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Biomedical subjects

Simon G Thompson

Publications and source records attributed to Simon G Thompson.

31 records · Page 2Linked to original sources
PubMed2004

Abdominal aortic aneurysm expansion: risk factors and time intervals for surveillance.

BACKGROUND: Intervention to reduce abdominal aortic aneurysm (AAA) expansion and optimization of screening intervals would improve current surveillance programs. The aim of this study was to characterize AAA growth in a national cohort of patients with AAA both overall and by cardiovascular risk factors. METHODS AND RESULTS: In this study, 1743 patients were monitored for changes in AAA diameter by ultrasonography over a mean follow-up of 1.9 years. Mean initial AAA diameter and growth rate were 43 mm (range 28 to 85 mm) and 2.6 mm/year (95% range, -1.0 to 6.1 mm/year), respectively. Baseline diameter was strongly associated with growth, suggesting that AAA growth accelerates as the aneurysm enlarges. AAA growth rate was lower in those with low ankle/brachial pressure index and diabetes but higher for current smokers (all P<0.001). No other factor (including lipids and blood pressure) was associated with AAA growth. Intervals of 36, 24, 12, and 3 months for aneurysms of 35, 40, 45, and 50 mm, respectively, would restrict the probability of breaching the 55-mm limit at rescreening to below 1%. CONCLUSIONS: Annual, or less frequent, surveillance intervals are safe for all AAAs < or =45 mm in diameter. Smoking increases AAA growth, but atherosclerosis plays a minor role.

Aged↗
PubMed2004

Controlling the risk of spurious findings from meta-regression.

Meta-regression has become a commonly used tool for investigating whether study characteristics may explain heterogeneity of results among studies in a systematic review. However, such explorations of heterogeneity are prone to misleading false-positive results. It is unclear how many covariates can reliably be investigated, and how this might depend on the number of studies, the extent of the heterogeneity and the relative weights awarded to the different studies. Our objectives in this paper are two-fold. First, we use simulation to investigate the type I error rate of meta-regression in various situations. Second, we propose a permutation test approach for assessing the true statistical significance of an observed meta-regression finding. Standard meta-regression methods suffer from substantially inflated false-positive rates when heterogeneity is present, when there are few studies and when there are many covariates. These are typical of situations in which meta-regressions are routinely employed. We demonstrate in particular that fixed effect meta-regression is likely to produce seriously misleading results in the presence of heterogeneity. The permutation test appropriately tempers the statistical significance of meta-regression findings. We recommend its use before a statistically significant relationship is claimed from a standard meta-regression analysis.

BCG Vaccine↗
PubMed2004

Allowing for imprecision of the intracluster correlation coefficient in the design of cluster randomized trials.

The sample size required for a cluster randomized trial depends on the magnitude of the intracluster correlation coefficient (ICC). The usual sample size calculation makes no allowance for the fact that the ICC is not known precisely in advance. We develop methods which allow for the uncertainty in a previously observed ICC, using a variety of distributional assumptions. Distributions for the power are derived, reflecting this uncertainty. Further, the observed ICC in a future study will not equal its true value, and we consider the impact of this on power. We implement calculations within a Bayesian simulation approach, and provide one simplification that can be performed using simple simulation within spreadsheet software. In our examples, recognizing the uncertainty in a previous ICC estimate decreases expected power, especially when the power calculated naively from the ICC estimate is high. To protect against the possibility of low power, sample sizes may need to be very substantially increased. Recognizing the variability in the future observed ICC has little effect if prior uncertainty has already been taken into account. We show how our method can be extended to the case in which multiple prior ICC estimates are available. The methods presented in this paper can be used by applied researchers to protect against loss of power, or to choose a design which reduces the impact of uncertainty in the ICC.

Bayes Theorem↗
PubMed2004

Bayesian methods for analysis of binary outcome data in cluster randomized trials on the absolute risk scale.

A Bayesian hierarchical modelling approach to the analysis of cluster randomized trials has advantages in terms of allowing for full parameter uncertainty, flexible modelling of covariates and variance structure, and use of prior information. Previously, such modelling of binary outcome data required use of a log-odds ratio scale for the treatment effect estimate and an approximation linking the intracluster correlation (ICC) to the between-cluster variance on a log-odds scale. In this paper we develop this method to allow estimation on the absolute risk scale, which facilitates clinical interpretation of both the treatment effect and the between-cluster variance. We describe a range of models and apply them to data from a trial of different interventions to promote secondary prevention of coronary heart disease in primary care. We demonstrate how these models can be used to incorporate prior data about typical ICCs, to derive a posterior distribution for the number needed to treat, and to consider both cluster and individual level covariates. Using these methods, we can benefit from the advantages of Bayesian modelling of binary outcome data at the same time as providing results on a clinically interpretable scale.

Bayes Theorem↗
PubMed2004

Poorer self assessed health in a prospective study of men with screen detected abdominal aortic aneurysm: a predictor or a consequence of screening outcome?

STUDY OBJECTIVES: To assess the extent to which poorer self assessed health in men in whom an abdominal aortic aneurysm (AAA) is detected at screening is a consequence or a predictor of screening outcome. DESIGN: Prospective study. SETTING: Community based screening. PARTICIPANTS: 23 654 men who attended for AAA screening as part of the UK multicentre aneurysm screening study completed a measure of self assessed health before screening. A total of 1156 had an aneurysm detected. A sub-sample of screened men (571 with an aneurysm and 609 with a normal aorta) also completed the measure of self assessed health six weeks after screening. MAIN RESULTS: Men in whom an aneurysm was detected at screening perceived their health to be poorer before screening than those with a normal aorta. Adjusting for risk factors for AAA made no difference to this RESULT: self assessed health remained a strong predictor of having an aneurysm (odds ratio 1.7 comparing the extreme quartiles of self assessed health, 95% confidence intervals: 1.4 to 2.0). Men with an aneurysm also perceived their health to be poorer after screening had detected their aneurysms, but only to an extent in line with their pre-screening perceptions. CONCLUSIONS: Self assessed health seems to predict having an aortic aneurysm, independently of known risk factors. This emphasises the importance of assessing baseline perceptions of health to prevent erroneously inferring that poorer self assessed health in those who screen positive is a consequence as compared with a predictor of screening outcome.

Aged↗
PubMed2004

Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals.

BACKGROUND: Randomized controlled trials almost always have some individuals with missing outcomes. Inadequate handling of these missing data in the analysis can cause substantial bias in the treatment effect estimates. We examine how missing outcome data are handled in randomized controlled trials in order to assess whether adequate steps have been taken to reduce nonresponse bias and to identify ways to improve procedures for missing data. METHODS: We reviewed all randomized trials published between July and December 2001 in BMJ, JAMA, Lancet and New England Journal of Medicine, excluding trials in which the primary outcome was described as a time-to-event. We focused on trial designs, how missing outcome data were described and the statistical methods used to deal with the missing outcome data, including sensitivity analyses. RESULTS: We identified 71 trials of which 63 (89%) reported having partly missing outcome data: 13 trials had more than 20% of patients with missing outcomes. In 26 trials that measured the outcome at a single time point, 92% performed a complete case analysis and 8% imputed the missing outcomes using baseline values or the worst case value. In 37 trials with repeated measures of the outcome, 46% performed complete case analyses, potentially excluding individuals with some follow-up data, while 14% performed a repeated measures analysis, 19% used the last observation carried forward, 11% imputed with the worst case value and 2% imputed using regression predictions. Thirteen (21%) of trials with missing data reported a sensitivity analysis. CONCLUSIONS: Our review shows that missing outcome data are a common problem in randomized controlled trials, and are often inadequately handled in the statistical analysis in the top tier medical journals. Authors should explicitly state the assumptions underlying the handling of the missing outcomes and justify them through data descriptions and sensitivity analyses.

