Biomedical subjects

John W Stevens

Publications and source records attributed to John W Stevens.

5 recordsLinked to original sources

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

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↗

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↗

PubMedDate not supplied

Bayesian decision procedures for binary and continuous bivariate dose-escalation studies.

In this paper, Bayesian decision procedures are developed for dose-escalation studies based on binary measures of undesirable events and continuous measures of therapeutic benefit. The methods generalize earlier approaches where undesirable events and therapeutic benefit are both binary. A logistic regression model is used to model the binary responses, while a linear regression model is used to model the continuous responses. Prior distributions for the unknown model parameters are suggested. A gain function is discussed and an optional safety constraint is included.

Bayes Theorem↗