Search PubMed⌕ Search

Biomedical subjects

S G Thompson

Publications and source records attributed to S G Thompson.

At least 37 records · Page 2Linked to original sources
PubMed2001

C-reactive protein, insulin resistance, central obesity, and coronary heart disease risk in Indian Asians from the United Kingdom compared with European whites.

BACKGROUND: Indian Asians in the United Kingdom have increased coronary heart disease (CHD) mortality compared with European whites, but the causes are not well understood. Increased circulating concentrations of C-reactive protein (CRP) are an independent risk factor for CHD. Therefore, we investigated this marker of inflammation in healthy UK Indian Asian and European white men. Methods and Results-- We measured serum CRP concentrations and conventional CHD risk factors in 1025 healthy male subjects (518 Indian Asians and 507 European whites) aged 35 to 60 years who were recruited at random from general practitioner lists. The geometric mean CRP concentration was 17% higher (95% confidence interval, 3% to 33%) in Indian Asians compared with European whites. CRP values were strongly associated with conventional CHD risk factors, measures of obesity, and metabolic disturbances associated with insulin resistance in both racial groups. The difference in CRP concentrations between Indian Asians and European whites remained after adjustment for conventional CHD risk factors but was eliminated by an adjustment for central obesity and insulin resistance score in Asians. On the basis of these results, we estimate that the processes underlying elevated CRP and/or increased CRP production itself are associated with an approximately 14% increase in population CHD risk among Indian Asians compared with European whites. CONCLUSIONS: CRP concentrations are higher in healthy Indian Asians than in European whites and are accounted for by greater central obesity and insulin resistance in Indian Asians. Our results suggest that inflammation or other mechanisms underlying elevated CRP values may contribute to the increased CHD risk among Indian Asians.

Adult↗
PubMed2001

Analysis of cluster randomized trials with repeated cross-sectional binary measurements.

Analytical techniques appropriate for cluster randomized trials that utilize a repeated cross-sectional design have not been extensively evaluated. This paper compares methods that can be used to evaluate the impact of an intervention on dichotomous outcomes. The methods are applied to data from a study on the implementation of Cochrane review evidence, in which 25 hospital obstetric units were randomized. Assessments were made for 30 pregnancies in each obstetric unit at baseline, and for 30 separate pregnancies at follow-up. The principal issues addressed are how best to take clustering into account and to allow for baseline imbalance. We compare cluster level analyses, the clustered Woolf method, marginal models based on generalized estimating equations, multilevel models, and methods based on random effects meta-analysis. Analyses which ignored the baseline assessments showed no effect of the intervention. There was substantial baseline imbalance, however, so that analyses taking into account the baseline were necessary. Yet, while analyses of change from baseline showed evidence of an effect of intervention, adjusting for baseline using analysis of covariance did not. Analysis of covariance required the use of cluster level rather than individual level responses, since different pregnancies were evaluated at baseline and follow-up. Also, when analysing change from baseline, we show it is important to allow for variation in the effect of secular trend between clusters in a multilevel model, or use robust variance estimates in a marginal model, for otherwise confidence intervals for the effect of intervention will be too narrow. We conclude however that analyses of change from baseline can be misleading since they are affected too much by baseline results, and that analysis of covariance approaches are preferable. To prevent difficulties in interpreting the results from repeated cross-sectional cluster trial designs, one should either attempt to achieve baseline balance by careful stratification of the clusters prior to randomization, or have sufficiently large samples for precise estimation of the effects of imbalance.

Cluster Analysis↗
PubMed2001

Bayesian methods of analysis for cluster randomized trials with binary outcome data.

We explore the potential of Bayesian hierarchical modelling for the analysis of cluster randomized trials with binary outcome data, and apply the methods to a trial randomized by general practice. An approximate relationship is derived between the intracluster correlation coefficient (ICC) and the between-cluster variance used in a hierarchical logistic regression model. By constructing an informative prior for the ICC on the basis of available information, we are thus able implicitly to specify an informative prior for the between-cluster variance. The approach also provides us with a credible interval for the ICC for binary outcome data. Several approaches to constructing informative priors from empirical ICC values are described. We investigate the sensitivity of results to the prior specified and find that the estimate of intervention effect changes very little in this data set, while its interval estimate is more sensitive. The Bayesian approach allows us to assume distributions other than normality for the random effects used to model the clustering. This enables us to gain insight into the robustness of our parameter estimates to the classical normality assumption. In a model with a more complex variance structure, Bayesian methods can provide credible intervals for a difference between two variance components, in order for example to investigate whether the effect of intervention varies across clusters. We compare our results with those obtained from classical estimation, discuss the relative merits of the Bayesian framework, and conclude that the flexibility of the Bayesian approach offers some substantial advantages, although selection of prior distributions is not straightforward.

Bayes Theorem↗
PubMed2001

Are we ignoring the importance of ankle pressures in patients with abdominal aortic aneurysm?

BACKGROUND AND PURPOSE: the ankle/brachial pressure index (ABPI) has been shown to be a reliable marker of cardiovascular risk in population studies. We investigated whether the ABPI was a useful prognostic index for patients with abdominal aortic aneurysm. METHODS: patients entered into the U.K. Small Aneurysm Trial and Study had their ABPI measured in both legs at baseline (mean ABPI reported) and were followed up until 30 June 1998, with information about cause of death being obtained from the Office of National Statistics. This study focussed on cardiovascular and all-cause mortality. RESULTS: a total of 1827 men and 478 women, mean age 69 years, median aneurysm diameter 4.4 cm, were followed up for a median of 5.7 years. A total of 829 deaths were reported (rate 8.1 per 100 person-years), 546 (66%) from cardiovascular causes. The all-cause mortality risk increased as the ABPI decreased, hazard ratio 1.25 per 0.2 unit decrease in ABPI (95% CI 1.17 to 1.34, p<0.001). For patients in the lowest tertile group (ABPI <0.87) there were 11.6 deaths per 100 person-years. This increased risk persisted after adjustment for age, sex, evidence of ischaemia on resting ECG and initial aneurysm diameter, adjusted hazard ratio 1.17 per 0.2 unit decrease in ABPI (95% CI 1.07 to 1.28, p<0.001). CONCLUSION: the ankle/brachial pressure index is an important prognostic indicator for patients with abdominal aortic aneurysm. Patients with an ABPI below 0.87 (limit of lowest tertile) have the highest mortality risk and best clinical practice demands that attention is focussed on active treatment to minimise their cardiovascular risk factors.

Aged↗
PubMed2001

Aortic aneurysm diameter and risk of cardiovascular mortality.

After successful surgical repair of an abdominal aortic aneurysm, patients have for many years an increased risk of death from cardiovascular causes. We have tested the hypothesis that for patients with abdominal aortic aneurysms, the risk of nonaneurysm cardiovascular mortality before and after surgery increased with aneurysm diameter. Records of aneurysm repair or rupture and mortality were available from 2305 patients entered into the UK Small Aneurysm Trial and Study. Two hundred fifty-nine deaths occurred before aneurysm repair or rupture (mean follow-up 1.7 years), and 325 occurred after surgical repair (mean follow-up 3.6 years). The risk of nonaneurysm-related mortality and cardiovascular death before and after surgery increased with aneurysm diameter at baseline, even after adjustment for other known risk factors. The adjusted hazard ratios for cardiovascular mortality, per standard deviation (0.8-cm) increase in aneurysm diameter, were 1.34 (95% CI 1.01 to 1.79) and 1.31 (95% CI 1.06 to 1.63) in the periods before aneurysm repair or rupture and after aneurysm repair, respectively. The significant association between aortic diameter and cardiovascular mortality, excluding aneurysm-related deaths, suggests that aneurysm diameter is an independent marker of cardiovascular disease risk.

Aged↗
PubMed2001

Multilevel models for meta-analysis, and their application to absolute risk differences.

Meta-analysis can be considered a multilevel statistical problem, since information within studies is combined in the presence of potential heterogeneity between studies. Here a general multilevel model framework is developed for meta-analysis to combine either summary data or individual patient outcome data from each study, and to include either study or individual level covariates that might explain heterogeneity. Classical and Bayesian approaches to estimation are contrasted. These methods are applied to a meta-analysis of trials of thrombolytic therapy after myocardial infarction. Subgroups within the trials were available, categorized by the time delay until treatment, so that a three-level random effects model that includes time delay as a covariate is proposed. In addition it was desired to represent the treatment effect as an absolute risk reduction, rather than the conventional odds ratio. We show how this can be achieved within a Bayesian analysis, while still recognizing the binary nature of the original outcome data.

Bayes Theorem↗
PubMed2000

A multilevel model framework for meta-analysis of clinical trials with binary outcomes.

In this paper we explore the potential of multilevel models for meta-analysis of trials with binary outcomes for both summary data, such as log-odds ratios, and individual patient data. Conventional fixed effect and random effects models are put into a multilevel model framework, which provides maximum likelihood or restricted maximum likelihood estimation. To exemplify the methods, we use the results from 22 trials to prevent respiratory tract infections; we also make comparisons with a second example data set comprising fewer trials. Within summary data methods, confidence intervals for the overall treatment effect and for the between-trial variance may be derived from likelihood based methods or a parametric bootstrap as well as from Wald methods; the bootstrap intervals are preferred because they relax the assumptions required by the other two methods. When modelling individual patient data, a bias corrected bootstrap may be used to provide unbiased estimation and correctly located confidence intervals; this method is particularly valuable for the between-trial variance. The trial effects may be modelled as either fixed or random within individual data models, and we discuss the corresponding assumptions and implications. If random trial effects are used, the covariance between these and the random treatment effects should be included; the resulting model is equivalent to a bivariate approach to meta-analysis. Having implemented these techniques, the flexibility of multilevel modelling may be exploited in facilitating extensions to standard meta-analysis methods.

Clinical Trials as Topic↗
PubMed2000

Analysis of cost data in randomized trials: an application of the non-parametric bootstrap.

Health economic evaluations are now more commonly being included in pragmatic randomized trials. However a variety of methods are being used for the presentation and analysis of the resulting cost data, and in many cases the approaches taken are inappropriate. In order to inform health care policy decisions, analysis needs to focus on arithmetic mean costs, since these will reflect the total cost of treating all patients with the disease. Thus, despite the often highly skewed distribution of cost data, standard non-parametric methods or use of normalizing transformations are not appropriate. Although standard parametric methods of comparing arithmetic means may be robust to non-normality for some data sets, this is not guaranteed. While the randomization test can be used to overcome assumptions of normality, its use for comparing means is still restricted by the need for similarly shaped distributions in the two groups. In this paper we show how the non-parametric bootstrap provides a more flexible alternative for comparing arithmetic mean costs between randomized groups, avoiding the assumptions which limit other methods. Details of several bootstrap methods for hypothesis tests and confidence intervals are described and applied to cost data from two randomized trials. The preferred bootstrap approaches are the bootstrap-t or variance stabilized bootstrap-t and the bias corrected and accelerated percentile methods. We conclude that such bootstrap techniques can be recommended either as a check on the robustness of standard parametric methods, or to provide the primary statistical analysis when making inferences about arithmetic means for moderately sized samples of highly skewed data such as costs.

Cognitive Behavioral Therapy↗
PubMed2000

A joint analysis of quality of life and survival using a random effect selection model.

