Measures of treatment effectiveness on tumour response and survival: a multi-state model approach

Chevret, Sylvie; Leporrier, M; Chastang, Claude

doi:10.1002/(sici)1097-0258(20000330)19:6<837::aid-sim394>3.0.co;2-3

Cited by 10 publications

(9 citation statements)

References 29 publications

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“…Multi-state models are now commonly used in areas other than cardiovascular clinical trials, including liver cirrhosis 1 , bone marrow transplant 2, 19 , breast cancer 4 , heart transplant 6 , lymphocytic leukemia 20 , psoriatic arthritis 21 , and colon cancer 22 . While of demonstrated utility, there are two important limitations to conducting the multi-state model analysis.…”

Section: Discussionmentioning

confidence: 99%

Analysis of the Bypass Angioplasty Revascularization Investigation Trial Using a Multistate Model of Clinical Outcomes

Zhang

Rogatko

et al. 2015

The American Journal of Cardiology

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Current cardiovascular randomized trials typically use composite outcomes. We hypothesized that the Bypass Angioplasty Revascularization Investigation (BARI) outcomes and conclusions would differ using a multi-state model relative to the intervention for the composite outcome of death and nonfatal Q-wave myocardial infarction (MI). We employed a multi-state model which uses transition paths to simultaneously assess multiple endpoints. Using the 10-year follow up BARI data, we post hoc analyzed outcomes according to three transition paths: 1) from intervention to MI; 2) from intervention to death; 3) from MI to death. Among 1829 patients randomized to the intervention of percutaneous transluminal coronary angioplasty (PTCA) or coronary artery bypass grafting (CABG), 700 (38%) experienced the composite event D/MI which included 230 (13%) nonfatal MI, and 470 (26%) death without antecedent nonfatal MI, while 79 of 230 (34%) experienced death following nonfatal MI. Outcomes of the three individual transition paths were analyzed by a multi-state model. Contrary to standard survival analyses, after adjustment for baseline clinical covariates, outcomes following PTCA or CABG were not significantly different for intervention to MI (p=0.33) or intervention to death (p=0.23), but MI to death favored CABG (p=0.02). Deconstruction of the BARI data using a multi-state model identifies a significant difference in individual transition stage outcomes and therefore trial conclusions in contrast to the standard methods of survival analysis. These observations suggest multi-state models should be considered in the design and analysis of randomized cardiovascular trials which use composite outcomes.

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Section: Discussionmentioning

confidence: 99%

Analysis of the Bypass Angioplasty Revascularization Investigation Trial Using a Multistate Model of Clinical Outcomes

Zhang

Rogatko

et al. 2015

The American Journal of Cardiology

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“…To our knowledge, the only other analyses in which this type of models was applied in the context of leukaemia were published by Chevret et al [24] and by Cailliod et al [25] for patients with chronic lymphocytic leukaemia (CLL). Cailliod et al described an illness-death model without recurrent events for patients with B cell CLL.…”

Section: Discussionmentioning

confidence: 99%

A multi-state model approach for prediction in chronic myeloid leukaemia

et al. 2014

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Multi-state models support prediction in medicine. With different states of disease, chronic myeloid leukaemia (CML) is particularly suited for the application of multi-state models. In this article, we tried to find a model for CML that allows predicting the prevalence of three different states (initial state of disease, remission and progression) in dependence on treatment, adjusted for age, sex and risk score. Based on the German CML Study IV, one of the largest randomised studies in CML, the model was able to represent the known effects of age and risk score on the probabilities of remission and progression. Patients achieving a major molecular remission had a better chance of surviving without progression, but this effect was not significant. Comparing treatments, patient of the high-dose arm had the greatest chance to be in the state "remission" at 5 years but did not seem to have an advantage considering "progression". The proposed illness-death model can be useful for predicting the course of CML based on the patient's individual covariates (trial registration: this is an explorative analysis of ClinicalTrials.gov Identifier: NCT00055874).

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“…Although this model is more and more frequently used in medical research [29], its application in survival analysis of B-CLL remains rare [33]. Nonetheless, by defining 'stage B or C' as a transient state in which patients could be at any time, the three-states Markov model applied in our study allowed for comparing the effects of each prognostic factor on the risk of disease progression with their effects on the risk of death in stage A patients without disease progression.…”

Section: Discussionmentioning

confidence: 99%

A Population-based Assessment of the Prognostic Value of the CD19 Positive Lymphocyte Count in B-cell Chronic Lymphocytic Leukemia using Cox and Markov Models

et al. 2005

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No population-based study has assessed the prognostic impact on survival of the CD19 positive lymphocyte count, evaluated by immunophenotyping at diagnosis, in B-cell chronic lymphocytic leukemia (B-CLL). Aiming at addressing this issue, we investigated the clinical outcome of a well-defined population of B-CLL patients. Survival of B-CLL patients, diagnosed between 1990 and 1999 and recorded by the Registry of Hematological Malignancies of the Côte d'Or, was analysed applying Cox's regression model to the 237 included cases and to the 195 Binet stage A patients. To assess simultaneously the predictive value of each parameter on the risk of disease progression and on the risk of death, we completed this analysis by applying a three-states homogeneous Markov model to the whole study population. Analysis of the entire population showed that age (p < 0.001), Binet stage (p = 0.008) and CD19 positive lymphocyte count (p = 0.038) were three independent prognostic factors. However, in stage A patients, only progression into a more advanced stage, analysed as a time-dependent variable, and age had a clear impact on survival (p < 0.001 for both). Markov model revealed that an increased CD19 positive lymphocyte count increased the risk of disease progression in stage A patients (p = 0.002) but did not have direct impact on survival of either stage A patients with stable disease or stage B or C patients. An increased CD19 positive lymphocyte count at diagnosis is a marker of an increased risk of disease progression in stage A patients. Thus, it can be a useful tool for the clinical management of these patients.

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Measures of treatment effectiveness on tumour response and survival: a multi-state model approach

Cited by 10 publications

References 29 publications

Analysis of the Bypass Angioplasty Revascularization Investigation Trial Using a Multistate Model of Clinical Outcomes

Analysis of the Bypass Angioplasty Revascularization Investigation Trial Using a Multistate Model of Clinical Outcomes

A multi-state model approach for prediction in chronic myeloid leukaemia

A Population-based Assessment of the Prognostic Value of the CD19 Positive Lymphocyte Count in B-cell Chronic Lymphocytic Leukemia using Cox and Markov Models

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