Biomedical subjects

Keith R Abrams

Publications and source records attributed to Keith R Abrams.

At least 19 recordsLinked to original sources

PubMed2007

Evidence-based sample size calculations based upon updated meta-analysis.

Meta-analyses of randomized controlled trials (RCTs) provide the highest level of evidence regarding the effectiveness of interventions and as such underpin much of evidence-based medicine. Despite this, meta-analyses are usually produced as observational by-products of the existing literature, with no formal consideration of future meta-analyses when individual trials are being designed. Basing the sample size of a new trial on the results of an updated meta-analysis which will include it, may sometimes make more sense than powering the trial in isolation. A framework for sample size calculation for a future RCT based on the results of a meta-analysis of the existing evidence is presented. Both fixed and random effect approaches are explored through an example. Bayesian Markov Chain Monte Carlo simulation modelling is used for the random effects model since it has computational advantages over the classical approach. Several criteria on which to base inference and hence power are considered. The prior expectation of the power is averaged over the prior distribution for the unknown true treatment effect. An extension to the framework allowing for consideration of the design for a series of new trials is also presented. Results suggest that power can be highly dependent on the statistical model used to meta-analyse the data and even very large studies may have little impact on a meta-analysis when there is considerable between study heterogeneity. This raises issues regarding the appropriateness of the use of random effect models when designing and drawing inferences across a series of studies.

Anti-Bacterial Agents↗

PubMed2007

Bivariate random-effects meta-analysis and the estimation of between-study correlation.

BACKGROUND: When multiple endpoints are of interest in evidence synthesis, a multivariate meta-analysis can jointly synthesise those endpoints and utilise their correlation. A multivariate random-effects meta-analysis must incorporate and estimate the between-study correlation (rhoB). METHODS: In this paper we assess maximum likelihood estimation of a general normal model and a generalised model for bivariate random-effects meta-analysis (BRMA). We consider two applied examples, one involving a diagnostic marker and the other a surrogate outcome. These motivate a simulation study where estimation properties from BRMA are compared with those from two separate univariate random-effects meta-analyses (URMAs), the traditional approach. RESULTS: The normal BRMA model estimates rhoB as -1 in both applied examples. Analytically we show this is due to the maximum likelihood estimator sensibly truncating the between-study covariance matrix on the boundary of its parameter space. Our simulations reveal this commonly occurs when the number of studies is small or the within-study variation is relatively large; it also causes upwardly biased between-study variance estimates, which are inflated to compensate for the restriction on rhoB. Importantly, this does not induce any systematic bias in the pooled estimates and produces conservative standard errors and mean-square errors. Furthermore, the normal BRMA is preferable to two normal URMAs; the mean-square error and standard error of pooled estimates is generally smaller in the BRMA, especially given data missing at random. For meta-analysis of proportions we then show that a generalised BRMA model is better still. This correctly uses a binomial rather than normal distribution, and produces better estimates than the normal BRMA and also two generalised URMAs; however the model may sometimes not converge due to difficulties estimating rhoB. CONCLUSION: A BRMA model offers numerous advantages over separate univariate synthesises; this paper highlights some of these benefits in both a normal and generalised modelling framework, and examines the estimation of between-study correlation to aid practitioners.

CD4 Lymphocyte Count↗

PubMed2007

Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis.

This article focuses on the modelling and prediction of costs due to disease accrued over time, to inform the planning of future services and budgets. It is well documented that the modelling of cost data is often problematic due to the distribution of such data; for example, strongly right skewed with a significant percentage of zero-cost observations. An additional problem associated with modelling costs over time is that cost observations measured on the same individual at different time points will usually be correlated. In this study we compare the performance of four different multilevel/hierarchical models (which allow for both the within-subject and between-subject variability) for analysing healthcare costs in a cohort of individuals with early inflammatory polyarthritis (IP) who were followed-up annually over a 5-year time period from 1990/1991. The hierarchical models fitted included linear regression models and two-part models with log-transformed costs, and two-part model with gamma regression and a log link. The cohort was split into a learning sample, to fit the different models, and a test sample to assess the predictive ability of these models. To obtain predicted costs on the original cost scale (rather than the log-cost scale) two different retransformation factors were applied. All analyses were carried out using Bayesian Markov chain Monte Carlo (MCMC) simulation methods.

Adult↗

PubMed2006

Effect of hypobaric hypoxia, simulating conditions during long-haul air travel, on coagulation, fibrinolysis, platelet function, and endothelial activation.

CONTEXT: The link between long-haul air travel and venous thromboembolism is the subject of continuing debate. It remains unclear whether the reduced cabin pressure and oxygen tension in the airplane cabin create an increased risk compared with seated immobility at ground level. OBJECTIVE: To determine whether hypobaric hypoxia, which may be encountered during air travel, activates hemostasis. DESIGN, SETTING, AND PARTICIPANTS: A single-blind, crossover study, performed in a hypobaric chamber, to assess the effect of an 8-hour seated exposure to hypobaric hypoxia on hemostasis in 73 healthy volunteers, which was conducted in the United Kingdom from September 2003 to November 2005. Participants were screened for factor V Leiden G1691A and prothrombin G20210A mutation and were excluded if they tested positive. Blood was drawn before and after exposure to assess activation of hemostasis. INTERVENTIONS: Individuals were exposed alternately (> or =1 week apart) to hypobaric hypoxia, similar to the conditions of reduced cabin pressure during commercial air travel (equivalent to atmospheric pressure at an altitude of 2438 m), and normobaric normoxia (control condition; equivalent to atmospheric conditions at ground level, circa 70 m above sea level). MAIN OUTCOME MEASURES: Comparative changes in markers of coagulation activation, fibrinolysis, platelet activation, and endothelial cell activation. RESULTS: Changes were observed in some hemostatic markers during the normobaric exposure, attributed to prolonged sitting and circadian variation. However, there were no significant differences between the changes in the hypobaric and the normobaric exposures. For example, the median difference in change between the hypobaric and normobaric exposure was 0 ng/mL for thrombin-antithrombin complex (95% CI, -0.30 to 0.30 ng/mL); -0.02 [corrected] nmol/L for prothrombin fragment 1 + 2 (95% CI, -0.03 to 0.01 nmol/L); 1.38 ng/mL for D-dimer (95% CI, -3.63 to 9.72 ng/mL); and -2.00% for endogenous thrombin potential (95% CI, -4.00% to 1.00%). CONCLUSION: Our findings do not support the hypothesis that hypobaric hypoxia, of the degree that might be encountered during long-haul air travel, is associated with prothrombotic alterations in the hemostatic system in healthy individuals at low risk of venous thromboembolism.

Adolescent↗

PubMed2006

Cerebral blood flow threshold of ischemic penumbra and infarct core in acute ischemic stroke: a systematic review.

BACKGROUND AND PURPOSE: Cerebral blood flow (CBF) reduction below critical thresholds discriminates between irreversible infarct core, penumbra, and benign oligemia (penumbra that recovers spontaneously). Thresholds are based on animal studies, and their diagnostic accuracy in humans has never been established. The purpose of this study was to assess the evidence available on CBF thresholds for infarct core and penumbra in adult stroke patients. METHODS: Electronic database searching using Medline, Embase and the Cochrane Library, crosschecking of references, and contact with experts and authors of primary studies was used. Studies on adult stroke patients were included if they compared CBF measurements with a diagnostic gold standard (follow-up brain CT/MRI), and reported CBF thresholds. Two reviewers independently extracted the data and assessed study quality. RESULTS: A meta-analysis could not be carried out because of insufficient data. The optimal reported CBF thresholds varied widely, from 14.1 to 35.0 and from 4.8 to 8.4 mL/100 g per minute for penumbra and infarct core, respectively. CONCLUSIONS: The use of CBF thresholds in commercial software for imaging methods cannot be recommended without further evaluation.