In studies of patients with advanced disease, longitudinal quality of life data may be truncated as a result of early death. Since survival and quality of life are likely to be related, modelling of the quality of life response needs to account for these different survival patterns. Here we discuss the application of a random effect selection model, in the form of a trivariate Normal model for the joint analysis of quality of life response (intercept and slope) and log survival time. Under certain assumptions this can give an unbiased description of the quality of life responses and valid inferences comparing treatment strategies in a clinical trial. It also indicates how quality of life and survival are related, by estimating the expected quality of life responses conditional on different survival times. Model parameters can be estimated using a restricted iterative generalized least-squares (RIGLS) procedure within standard software, extended to handle censoring of survival outcome using an EM algorithm. The model is applied to a physical quality of life score and survival data from a trial of treatment for patients with colorectal hepatic metastases. Survival differed between the treatment groups, and quality of life repsonse tended to be worse, both in initial level and change over time, for those patients who died earlier. The parameter estimates obtained agreed well with those from analysing the extended trial data set with complete survival information. Residual diagnostics used to check the necessary underlying assumptions of the model are exemplified. We conclude that such models can give an informative description of longitudinal responses when these are truncated by differential survival patterns.

Algorithms↗
PubMed2000

Analysing the relationship between treatment effect and underlying risk in meta-analysis: comparison and development of approaches.

Three approaches for estimating the relationship between treatment effect and underlying risk in a meta-analysis of clinical trials have recently been published. The aim of each is to overcome the bias inherent in conventional regressions of treatment effect on control group risk, which arises from the measurement error in the observed control group risks in different trials. Here we describe these published approaches, and compare them with respect to their underlying models and methods of implementation. The underlying model for one of them is shown to be seriously flawed, while the other two are both statistically more appropriate than the conventional approaches, and differ from each other in only two assumptions. Both may be implemented using the Gibbs sampling algorithm in BUGS, and are exemplified here using a meta-analysis of mortality and bleeding data in trials of sclerotherapy for patients with cirrhosis. One approach is developed further; for the illustrative example considered, it is shown to be robust to different choices of prior distributions for the model parameters, and to the assumption of a linear relationship on a log-odds scale. It can also be used to estimate the level of underlying risk (and its standard error) at which the treatment effect crosses from benefit to harm, and other trial-level covariates may be included in the model as confounders. The BUGS code is provided in an Appendix, to enable applied researchers to perform the various analyses described.

Algorithms↗
PubMed2000

Analysis of a cluster randomized trial with binary outcome data using a multi-level model.

The use of multi-level logistic regression models was explored for the analysis of data from a cluster randomized trial investigating whether a training programme for general practitioners' reception staff could improve women's attendance at breast screening. Twenty-six general practices were randomized with women nested within them, requiring a two-level model which allowed for between-practice variability. Comparisons were made with fixed effect (FE) and random effects (RE) cluster summary statistic methods, ordinary logistic regression and a marginal model based on generalized estimating equations with robust variance estimates. An FE summary statistic method and ordinary logistic regression considerably understated the variance of the intervention effect, thus overstating its statistical significance. The marginal model produced a higher statistical significance for the intervention effect compared to that obtained from the RE summary statistic method and the multi-level model. Because there was only a moderate number of practices and these had unbalanced cluster sizes, reliable asymptotic properties for the robust standard errors used in the marginal model may not have been achieved. While the RE summary statistic method cannot handle multiple covariates easily, marginal and multi-level models can do so. In contrast to multi-level models however, marginal models do not provide direct estimates of variance components, but treat these as nuisance parameters. Estimates of the variance components were of particular interest in this example. Additionally, parametric bootstrap methods within the multi-level model framework provide confidence intervals for these variance components, as well as a confidence interval for the effect of intervention which allows for the imprecision in the estimated variance components. The assumption of normality of the random effects can be checked, and the models extended to investigate multiple sources of variability.

Breast Neoplasms↗
PubMed2000

Plasma homocysteine concentrations and risk of coronary heart disease in UK Indian Asian and European men.

