A novel equivalence probability weighted power prior for using historical control data in an adaptive clinical trial design: A comparison to standard methods.

A standard two-arm randomised controlled trial usually compares an intervention to a control treatment with equal numbers of patients randomised to each treatment arm and only data from within the current trial are used to assess the treatment effect. Historical data are used when designing new trials and have recently been considered for use in the analysis when the required number of patients under a standard trial design cannot be achieved. Incorporating historical control data could lead to more efficient trials, reducing the number of controls required in the current study when the historical and current control data agree. However, when the data are inconsistent, there is potential for biased treatment effect estimates, inflated type I error and reduced power. We introduce two novel approaches for binary data which discount historical data based on the agreement with the current trial controls, an equivalence approach and an approach based on tail area probabilities. An adaptive design is used where the allocation ratio is adapted at the interim analysis, randomising fewer patients to control when there is agreement. The historical data are down-weighted in the analysis using the power prior approach with a fixed power. We compare operating characteristics of the proposed design to historical data methods in the literature: the modified power prior; commensurate prior; and robust mixture prior. The equivalence probability weight approach is intuitive and the operating characteristics can be calculated exactly. Furthermore, the equivalence bounds can be chosen to control the maximum possible inflation in type I error.

Assessing efficacy in important subgroups in confirmatory trials: An example using Bayesian dynamic borrowing.

Assessment of efficacy in important subgroups - such as those defined by sex, age, race and region - in confirmatory trials is typically performed using separate analysis of the specific subgroup. This ignores relevant information from the complementary subgroup. Bayesian dynamic borrowing uses an informative prior based on analysis of the complementary subgroup and a weak prior distribution centred on a mean of zero to construct a robust mixture prior. This combination of priors allows for dynamic borrowing of prior information; the analysis learns how much of the complementary subgroup prior information to borrow based on the consistency between the subgroup of interest and the complementary subgroup. A tipping point analysis can be carried out to identify how much prior weight needs to be placed on the complementary subgroup component of the robust mixture prior to establish efficacy in the subgroup of interest. An attractive feature of the tipping point analysis is that it enables the evidence from the source subgroup, the evidence from the target subgroup, and the combined evidence to be displayed alongside each other. This method is illustrated with an example trial in severe asthma where efficacy in the adolescent subgroup was assessed using a mixture prior combining an informative prior from the adult data in the same trial with a non-informative prior.

A causal modelling framework for reference-based imputation and tipping point analysis in clinical trials with quantitative outcome.

We consider estimation in a randomised placebo-controlled or standard-of-care-controlled drug trial with quantitative outcome, where participants who discontinue an investigational treatment are not followed up thereafter, and the estimand follows a treatment policy strategy for handling treatment discontinuation. Our approach is also useful in situations where participants take rescue medication or a subsequent line of therapy and the estimand follows a hypothetical strategy to estimate the effect of initially randomised treatment in the absence of rescue or other active treatment. Carpenter et al proposed reference-based imputation methods which use a reference arm to inform the distribution of post-discontinuation outcomes and hence to inform an imputation model. However, the reference-based imputation methods were not formally justified. We present a causal model which makes an explicit assumption in a potential outcomes framework about the maintained causal effect of treatment after discontinuation. We use mathematical argument and a simulation study to show that the "jump to reference", "copy reference" and "copy increments in reference" reference-based imputation methods, with the control arm as the reference arm, are special cases of the causal model with specific assumptions about the causal treatment effect. We also show that the causal model provides a flexible and transparent framework for a tipping point sensitivity analysis in which we vary the assumptions made about the causal effect of discontinued treatment. We illustrate the approach with data from two longitudinal clinical trials.

Better decision making in drug development through adoption of formal prior elicitation.