Adult↗

PubMed2006

Comparison of two methods to detect publication bias in meta-analysis.

CONTEXT: Egger's regression test is often used to help detect publication bias in meta-analyses. However, the performance of this test and the usual funnel plot have been challenged particularly when the summary estimate is the natural log of the odds ratio (lnOR). OBJECTIVE: To compare the performance of Egger's regression test with a regression test based on sample size (a modification of Macaskill's test) with lnOR as the summary estimate. DESIGN: Simulation of meta-analyses under a number of scenarios in the presence and absence of publication bias and between-study heterogeneity. MAIN OUTCOME MEASURES: Type I error rates (the proportion of false-positive results) for each regression test and their power to detect publication bias when it is present (the proportion of true-positive results). RESULTS: Type I error rates for Egger's regression test are higher than those for the alternative regression test. The alternative regression test has the appropriate type I error rates regardless of the size of the underlying OR, the number of primary studies in the meta-analysis, and the level of between-study heterogeneity. The alternative regression test has comparable power to Egger's regression test to detect publication bias under conditions of low between-study heterogeneity. CONCLUSION: Because of appropriate type I error rates and reduction in the correlation between the lnOR and its variance, the alternative regression test can be used in place of Egger's regression test when the summary estimates are lnORs.

Meta-Analysis as Topic↗

PubMed2006

A systematic review of systematic reviews and meta-analyses of animal experiments with guidelines for reporting.

To maximize the findings of animal experiments to inform likely health effects in humans, a thorough review and evaluation of the animal evidence is required. Systematic reviews and, where appropriate, meta-analyses have great potential in facilitating such an evaluation, making efficient use of the animal evidence while minimizing possible sources of bias. The extent to which systematic review and meta-analysis methods have been applied to evaluate animal experiments to inform human health is unknown. Using systematic review methods, we examine the extent and quality of systematic reviews and meta-analyses of in vivo animal experiments carried out to inform human health. We identified 103 articles meeting the inclusion criteria: 57 reported a systematic review, 29 a systematic review and a meta-analysis, and 17 reported a meta-analysis only. The use of these methods to evaluate animal evidence has increased over time. Although the reporting of systematic reviews is of adequate quality, the reporting of meta-analyses is poor. The inadequate reporting of meta-analyses observed here leads to questions on whether the most appropriate methods were used to maximize the use of the animal evidence to inform policy or decision-making. We recommend that guidelines proposed here be used to help improve the reporting of systematic reviews and meta-analyses of animal experiments. Further consideration of the use and methodological quality and reporting of such studies is needed.

Animal Experimentation↗

PubMed2006

A randomized controlled trial of the effectiveness of pelvic floor therapies for urodynamic stress and mixed incontinence.

OBJECTIVES: To assess the efficacy and cost-effectiveness of pelvic floor muscle therapies (PFMT) in women aged > or = 40 years with urodynamic stress incontinence (USI) and mixed UI. PATIENTS AND METHODS: In a three-arm randomized controlled trial in Leicestershire and Rutland UK, 238 community-dwelling women aged > or = 40 years with USI in whom previous primary behavioural intervention had failed were randomized to receive either intensive PFMT (79), vaginal cone therapy (80) or to continue with primary behavioural intervention (79) for 3 months. The main outcome measure was the frequency of primary UI episodes, and secondary measures were pad-test urine loss, patient perception of problem, assessment of PF function, voiding frequency, and pad usage. Validated scales for urinary dysfunction, and impact on quality of life and satisfaction were collected at an independent interview. RESULTS: All three groups had a moderate reduction in UI episodes after intervention but there was no statistically significant difference among the groups. There were marginal improvements in voiding frequency for all groups, with no statistically significant difference among them. CONCLUSIONS: In women who have already had simple behavioural therapies (including advice on PFM exercises) for urinary dysfunction, the continuation of these behavioural therapies can lead to further improvement. The addition of vaginal cone therapy or intensive PFMT does not seem to contribute to further improvement. The improvement in pelvic floor function was significantly greater in the PFMT arm than in the control arm although this did not translate into changes in urinary symptoms.

