Multiple testing in spatial epidemiology: a Bayesian approach

In this work we aim to propose a new approach for preliminary epidemiological studies on Standardized Mortality Ratios (SMR) collected in many spatial regions. A preliminary study on SMRs aims to formulate hypotheses to be investigated via individual epidemiological studies that avoid bias carried on by aggregated analyses. Starting from collecting disease counts and calculating expected disease counts by means of reference population disease rates, in each area an SMR is derived as the MLE under the Poisson assumption on each observation. Such estimators have high standard errors in small areas, i.e. where the expected count is low either because of the low population underlying the area or the rarity of the disease under study. Disease mapping models and other techniques for screening disease rates among the map aiming to detect anomalies and possible high-risk areas have been proposed in literature according to the classic and the Bayesian paradigm. Our proposal is approaching this issue by a decision-oriented method, which focus on multiple testing control, without however leaving the preliminary study perspective that an analysis on SMR indicators is asked to. We implement the control of the FDR, a quantity largely used to address multiple comparisons problems in the eld of microarray data analysis but which is not usually employed in disease mapping. Controlling the FDR means providing an estimate of the FDR for a set of rejected null hypotheses. The small areas issue arises diculties in applying traditional methods for FDR estimation, that are usually based only on the p-values knowledge (Benjamini and Hochberg, 1995; Storey, 2003). Tests evaluated by a traditional p-value provide weak power in small areas, where the expected number of disease cases is small. Moreover tests cannot be assumed as independent when spatial correlation between SMRs is expected, neither they are identical distributed when population underlying the map is heterogeneous. The Bayesian paradigm oers a way to overcome the inappropriateness of p-values based methods. Another peculiarity of the present work is to propose a hierarchical full Bayesian model for FDR estimation in testing many null hypothesis of absence of risk.We will use concepts of Bayesian models for disease mapping, referring in particular to the Besag York and Mollié model (1991) often used in practice for its exible prior assumption on the risks distribution across regions. The borrowing of strength between prior and likelihood typical of a hierarchical Bayesian model takes the advantage of evaluating a singular test (i.e. a test in a singular area) by means of all observations in the map under study, rather than just by means of the singular observation. This allows to improve the power test in small areas and addressing more appropriately the spatial correlation issue that suggests that relative risks are closer in spatially contiguous regions. The proposed model aims to estimate the FDR by means of the MCMC estimated posterior probabilities b i's of the null hypothesis (absence of risk) for each area. An estimate of the expected FDR conditional on data (\FDR) can be calculated in any set of b i's relative to areas declared at high-risk (where thenull hypothesis is rejected) by averaging the b i's themselves. The\FDR can be used to provide an easy decision rule for selecting high-risk areas, i.e. selecting as many as possible areas such that the\FDR is non-lower than a prexed value; we call them\FDR based decision (or selection) rules. The sensitivity and specicity of such rule depend on the accuracy of the FDR estimate, the over-estimation of FDR causing a loss of power and the under-estimation of FDR producing a loss of specicity. Moreover, our model has the interesting feature of still being able to provide an estimate of relative risk values as in the Besag York and Mollié model (1991). A simulation study to evaluate the model performance in FDR estimation accuracy, sensitivity and specificity of the decision rule, and goodness of estimation of relative risks, was set up. We chose a real map from which we generated several spatial scenarios whose counts of disease vary according to the spatial correlation degree, the size areas, the number of areas where the null hypothesis is true and the risk level in the latter areas. In summarizing simulation results we will always consider the FDR estimation in sets constituted by all b i's selected lower than a threshold t. We will show graphs of the\FDR and the true FDR (known by simulation) plotted against a threshold t to assess the FDR estimation. Varying the threshold we can learn which FDR values can be accurately estimated by the practitioner willing to apply the model (by the closeness between\FDR and true FDR). By plotting the calculated sensitivity and specicity (both known by simulation) vs the\FDR we can check the sensitivity and specicity of the corresponding\FDR based decision rules. For investigating the over-smoothing level of relative risk estimates we will compare box-plots of such estimates in high-risk areas (known by simulation), obtained by both our model and the classic Besag York Mollié model. All the summary tools are worked out for all simulated scenarios (in total 54 scenarios). Results show that FDR is well estimated (in the worst case we get an overestimation, hence a conservative FDR control) in small areas, low risk levels and spatially correlated risks scenarios, that are our primary aims. In such scenarios we have good estimates of the FDR for all values less or equal than 0.10. The sensitivity of\FDR based decision rules is generally low but specicity is high. In such scenario the use of\FDR = 0:05 or\FDR = 0:10 based selection rule can be suggested. In cases where the number of true alternative hypotheses (number of true high-risk areas) is small, also FDR = 0:15 values are well estimated, and \FDR = 0:15 based decision rules gains power maintaining an high specicity. On the other hand, in non-small areas and non-small risk level scenarios the FDR is under-estimated unless for very small values of it (much lower than 0.05); this resulting in a loss of specicity of a\FDR = 0:05 based decision rule. In such scenario\FDR = 0:05 or, even worse,\FDR = 0:1 based decision rules cannot be suggested because the true FDR is actually much higher. As regards the relative risk estimation, our model achieves almost the same results of the classic Besag York Molliè model. For this reason, our model is interesting for its ability to perform both the estimation of relative risk values and the FDR control, except for non-small areas and large risk level scenarios. A case of study is nally presented to show how the method can be used in epidemiology.