With the continued increase in the use of Bayesian methods in drug development, there is a need for statisticians to have tools to develop robust and defensible informative prior distributions. Whilst relevant empirical data should, where possible, provide the basis for such priors, it is often the case that limitations in data and/or our understanding may preclude direct construction of a data-based prior. Formal expert elicitation methods are a key technique that can be used to determine priors in these situations. Within GlaxoSmithKline, we have adopted a structured approach to prior elicitation on the basis of the SHELF elicitation framework and routinely use this in conjunction with calculation of probability of success (assurance) of the next study(s) to inform internal decision making at key project milestones. The aim of this paper is to share our experiences of embedding the use of prior elicitation within a large pharmaceutical company, highlighting both the benefits and challenges of prior elicitation through a series of case studies. We have found that putting team beliefs into the shape of a quantitative probability distribution provides a firm anchor for all internal decision making, enabling teams to provide investment boards with formally appropriate estimates of the probability of trial success as well as robust plans for interim decision rules where appropriate. As an added benefit, the elicitation process provides transparency about the beliefs and risks of the potential medicine, ultimately enabling better portfolio and company-wide decision making.

Practical experiences of adopting assurance as a quantitative framework to support decision making in drug development.

All clinical trials are designed for success of their primary objectives. Hence, evaluating the probability of success (PoS) should be a key focus at the design stage both to support funding approval from sponsor governance boards and to inform trial design itself. Use of assurance-that is, expected success probability averaged over a prior probability distribution for the treatment effect-to quantify PoS of a planned study has grown across the industry in recent years, and has now become routine within the authors' company. In this paper, we illustrate some of the benefits of systematically adopting assurance as a quantitative framework to support decision making in drug development through several case-studies where evaluation of assurance has proved impactful in terms of trial design and in supporting governance-board reviews of project proposals. In addition, we describe specific features of how the assurance framework has been implemented within our company, highlighting the critical role that prior elicitation plays in this process, and illustrating how the overall assurance calculation may be decomposed into a sequence of conditional PoS estimates which can provide greater insight into how and when different development options are able to discharge risk.

The future of life expectancy and life expectancy inequalities in England and Wales: Bayesian spatiotemporal forecasting.

BACKGROUND: To plan for pensions and health and social services, future mortality and life expectancy need to be forecast. Consistent forecasts for all subnational units within a country are very rare. Our aim was to forecast mortality and life expectancy for England and Wales' districts. METHODS: We developed Bayesian spatiotemporal models for forecasting of age-specific mortality and life expectancy at a local, small-area level. The models included components that accounted for mortality in relation to age, birth cohort, time, and space. We used geocoded mortality and population data between 1981 and 2012 from the Office for National Statistics together with the model with the smallest error to forecast age-specific death rates and life expectancy to 2030 for 375 of England and Wales' 376 districts. We measured model performance by withholding recent data and comparing forecasts with this withheld data. FINDINGS: Life expectancy at birth in England and Wales was 79·5 years (95% credible interval 79·5-79·6) for men and 83·3 years (83·3-83·4) for women in 2012. District life expectancies ranged between 75·2 years (74·9-75·6) and 83·4 years (82·1-84·8) for men and between 80·2 years (79·8-80·5) and 87·3 years (86·0-88·8) for women. Between 1981 and 2012, life expectancy increased by 8·2 years for men and 6·0 years for women, closing the female-male gap from 6·0 to 3·8 years. National life expectancy in 2030 is expected to reach 85·7 (84·2-87·4) years for men and 87·6 (86·7-88·9) years for women, further reducing the female advantage to 1·9 years. Life expectancy will reach or surpass 81·4 years for men and reach or surpass 84·5 years for women in every district by 2030. Longevity inequality across districts, measured as the difference between the 1st and 99th percentiles of district life expectancies, has risen since 1981, and is forecast to rise steadily to 8·3 years (6·8-9·7) for men and 8·3 years (7·1-9·4) for women by 2030. INTERPRETATION: Present forecasts underestimate the expected rise in life expectancy, especially for men, and hence the need to provide improved health and social services and pensions for elderly people in England and Wales. Health and social policies are needed to curb widening life expectancy inequalities, help deprived districts catch up in longevity gains, and avoid a so-called grand divergence in health and longevity. FUNDING: UK Medical Research Council and Public Health England.

Protective Effects of Smoke-free Legislation on Birth Outcomes in England: A Regression Discontinuity Design.

