Multistate models are useful for studying exposures that affect transitions among a set of health states. However, they can be challenging to apply when exposures are time-varying. We develop a ...multistate model and a method of likelihood construction that allows application of the model to data in which interventions or other exposures can be time-varying and an individual may to be exposed to multiple intervention conditions while progressing through states. The model includes cure proportions, reflecting the possibility that some individuals will never leave certain states. We apply the approach to analyze patient vaccination data from a stepped wedge design trial evaluating two interventions to increase uptake of human papillomavirus vaccination. The states are defined as the number of vaccine doses the patient has received. We model state transitions as a semi-Markov process and include cure proportions to account for individuals who will never leave a given state (e.g. never receive their next dose). Multistate models typically quantify intervention effects as hazard ratios contrasting the intensities of transitions between states in intervention versus control conditions. For multistate processes, another clinically meaningful outcome is the change in the percentage of the study population that has achieved a specific state (e.g. completion of all required doses) by a specific point in time due to an intervention. We present a method for quantifying intervention effects in this manner. We apply the model to both simulated and real-world data and also explore some conditions under which such models may give biased results.
is an R package for fully-parametric modeling of survival data. Any parametric time-to-event distribution may be fitted if the user supplies a probability density or hazard function, and ideally also ...their cumulative versions. Standard survival distributions are built in, including the three and four-parameter generalized gamma and F distributions. Any parameter of any distribution can be modeled as a linear or log-linear function of covariates. The package also includes the spline model of Royston and Parmar (2002), in which both baseline survival and covariate effects can be arbitrarily flexible parametric functions of time. The main model-fitting function, flexsurvreg, uses the familiar syntax of survreg from the standard
package (Therneau 2016). Censoring or left-truncation are specified in 'Surv' objects. The models are fitted by maximizing the full log-likelihood, and estimates and confidence intervals for any function of the model parameters can be printed or plotted.
also provides functions for fitting and predicting from fully-parametric multi-state models, and connects with the
package (de Wreede, Fiocco, and Putter 2011). This article explains the methods and design principles of the package, giving several worked examples of its use.
Clinical states of cirrhosis and competing risks D'Amico, Gennaro; Morabito, Alberto; D'Amico, Mario ...
Journal of hepatology,
March 2018, 2018-03-00, 20180301, Letnik:
68, Številka:
3
Journal Article
Recenzirano
Odprti dostop
The clinical course of cirrhosis is mostly determined by the progressive increase of portal hypertension, hyperdynamic circulation, bacterial translocation and activation of systemic inflammation. ...Different disease states, encompassing compensated and decompensated cirrhosis and a late decompensated state, are related to the progression of these mechanisms and may be recognised by haemodynamic or clinical characteristics. While these disease states do not follow a predictable sequence, they correspond to varying mortality risk. Acute-on-chronic liver failure may occur either in decompensated or in compensated cirrhosis and is always associated with a high short-term mortality. The increasing severity of these disease states prompted the concept of clinical states of cirrhosis. A multistate approach has been considered to describe the clinical course of the disease. Such an approach requires the assessment of the probabilities of different outcomes in each state, which compete with each other to occur first and mark the transition towards a different state. This requires the use of competing risks analysis, since the traditional Kaplan-Meier analysis should only be used in two-state settings. Accounting for competing risks also has implications for prognosis and treatment efficacy research. The aim of this review is to summarise relevant clinical states and to show examples of competing risks analysis in multistate models of cirrhosis.
Increased length of stay (LOS) for patients is an important measure of the burden of healthcare-associated infection (HAI).
To estimate the excess LOS attributable to HAI.
This was a one-year ...prospective incidence study of HAI observed in one teaching hospital and one general hospital in NHS Scotland as part of the Evaluation of Cost of Nosocomial Infection (ECONI) study. All adult inpatients with an overnight stay were included. HAI was diagnosed using European Centres for Disease Prevention and Control definitions. A multi-state model was used to account for the time-varying nature of HAI and the competing risks of death and discharge.
The excess LOS attributable to HAI was 7.8 days (95% confidence interval (CI): 5.7–9.9). Median LOS for HAI patients was 30 days and for non-HAI patients was 3 days. Using a simple comparison of duration of hospital stay for HAI cases and non-cases would overestimate the excess LOS by 3.5 times (27 days compared with 7.8 days). The greatest impact on LOS was due to pneumonia (16.3 days; 95% CI: 7.5–25.2), bloodstream infections (11.4 days; 5.8–17.0) and surgical site infection (SSI) (9.8 days; 4.5–15.0). It is estimated that 58,000 bed-days are occupied due to HAI annually.
A reduction of 10% in HAI incidence could make 5800 bed-days available. These could be used to treat 1706 elective patients in Scotland annually and help reduce the number of patients awaiting planned treatment. This study has important implications for investment decisions in infection prevention and control interventions locally, nationally, and internationally.
We survey state-of-the-art approaches to study trajectories in their entirety, adopting a holistic perspective, and discuss their strengths and weaknesses. We begin by considering sequence analysis ...(SA), one of the most established holistic approaches. We discuss the inherent problems arising in SA, particularly in the study of the relationship between trajectories and covariates. We describe some recent developments combining SA and Event History Analysis, and illustrate how weakening the holistic perspective—focusing on sub-trajectories—might result in a more flexible analysis of life courses. We then move to some model-based approaches (included in the broad classes of multistate and of mixture latent Markov models) that further weaken the holistic perspective, assuming that the difficult task of predicting and explaining trajectories can be simplified by focusing on the collection of observed transitions.
Our goal is twofold. On one hand, we aim to provide social scientists with indications for informed methodological choices and to emphasize issues that require consideration for proper application of the described approaches. On the other hand, by identifying relevant and open methodological challenges, we highlight and encourage promising directions for future research.
Cognitively impaired adults without a partner are highly disadvantaged, as partners constitute an important source of caregiving and emotional support. With the application of innovative multistate ...models to the Health and Retirement Study, this paper is the first to estimate joint expectancies of cognitive and partnership status at age 50 by sex, race/ethnicity, and education in the United States. We find that women live a decade longer unpartnered than men. Women are also disadvantaged as they experience three more years as both cognitively impaired and unpartnered than men. Black women live over twice as long as cognitively impaired and unpartnered compared with White women. Lower-educated men and women live around three and five years longer, respectively, as cognitively impaired and unpartnered than more highly educated men and women. This study addresses a novel facet of partnership and cognitive status dynamics and examines their variations by key socio-demographic factors.
The depth of information collected in participants’ daily lives with active (e.g., experience sampling surveys) and passive (e.g., smartphone sensors) ambulatory measurement methods is immense. When ...measuring participants’ behaviors in daily life, the timing of particular events—such as social interactions—is often recorded. These data facilitate the investigation of new types of research questions about the timing of those events, including whether individuals’ affective state is associated with the rate of social interactions (binary event occurrence) and what types of social interactions are likely to occur (multicategory event occurrences, e.g., interactions with friends or family). Although survival analysis methods have been used to analyze time-to-event data in longitudinal settings for several decades, these methods have not yet been incorporated into ambulatory assessment research. This article illustrates how multilevel and multistate survival analysis methods can be used to model the social interaction dynamics captured in intensive longitudinal data, specifically when individuals exhibit particular categories of behavior. We provide an introduction to these models and a tutorial on how the timing and type of social interactions can be modeled using the R statistical programming language. Using event-contingent reports ( N = 150, N events = 64,112) obtained in an ambulatory study of interpersonal interactions, we further exemplify an empirical application case. In sum, this article demonstrates how survival models can advance the understanding of (social interaction) dynamics that unfold in daily life. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Source: journal abstract)
Introduction: Diabetes is a chronic, non-communicable disease characterized by elevated blood glucose levels. The purpose of this study was to jointly model the transition of diabetic patients in a ...series of clinical states and to assess the relationship between each state and different patient characteristics.
Methods: A hospital-based retrospective study was conducted on 524 patients with type II diabetes, aged 18 years or older, who attended their medication between January 1, 2005, and December 31, 2017. Multistate models with different assumptions were considered to explore the effects of different prognostic factors on the transition intensity of type II diabetes mellitus patients.
Results: During a median follow-up time of 7.4 years (Inter-Quartile Range=4.01), 54.8% of diabetic patients developed either microvascular or macrovascular complications, and 10.5% of them experienced both microand macrocomplications, and 16.66% of diabetes patients died. The assumption Markov was assessed by using the likelihood ratio test showed that Markov assumption was not held just for the transition. The transition rate of patients from the macrovascular state to the death state was affected by the residence of the patients (P=0.05) and age at diagnosis (p=0.01). The transition rates of patients with microvascular complications to death were significantly affected by baseline triglyceride level (P<0.001), age at first diagnosis (P=0.01), baseline glucose level (P=0.03, and baseline serum creatinine level (P=0.04).
Conclusion: The semi-Markov model fitted the data well and could be used as a convenient model for the analysis of time to diabetes-related complications or death.
Abstract
Prevalences of overweight and obesity in young children have risen dramatically in the last several decades in most developed countries. Childhood overweight and obesity are known to have ...immediate and long-term health consequences and are now recognized as important public health concerns. We used a Markov 4-state model with states defined by 4 body mass index (BMI; weight (kg)/height (m)2) categories (underweight (<−2 standard deviations (SDs) of BMI z score), normal weight (−2 ≤ SD ≤ 1), overweight (1 < SD ≤ 2), and obese (>2 SDs of BMI z score)) to study the rates of transition to higher or lower BMI categories among children aged 4–10 years. We also used this model to study the relationships between explanatory variables and their transition rates. The participants consisted of 4,887 children from the Generation XXI Birth Cohort Study (Porto, Portugal; 2005–2017) who underwent anthropometric evaluation at age 4 years and in at least 1 of the subsequent follow-up waves (ages 7 and 10 years). Children who were normal weight were more likely to move to higher BMI categories than to lower categories, whereas overweight children had similar rates of transition to the 2 adjacent categories. We evaluated the associations of maternal age and education, type of delivery, sex, and birth weight with childhood overweight and obesity, but we observed statistically significant results only for sex and maternal education with regard to the progressive transitions.
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