Adult↗

PubMed2005

Bayesian implementation of a genetic model-free approach to the meta-analysis of genetic association studies.

A genetic model-free method for the meta-analysis of genetic association studies is described that estimates the mode of inheritance from the data rather than assuming that it is known. For a bi-allelic polymorphism, with G as risk allele and g as wild-type, the genetic model depends on the ratio of the two log odds ratios, lambda = log OR(Gg)/log OR(GG), where OR(GG) compares GG with gg and OR(Gg) compares Gg with gg. Modelling log OR(GG) as a random effect creates a hierarchical model that can be implemented within a Bayesian framework. In Bayesian modelling, vague prior distributions have to be specified for all unknown parameters when no external information is available. When the data are sparse even supposedly vague prior distributions may have an influence on the posterior estimates. We investigate the impact of different vague prior distributions for the between-study standard deviation of log OR(GG) and for lambda, by considering three published meta-analyses and associated simulations. Our results show that depending on the characteristics of the meta-analysis the results may indeed be sensitive to the choice of vague prior distribution for either parameter. Genetic association studies usually use a case-control design that should be analysed by the corresponding retrospective likelihood. However, under some circumstances the prospective likelihood has been shown to produce identical results and it is usually preferred for its simplicity. In our meta-analyses the two likelihoods give very similar results.

Bayes Theorem↗

PubMed2005

Meta-analysis of heterogeneously reported trials assessing change from baseline.

This paper considers the quantitative synthesis of published comparative study results when the outcome measures used in the individual studies and the way in which they are reported varies between studies. Whilst the former difficulty may be overcome, at least to a limited extent, by the use of standardized effects, the latter is often more problematic. Two potential solutions to this problem are; sensitivity analyses and a fully Bayesian approach, in which pertinent external information is included. Both approaches are illustrated using the results of two systematic reviews and meta-analyses which consider the difference in mean change in systolic blood pressure and the difference in physical functioning between an intervention and control group. The two examples illustrate that by adopting a fully Bayesian approach, as opposed to undertaking sensitivity analyses assuming fixed values for unknown parameters, the overall intervention effect can be estimated with greater uncertainty, but that assessing the sensitivity of results to choice of prior distributions in such analyses is crucial.

Bayes Theorem↗

PubMed2005

Rescue angioplasty after failed thrombolytic therapy for acute myocardial infarction.

BACKGROUND: The appropriate treatment for patients in whom reperfusion fails to occur after thrombolytic therapy for acute myocardial infarction remains unclear. There are few data comparing emergency percutaneous coronary intervention (rescue PCI) with conservative care in such patients, and none comparing rescue PCI with repeated thrombolysis. METHODS: We conducted a multicenter trial in the United Kingdom involving 427 patients with ST-segment elevation myocardial infarction in whom reperfusion failed to occur (less than 50 percent ST-segment resolution) within 90 minutes after thrombolytic treatment. The patients were randomly assigned to repeated thrombolysis (142 patients), conservative treatment (141 patients), or rescue PCI (144 patients). The primary end point was a composite of death, reinfarction, stroke, or severe heart failure within six months. RESULTS: The rate of event-free survival among patients treated with rescue PCI was 84.6 percent, as compared with 70.1 percent among those receiving conservative therapy and 68.7 percent among those undergoing repeated thrombolysis (overall P=0.004). The adjusted hazard ratio for the occurrence of the primary end point for repeated thrombolysis versus conservative therapy was 1.09 (95 percent confidence interval, 0.71 to 1.67; P=0.69), as compared with adjusted hazard ratios of 0.43 (95 percent confidence interval, 0.26 to 0.72; P=0.001) for rescue PCI versus repeated thrombolysis and 0.47 (95 percent confidence interval, 0.28 to 0.79; P=0.004) for rescue PCI versus conservative therapy. There were no significant differences in mortality from all causes. Nonfatal bleeding, mostly at the sheath-insertion site, was more common with rescue PCI. At six months, 86.2 percent of the rescue-PCI group were free from revascularization, as compared with 77.6 percent of the conservative-therapy group and 74.4 percent of the repeated-thrombolysis group (overall P=0.05). CONCLUSIONS: Event-free survival after failed thrombolytic therapy was significantly higher with rescue PCI than with repeated thrombolysis or conservative treatment. Rescue PCI should be considered for patients in whom reperfusion fails to occur after thrombolytic therapy.

Adult↗

PubMed2005

The choice of a genetic model in the meta-analysis of molecular association studies.

BACKGROUND: To evaluate gene-disease associations, genetic epidemiologists collect information on the disease risk in subjects with different genotypes (for a bi-allelic polymorphism: gg, Gg, GG). Meta-analyses of such studies usually reduce the problem to a single comparison, either by performing two separate pairwise comparisons or by assuming a specific underlying genetic model (recessive, co-dominant, dominant). A biological justification for the choice of the genetic model is seldom available. METHODS: We present a genetic model-free approach, which does not assume that the underlying genetic model is known in advance but still makes use of the information available on all genotypes. The approach uses OR(GG), the odds ratio between the homozygous genotypes, to capture the magnitude of the genetic effect, and lambda, the heterozygote log odds ratio as a proportion of the homozygote log odds ratio, to capture the genetic mode of inheritance. The analysis assumes that the same unknown genetic model, i.e. the same lambda, applies in all studies, and this is investigated graphically. The approach is illustrated using five examples of published meta-analyses. RESULTS: Analyses based on specific genetic models can produce misleading estimates of the odds ratios when an inappropriate model is assumed. The genetic model-free approach gives appropriately wider confidence intervals than genetic model-based analyses because it allows for uncertainty about the genetic model. In terms of assessment of model fit, it performs at least as well as a bivariate pairwise analysis in our examples. CONCLUSIONS: The genetic model-free approach offers a unified approach that efficiently estimates the genetic effect and the underlying genetic model. A bivariate pairwise analysis should be used if the assumption of a common genetic model across studies is in doubt.

Cardiovascular Diseases↗

PubMed2005

How vague is vague? A simulation study of the impact of the use of vague prior distributions in MCMC using WinBUGS.

There has been a recent growth in the use of Bayesian methods in medical research. The main reasons for this are the development of computer intensive simulation based methods such as Markov chain Monte Carlo (MCMC), increases in computing power and the introduction of powerful software such as WinBUGS. This has enabled increasingly complex models to be fitted. The ability to fit these complex models has led to MCMC methods being used as a convenient tool by frequentists, who may have no desire to be fully Bayesian. Often researchers want 'the data to dominate' when there is no prior information and thus attempt to use vague prior distributions. However, with small amounts of data the use of vague priors can be problematic. The results are potentially sensitive to the choice of prior distribution. In general there are fewer problems with location parameters. The main problem is with scale parameters. With scale parameters, not only does one have to decide the distributional form of the prior distribution, but also whether to put the prior distribution on the variance, standard deviation or precision. We have conducted a simulation study comparing the effects of 13 different prior distributions for the scale parameter on simulated random effects meta-analysis data. We varied the number of studies (5, 10 and 30) and compared three different between-study variances to give nine different simulation scenarios. One thousand data sets were generated for each scenario and each data set was analysed using the 13 different prior distributions. The frequentist properties of bias and coverage were investigated for the between-study variance and the effect size. The choice of prior distribution was crucial when there were just five studies. There was a large variation in the estimates of the between-study variance for the 13 different prior distributions. With a large number of studies the choice of prior distribution was less important. The effect size estimated was not biased, but the precision with which it was estimated varied with the choice of prior distribution leading to varying coverage intervals and, potentially, to different statistical inferences. Again there was less of a problem with a larger number of studies. There is a particular problem if the between-study variance is close to the boundary at zero, as MCMC results tend to produce upwardly biased estimates of the between-study variance, particularly if inferences are based on the posterior mean. The choice of 'vague' prior distribution can lead to a marked variation in results, particularly in small studies. Sensitivity to the choice of prior distribution should always be assessed.

Anti-Bacterial Agents↗

PubMed2005

Meta-analysis of genetic studies using Mendelian randomization--a multivariate approach.

In traditional epidemiological studies the association between phenotype (risk factor) and disease is often biased by confounding and reverse causation. As a person's genotype is assigned by a seemingly random process, genes are potentially useful instrumental variables for adjusting for such bias. This type of adjustment combines information on the genotype-disease association and the genotype-phenotype association to estimate the phenotype-disease association and has become known as Mendelian randomization. The information on genotype-disease and genotype-phenotype may well come from a meta-analysis. In such a synthesis, a multivariate approach needs to be used whenever some studies provide evidence on both the genotype-phenotype and genotype-disease associations. This paper presents two multivariate meta-analytical models, which differ in their treatment of the heterogeneities (between-study variances). Heterogeneities on the genotype-phenotype and genotype-disease associations may be highly correlated, but a multivariate model that parameterizes the heterogeneity directly is difficult to fit because that correlation is poorly estimated. We advocate an alternative model that treats the heterogeneities on genotype-phenotype and phenotype-disease as being independent. This model fits readily and implicitly defines the correlation between the heterogeneities on genotype-phenotype and genotype-disease. We show how either maximum likelihood or a Bayesian approach with vague prior distributions can be used to fit the alternative model.

Coronary Disease↗

PubMed2005

A Bayesian approach to evaluating net clinical benefit allowed for parameter uncertainty.

BACKGROUND AND OBJECTIVE: Although randomized controlled trials (RCTs) are conducted to establish whether novel interventions work on average in the patient population, there is a growing desire to move to a more individualized approach to evaluation. The potential benefits and harms of a treatment policy may differ between individuals. If these benefits and harms are not evaluated distinctly, and in a quantitative framework, transparency can be lost in the decision-making process. METHODS: Glasziou and Irwig have outlined the concept of net clinical treatment benefit for identifying the patients for whom the potential benefits of treatment outweigh the possible side effects. This study revisits the decision whether to use warfarin to treat atrial fibrillation. In this analysis, RCT and various sorts of observational data are synthesized. RESULTS: This reanalysis brings into question the conclusions of the original analysis on who would benefit from warfarin; however, caution is advised, due to limitations in the quality of life data available. CONCLUSION: A fully realized Bayesian implementation of the model is presented. This provides a framework for including uncertainty related to the estimation of all model parameters, and permits both direct probability statements and credible intervals for specific patient groups to be expressed.

Anticoagulants↗

PubMed2005

Normative values of Doppler velocimetry of five major fetal arteries as determined by color power angiography.

OBJECTIVE: To produce normograms of Doppler indices of major fetal arteries and their ratios relative to the ascending aorta in a cohort of appropriately grown for gestational age fetuses. METHODS: Prospective longitudinal study of 70 women with appropriately grown for gestational age fetuses between 24 and 38 weeks' gestation attending the Fetal Growth Clinic of a large UK teaching hospital. Doppler velocimetry of the middle cerebral (MCA), umbilical (UmA) and renal arteries (RA) and the ascending (AAO) and descending (DAO) aortas were studied using color power angiography. Ratios of the Doppler indices [pulsatility index (PI), resistance index (RI), systolic/diastolic (S/D) ratio] were then calculated using the ascending aorta as the reference numerator for the other four vessels to produce normograms. Regression analysis was performed to determined the significance, if any, of the changes in these ratios with gestation. RESULTS: The normograms of the various Doppler indices were similar for the middle cerebral artery, ascending and descending aortas. There was an initial rise to a peak between 30 and 32 weeks and then a gradual return to values at 38 weeks similar to those at 24 weeks' gestation. In the renal artery, the indices showed very little variation with gestation. However, there was a gradual fall in the indices with gestation in the umbilical artery. The ratios of the various indices relative to that of the ascending aorta demonstrated an increase with gestation. The changes with gestation were statistically significant for the ratios of the indices from the ascending aorta to those of the middle cerebral, renal and umbilical arteries but not for those of the descending aorta. CONCLUSIONS: The vascular resistance in the five fetal arteries decreased towards the end of pregnancy and the ratios of their indices relative to those of the ascending aorta decreased from 24 to 38 weeks' gestation. Early subtle changes in circulation in compromised fetuses may be identified early from deviations in these normograms.

Angiography↗

PubMed2005

Clinical and cost-effectiveness of a new nurse-led continence service: a randomised controlled trial.

BACKGROUND: Continence services in the UK have developed at different rates within differing care models, resulting in scattered and inconsistent services. Consequently, questions remain about the most cost-effective method of delivering these services. AIM: To evaluate the impact of a new service led by a continence nurse practitioner compared with existing primary/secondary care provision for people with urinary incontinence and storage symptoms. DESIGN OF STUDY: Randomised controlled trial with a 3- and 6-month follow-up in men and women (n = 3746) aged 40 years and over living in private households (intervention [n = 2958]; control [n = 788]). SETTING: Leicestershire and Rutland, UK. METHOD: The continence nurse practitioner intervention comprised a continence service provided by specially trained nurses delivering evidence-based interventions using predetermined care pathways. They delivered an 8-week primary intervention package that included advice on diet and fluids; bladder training; pelvic floor awareness and lifestyle advice. The standard care arm comprised access to existing primary care including GP and continence advisory services in the area. Outcome measures were recorded at 3 and 6 months post-randomisation. RESULTS: The percentage of individuals who improved (with at least one symptom alleviated) at 3 months was 59% in the intervention group compared with 48% in the standard care group (difference of 11%, 95% CI = 7 to 16; P<0.001) The percentage of people reporting no symptoms or 'cured' was 25% in the intervention group and 15% in the standard care group (difference of 10%, 95% CI = 6 to 13, P = 0.001). At 6 months the difference was maintained. There was a significant difference in impact scores between the two groups at 3 and 6 months. CONCLUSIONS: The continence nurse practitioner-led intervention reduced the symptoms of incontinence, frequency, urgency and nocturia at 3 and 6 months; impact was reduced; and satisfaction with the new service was high.

Adult↗

PubMed2004

The analysis of peak expiratory flow data using a three-level hierarchical model.

Peak expiratory flow (PEF) is a measure commonly used in clinical practice and research for respiratory diseases such as asthma. In research, PEF is usually recorded in a diary for a 2-week period with two or more measurements per day. Interest may lie in whether certain groups of individuals tend to have higher or lower PEF. In addition the variability of PEF may be of interest as, for example, asthmatics tend to have more variable airways. In this paper we develop a three-level hierarchical model that can simultaneously model the mean level and variability of PEF. The variability is broken down into three components, between-subject variability, between-day within-subject variability, and within-day within-subject variability. The latter two components are of specific clinical interest. We fit both classical and Bayesian models. The Bayesian models have the advantage of taking the uncertainty in the variance component estimates into account when estimating the standard errors of the fixed effects. In addition, the Bayesian models provide an intuitive and simple way to investigate the within-subject variance components.

Asthma↗