BACKGROUND: Environmental tobacco smoke has an adverse association with preterm birth and birth weight. England introduced a new law to make virtually all enclosed public places and workplaces smoke free on July 1, 2007. We investigated the effect of smoke-free legislation on birth outcomes in England using Hospital Episode Statistics (HES) maternity data. METHODS: We used regression discontinuity, a quasi-experimental study design, which can facilitate valid causal inference, to analyze short-term effects of smoke-free legislation on birth weight, low birth weight, gestational age, preterm birth, and small for gestational age. RESULTS: We analyzed 1,800,906 pregnancies resulting in singleton live-births in England between 1 January 2005 and 31 December 2009. In the 1 to 5 months following the introduction of the smoke-free legislation, for those entering their third trimester, the risk of low birth weight decreased by between 8% (95% confidence interval [CI]: 4%, 12%) and 14% (95% CI: 5%, 23%), very low birth weight between 28% (95% CI: 19%, 36%) and 32% (95% CI: 21%, 41%), preterm birth between 4% (95% CI: 1%, 8%) and 9% (95% CI: 2%, 16%), and small for gestational age between 5% (95% CI: 2%, 8%) and 9% (95% CI: 2%, 15%). The estimated impact of the smoke-free legislation varied by maternal age, deprivation, ethnicity, and region. CONCLUSIONS: The introduction of smoke-free legislation in England had an immediate estimated beneficial impact on birth outcomes overall, although we did not observe improvements across all age, ethnic, or deprivation groups.See video abstract at http://links.lww.com/EDE/B85.

Time trends in biological fertility in Western Europe.

We investigated trends in biological fertility in a comprehensive analysis of 5 major European data sets with data on time to pregnancy (TTP) and proportion of contraceptive failures. In particular, we distinguished a period effect from a birth cohort effect (lifelong tendency) in both sexes. Attempts at conception not resulting in birth were excluded. We analyzed data on pregnancies occurring in 9,247 couples between 1953 and 1993 and performed sensitivity analyses to check the robustness of findings. Separate analyses of each time effect showed an increasing fertility trend. Mutually adjusted analyses demonstrated that this rise was visible as a male cohort effect for both TTP and contraceptive failure. On the other hand, the female birth cohort effect showed a slight fall in the first half of the study period for both TTP and contraceptive failure. As a period effect, fertility remained generally stable, the slight trends in TTP and contraceptive failure being in opposite directions, likely indicating an artifact. The rising trend accords with most previous evidence. The increasing trend in male fertility does not contradict the previously reported semen quality deterioration, the effects of which are calculated to be small. The declining female fertility accords with a falling dizygotic twinning rate during the same period.

BaySTDetect: detecting unusual temporal patterns in small area data via Bayesian model choice.

Space-time modeling of small area data is often used in epidemiology for mapping chronic disease rates and by government statistical agencies for producing local estimates of, for example, unemployment or crime rates. Although there is typically a general temporal trend, which affects all areas similarly, abrupt changes may occur in a particular area, e.g. due to emergence of localized predictors/risk factor(s) or impact of a new policy. Detection of areas with "unusual" temporal patterns is therefore important as a screening tool for further investigations. In this paper, we propose BaySTDetect, a novel detection method for short-time series of small area data using Bayesian model choice between two competing space-time models. The first model is a multiplicative decomposition of the area effect and the temporal effect, assuming one common temporal pattern across the whole study region. The second model estimates the time trends independently for each area. For each area, the posterior probability of belonging to the common trend model is calculated, which is then used to classify the local time trend as unusual or not. Crucial to any detection method, we provide a Bayesian estimate of the false discovery rate (FDR). A comprehensive simulation study has demonstrated the consistent good performance of BaySTDetect in detecting various realistic departure patterns in addition to estimating well the FDR. The proposed method is applied retrospectively to mortality data on chronic obstructive pulmonary disease (COPD) in England and Wales between 1990 and 1997 (a) to test a hypothesis that a government policy increased the diagnosis of COPD and (b) to perform surveillance. While results showed no evidence supporting the hypothesis regarding the policy, an identified unusual district (Tower Hamlets in inner London) was later recognized to have higher than national rates of hospital readmission and mortality due to COPD by the National Health Service, which initiated various local enhanced services to tackle the problem. Our method would have led to an early detection of this local health issue.

Diagram-based Analysis of Causal Systems (DACS): elucidating inter-relationships between determinants of acute lower respiratory infections among children in sub-Saharan Africa.

