Biomedical subjects

Anthony O'Hagan

Publications and source records attributed to Anthony O'Hagan.

12 recordsLinked to original sources

PubMed2007

Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA.

Probabilistic sensitivity analysis (PSA) is required to account for uncertainty in cost-effectiveness calculations arising from health economic models. The simplest way to perform PSA in practice is by Monte Carlo methods, which involves running the model many times using randomly sampled values of the model inputs. However, this can be impractical when the economic model takes appreciable amounts of time to run. This situation arises, in particular, for patient-level simulation models (also known as micro-simulation or individual-level simulation models), where a single run of the model simulates the health care of many thousands of individual patients. The large number of patients required in each run to achieve accurate estimation of cost-effectiveness means that only a relatively small number of runs is possible. For this reason, it is often said that PSA is not practical for patient-level models. We develop a way to reduce the computational burden of Monte Carlo PSA for patient-level models, based on the algebra of analysis of variance. Methods are presented to estimate the mean and variance of the model output, with formulae for determining optimal sample sizes. The methods are simple to apply and will typically reduce the computational demand very substantially.

Analysis of Variance↗

PubMed2006

Modelling covariates for the SF-6D standard gamble health state preference data using a nonparametric Bayesian method.

It has long been recognised that respondent characteristics can impact on the values they give to health states. This paper reports on the findings from applying a non-parametric approach to estimate the covariates in a model of SF-6D health state values using Bayesian methods. The data set is the UK SF-6D valuation study, where a sample of 249 states defined by the SF-6D (a derivate of the SF-36) was valued by a sample of the UK general population using standard gamble. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics and that it allows for an impact to vary by health state (whilst ensuring that full health passes through unity). The results suggest an important age effect, with sex, class, education, employment and physical functioning probably having some effect, but the remaining covariates having no discernable effect. Adjusting for covariates in the UK sample made little difference to mean health state values. The paper discusses the implications of these results for policy.

Adult↗

PubMed2006

Modelling SF-6D health state preference data using a nonparametric Bayesian method.

This paper reports on the findings from applying a new approach to modelling health state valuation data. The approach applies a nonparametric model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the nonparametric model and comparing it to the original model estimated using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field.

Bayes Theorem↗

PubMed2006

Using rank data to estimate health state utility models.

In this paper we report the estimation of conditional logistic regression models for the Health Utilities Index Mark 2 and the SF-6D, using ordinal preference data. The results are compared to the conventional regression models estimated from standard gamble data, and to the observed mean standard gamble health state valuations. For both the HUI2 and the SF-6D, the models estimated using ordinal data are broadly comparable to the models estimated on standard gamble data and the predictive performance of these models is close to that of the standard gamble models. Our research indicates that ordinal data have the potential to provide useful insights into community health state preferences. However, important questions remain.

Health Status Indicators↗

PubMed2006

Bayesian and time-independent species sensitivity distributions for risk assessment of chemicals.

Species sensitivity distributions (SSDs) are increasingly used to analyze toxicity data but have been criticized for a lack of consistency in data inputs, lack of relevance to the real environment, and a lack of transparency in implementation. This paper shows how the Bayesian approach addresses concerns arising from frequentist SSD estimation. Bayesian methodologies are used to estimate SSDs and compare results obtained with time-dependent (LC50) and time-independent (predicted no observed effect concentration) endpoints for the insecticide chlorpyrifos. Uncertainty in the estimation of each SSD is obtained either in the form of a pointwise percentile confidence interval computed by bootstrap regression or an associated credible interval. We demonstrate that uncertainty in SSD estimation can be reduced by applying a Bayesian approach that incorporates expert knowledge and that use of Bayesian methodology permits estimation of an SSD that is more robust to variations in data. The results suggest that even with sparse data sets theoretical criticisms of the SSD approach can be overcome.

Animals↗

PubMed2005

Incorporation of uncertainty in health economic modelling studies.

In a recent leading article in PharmacoEconomics, Nuijten described some methods for incorporating uncertainty into health economic models and for utilising the information on uncertainty regarding the cost effectiveness of a therapy in resource allocation decision-making. His proposals are found to suffer from serious flaws in statistical and health economic reasoning.Nuijten's suggestions for incorporating uncertainty: (a) wrongly interpret the p-value as the probability that the null hypothesis is true; (b) represent this probability wrongly by truncating the input distribution; and (c) in the specific example of an antiparkinsonian drug uses a completely inappropriate p-value of 0.05 when the null hypothesis would, in reality, be emphatically disproved by the data.His suggestions regarding minimum important differences in cost effectiveness: (a) introduce areas of indifference that suggest inappropriate reliance on cost minimisation while failing to recognise that decisions should be based on expected costs versus benefits; and (b) offer no guidance on how the probabilities associated with these areas could be used in decision-making. Furthermore, Nuijten's model for Parkinson's disease is over-simplified to the point of providing a bad example of modelling practice, which may mislead the readers of PharmacoEconomics. The rationale for this paper is to ensure that readers do not apply inappropriate analyses as a result of following the proposals contained in Nuijten's paper. In addition to a detailed critique of Nuijten's proposals, we provide brief summaries of the currently accepted best practice in cost-effectiveness decision-making under uncertainty.

Antiparkinson Agents↗

PubMed2004

On estimators of medical costs with censored data.

In the assessment of cost-effectiveness of alternative medical technologies, it is necessary to estimate the mean total cost per patient over the relevant patient population. Where information about costs comes from a clinical trial with censored data, care is needed to estimate mean total costs. We examine the theoretical connections between the two most widely used of a growing range of nonparametric estimators of costs under censoring. By clarifying the relationships between these simple methods we hope to make them more accessible and to facilitate the take-up of more sophisticated techniques. Recommendations are offered regarding the most appropriate of the available methods, but also on the potential for greater efficiency through parametric modelling.