Avoidance and inhibition do not predict nonrespondent bias among patients with inflammatory bowel disease

It has been suggested that participant withdrawal from studies can bias estimates. However, this is only possible when withdrawers and nonwithdrawers differ in an important way. We tested the hypothesis that withdrawers are more likely than nonwithdrawers to be avoidant and negatively affected.

The GABRIEL Advanced Surveys: study design, participation and evaluation of bias

Exposure to farming environments has been shown to protect substantially against asthma and atopic disease across Europe and in other parts of the world. The GABRIEL Advanced Surveys (GABRIELA) were conducted to determine factors in farming environments which are fundamental to protecting against asthma and atopic disease. The GABRIEL Advanced Surveys have a multi-phase stratified design. In a first-screening phase, a comprehensive population-based survey was conducted to assess the prevalence of exposure to farming environments and of asthma and atopic diseases (n = 103,219). The second phase was designed to ascertain detailed exposure to farming environments and to collect biomaterial and environmental samples in a stratified random sample of phase 1 participants (n = 15,255). A third phase was carried out in a further stratified sample only in Bavaria, southern Germany, aiming at in-depth respiratory disease and exposure assessment including extensive environmental sampling (n = 895). Participation rates in phase 1 were around 60% but only about half of the participating study population consented to further study modules in phase 2. We found that consenting behaviour was related to familial allergies, high parental education, wheeze, doctor diagnosed asthma and rhinoconjunctivitis, and to a lesser extent to exposure to farming environments. The association of exposure to farm environments with asthma or rhinoconjunctivitis was not biased by participation or consenting behaviour. The GABRIEL Advanced Surveys are one of the largest studies to shed light on the protective 'farm effect' on asthma and atopic disease. Bias with regard to the main study question was able to be ruled out by representativeness and high participation rates in phases 2 and 3. The GABRIEL Advanced Surveys have created extensive collections of questionnaire data, biomaterial and environmental samples promising new insights into this area of research.

Association between reported exposure to road traffic and respiratory symptoms in children: evidence of bias

BACKGROUND: Many studies showing effects of traffic-related air pollution on health rely on self-reported exposure, which may be inaccurate. We estimated the association between self-reported exposure to road traffic and respiratory symptoms in preschool children, and investigated whether the effect could have been caused by reporting bias. METHODS: In a random sample of 8700 preschool children in Leicestershire, UK, exposure to road traffic and respiratory symptoms were assessed by a postal questionnaire (response rate 80%). The association between traffic exposure and respiratory outcomes was assessed using unconditional logistic regression and conditional regression models (matching by postcode). RESULTS: Prevalence odds ratios (95% confidence intervals) for self-reported road traffic exposure, comparing the categories 'moderate' and 'dense', respectively, with 'little or no' were for current wheezing: 1.26 (1.13-1.42) and 1.30 (1.09-1.55); chronic rhinitis: 1.18 (1.05-1.31) and 1.31 (1.11-1.56); night cough: 1.17 (1.04-1.32) and 1.36 (1.14-1.62); and bronchodilator use: 1.20 (1.04-1.38) and 1.18 (0.95-1.46). Matched analysis only comparing symptomatic and asymptomatic children living at the same postcode (thus exposed to similar road traffic) showed similar ORs, suggesting that parents of children with respiratory symptoms reported more road traffic than parents of asymptomatic children. CONCLUSIONS: Our study suggests that reporting bias could explain some or even all the association between reported exposure to road traffic and disease. Over-reporting of exposure by only 10% of parents of symptomatic children would be sufficient to produce the effect sizes shown in this study. Future research should be based only on objective measurements of traffic exposure.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Analyzing Bivariate Survival Data with Interval Sampling and Application to Cancer Epidemiology

In medical follow-up studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as the outcomes to identify the progression of a disease. In cancer studies interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme where the first failure event (cancer onset) is identified within a calendar time interval, the time of the initiating event (birth) can be retrospectively confirmed, and the occurrence of the second event (death) is observed sub ject to right censoring. To analyze this type of bivariate failure time data, it is important to recognize the presence of bias arising due to interval sampling. In this paper, nonparametric and semiparametric methods are developed to analyze the bivariate survival data with interval sampling under stationary and semi-stationary conditions. Numerical studies demonstrate the proposed estimating approaches perform well with practical sample sizes in different simulated models. We apply the proposed methods to SEER ovarian cancer registry data for illustration of the methods and theory.

Multivariable adjustments counteract spectrum and test review bias in accuracy studies

OBJECTIVES: The STAndards for Reporting studies of Diagnostic accuracy (STARD) for investigators and editors and the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) for reviewers and readers offer guidelines for the quality and reporting of test accuracy studies. These guidelines address and propose some solutions to two major threats to validity: spectrum bias and test review bias. STUDY DESIGN AND SETTING: Using a clinical example, we demonstrate that these solutions fail and propose an alternative solution that concomitantly addresses both sources of bias. We also derive formulas that prove the generality of our arguments. RESULTS: A logical extension of our ideas is to extend STARD item 23 by adding a requirement for multivariable statistical adjustment using information collected in QUADAS items 1, 2, and 12 and STARD items 3-5, 11, 15, and 18. CONCLUSION: We recommend reporting not only variation of diagnostic accuracy across subgroups (STARD item 23) but also the effects of the multivariable adjustments on test performance. We also suggest that the QUADAS be supplemented by an item addressing the appropriateness of statistical methods, in particular whether multivariable adjustments have been included in the analysis.

Effects of study precision and risk of bias in networks of interventions: a network meta-epidemiological study

BACKGROUND Empirical research has illustrated an association between study size and relative treatment effects, but conclusions have been inconsistent about the association of study size with the risk of bias items. Small studies give generally imprecisely estimated treatment effects, and study variance can serve as a surrogate for study size. METHODS We conducted a network meta-epidemiological study analyzing 32 networks including 613 randomized controlled trials, and used Bayesian network meta-analysis and meta-regression models to evaluate the impact of trial characteristics and study variance on the results of network meta-analysis. We examined changes in relative effects and between-studies variation in network meta-regression models as a function of the variance of the observed effect size and indicators for the adequacy of each risk of bias item. Adjustment was performed both within and across networks, allowing for between-networks variability. RESULTS Imprecise studies with large variances tended to exaggerate the effects of the active or new intervention in the majority of networks, with a ratio of odds ratios of 1.83 (95% CI: 1.09,3.32). Inappropriate or unclear conduct of random sequence generation and allocation concealment, as well as lack of blinding of patients and outcome assessors, did not materially impact on the summary results. Imprecise studies also appeared to be more prone to inadequate conduct. CONCLUSIONS Compared to more precise studies, studies with large variance may give substantially different answers that alter the results of network meta-analyses for dichotomous outcomes.

Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID): an extension of the STROBE statement

Molecular data are now widely used in epidemiological studies to investigate the transmission, distribution, biology, and diversity of pathogens. Our objective was to establish recommendations to support good scientific reporting of molecular epidemiological studies to encourage authors to consider specific threats to valid inference. The statement Strengthening the Reporting of Molecular Epidemiology for Infectious Diseases (STROME-ID) builds upon the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative. The STROME-ID statement was developed by a working group of epidemiologists, statisticians, bioinformaticians, virologists, and microbiologists with expertise in control of infection and communicable diseases. The statement focuses on issues relating to the reporting of epidemiological studies of infectious diseases using molecular data that were not addressed by STROBE. STROME-ID addresses terminology, measures of genetic diversity within pathogen populations, laboratory methods, sample collection, use of molecular markers, molecular clocks, timeframe, multiple-strain infections, non-independence of infectious-disease data, missing data, ascertainment bias, consistency between molecular and epidemiological data, and ethical considerations with respect to infectious-disease research. In total, 20 items were added to the 22 item STROBE checklist. When used, the STROME-ID recommendations should advance the quality and transparency of scientific reporting, with clear benefits for evidence reviews and health-policy decision making.

Clinical Accuracy of the MedGem Indirect Calorimeter for Measuring Resting Energy Expenditure in Cancer Patients

OBJECTIVE: To compare, in patients with cancer and in healthy subjects, measured resting energy expenditure (REE) from traditional indirect calorimetry to a new portable device (MedGem) and predicted REE. DESIGN: Cross-sectional clinical validation study. SETTING: Private radiation oncology centre, Brisbane, Australia. SUBJECTS: Cancer patients (n = 18) and healthy subjects (n = 17) aged 37-86 y, with body mass indices ranging from 18 to 42 kg/m(2). INTERVENTIONS: Oxygen consumption (VO(2)) and REE were measured by VMax229 (VM) and MedGem (MG) indirect calorimeters in random order after a 12-h fast and 30-min rest. REE was also calculated from the MG without adjustment for nitrogen excretion (MGN) and estimated from Harris-Benedict prediction equations. Data were analysed using the Bland and Altman approach, based on a clinically acceptable difference between methods of 5%. RESULTS: The mean bias (MGN-VM) was 10% and limits of agreement were -42 to 21% for cancer patients; mean bias -5% with limits of -45 to 35% for healthy subjects. Less than half of the cancer patients (n = 7, 46.7%) and only a third (n = 5, 33.3%) of healthy subjects had measured REE by MGN within clinically acceptable limits of VM. Predicted REE showed a mean bias (HB-VM) of -5% for cancer patients and 4% for healthy subjects, with limits of agreement of -30 to 20% and -27 to 34%, respectively. CONCLUSIONS: Limits of agreement for the MG and Harris Benedict equations compared to traditional indirect calorimetry were similar but wide, indicating poor clinical accuracy for determining the REE of individual cancer patients and healthy subjects.

Correcting for bias when estimating the cost of hospital-acquired infection : an analysis of lower respiratory tract infections in non-surgical patients

Hospital acquired infections (HAI) are costly but many are avoidable. Evaluating prevention programmes requires data on their costs and benefits. Estimating the actual costs of HAI (a measure of the cost savings due to prevention) is difficult as HAI changes cost by extending patient length of stay, yet, length of stay is a major risk factor for HAI. This endogeneity bias can confound attempts to measure accurately the cost of HAI. We propose a two-stage instrumental variables estimation strategy that explicitly controls for the endogeneity between risk of HAI and length of stay. We find that a 10% reduction in ex ante risk of HAI results in an expected savings of £693 ($US 984).