BACKGROUND: Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa. RESULTS: Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality. CONCLUSIONS: Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce.

Chlorination by-products in tap water and semen quality in England and Wales.

OBJECTIVES: Disinfection by-products (DBPs) have been associated with adverse semen outcomes in laboratory animals, although the evidence for trihalomethanes (THMs) is limited. Three small epidemiological studies found little evidence for an association between DBPs and adverse semen outcomes in humans. Using data from a large case-referent study (Chemicals and Pregnancy Study, Chaps-UK), we investigated the association between total THM (TTHM), chloroform and total brominated THMs and sperm concentration, percent motile sperm and motile sperm concentration (MSC). METHODS: Chaps-UK recruited men from 13 fertility clinics in nine urban centres across England and Wales between 1999 and 2002. We linked modelled THM concentrations in water zones to semen quality data for 642 cases (men with low MSC) and 926 referents (other men investigated for infertility), based on the men's residence during semen sampling. We assessed risk of low MSC in relation to DBP exposure using continuous THM concentrations. A secondary analysis investigated continuous outcomes (MSC, sperm concentration and percent motile sperm). RESULTS: In the case-referent analysis there was little evidence of elevated risk associated with chloroform, total brominated THM or TTHM concentration after adjustment (OR per 10 µg/L TTHM 1.01; 95% CI 0.91 to 1.12). Similarly, there was no significant effect of THMs on the continuous outcomes. CONCLUSIONS: In the largest study to date on DBPs in public water supplies, and semen quality we found that concentrations of THMs were not associated with poor semen quality. Large-scale investigation of other DBPs (eg, haloacetic acids) and other semen quality parameters (eg, sperm morphology and/or sperm DNA integrity) is recommended.

Hyponatraemia: A strong predictor of mortality in adults with congenital heart disease.

AIMS: We studied the prevalence of hyponatraemia and its prognostic implications in a large population of adult patients with congenital heart disease (ACHD). METHODS AND RESULTS: A total of 1004 ACHD patients were retrospectively entered in this study (mean age 36.2 +/- 14.4 years, 48.7% male). Cox regression was used to estimate mortality associated with hyponatraemia, adjusted for potential confounders using both multivariable regression and propensity score matching. Mean sodium concentration in this ACHD cohort was 137.6 +/- 2.6 mmol/L. The overall prevalence of hyponatraemia in this cohort was 15.5% and was highest in congenitally corrected transposition (33.3%), after Fontan operation (29.6%), and in patients with Eisenmenger syndrome (22.0%). Predictors of hyponatraemia were worse functional class, cyanosis, higher serum creatinine levels, and treatment with diuretics. Patients were followed for a median of 4.1 years, during which there were 96 deaths. Hyponatraemia was a strong predictor of death, independent of age, previous surgery, functional class, systemic ventricular function, creatinine levels, and the use of diuretics (adjusted HR 2.82, 95% CI: 1.72-4.63, P < 0.0001). CONCLUSION: Hyponatraemia is relatively common in ACHD. Hyponatraemia carries a three-fold higher risk of death in ACHD and is a simple, cheap but powerful marker of mortality.

Adjusting for selection bias in retrospective, case-control studies.

Retrospective case-control studies are more susceptible to selection bias than other epidemiologic studies as by design they require that both cases and controls are representative of the same population. However, as cases and control recruitment processes are often different, it is not always obvious that the necessary exchangeability conditions hold. Selection bias typically arises when the selection criteria are associated with the risk factor under investigation. We develop a method which produces bias-adjusted estimates for the odds ratio. Our method hinges on 2 conditions. The first is that a variable that separates the risk factor from the selection criteria can be identified. This is termed the "bias breaking" variable. The second condition is that data can be found such that a bias-corrected estimate of the distribution of the bias breaking variable can be obtained. We show by means of a set of examples that such bias breaking variables are not uncommon in epidemiologic settings. We demonstrate using simulations that the estimates of the odds ratios produced by our method are consistently closer to the true odds ratio than standard odds ratio estimates using logistic regression. Further, by applying it to a case-control study, we show that our method can help to determine whether selection bias is present and thus confirm the validity of study conclusions when no evidence of selection bias can be found.