Cost-Benefit Analysis↗

PubMed2003

Modelling the cost effectiveness of interferon beta and glatiramer acetate in the management of multiple sclerosis. Commentary: evaluating disease modifying treatments in multiple sclerosis.

OBJECTIVE: To evaluate the cost effectiveness of four disease modifying treatments (interferon betas and glatiramer acetate) for relapsing remitting and secondary progressive multiple sclerosis in the United Kingdom. DESIGN: Modelling cost effectiveness. SETTING: UK NHS. PARTICIPANTS: Patients with relapsing remitting multiple sclerosis and secondary progressive multiple sclerosis. MAIN OUTCOME MEASURES: Cost per quality adjusted life year gained. RESULTS: The base case cost per quality adjusted life year gained by using any of the four treatments ranged from pound 42,000 (66,469 dollars; 61,630 euro) to pound 98,000 based on efficacy information in the public domain. Uncertainty analysis suggests that the probability of any of these treatments having a cost effectiveness better than pound 20,000 at 20 years is below 20%. The key determinants of cost effectiveness were the time horizon, the progression of patients after stopping treatment, differential discount rates, and the price of the treatments. CONCLUSIONS: Cost effectiveness varied markedly between the interventions. Uncertainty around point estimates was substantial. This uncertainty could be reduced by conducting research on the true magnitude of the effect of these drugs, the progression of patients after stopping treatment, the costs of care, and the quality of life of the patients. Price was the key modifiable determinant of the cost effectiveness of these treatments.

Cost-Benefit Analysis↗

PubMed2003

Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?

This article addresses and challenges some common perceptions in the statistical assessment of costs and cost-effectiveness in health economics. Cost data typically exhibit highly skew distributions. Two techniques whose validity does not depend on any specific form of underlying distribution are the bootstrap and methods based on asymptotic normality of sample means. These methods are generally thought to be appropriate for the analysis of cost data. We argue that, even when these methods are technically valid, they may often lead to inefficient and even misleading inferences. It is important to apply methods that recognise the skewness in cost data. We further demonstrate that it may also be important to incorporate relevant prior information in a Bayesian analysis.

Bayes Theorem↗

PubMed2003

The cost effectiveness of two new antiepileptic therapies in the absence of direct comparative data: a first approximation.

BACKGROUND: A number of new antiepileptic agents have been introduced within a short period of time. Direct comparisons are not available, and information about the balance between costs and effects for these new therapies is lacking. OBJECTIVE: To introduce a first approximation of the cost effectiveness of the new therapeutic agents (topiramate and lamotrigine) for epilepsy that have been assessed in clinical trials against placebo. METHODS: Without head to head comparative data no formal methods are available to assess the relative cost effectiveness of two products; therefore, a Bayesian approach was developed. The approach starts with the 'proportionality assumption' saying that the differences in healthcare expenditure (less the direct cost of therapy) are directly proportional to the differences in effectiveness. Given this assumption, a therapy that is x times as expensive as an alternative therapy has an equivalent cost-effectiveness profile if the acquisition cost is x times as high. Moreover, simple formulas can be derived to calculate the probabilities that a therapy is dominant (more effective and less expensive) and that it is weakly dominant (more effective and a better cost-effectiveness profile). The approach is applied to data from published fixed dosage, parallel-design studies comparing both topiramate and lamotrigine with placebo. RESULTS: Assuming that the 'proportionality assumption' holds for the medical treatment of epilepsy, and disregarding uncertainties, it is estimated that topiramate may be priced more than 2.2 times its current acquisition cost and still be more cost effective than lamotrigine. Taking uncertainties into account, it is estimated that lamotrigine 500 mg/day is dominated by topiramate 200 mg/day with a probability of 0.875 and by topiramate 400 mg/day with a probability of 0.986. CONCLUSIONS: A simple method can be applied to assess the relative cost effectiveness of two therapies in the absence of direct comparative data. Applying this method to compare topiramate and lamotrigine leads to a strong preference for topiramate. However, to be able to draw this conclusion, some heroic assumptions need to be made. As such the method as developed here only reflects a first approximation. It needs to be used with care and is not intended to replace good comparative research.

Anticonvulsants↗

PubMed2002

The probability of cost-effectiveness.

BACKGROUND: The study of cost-effectiveness comparisons between competing medical interventions has led to a variety of proposals for quantifying cost-effectiveness. The differences between the various approaches can be subtle, and one purpose of this article is to clarify some important distinctions. DISCUSSION: We discuss alternative measures in the framework of individual, patient-level, incremental net benefits. In particular we examine the probability of cost-effectiveness for an individual, proposed by Willan. SUMMARY: We argue that this is a useful addition to the range of cost-effectiveness measures, but will be of secondary interest to most decision makers. We also demonstrate that Willan's proposed estimate of this probability is logically flawed.

Bayes Theorem↗

PubMed2002

Incorporation of genuine prior information in cost-effectiveness analysis of clinical trial data.

The Bayesian approach to statistics has been growing rapidly in popularity as an alternative to the frequentist approach in the appraisal of healthcare technologies in clinical trials. Bayesian methods have significant advantages over classical frequentist statistical methods and the presentation of evidence to decision makers. A fundamental feature of a Bayesian analysis is the use of prior information as well as the clinical trial data in the final analysis. However, the incorporation of prior information remains a controversial subject that provides a potential barrier to the acceptance of practical uses of Bayesian methods. The purpose of this paper is to stimulate a debate on the use of prior information in evidence submitted to decision makers. We discuss the advantages of incorporating genuine prior information in cost-effectiveness analyses of clinical trial data and explore mechanisms to safeguard scientific rigor in the use of such prior information.

Bayes Theorem↗