Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

Skourlis, Nikolaos; Crowther, Michael J.; Andersson, Therése; Lambert, Paul C.

doi:10.1186/s12874-021-01420-9

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…The graphs presented in Fig. 1 were produced via the interactive web-tool MSMplus [ 19 ]. The traits, results, interpretation, advantages and drawbacks of each structure are presented in the results section.…”

Section: Methodsmentioning

confidence: 99%

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

Skourlis

Crowther²,

Andersson

et al. 2023

BMC Med Res Methodol

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Background Multi-state models are used to study several clinically meaningful research questions. Depending on the research question of interest and the information contained in the data, different multi-state structures and modelling choices can be applied. We aim to explore different research questions using a series of multi-state models of increasing complexity when studying repeated prescriptions data, while also evaluating different modelling choices. Methods We develop a series of research questions regarding the probability of being under antidepressant medication across time using multi-state models, among Swedish women diagnosed with breast cancer (n = 18,313) and an age-matched population comparison group of cancer-free women (n = 92,454) using a register-based database (Breast Cancer Data Base Sweden 2.0). Research questions were formulated ranging from simple to more composite ones. Depending on the research question, multi-state models were built with structures ranging from simpler ones, like single-event survival analysis and competing risks, up to complex bidirectional and recurrent multi-state structures that take into account the recurring start and stop of medication. We also investigate modelling choices, such as choosing a time-scale for the transition rates and borrowing information across transitions. Results Each structure has its own utility and answers a specific research question. However, the more complex structures (bidirectional, recurrent) enable accounting for the intermittent nature of prescribed medication data. These structures deliver estimates of the probability of being under medication and total time spent under medication over the follow-up period. Sensitivity analyses over different definitions of the medication cycle and different choices of timescale when modelling the transition intensity rates show that the estimates of total probabilities of being in a medication cycle over follow-up derived from the complex structures are quite stable. Conclusions Each research question requires the definition of an appropriate multi-state structure, with more composite ones requiring such an increase in the complexity of the multi-state structure. When a research question is related with an outcome of interest that repeatedly changes over time, such as the medication status based on prescribed medication, the use of novel multi-state models of adequate complexity coupled with sensible modelling choices can successfully address composite, more realistic research questions.

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Section: Methodsmentioning

confidence: 99%

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

Skourlis

Crowther²,

Andersson

et al. 2023

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

show abstract

“…The graphs presented in Fig. 1 were produced via the interactive web-tool MSMplus (23). The traits, results, interpretation, advantages and drawbacks of each structure are presented in section 4.4.…”

Section: Use Of Different Multi-state Structures For Analyzing the An...mentioning

confidence: 99%

Exploring different multi-state structures when studying patterns of antidepressant medication use in women with breast cancer and matched healthy controls

Skourlis

Crowther

Andersson

et al. 2022

Preprint

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Background: A breast cancer diagnosis is related with psychological distress and possibly with an increased use of antidepressants. Multi-state models can be used to study a number of clinically meaningful research questions related to patterns of medication use such as the total probability of being or total length of stay in an antidepressant medication or discontinuation period over time. Methods: Different multi-state structures may correspond to different research questions and vary from simple ones, such as a single-event survival analysis setting, up to bidirectional and recurrent multi-state structures, with each structure having its own traits, advantages, interpretations, and limitations. There are also a number of factors to consider when applying a multi-state model such as choosing a time-scale for the transition rates, borrowing information across transitions and relaxing the proportional hazards assumption. We explore the use of different multi-state structures when studying antidepressant medication use patterns among women diagnosed with invasive breast cancer (n=18313) and age-matched cancer-free women (n=92454) from the Swedish population, using the information in Breast Cancer Data Base Sweden 2.0 (BCBaSe 2.0). Results: While each structure has its own utility, the more complex structures -bidirectional and recurrent– are able to take into account the intermittent nature of the prescribed medication data and deliver estimates about the total probability and total length of stay of an individual in an antidepressant medication status or discontinuation periods during their follow-up. Sensitivity analyses over different definitions of the medication cycle and different approaches when modelling the transition intensity rates (Markov, semi-Markov or mix of the two) show that the estimates of the complex structures are quite stable. However, even these structures present limitations that should be taken into account during the implementation of the models. Conclusions: In this study we provide insight into the use, interpretation and limitations of different multi-state structures when applied to prescribed medication data. The conclusions drawn provide a better understanding of the application of multi-state models in epidemiology.

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MSMpred: interactive modelling and prediction of individual evolution via multistate models

Martínez¹,

Melis²

2023

BMC Med Res Methodol

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Background Modelling the course of a disease regarding severe events and identifying prognostic factors is of great clinical relevance. Multistate models (MSM) can be used to describe diseases or processes that change over time using different states and the transitions between them. Specifically, they are useful to analyse a disease with an increasing degree of severity, that may precede death. The complexity of these models changes depending on the number of states and transitions taken into account. Due to that, a web tool has been developed making easier to work with those models. Results MSMpred is a web tool created with the shiny package that has two main features: 1) to allow to fit a MSM from specific data; 2) to predict the clinical evolution for a given subject. To fit the model, the data to be analysed must be upload in a prespecified format. Then, the user has to define the states and transitions as well as the covariates (e.g., age or gender) involved in each transition. From this information, the app returns histograms or barplots, as appropriate, to represent the distributions of the selected covariates and boxplots to show the patient’ length of stay (for uncensored data) in each state. To make predictions, the values of selected covariates from a new subject at baseline has to be provided. From these inputs, the app provides some indicators of the subject’s evolution such as the probability of 30-day death or the most likely state at a fixed time. Furthermore, visual representations (e.g., the stacked transition probabilities plot) are given to make predictions more understandable. Conclusions MSMpred is an intuitive and visual app that eases the work of biostatisticians and facilitates to the medical personnel the interpretation of MSMs.

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Development of a dynamic interactive web tool to enhance understanding of multi-state model analyses: MSMplus

Cited by 3 publications

References 32 publications

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

Exploring different research questions via complex multi-state models when using registry-based repeated prescriptions of antidepressants in women with breast cancer and a matched population comparison group

Exploring different multi-state structures when studying patterns of antidepressant medication use in women with breast cancer and matched healthy controls

MSMpred: interactive modelling and prediction of individual evolution via multistate models

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