Bias↗
PubMed2003

Choice of test for comparing two groups, with particular application to skewed outcomes.

We consider a clinical trial where a skewed outcome variable is to be compared between two groups. While comparison of sample means may lack power, we show that power also depends on the nature of the anticipated treatment effect. For any given distribution in the control arm, there is a family of true distributions in the intervention arm for which the most powerful test is a comparison of arithmetic means. Similar results hold for a comparison of geometric means, and approximately for the Wilcoxon rank sum test and a comparison of medians. We discuss how these methods could be used in planning the analysis of a clinical trial in which the intervention effect alters the shape of the distribution. These ideas are illustrated by a trial in community psychiatry, where the primary outcome (days in hospital) was highly skewed but the intervention was mainly expected to reduce the frequency of values in the tail. We show that a comparison of sample means is a reasonable choice in this case despite the skewness.

Bias↗
PubMed2003

A modelling strategy for the analysis of clinical trials with partly missing longitudinal data.

Standard statistical analyses of randomized controlled trials with partially missing outcome data often exclude valuable information from individuals with incomplete follow-up. This may lead to biased estimates of the intervention effect and loss of precision. We consider a randomized trial with a repeatedly measured outcome, in which the value of the outcome on the final occasion is of primary interest. We propose a modelling strategy in which the model is successively extended to include baseline values of the outcome, then intermediate values of the outcome, and finally values of other outcome variables. Likelihood-based estimation of random effects models is used, allowing the incorporation of data from individuals with some missing outcomes. Each estimated intervention effect is free of non-response bias under a different missing-at-random assumption. These assumptions become more plausible as the more complex models are fitted, so we propose using the trend in estimated intervention effects to assess the nature of any non-response bias. The methods are applied to data from a trial comparing intensive case management with standard case management for severely psychotic patients. All models give similar estimates of the intervention effect and we conclude that non-response bias is likely to be small.

Humans↗
PubMed2002

Quantifying heterogeneity in a meta-analysis.

The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity.

Albumins↗
PubMed2002

How should meta-regression analyses be undertaken and interpreted?

Appropriate methods for meta-regression applied to a set of clinical trials, and the limitations and pitfalls in interpretation, are insufficiently recognized. Here we summarize recent research focusing on these issues, and consider three published examples of meta-regression in the light of this work. One principal methodological issue is that meta-regression should be weighted to take account of both within-trial variances of treatment effects and the residual between-trial heterogeneity (that is, heterogeneity not explained by the covariates in the regression). This corresponds to random effects meta-regression. The associations derived from meta-regressions are observational, and have a weaker interpretation than the causal relationships derived from randomized comparisons. This applies particularly when averages of patient characteristics in each trial are used as covariates in the regression. Data dredging is the main pitfall in reaching reliable conclusions from meta-regression. It can only be avoided by prespecification of covariates that will be investigated as potential sources of heterogeneity. However, in practice this is not always easy to achieve. The examples considered in this paper show the tension between the scientific rationale for using meta-regression and the difficult interpretative problems to which such analyses are prone.

Adrenergic beta-Antagonists↗
PubMedDate not supplied

Treating individuals 4: can meta-analysis help target interventions at individuals most likely to benefit?

Meta-analyses of randomised trials aim to summarise the effects of interventions across many patients, and can seem remote from the clinical issue of how individual patients should be treated and which patient groups will benefit the most from treatment. One method that attempts to address this point entails relating the overall effect in every trial to summaries of patient characteristics. This is called meta-regression. The interpretation of such analyses is not straightforward, however, because of a combination of confounding and other biases. Much more useful is to compare the outcomes for patient subgroups within trials and combine these results across trials. Unfortunately this method is rarely possible using published information, so analyses of individual patient data from trials are necessary. Also, although meta-analyses generally summarise an intervention's effect as a relative risk reduction, the groups of patients with the greatest absolute risk reduction have the most to gain.

Age Factors↗
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↗