Dataset Information

Estimating incremental cost-effectiveness ratios and their confidence intervals with different terminating events for survival time and costs.

ABSTRACT: Cost-effectiveness analysis (CEA) is an important component of the economic evaluation of new treatment options. In many clinical and observational studies of costs, censored data pose challenges to the CEA. We consider a special situation where the terminating events for the survival time and costs are different. Traditional methods for statistical inference offer no means for dealing with censored data in these circumstances. To address this gap, we propose a new method for deriving the confidence interval for the incremental cost-effectiveness ratio. The simulation studies and real data example show that our method performs very well for some practical settings, revealing a great potential for application to actual settings in which terminating events for the survival time and costs differ.

SUBMITTER: Chen S

PROVIDER: S-EPMC3732026 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Estimating incremental cost-effectiveness ratios and their confidence intervals with different terminating events for survival time and costs.

Chen Shuai S Zhao Hongwei H

Biostatistics (Oxford, England) 20130219 3

Cost-effectiveness analysis (CEA) is an important component of the economic evaluation of new treatment options. In many clinical and observational studies of costs, censored data pose challenges to the CEA. We consider a special situation where the terminating events for the survival time and costs are different. Traditional methods for statistical inference offer no means for dealing with censored data in these circumstances. To address this gap, we propose a new method for deriving the confid ...[more]

PMID: 23426525

Similar Datasets

Project description:BackgroundThe dynamics of gene regulation play a crucial role in a cellular control: allowing the cell to express the right proteins to meet changing needs. Some needs, such as correctly anticipating the day-night cycle, require complicated oscillatory features. In the analysis of gene regulatory networks, mathematical models are frequently used to understand how a network's structure enables it to respond appropriately to external inputs. These models typically consist of a set of ordinary differential equations, describing a network of biochemical reactions, and unknown kinetic parameters, chosen such that the model best captures experimental data. However, since a model's parameter values are uncertain, and since dynamic responses to inputs are highly parameter-dependent, it is difficult to assess the confidence associated with these in silico predictions. In particular, models with complex dynamics - such as oscillations - must be fit with computationally expensive global optimization routines, and cannot take advantage of existing measures of identifiability. Despite their difficulty to model mathematically, limit cycle oscillations play a key role in many biological processes, including cell cycling, metabolism, neuron firing, and circadian rhythms.ResultsIn this study, we employ an efficient parameter estimation technique to enable a bootstrap uncertainty analysis for limit cycle models. Since the primary role of systems biology models is the insight they provide on responses to rate perturbations, we extend our uncertainty analysis to include first order sensitivity coefficients. Using a literature model of circadian rhythms, we show how predictive precision is degraded with decreasing sample points and increasing relative error. Additionally, we show how this method can be used for model discrimination by comparing the output identifiability of two candidate model structures to published literature data.ConclusionsOur method permits modellers of oscillatory systems to confidently show that a model's dynamic characteristics follow directly from experimental data and model structure, relaxing assumptions on the particular parameters chosen. Ultimately, this work highlights the importance of continued collection of high-resolution data on gene and protein activity levels, as they allow the development of predictive mathematical models.

Dataset Information

Estimating incremental cost-effectiveness ratios and their confidence intervals with different terminating events for survival time and costs.

Publications

Estimating incremental cost-effectiveness ratios and their confidence intervals with different terminating events for survival time and costs.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets