Calculating total health service utilisation and costs from routinely collected electronic health records using the example of patients with irritable bowel syndrome before and after their first gastroenterology appointment

INTRODUCTION: Health economic models are increasingly important in funding decisions but most are based on data, which may therefore not represent the general population. We sought to establish the potential of real-world data available within the Clinical Practice Research Datalink (CPRD) and linked Hospital Episode Statistics (HES) to determine comprehensive healthcare utilisation and costs as input variables for economic modelling. METHODS: A cohort of patients with irritable bowel syndrome (IBS) who first saw a gastroenterologist in 2008 or 2009, and with 3 years of data before and after their appointment, was created in the CPRD. Primary care, outpatient, inpatient, prescription and colonoscopy data were extracted from the linked CPRD and HES. The appropriate cost to the NHS was attached to each event. Total and stratified annual healthcare utilisation rates and costs were calculated before and after the gastroenterology appointment with distribution parameters. Absolute differences were calculated with 95 % confidence intervals. RESULTS: Total annual healthcare costs over 3 years increase by £935 (95 % CI £928–941) following a gastroenterology appointment for IBS. We derived utilisation and cost data with parameter distributions stratified by demographics and time. Women, older patients, smokers and patients with greater comorbidity utilised more healthcare resources, which generated higher costs. CONCLUSIONS: These linked datasets provide comprehensive primary and secondary care data for large numbers of patients, which allows stratification of outcomes. It is possible to derive input parameters appropriate for economic models and their distributions directly from the population of interest

Simulating Survival Data Using the simsurv R Package

The simsurv R package allows users to simulate survival (i.e., time-to-event) data from standard parametric distributions (exponential, Weibull, and Gompertz), two-component mixture distributions, or a user-defined hazard function. Baseline covariates can be included under a proportional hazards assumption. Clustered event times, for example individuals within a family, are also easily accommodated. Time-dependent effects (i.e., nonproportional hazards) can be included by interacting covariates with linear time or a user-defined function of time. Under a user-defined hazard function, event times can be generated for a variety of complex models such as flexible (spline-based) baseline hazards, models with time-varying covariates, or joint longitudinal-survival models

Joint longitudinal and time-to-event models for multilevel hierarchical data

Joint modelling of longitudinal and time-to-event data has received much attention recently. Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper, we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progression-free survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation