Biomedical subjects

Paul C Lambert

Publications and source records attributed to Paul C Lambert.

18 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

Estimating and modeling the cure fraction in population-based cancer survival analysis.

In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and is a useful measure to monitor trends in survival of curable disease. There are 2 main types of cure fraction model, the mixture cure fraction model and the non-mixture cure fraction model, with most previous work concentrating on the mixture cure fraction model. In this paper, we extend the parametric non-mixture cure fraction model to incorporate background mortality, thus providing estimates of the cure fraction in population-based cancer studies. We compare the estimates of relative survival and the cure fraction between the 2 types of model and also investigate the importance of modeling the ancillary parameters in the selected parametric distribution for both types of model.

Aged↗

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

Additive and multiplicative covariate regression models for relative survival incorporating fractional polynomials for time-dependent effects.

Relative survival is used to estimate patient survival excluding causes of death not related to the disease of interest. Rather than using cause of death information from death certificates, which is often poorly recorded, relative survival compares the observed survival to that expected in a matched group from the general population. Models for relative survival can be expressed on the hazard (mortality) rate scale as the sum of two components where the total mortality rate is the sum of the underlying baseline mortality rate and the excess mortality rate due to the disease of interest. Previous models for relative survival have assumed that covariate effects act multiplicatively and have thus provided relative effects of differences between groups using excess mortality rate ratios. In this paper we consider (i) the use of an additive covariate model, which provides estimates of the absolute difference in the excess mortality rate; and (ii) the use of fractional polynomials in relative survival models for the baseline excess mortality rate and time-dependent effects. The approaches are illustrated using data on 115 331 female breast cancer patients diagnosed between 1 January 1986 and 31 December 1990. The use of additive covariate relative survival models can be useful in situations when the excess mortality rate is zero or slightly less than zero and can provide useful information from a public health perspective. The use of fractional polynomials has advantages over the usual piecewise estimation by providing smooth estimates of the baseline excess mortality rate and time-dependent effects for both the multiplicative and additive covariate models. All models presented in this paper can be estimated within a generalized linear models framework and thus can be implemented using standard software.

Breast Neoplasms↗

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

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

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↗

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↗

PubMed2004

What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of sparse data.

OBJECTIVES: To compare the performance of different meta-analysis methods for pooling odds ratios when applied to sparse event data with emphasis on the use of continuity corrections. BACKGROUND: Meta-analysis of side effects from RCTs or risk factors for rare diseases in epidemiological studies frequently requires the synthesis of data with sparse event rates. Combining such data can be problematic when zero events exist in one or both arms of a study as continuity corrections are often needed, but, these can influence results and conclusions. METHODS: A simulation study was undertaken comparing several meta-analysis methods for combining odds ratios (using various classical and Bayesian methods of estimation) on sparse event data. Where required, the routine use of a constant and two alternative continuity corrections; one based on a function of the reciprocal of the opposite group arm size; and the other an empirical estimate of the pooled effect size from the remaining studies in the meta-analysis, were also compared. A number of meta-analysis scenarios were simulated and replicated 1000 times, varying the ratio of the study arm sizes. RESULTS: Mantel-Haenszel summary estimates using the alternative continuity correction factors gave the least biased results for all group size imbalances. Logistic regression was virtually unbiased for all scenarios and gave good coverage properties. The Peto method provided unbiased results for balanced treatment groups but bias increased with the ratio of the study arm sizes. The Bayesian fixed effect model provided good coverage for all group size imbalances. The two alternative continuity corrections outperformed the constant correction factor in nearly all situations. The inverse variance method performed consistently badly, irrespective of the continuity correction used. CONCLUSIONS: Many routinely used summary methods provide widely ranging estimates when applied to sparse data with high imbalance between the size of the studies' arms. A sensitivity analysis using several methods and continuity correction factors is advocated for routine practice.

Bayes Theorem↗

PubMed2004

A systematic review of molecular and biological tumor markers in neuroblastoma.

PURPOSE: The aim of this study was to conduct a systematic review, and where possible meta-analyses, of molecular and biological tumor markers described in neuroblastoma, and to establish an evidence-based perspective on their clinical value for the screening, diagnosis, prognosis, and monitoring of patients. EXPERIMENTAL DESIGN: A well-defined, reproducible search strategy was used to identify the relevant literature from 1966 to February 2000. RESULTS: A total of 428 papers studying the use of 195 different tumor markers in neuroblastoma were identified. Small sample sizes, poor statistical reporting, large heterogeneity across studies (e.g., in cutoff levels), and publication bias limited meta-analysis to the area of prognosis only; MYCN, chromosome 1p, DNA index, vanillylmandelic acid:homovanillic acid ratio, CD44, Trk-A, neuron-specific enolase, lactate dehydrogenase, ferritin, and multidrug resistance were all identified as potentially important prognostic tools. CONCLUSIONS: This systematic review forms a knowledge base of the tumor markers studied thus far in neuroblastoma, and has identified some of the most important prognostic markers, which should be considered in future research and treatment strategies. Importantly, the review has also highlighted some general problems across primary tumor marker studies, in particular poor and heterogeneous reporting. These need to be addressed to allow better clinical interpretation and enable more appropriate evidence-based reviews in the future. In particular, collaboration of cancer research groups is needed to enable bigger sample sizes, standardize methods of analysis and reporting, and facilitate the pooling of individual patient data.

Biomarkers, Tumor↗

PubMed2004

Sensitivity analyses allowed more appropriate and reliable meta-analysis conclusions for multiple outcomes when missing data was present.

OBJECTIVE: A major problem for meta-analysis of multiple outcomes is the unavailability of some estimates from published and unpublished studies. Dissemination bias, in how and what outcomes are reported or published, may be causing this incompleteness. This article illustrates these problems and presents possible sensitivity analyses to allow the most reliable conclusions. STUDY DESIGN AND SETTING: In a systematic review of prognostic marker MYC-N in neuroblastoma, meta-analysis for overall survival (OS) and disease-free survival (DFS) was of interest. Only 17 published studies enabled extraction of both outcome estimates, 25 enabled only DFS, 39 enabled only OS, and 70 enabled neither outcome. Unidentified unpublished studies may also exist. We assessed the robustness of the pooled estimates to the problem of missing information. Because OS and DFS estimates seemed to be related, we used the known outcome estimates to predict estimates known to be missing, and combined this approach with existing methods for assessing dissemination bias. RESULTS: The results of the sensitivity analyses suggested that the original meta-analysis results were likely to be an overestimate of the true OS and DFS effect-sizes but strengthened the belief that MYC-N is a potentially important prognostic marker in neuroblastoma. CONCLUSION: Sensitivity analyses in meta-analysis allow more appropriate and reliable conclusions when problems such as unavailable estimates and dissemination bias are present.

Biomarkers, Tumor↗

PubMed2004

Providing more up-to-date estimates of patient survival: a comparison of standard survival analysis with period analysis using life-table methods and proportional hazards models.

OBJECTIVE: Standard survival methods can yield out-of-date estimates of long-term survival. Period analysis, based on life-table methodology, provides more up-to-date survival estimates by exploring survival during a restricted recent period of interest. It excludes the short-term survival of patients recruited at the start of the study. We use statistical models to further develop the method of period analysis, providing more up-to-date estimates of survival and the ability to explore differences in survival by covariates and adjust for case mix. METHODS: We use cancer registry data for colorectal cancer in Leicestershire, UK, to illustrate the use of Cox proportional hazards (CPH) models to estimate period and standard survival. We compare these estimates with those obtained using life-table methodology. RESULTS: Period estimates were slightly higher than the standard estimates as they reflect recent improvements in short-term survival. The results for period analysis using the life-table approach and using CPH models were similar. However, CPH models allowed further investigation of other risk factors and the ability to control for potential confounding variables. CONCLUSION: Using period survival estimates, more up-to-date information is available to clinicians and others with an interest in monitoring survival. Period CHP models offer all the advantages of statistical modeling, and are straightforward to fit in standard statistical packages.

Colorectal Neoplasms↗

PubMed2003

Efficacy of a short course of parent-initiated oral prednisolone for viral wheeze in children aged 1-5 years: randomised controlled trial.

BACKGROUND: Episodic wheeze triggered by viral colds is common in children aged between 1 and 5 years (preschool viral wheeze). Most affected children are asymptomatic by age 6 years. Persistence of wheeze is associated with above-average systemic eosinophil priming. Use of parental-initiated oral prednisolone is recommended at the first sign of preschool viral wheeze. However, evidence for this treatment strategy is conflicting. We therefore aimed to assess the efficacy of a short course of oral prednisolone for preschool viral wheeze, with stratification for systemic eosinophil priming. METHODS: Children aged 1-5 years admitted to hospital with viral wheeze were allocated to either a high-primed or low-primed stratum according to amounts of serum eosinophil cationic protein and eosinophil protein X, and randomised to parent-initiated prednisolone (20 mg one daily for 5 days) or placebo for the next episode. The primary outcomes were the 7-day mean daytime and night-time respiratory symptom scores, which were analysed by mean differences between treatment groups. FINDINGS: 108 children were randomised to placebo and 109 to prednisolone. Outcome data were available for 120 (78%) of 153 children who had a further episode of viral wheeze, of whom 51 received prednisolone and 69 placebo. Mean daytime (difference in means -0.01 [-0.22 to 0.20]) and night-time (0.10 [-0.12 to 0.32]) respiratory symptom scores and need for hospital admission did not differ between treatment groups. Within the high-primed (n=59) and low-primed (n=61) strata there was no difference in primary outcome between treatment groups. INTERPRETATION: There is no clear benefit of a short course of parent-initiated oral prednisolone for viral wheeze in children aged 1-5 years even in those with above-average eosinophil priming.

Administration, Oral↗

PubMed2003

Effect of NHS walk-in centre on local primary healthcare services: before and after observational study.

OBJECTIVE: To assess the effect of an NHS walk-in centre on local primary and emergency healthcare services. DESIGN: Before and after observational study. SETTING: Loughborough, which had an NHS walk-in centre, and Market Harborough, the control town. PARTICIPANTS: 12 general practices. MAIN OUTCOME MEASURES: Mean daily rate of emergency general practitioner consultations, mean number of half days to the sixth bookable routine appointment, and attendance rates at out of hours services, minor injuries units, and accident and emergency departments. RESULTS: The change between the before and after study periods was not significantly different in the two towns for daily rate of emergency general practice consultations (mean difference -0.02/1000 population, 95% confidence interval -0.75 to 0.71), the time to the sixth bookable routine appointment (-0.24 half-days, -1.85 to 1.37), and daily rate of attendances at out of hours services (0.07/1000 population, -0.06 to 0.19). However, attendance at the local minor injuries unit was significantly higher in Loughborough than Market Harborough (rate ratio 1.22, 1.12 to 1.33). Non-ambulance attendances at accident and emergency departments fell less in Loughborough than Market Harborough (rate ratio 1.17, 1.03 to 1.33). CONCLUSIONS: The NHS walk-in centre did not greatly affect the workload of local general practitioners. However, the workload of the local minor injuries unit increased significantly, probably because it was in the same building as the walk-in centre.

After-Hours Care↗

PubMed2003

Urinary microalbumin/creatinine ratios: reference range in uncomplicated pregnancy.

During uncomplicated pregnancy, the development of proteinuria is accepted as a poor prognostic sign and is associated with increasing maternal and perinatal mortality and morbidity. Physiological proteinuria increases with increasing gestation and one of its largest constituents is albumin. The reference range for the (micro)albumin/creatinine ratio (ACR) has not been described for normal pregnancy. This prospective cross-sectional study describes the gestation-specific 95% reference ranges for urinary microalbumin concentration, creatinine concentration and ACR in uncomplicated pregnancy. There is a significant increase ( P =0.016) in the ACR in the third trimester. The mean difference is 0.091 mg of albumin/mmol of creatinine (95% confidence interval, 0.014-0.168). Our results describe the first well-defined gestation-specific 95% reference range for a point-of-care measurement of the ACR. These data are essential if such testing is to be employed in antenatal care.

Adolescent↗

PubMed2003

Randomised controlled trial of the effectiveness of feedback in improving test ordering in general practice.

OBJECTIVE: To assess the effectiveness of feedback on the number of pathology tests ordered by general practices. DESIGN: Randomised controlled trial. SETTING: General practices in two primary care groups in Leicestershire, UK. SUBJECTS: 58 general practitioners in 17 practices received guidelines then feedback at 3-month intervals for 1 year about the numbers of thyroid function, rheumatoid factor tests and urine cultures they ordered, and 38 general practitioners in 16 practices received guidelines then feedback about lipid and plasma viscosity tests. MAIN OUTCOME MEASURES: Numbers of tests requested per thousand patients registered with each practice. RESULTS; There were no changes in the numbers of tests per thousand requested in either of the study groups for any of the tests. CONCLUSIONS: Feedback did not have an influence on test ordering by general practitioners in this study. More intensive strategies may be required to change the use of laboratory tests.

Diagnostic Tests, Routine↗