Multistate models for the natural history of cancer progression

Cheung, Li C.; Albert, Paul S.; Das, Shrutikona; Cook, Richard J.

doi:10.1038/s41416-022-01904-5

Cited by 6 publications

(4 citation statements)

References 88 publications

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“…For analyses that involve patient histories with several events of interest occurrence, multistate models could be used to characterize the competing risks. Applications of multistate models could be found for various care levels 10 , 11 and patient groups such as kidney disease 12 , diabetic 13 , surgical 14 , cancer 15 , COVID-19 16 , and geriatric 17 cohorts.…”

Section: Background and Significancementioning

confidence: 99%

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Adiyeke,

Ren,

Guan

et al. 2023

Sci Rep

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Persistence of acute kidney injury (AKI) or insufficient recovery of renal function was associated with reduced long-term survival and life quality. We quantified AKI trajectories and describe transitions through progression and recovery among hospitalized patients. 245,663 encounters from 128,271 patients admitted to UF Health between 2012 and 2019 were retrospectively categorized according to the worst AKI stage experienced within 24-h periods. Multistate models were fit for describing characteristics influencing transitions towards progressed or regressed AKI, discharge, and death. Effects of age, sex, race, admission comorbidities, and prolonged intensive care unit stay (ICU) on transition rates were examined via Cox proportional hazards models. About 20% of encounters had AKI; where 66% of those with AKI had Stage 1 as their worst AKI severity during hospitalization, 18% had Stage 2, and 16% had Stage 3 AKI (12% with kidney replacement therapy (KRT) and 4% without KRT). At 3 days following Stage 1 AKI, 71.1% (70.5–71.6%) were either resolved to No AKI or discharged, while recovery proportion was 38% (37.4–38.6%) and discharge proportion was 7.1% (6.9–7.3%) following AKI Stage 2. At 14 days following Stage 1 AKI, patients with additional frail conditions stay had lower transition proportion towards No AKI or discharge states. Multistate modeling framework is a facilitating mechanism for understanding AKI clinical course and examining characteristics influencing disease process and transition rates.

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Section: Background and Significancementioning

confidence: 99%

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Adiyeke,

Ren,

Guan

et al. 2023

Sci Rep

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“…The following year the multistate survival model was linked to a tumor growth model in order to enhance the predictions of survival 3 . The natural history of cancer can be predicted by time‐inhomogenous semi‐Markov processes and by an unmodeled precancer disease state, both implemented in a multistate model 4 . An earlier paper identified the importance of competing risks in modeling survival after hematopoietic stem cell transplantation.…”

Section: Introductionmentioning

confidence: 99%

“… 3 The natural history of cancer can be predicted by time‐inhomogenous semi‐Markov processes and by an unmodeled precancer disease state, both implemented in a multistate model. 4 An earlier paper identified the importance of competing risks in modeling survival after hematopoietic stem cell transplantation. The contribution of repeated episodes of graft‐versus‐host‐disease during decades of survival and the risk of dying from disease‐unrelated causes were implemented in a multistate model.…”

Section: Introductionmentioning

confidence: 99%

Application of multistate modeling to clinical data analysis

Grevel,

Veasy,

Chacko

2024

CPT Pharmacom & Syst Pharma

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Multistate models have been used for decades to analyze the economics of expensive and long‐lasting treatments. More recently they also served to address questions in clinical drug development. It seems timely to introduce the broader pharmacometrics community to the technical aspects and the problem‐solving capabilities of these models. A minimal model is introduced that can answer questions of interest to drug developers, regulatory agencies, and patients (with their carers and payers). A clinical study is simulated where 1000 patients are randomly allocated (1:1) to placebo and active treatment. After a recruitment phase, deaths are counted, and an administrative data cutoff occurs 858 days after the first patient is randomized. The minimal model has one initial state, two transient states, and two absorbing states. Fully parameterized semi‐Markov processes govern the unidirectional transitions between states. Simulations explore the influence of parameter uncertainty and sample size on the validity of statistical inferences. The questions of interest to stakeholders are addressed predominantly with graphic displays. All programming codes are made available. Both drug developers and regulators are invited to re‐evaluate the methods currently in use to assess the benefits and risks of new treatments.

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“…In recent years, as an alternative to multi-state models, several continuous tumour growth models have been developed for the analysis of breast cancer screening data. 19,20 These represent a relatively novel and useful approach for analysing screening data (see Discussion in Cheung et al 21 ). Continuous tumour growth models use random effects and growth functions to model the (latent) growth in the size of tumours.…”

Section: Introductionmentioning

confidence: 99%

Random effects models of tumour growth for investigating interval breast cancer

Orsini,

Czene,

Humphreys

2024

Statistics in Medicine

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In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed “interval cancers”. Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1‐sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non‐screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model‐based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population‐based breast cancer screening programs.

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Multistate models for the natural history of cancer progression

Cited by 6 publications

References 88 publications

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Clinical courses of acute kidney injury in hospitalized patients: a multistate analysis

Application of multistate modeling to clinical data analysis

Random effects models of tumour growth for investigating interval breast cancer

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