BACKGROUND: Reasons for the increase in mortality due to coronary heart disease (CHD) in UK Indian Asians are not well understood. In this study, we tested the hypotheses that elevated plasma homocysteine concentrations are a risk factor for CHD in Indian Asians, and explain part of their increased CHD risk, compared with Europeans. METHODS: We undertook two parallel case-control studies, one in Europeans and one in Indian Asians. We recruited 551 male cases (294 European, 257 Indian Asian) and 1025 healthy male controls (507 European, 518 Indian Asian). Fasting and post-methionine load homocysteine, vitamin B12 and folate concentrations, and conventional CHD risk factors were measured. FINDINGS: Fasting homocysteine concentrations were 8% higher (95% CI 3-14) in cases compared with controls, in both ethnic groups. The odds ratio of CHD for a 5 micromol/L increment in fasting plasma homocysteine was 1.3 (1.1-1.6) in Europeans and 1.2 (1.0-1.4) in Indian Asians. The association between fasting plasma homocysteine and CHD was independent of conventional CHD risk factors in both ethnic groups. Post-load homocysteine concentrations were not significantly different in cases compared with controls. Among the controls, fasting homocysteine concentrations were 6% (2-10) higher in Indian Asians than in Europeans. From the results we estimate that elevated homocysteine may contribute to twice as many CHD deaths in Indian Asians, compared with Europeans. The differences in homocysteine concentrations between the two ethnic groups were explained by lower vitamin B12 and folate levels in Asians. INTERPRETATION: Plasma homocysteine is a novel and independent risk factor for CHD in Indian Asians, and may contribute to their increased CHD risk. Raised homocysteine concentrations in Indian Asians may be related to their reduced vitamin B12 and folate levels, implying that the increased CHD risk in this group may be reduced by dietary vitamin supplementation.

Case-Control Studies↗
PubMed2000

Vibrio gastroenteritis in the US Gulf of Mexico region: the role of raw oysters.

We examined clinical and epidemiological features of 575 laboratory-confirmed cases of vibrio gastroenteritis in Alabama, Florida, Louisiana, and Texas from 1988 to 1997 (the US Gulf of Mexico Regional Vibrio Surveillance System). Illnesses occurred year round, with peaks in spring and autumn. Illnesses lasted a median of 7 days and included fever in half of patients and bloody stools in 25% of patients with relevant information. Seventy-two percent of patients reported no underlying illnesses. In the week before onset, 236 (53%) of 445 patients for whom data were available ate raw oysters, generally at a restaurant or bar. Educational efforts should address the risk of vibrio gastroenteritis for raw oyster consumers, including healthy individuals. Further studies should examine environmental conditions affecting vibrio counts on seafood and processing technologies to enhance the safety of raw oysters.

Adult↗
PubMed2000

Risk factors for postoperative death following elective surgical repair of abdominal aortic aneurysm: results from the UK Small Aneurysm Trial. On behalf of the UK Small Aneurysm Trial participants.

BACKGROUND: In regional and population studies, the mortality rate within 30 days of elective surgical repair of abdominal aortic aneurysm is approximately 8 per cent. Identification of preoperative factors associated with this mortality risk is important for informing surgical policy and may suggest suitable preoperative interventions. METHODS: In the UK Small Aneurysm Trial, 820 patients aged 60-80 years underwent elective open surgical repair of an abdominal aortic aneurysm. The relationship between 30-day mortality rate and 13 prespecified potential prognostic factors was investigated. The value of a published clinical prediction rule was also evaluated. RESULTS: The postoperative mortality rate was 5.6 per cent overall (46 deaths in 820 patients). Postoperative mortality risk was significantly associated with older age (P = 0. 03), higher serum creatinine level (P = 0.002) and lower forced expiratory volume in 1 s (FEV1) (P = 0.003) in univariate analyses. Evidence of a relationship between age and postoperative death was weakened (P = 0.08) after adjustment for creatinine level and FEV1. The predicted postoperative mortality risk ranged from 2.7 per cent in younger patients with below average creatinine levels and above average FEV1, to 7.8 per cent in older patients with above average creatinine levels and below average FEV1. The published clinical prediction rule did not validate well on these data; observed risk did not correlate with predicted risk except for a small group of high-risk patients. CONCLUSION: Poor preoperative lung and renal function was strongly associated with postoperative death. Age was less important once these two important prognostic factors had been taken into account. The potential for preoperative improvement in lung and renal function to reduce postoperative mortality rates should be addressed in future studies.