Bayesian modelling of household solid fuel use: insights towards designing effective interventions to promote fuel switching in Africa.

Indoor air pollution from solid fuel use is a significant risk factor for acute lower respiratory infections among children in sub-Saharan Africa. Interventions that promote a switch to modern fuels hold a large health promise, but their effective design and implementation require an understanding of the web of upstream and proximal determinants of household fuel use. Using Demographic and Health Survey data for Benin, Kenya and Ethiopia together with Bayesian hierarchical and spatial modelling, this paper quantifies the impact of household-level factors on cooking fuel choice, assesses variation between communities and districts and discusses the likely nature of contextual effects. Household- and area-level characteristics appear to interact as determinants of cooking fuel choice. In all three countries, wealth and the educational attainment of women and men emerge as important; the nature of area-level factors varies between countries. In Benin, a two-level model with spatial community random effects best explains the data, pointing to an environmental explanation. In Ethiopia and Kenya, a three-level model with unstructured community and district random effects is selected, implying relatively autonomous economic and social areas. Area-level heterogeneity, indicated by large median odds ratios, appears to be responsible for a greater share of variation in the data than household-level factors. This may be an indication that fuel choice is to a considerable extent supply-driven rather than demand-driven. Consequently, interventions to promote fuel switching will carefully need to assess supply-side limitations and devise appropriate policy and programmatic approaches to overcome them. To our knowledge, this paper represents the first attempt to model the determinants of solid fuel use, highlighting socio-economic differences between households and, notably, the dramatic influence of contextual effects. It illustrates the potential that multilevel and spatial modelling approaches hold for understanding determinants of major public health problems in the developing world.

Reducing Patient Burden in Clinical Trials Through the Use of Historical Controls: Appropriate Selection of Historical Data to Minimize Risk of Bias.

Historical data have been used to augment or replace control arms in some rare disease and pediatric clinical trials. With greater availability of historical data and new methodology such as dynamic borrowing, the inclusion of historical data in clinical trials is an increasingly appealing approach for larger disease areas as well, as this can result in increased power and precision and can minimize the burden on patients in clinical trials. However, sponsors must assess whether the potential biases incurred with this approach outweigh the benefits and discuss this trade-off with the regulatory agencies. This paper discusses important points for the appropriate selection of historical controls for inclusion in the analysis of primary and/or key secondary endpoint(s) in clinical trials. The general steps are as follows: (1) Assess whether a trial is a suitable candidate for this approach. (2) If it is, then carefully identify appropriate historical trials to minimize selection bias. (3) Refine the historical control set if appropriate, for example, by selecting subsets of studies or patients. Identification of trial settings that are amenable to historical borrowing and selection of appropriate historical data using the principles discussed in this paper has the potential to lead to more efficient estimation and decision making. Ultimately, this efficiency gain results in lower patient burden and gets effective drugs to patients more quickly.

Prevalence, predictors, and prognostic value of renal dysfunction in adults with congenital heart disease.

BACKGROUND: Renal insufficiency in patients with ischemic heart disease and acquired heart failure is associated with higher mortality and morbidity. We studied the prevalence of renal dysfunction in adult patients with congenital heart disease (ACHD) and its relation to outcome. METHODS AND RESULTS: A total of 1102 adult patients with congenital heart disease (age 36.0+/-14.2 years) attending our institution between 1999 and 2006 had creatinine concentration measured. Glomerular filtration rate (GFR) was calculated with the Modification of Diet in Renal Disease equation. Patients were divided into groups of normal GFR (> or =90 mL . min(-1) . 1.73 m(-2)), mildly impaired GFR (60 to 89 mL . min(-1) . 1.73 m(-2)), and moderately/severely impaired GFR (<60 mL . min(-1) . 1.73 m(-2)). Survival was compared between GFR groups by Cox regression. Median follow-up was 4.1 years, during which 103 patients died. Renal dysfunction was mild in 41% of patients and moderate or severe in 9%. A decrease in GFR was more common among patients with Eisenmenger physiology, of whom 72% had reduced GFR (<90 mL . min(-1) . 1.73 m(-2), P<0.0001 compared with the remainder), and in 18%, this was moderate or severe (P=0.007). Renal dysfunction had a substantial impact on mortality (propensity score-weighted hazard ratio 3.25, 95% CI 1.54 to 6.86, P=0.002 for moderately or severely impaired versus normal GFR). CONCLUSIONS: Deranged physiology in adult patients with congenital heart disease is not limited to the heart but also affects the kidney. Mortality is 3-fold higher than normal in the 1 in 11 patients who have moderate or severe GFR reduction.

