for summarizing treatment pathways and characterizing patient subgroups in real-world practice.

stat.ME↗

arXiv2024

Improving prediction models by incorporating external data with weights based on similarity

In clinical settings, we often face the challenge of building prediction models based on small observational data sets. For example, such a data set might be from a medical center in a multi-center study. Differences between centers might be large, thus requiring specific models based on the data set from the target center. Still, we want to borrow information from the external centers, to deal with small sample sizes. There are approaches that either assign weights to each external data set or each external observation. To incorporate information on differences between data sets and observations, we propose an approach that combines both into weights that can be incorporated into a likelihood for fitting regression models. Specifically, we suggest weights at the data set level that incorporate information on how well the models that provide the observation weights distinguish between data sets. Technically, this takes the form of inverse probability weighting. We explore different scenarios where covariates and outcomes differ among data sets, informing our simulation design for method evaluation. The concept of effective sample size is used for understanding the effectiveness of our subgroup modeling approach. We demonstrate our approach through a clinical application, predicting applied radiotherapy doses for cancer patients. Generally, the proposed approach provides improved prediction performance when external data sets are similar. We thus provide a method for quantifying similarity of external data sets to the target data set and use this similarity to include external observations for improving performance in a target data set prediction modeling task with small data.

stat.ME↗