Aged↗
PubMed2000

A national Survey of Acute Myocardial Infarction and Ischaemia (SAMII) in the U.K.: characteristics, management and in-hospital outcome in women compared to men in patients under 70 years.

AIMS: To assess the clinical characteristics, management and outcome of women compared to men with acute myocardial infarction or ischaemia. DESIGN: A prospective clinical survey was made in a random sample of 94 District General Hospitals in the U.K. 1064 patients, <70 years of age, comprising six consecutive females and six consecutive males from each hospital, diagnosed on admission as acute coronary syndromes (myocardial infarction or myocardial ischaemia) were studied. Outcome measures included: admission and final diagnosis, time to delivery of care, inpatient management, complications and clinical outcome. RESULTS: Five hundred and three women and 561 men were admitted with a diagnosis of acute myocardial infarction or myocardial ischaemia. Women were older, waited longer between seeking and receiving advice, and much less likely to have infarction than men. After adjustment for age, diagnosis and past medical history there were no gender differences in initial and subsequent hospital management, in complications (recurrent ischaemia, arrhythmias, temporary pacing, heart failure), any routine procedure or outcome. Of all patients, 3.4% died in a District General Hospital, 12.2% were transferred to Specialist Cardiac Centres and 84.4% discharged home. Prophylactic medication on discharge was similar for men and women. CONCLUSION: After adjustment for age, diagnosis and past medical history, although women waited longer between seeking and receiving medical advice, in hospital their assessment, management, complications, outcome and follow-up arrangements were the same as for men. In hospital, management and outcomes were mainly influenced by age, diagnosis (infarction or ischaemia), a past history of coronary disease, but not by gender. This large, nationally representative, survey has found no evidence of important gender difference in the hospital management of acute ischaemic syndromes.

Adult↗
PubMed2000

Survival of patients with a new diagnosis of heart failure: a population based study.

OBJECTIVE: To describe the survival of a population based cohort of patients with incident (new) heart failure and the clinical features associated with mortality. DESIGN: A population based observational study. SETTING: Population of 151 000 served by 82 general practitioners in west London. PATIENTS: New cases of heart failure were identified by daily surveillance of acute hospital admissions to the local district general hospital, and by general practitioner referral of all suspected new cases of heart failure to a rapid access clinic. INTERVENTIONS: All patients with suspected heart failure underwent clinical assessment, and chest radiography, ECG, and echocardiogram were performed. A panel of three cardiologists reviewed all the data and determined whether the definition of heart failure had been met. Patients were subsequently managed by the general practitioner in consultation with the local cardiologist or admitting physician. MAIN OUTCOME MEASURES: Death, overall and from cardiovascular causes. RESULTS: There were 90 deaths (83 cardiovascular deaths) in the cohort of 220 patients with incident heart failure over a median follow up of 16 months. Survival was 81% at one month, 75% at three months, 70% at six months, 62% at 12 months, and 57% at 18 months. Lower systolic blood pressure, higher serum creatinine concentration, and greater extent of crackles on auscultation of the lungs were independently predictive of cardiovascular mortality (all p < 0.001). CONCLUSIONS: In patients with new heart failure, mortality is high in the first few weeks after diagnosis. Simple clinical features can identify a group of patients at especially high risk of death.

Adult↗