Biomedical subjects

Richard M Nixon

Publications and source records attributed to Richard M Nixon.

6 recordsLinked to original sources

PubMed2006

Addressing the issues that arise in analysing multicentre cost data, with application to a multinational study.

Differences in the mean, spread and skewness of cost data collected from different countries present problems for analysis and interpretation. Here we develop generalised linear multilevel models to estimate the effects of patient and national characteristics on costs. Using gamma distributions and multiplicative effects for patient characteristics fitted the data better than models which assumed normal distributions or estimated additive effects. A multilevel gamma model is employed to allow for heterogeneity in the effects of patient case-mix across centres. Analysis of multinational cost data must recognise differences in mean, spread and skewness across centres, as well as the data's hierarchical structure.

Costs and Cost Analysis↗

PubMed2005

Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations.

BACKGROUND: Overall assessments of cost-effectiveness are now commonplace in informing medical policy decision making. It is often important, however, also to investigate how cost-effectiveness varies between patient subgroups. Yet such analyses are rarely undertaken, because appropriate methods have not been sufficiently developed. METHODS: We propose a coherent set of Bayesian methods to extend cost-effectiveness analyses to adjust for baseline covariates, to investigate differences between subgroups, and to allow for differences between centres in a multicentre study using a hierarchical model. These methods consider costs and effects jointly, and allow for the typically skewed distribution of cost data. The results are presented as inferences on the cost-effectiveness plane, and as cost-effectiveness acceptability curves. RESULTS: In applying these methods to a randomised trial of case management of psychotic patients, we show that overall cost-effectiveness can be affected by ignoring the skewness of cost data, but that it may be difficult to gain substantial precision by adjusting for baseline covariates. While analyses of overall cost-effectiveness can mask important subgroup differences, crude differences between centres may provide an unrealistic indication of the true differences between them. CONCLUSIONS: The methods developed allow a flexible choice for the distributions used for cost data, and have a wide range of applicability--to both randomised trials and observational studies. Experience needs to be gained in applying these methods in practice, and using their results in decision making.

Bayes Theorem↗

PubMed2003

Imputation of a true endpoint from a surrogate: application to a cluster randomized controlled trial with partial information on the true endpoint.

BACKGROUND: The Anglia Menorrhagia Education Study (AMES) is a randomized controlled trial testing the effectiveness of an education package applied to general practices. Binary data are available from two sources; general practitioner reported referrals to hospital, and referrals to hospital determined by independent audit of the general practices. The former may be regarded as a surrogate for the latter, which is regarded as the true endpoint. Data are only available for the true end point on a sub set of the practices, but there are surrogate data for almost all of the audited practices and for most of the remaining practices. METHODS: The aim of this paper was to estimate the treatment effect using data from every practice in the study. Where the true endpoint was not available, it was estimated by three approaches, a regression method, multiple imputation and a full likelihood model. RESULTS: Including the surrogate data in the analysis yielded an estimate of the treatment effect which was more precise than an estimate gained from using the true end point data alone. CONCLUSIONS: The full likelihood method provides a new imputation tool at the disposal of trials with surrogate data.

Bayes Theorem↗

PubMed2003

Bayesian evaluation of breast cancer screening using data from two studies.

The mean sojourn time (the duration of the period during which a cancer is symptom free but potentially detectable by screening) and the screening sensitivity (the probability that a screen applied to a cancer in the preclinical screen detectable period will result in a positive diagnosis) are two important features of a cancer screening programme. Little data from any single study are available on the potential effectiveness of mammographic screening for breast cancer in women with a family history of the disease, despite this being an important public health issue. We develop a method of estimation, from two separate studies, of the two parameters, assuming that transition from no disease to preclinical screen detectable disease, and from preclinical disease to clinical disease, are Poisson processes. Estimation is performed by a Markov chain Monte Carlo algorithm. The method is applied to the synthesis of two studies of mammographic screening in women with a family history of breast cancer, one in Manchester and one in Kopparberg, Sweden.

Adult↗

PubMed2002

A comparison of clinical assessment with ultrasound in the management of secondary postpartum haemorrhage.

OBJECTIVE: To compare the diagnostic accuracy of clinical assessment with transabdominal ultrasound in the management of secondary postpartum haemorrhage (PPH). DESIGN: A prospective cohort study. METHODS: Fifty-three women who presented to a teaching hospital obstetric unit with secondary PPH were studied. Patients were divided into those in whom retained placental tissue was or was not the suspected cause of bleeding. This diagnosis was based on history/examination and transabdominal pelvic ultrasound scan. The definitive diagnosis was made following uterine evacuation or was assumed in women who stopped bleeding without surgical management. Likelihood ratio (LR) was used as an accuracy measure. RESULTS: The positive LR for clinical assessment was 5.5 (95% CI 2.7-12.1) compared with 2.4 (95% CI 1.5-3.7) for ultrasound. The negative LRs were 0.1 (95% CI 0.04-0.5) and 0.1 (95% CI 0.02-0.5) for clinical and ultrasound assessment, respectively. CONCLUSION: Clinical examination and ultrasound scan assessment have limited diagnostic accuracy in secondary PPH.

Anti-Bacterial Agents↗

PubMedDate not supplied

How sensitive are cost-effectiveness analyses to choice of parametric distributions?

BACKGROUND: Cost-effectiveness analyses of clinical trial data are based on assumptions about the distributions of costs and effects. Cost data usually have very skewed distributions and can be difficult to model. The authors investigate whether choice of distribution can make a difference to the conclusions drawn. METHODS: The authors compare 3 distributions for cost data-normal, gamma, and lognormal-using similar parametric models for the cost-effectiveness analyses. Inferences on the cost-effectiveness plane are derived, together with cost-effectiveness acceptability curves. These methods are applied to data from a trial of rapid magnetic resonance imaging (rMRI) investigation in patients with low back pain. RESULTS: The gamma and lognormal distributions fitted the cost data much better than the normal distribution. However, in terms of inferences about cost-effectiveness, it was the normal and gamma distributions that gave similar results. Using the lognormal distribution led to the conclusion that rMRI was cost-effective for a range of willingness-to-pay values where assuming a gamma or normal distribution did not. CONCLUSIONS: Conclusions from cost-effectiveness analyses are sensitive to choice of distribution and, in particular, to how the upper tail of the cost distribution beyond the observed data is modeled. How well a distribution fits the data is an insufficient guide to model choice. A sensitivity analysis is therefore necessary to address uncertainty about choice of distribution.

Cost-Benefit Analysis↗