Methodological issues in analyzing time trends in biologic fertility: protection bias.

One method of assessing biologic fertility is to measure time to pregnancy (TTP). Accidental pregnancies do not generate a valid TTP value and lead to nonrandom missing data if couples experiencing accidental pregnancies are more fertile than the general population. If factors affecting the rate of accidental pregnancies, such as availability of effective contraception and induced abortion, vary over time, then the result may be protection bias in the estimates of fertility time trends. Six European data sets were analyzed to investigate whether evidence of protection bias exists in TTP studies of fertility trends in Europe over the past 50 years. Couples experiencing accidental pregnancies tended to be more fertile than the general population. However, trends in accidental pregnancy rates were inconsistent across countries and were insufficient to produce substantial bias in fertility trends in simulated data. Where protection bias is suspected, the authors demonstrate use of 2 multiple imputation methods to generate realizations for the missing TTP values for accidental pregnancies. Simulation studies show that both methods successfully reduce or eliminate protection bias. The authors also demonstrate that standard sensitivity analyses for dealing with accidental pregnancies provide an upper bound on the extent of any bias.

Geographic variations in risk: adjusting for unmeasured confounders through joint modeling of multiple diseases.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an important cause of mortality with marked geographic variations in Great Britain. Additional factors beyond cigarette smoking are likely to influence these variations, but direct information on smoking by area is not readily available. We compared methods of jointly modeling the spatial distribution of mortality from COPD and lung cancer, using the latter as a proxy for smoking, to identify areas in which risk factors other than smoking may be important. METHODS: We obtained district-level mortality and population data for men aged 45 years or older in 1981-1999 in Great Britain. Three models were compared: Bayesian ecological regression using observed (model 1) or spatially smoothed (model 2) lung cancer standardized mortality ratio (SMR) as a smoking proxy, and bivariate regression (model 3) treating smoking as a spatial latent variable common to both diseases. RESULTS: Model selection criteria favored models 2 and 3 over model 1. Between 9% (model 3) and 25% (model 2) of spatial variation in COPD mortality was estimated to be unrelated to smoking. After adjustment for lung cancer as a proxy for smoking, both models showed similar geographic patterns of higher COPD mortality in conurbation and mining areas, historically associated with heavy industry and higher air pollution levels. CONCLUSIONS: Joint modeling of multiple diseases can be used to investigate geographic variations in risk. These models reveal patterns that are adjusted for the effects of shared area-level risk factors for which no direct data are available.

The BUGS project: Evolution, critique and future directions.

BUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives.

Health impacts of long-term exposure to disinfection by-products in drinking water in Europe: HIWATE.

There appears to be very good epidemiological evidence for a relationship between chlorination by-products, as measured by trihalomethanes (THMs), in drinking water and bladder cancer, but the evidence for other cancers, including colorectal cancer appears to be inconclusive and inconsistent. There appears to be some evidence for a relationship between chlorination by-products, as measured by THMs, and small for gestational age (SGA)/intrauterine growth retardation (IUGR) and preterm delivery, but evidence for other outcomes such as low birth weight (LBW), stillbirth, congenital anomalies and semen quality appears to be inconclusive and inconsistent.The overall aim of the HIWATE study is to investigate potential human health risks (e.g. bladder and colorectal cancer, premature births, SGA, semen quality, stillbirth, congenital anomalies) associated with long-term exposure to low levels of disinfectants (such as chlorine) and DBPs occurring in water for human consumption and use in the food industry. The study will comprise risk-benefit analyses including quantitative assessments of risk associated with microbial contamination of drinking water versus chemical risk and will compare alternative treatment options. The outcome will be improved risk assessment and better information for risk management. The work is divided into different topics (exposure assessment, epidemiology, risk assessment and management) and studies.