Competing Risks and Multistate Models with R

Beyersmann, Jan; Allignol, Arthur; Schumacher, Martin

doi:10.1007/978-1-4614-2035-4

Cited by 310 publications

(365 citation statements)

References 85 publications

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“…Patients lost to follow-up were censored on the date of last known follow-up. Data were analyzed through a competingrisks multistate model among death, amputation, and healing (27). Cumulative incidence functions for amputation and healing were calculated for the telemedical and control groups separately using the Aalen-Johansen estimator.…”

Section: Methodsmentioning

confidence: 99%

A Randomized Controlled Trial Comparing Telemedical and Standard Outpatient Monitoring of Diabetic Foot Ulcers

et al. 2015

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OBJECTIVEThe role of telemedical monitoring in diabetic foot ulcer care is still uncertain. Our aim was to compare telemedical and standard outpatient monitoring in the care of patients with diabetic foot ulcers in a randomized controlled trial. RESEARCH DESIGN AND METHODSOf the 736 screened individuals with diabetic foot ulcers, 401 met the eligibility criteria and were randomized between October 2010 and November 2014. The per-protocol telemedical monitoring consisted of two consultations in the patient's own home and one consultation at the outpatient clinic. Standard practice consisted of three outpatient clinic visits. The three-visit cycle was repeated until study end point. The study end points were defined as complete ulcer healing, amputation, or death. RESULTSOne hundred ninety-three individuals were randomized to telemedical monitoring and 181 to standard care. Demographics were similar in both groups. A causespecific Cox proportional hazards model showed no difference in individuals monitored through telemedicine regarding wound healing (hazard ratio 1.11 [95% CI 0.87, 1.42], P = 0.42) or amputation (0.87 [0.54, 1.42], P = 0.59). We found a higher mortality incidence in the telemedical monitoring group compared with the standard outpatient monitoring group (8.68 [6.93, 10.88], P = 0.0001). CONCLUSIONSThe findings of no significant difference regarding amputation and healing between telemedical and standard outpatient monitoring seem promising; however, for telemedical monitoring, a higher mortality throws into question the role of telemedicine in monitoring diabetic foot ulcers. Further studies are needed to investigate effects of telemedicine on mortality and other clinical outcomes and to identify patient subgroups that may have a poorer outcome through telemedical monitoring.Diabetes currently affects .387 million people worldwide, and this number is expected to rise to .592 million individuals worldwide by 2035 (1). In Denmark, 320,545 people suffer from diabetes, and it is estimated that a further 200,000 individuals are undiagnosed; a further 750,000 have impaired glucose tolerance (2). Approximately 7-15% of the population with diabetes will have one or several foot ulcers during a lifetime, and up to 70% of these ulcers will recur during a 5-year period (3). Diabetic foot ulcers lead to ;500 major amputations every year in

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Section: Methodsmentioning

confidence: 99%

A Randomized Controlled Trial Comparing Telemedical and Standard Outpatient Monitoring of Diabetic Foot Ulcers

et al. 2015

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“…Estimation methods should allow for observations that are left-truncated and right-censored. The theory of counting processes, pioneered by Aalen (1975) in his dissertation, satisfies that requirement (for details, see Andersen et al 1993;Andersen and Keiding 2002;Aalen et al 2008;Beyersmann et al 2012). Andersen and Gill (1982) suggested a format for counting process data that applies to multistate models: a period of observation (episode) is described by a starting time, an ending time, and a reason for ending (transition or censoring).…”

Section: Statistical Inferencementioning

confidence: 99%

Software for multistate analysis

Willekens¹,

Putter²

2014

DemRes

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Section: Introductionmentioning

confidence: 99%

“…The continuous-time Markov process incorporates the theory of competing risks, which determines the destination state entered after leaving a given (origin) state. For an explicit treatment of the theory of competing risks in the context of multistate models, see Aalen et al (2008) and Beyersmann et al (2012).Transition rates, all of which are estimated from actual data apart from the transition rates to migration, may depend on the systematic factors mentioned, and may vary by age and over time. Our main data sources are censuses and surveys.…”

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confidence: 99%

Multistate modelling extended by behavioural rules: An application to migration

et al. 2017

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We propose to extend demographic multistate models by adding a behavioural element: behavioural rules explain intentions and thus transitions. Our framework is inspired by the Theory of Planned Behaviour. We exemplify our approach with a model of migration from Senegal to France. Model parameters are determined using empirical data where available. Parameters for which no empirical correspondence exists are determined by calibration. Age-and period-specific migration rates are used for model validation. Our approach adds to the toolkit of demographic projection by allowing for shocks and social influence, which alter behaviour in non-linear ways, while sticking to the general framework of multistate modelling. Our simulations yield that higher income growth in Senegal leads to higher emigration rates in the medium term, while a decrease in fertility yields lower emigration rates.Keywords: international migration; life course modelling; Theory of Planned Behaviour; multistate modelling; microsimulation; agent-based modelling IntroductionFor policymakers and researchers alike, it is of paramount interest to be able to predict how people make demographically relevant decisions. In this paper, we present a novel approach to projection by incorporating decision-making and interaction between individuals in a demographic projection model. We use an individual-based model rather than a population-based model such as the popular cohort-component model. For an overview of approaches to forecasting migration, see Bijak (2011), as well as other recent contributions from Hatton and Williamson (2011), Azose and Raftery (2013), and Abel and Sander (2014. This individual-based micro perspective enables us to incorporate behavioural mechanisms and social processes that influence demographic behaviour and population change into our model. We can thus predict the effect of external shocks, such as policy changes, by making the causal mechanism through which shocks alter behaviour explicit. Comparing model predictions with empirical outcomes facilitates the subsequent drawing of conclusions on the plausibility of the assumed behavioural mechanism. In this way, the model can be improved gradually over time, by refining the parameters and functional forms of its main component, in this case the decision-making on international migration.In the model, as in real life, demographically relevant decisions are embedded in the human life course. Choices are motivated by aspirations and preferences in different domains of life, such as work and family, and constrained by available resources (not only financial, but also cognitive and social resources, and time). We take into consideration that people do not have perfect foresight and usually lack the time and cognitive abilities to acquire the full and unbiased information necessary to make a rational choice. Preferences vary over the life course, as do both the availability of and need for resources, because of events or changing conditions (e.g., available social support). As a result, t...

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Competing Risks and Multistate Models with R

Cited by 310 publications

References 85 publications

A Randomized Controlled Trial Comparing Telemedical and Standard Outpatient Monitoring of Diabetic Foot Ulcers

A Randomized Controlled Trial Comparing Telemedical and Standard Outpatient Monitoring of Diabetic Foot Ulcers

Software for multistate analysis

Multistate modelling extended by behavioural rules: An application to migration

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