Beta-binomial Regression Model Research Articles

Comparison of the EQ-5D-5L and the patient-reported outcomes measurement information system preference score (PROPr) in the United States

BackgroundIn contrast to prior research, our study presents longitudinal comparisons of the EQ-5D-5L and Patient-Reported Outcomes Measurement Information System (PROMIS) preference (PROPr) scores. This fills a gap in the literature, providing a much-needed understanding of these preference-based measures and their applications in healthcare research. Furthermore, our study provides equations to estimate one measure from the other, a tool that can significantly facilitate comparisons across studies.MethodsWe administered a health survey to 4,098 KnowledgePanel® members living in the United States. A subset of 1,256 (82% response rate) with back pain also completed the six-month follow-up survey. We then conducted thorough cross-sectional and longitudinal analyses of the two measures, including product-moment correlations between scores, associations with demographic variables, and health conditions. To estimate one measure from the other, we used ordinary least squares (OLS) regression with the baseline data from the general population.ResultsThe correlation between the EQ-5D-5L and PROPr scores was 0.69, but the intraclass correlation was only 0.34 because the PROPr had lower (less positive) mean scores on the 0 (dead) to 1 (perfect health) continuum than the EQ-5D-5L. The associations between the two preference measures and demographic variables were similar at baseline. The product-moment correlation between unstandardized beta coefficients for each preference measure regressed on 22 health conditions was 0.86, reflecting similar patterns of unique associations. Correlations of change from baseline to 6 months in the two measures with retrospective perceptions of change were similar. Adjusted variance explained in OLS regressions predicting one measure from the other was 48%. On average, the predicted values were within a half-standard deviation of the observed EQ-5D-5L and PROPr scores. The beta-binomial regression model slightly improved over the OLS model in predicting the EQ-5D-5L from the PROPr but was equivalent to the OLS model in predicting the PROPr.ConclusionDespite substantial mean differences, the EQ-5D-5L and PROPr have similar cross-sectional and longitudinal associations with other variables. We provide the OLS regression equations for use in cost-effectiveness research and meta-analyses. Future studies are needed to compare these measures with different conditions and interventions to provide more information on their relative validity.

Tilted beta regression and beta-binomial regression models: Mean and variance modeling

Tilted beta regression and beta-binomial regression models: Mean and variance modeling

The Association Between Type of Insurance Plan, Out-of-Pocket Cost, and Adherence to Antihypertensive Medications in Medicare Supplement Insurance Enrollees.

Medicare supplement insurance, or Medigap, covers 21% of Medicare beneficiaries. Despite offsetting some out-of-pocket (OOP) expenses, remaining OOP costs may pose a barrier to medication adherence. This study aims to evaluate how OOP costs and insurance plan types influence medication adherence among beneficiaries covered by Medicare supplement plans. We conducted a retrospective analysis of the Merative MarketScan Medicare Supplement Database (2017-2019) in Medigap enrollees (≥65 years) with hypertension. The proportion of days covered (PDC) was a continuous measure of medication adherence and was also dichotomized (PDC ≥0.8) to quantify adequate adherence. Beta-binomial and logistic regression models were used to estimate associations between these outcomes and insurance plan type and log-transformed OOP costs, adjusting for patient characteristics. Among 27,407 patients with hypertension, the average PDC was 0.68 ± 0.31; 47.5% achieved adequate adherence. A mean $1 higher in 30-day OOP costs were associated with a 0.06 (95% confidence intervals [CIs]: -0.09 to -0.03) lower probability of adequate adherence, or a 5% (95% CI: 4%-7%) decrease in PDC. Compared with comprehensive plan enrollees, the odds of adequate adherence were lower among those with point-of-service plans (odds ratio [OR]: 0.69, 95% CI: 0.62-0.77), but higher among those with preferred provider organization (PPO) plans (OR: 1.08, 95% CI: 1.01-1.15). Moreover, the association between OOP costs and PDC was significantly greater for PPO enrollees. While Medicare supplement insurance alleviates some OOP costs, different insurance plans and remaining OOP costs influence medication adherence. Reducing patient cost-sharing may improve medication adherence.

Model Selection in Generalized Linear Models

The problem of model selection in regression analysis through the use of forward selection, backward elimination, and stepwise selection has been well explored in the literature. The main assumption in this, of course, is that the data are normally distributed and the main tool used here is either a t test or an F test. However, the properties of these model selection procedures are not well-known. The purpose of this paper is to study the properties of these procedures within generalized linear regression models, considering the normal linear regression model as a special case. The main tool that is being used is the score test. However, the F test and other large sample tests, such as the likelihood ratio and the Wald test, the AIC, and the BIC, are included for the comparison. A systematic study, through simulations, of the properties of this procedure was conducted, in terms of level and power, for symmetric and asymmetric distributions, such as normal, Poisson, and binomial regression models. Extensions for skewed distributions, over-dispersed Poisson (the negative binomial), and over-dispersed binomial (the beta-binomial) regression models, are also given and evaluated. The methods are applied to analyze two health datasets.

Prevalence and Etiological Characteristics of Norovirus Infection in China: A Systematic Review and Meta-Analysis.

Norovirus is a common cause of sporadic cases and outbreaks of gastroenteritis worldwide, although its prevalence and the dominant genotypes responsible for gastroenteritis outbreaks remain obscure. A systematic review was conducted on norovirus infection in China between January 2009 and March 2021. A meta-analysis and beta-binomial regression model were used to explore the epidemiological and clinical characteristics of norovirus infection and the potential factors contributing to the attack rate of the norovirus outbreaks, respectively. A total of 1132 articles with 155,865 confirmed cases were included, with a pooled positive test rate of 11.54% among 991,786 patients with acute diarrhea and a pooled attack rate of 6.73% in 500 norovirus outbreaks. GII.4 was the predominant genotype in both the etiological surveillance and outbreaks, followed by GII.3 in the etiological surveillance, and GII.17 in the outbreaks, with the proportion of recombinant genotypes increasing in recent years. A higher attack rate in the norovirus outbreaks was associated with age group (older adults), settings (nurseries, primary schools, etc.) and region (North China). The nation-wide pooled positive rate in the etiological surveillance of norovirus is lower than elsewhere in the global population, while the dominant genotypes are similar in both the etiological surveillance and the outbreak investigations. This study contributes to the understanding of norovirus infection with different genotypes in China. The prevention and control of norovirus outbreaks during the cold season should be intensified, with special attention paid to and enhanced surveillance performed in nurseries, schools and nursing homes from November to March.

Pairwise ratio-based differential abundance analysis of infant microbiome 16S sequencing data.

Differential abundance analysis of infant 16S microbial sequencing data is complicated by challenging data properties, including high sparsity, extreme dispersion and the relative nature of the information contained within the data. In this study, we propose a pairwise ratio analysis that uses the compositional data analysis principle of subcompositional coherence and merges it with a beta-binomial regression model. The resulting method provides a flexible and easily interpretable approach to infant 16S sequencing data differential abundance analysis that does not require zero imputation. We evaluate the proposed method using infant 16S data from clinical trials and demonstrate that the proposed method has the power to detect differences, and demonstrate how its results can be used to gain insights. We further evaluate the method using data-inspired simulations and compare its power against related methods. Our results indicate that power is high for pairwise differential abundance analysis of taxon pairs that have a large abundance. In contrast, results for sparse taxon pairs show a decrease in power and substantial variability in method performance. While our method shows promising performance on well-measured subcompositions, we advise strong filtering steps in order to avoid excessive numbers of underpowered comparisons in practical applications.

μσ2-Beta and μσ2-Beta Binomial Regression Models

This paper proposes new parameterizations of the beta and beta binomial distributions as functions of the mean and variance parameters. From these new parameterizations, new beta and beta binomial linear regression models are formulated by assuming that appropriate real functions of the mean and variance follow linear regression structures. These models were fitted to real datasets by applying Bayesian methods, using the OpenBUGS software. The new beta regression models were fitted to the Dyslexia Reading Accuracy dataset and the new beta binomial regression models were applied to the School Absenteeism Dataset. In both cases, the results obtained by fitting these models were compared with those obtained by fitting the usual mean and dispersion beta regression models and the mean and dispersion beta binomial regression models, respectively.

Abstract P151: Higher Gut Microbiota Diversity And Abundance Of Butyrogenic Species Are Associated With Lower Pulse Wave Velocity In The Baltimore Longitudinal Study Of Aging

Background: Gut microbiota, particularly butyrogenic species, may affect risk of cardiovascular disease through effects on arterial stiffness. However, studies on gut microbiota and arterial stiffness in human population studies are sparse, with no prior studies having used shotgun metagenomic sequencing to identify species. Objective: To investigate associations of gut microbiota with measures of arterial stiffness as measured by pulse wave velocity (PWV). Methods: We studied 780 adults from the Baltimore Longitudinal Study of Aging who provided feces for shotgun metagenomic microbiome sequencing and at least 1 measure of carotid-femoral PWV between 2013 and 2017. We examined associations of PWV (continuous and as quartiles) with gut microbiome features using beta-binomial and linear regression models for microbiota relative abundances; linear regression models for microbial alpha diversity (Shannon index); and PERMANOVA for microbial beta diversity (Weighted Unifrac distance). We adjusted all models for age, sex and race. We used an FDR-threshold of q <0.05 for abundance models. Results: Approximately 55% of our sample were female and 65% identified as white, with a mean age of 71 years. Microbial beta diversity was significantly associated with PWV (Weighted UniFrac p=0.003, R 2 =0.01), and a 1-SD increase in (Shannon index) alpha diversity was associated with a 0.21 decrease in PWV (p=0.002). Twelve species were associated with continuous PWV, including 4 key butyrate producers in dose-response fashion (Figure). These findings persisted after further adjustment for Mediterranean diet score. Conclusion: Higher microbial diversity and abundance of key butyrogenic bacteria were associated with lower arterial stiffness in a racially diverse cohort of US adults. Some of the species associated with arterial stiffness, e.g. F. prausnitzii and other taxa in Figure, have been previously associated with lower cardiovascular disease risk. Clinical trials are needed to determine if these relationships are causal.

Identification of drivers of Rift Valley fever after the 2013-14 outbreak in Senegal using serological data in small ruminants.

Rift Valley fever (RVF) is a mosquito-borne disease mostly affecting wild and domestic ruminants. It is widespread in Africa, with spillovers in the Arab Peninsula and the southwestern Indian Ocean. Although RVF has been circulating in West Africa for more than 30 years, its epidemiology is still not clearly understood. In 2013, an RVF outbreak hit Senegal in new areas that weren’t ever affected before. To assess the extent of the spread of RVF virus, a national serological survey was implemented in young small ruminants (6–18 months old), between November 2014 and January 2015 (after the rainy season) in 139 villages. Additionally, the drivers of this spread were identified. For this purpose, we used a beta-binomial () logistic regression model. An Integrated Nested Laplace Approximation (INLA) approach was used to fit the spatial model. Lower cumulative rainfall, and higher accessibility were both associated with a higher RVFV seroprevalence. The spatial patterns of fitted RVFV seroprevalence pointed densely populated areas of western Senegal as being at higher risk of RVFV infection in small ruminants than rural or southeastern areas. Thus, because slaughtering infected animals and processing their fresh meat is an important RVFV transmission route for humans, more human populations might have been exposed to RVFV during the 2013–2014 outbreak than in previous outbreaks in Senegal.

Application of Binomial and Beta-Binomial Regression Models on Incidence of Diarrhea Among children Aged 6-59 Months in Miesso District, Oromia, Ethiopia

In binomial regression analysis model dependent variable follows a binomial distribution. β-binomial model is a regression model for outcomes following the β-binomial probability distribution. The aim of present study was to fit binomial regression and beta-binomial regression models for incidence of diarrhea, a prominent cause of health disorders/ deaths in under-five year age children. The study was conducted in the children aged 6-59 months in Miesso Wereda, Oromia, Ethiopia from June to September 2017. The data used was obtained from a cross-sectional study using well-structured questionnaires. The Predictors used for the current study were child sex, household food security status, sources of drinking water, and toilet ownership status. Binomial and β-binomial models were fitted to identify the significant predictors. The overdispersion status of the data was tested and revealed that there was no over-dispersion and the fitted models had similar Akaiki information criteria and Bayesian information Criteria with similar results. The source of water (pipe water) and having toilet facility significantly affected the incidence of diarrhea among the studied group of children. The study recommended that giving attention to environmental sanitation and creating pure water facility shall reduce diarrhea incidence in children.

Factors modifying the likelihood of speeding behaviors based on naturalistic driving data

Speeding behaviors are quite common and are known to affect the risk and severity outcomes of vehicular crashes. Naturalistic driving data allows for the direct observation of speeding and the development of evidence-based causal structures for this behavior. This limits biases associated with self-reported speeding prevalence, allowing for more precise speeding measures than post-crash investigations and for the evaluation of driver attributes associated with speeding across a wide variety of locations and road types. Data from the Second Strategic Highway Research Program Naturalistic Driving Study were used to identify speeding events that were aggregated based on their duration and degree of speeding above the posted speed limit. The events were summarized as a likelihood of speeding metric for each trip in the dataset. These likelihood of speeding measurements were also aggregated across drivers and compared, using a beta binomial regression model, to driver questionnaire answers that addressed drivers’ perception of their speeding as well as different driver-specific factors that are suspected of having an influence on speeding behaviors. Results showed that, consistent with past studies, age and gender significantly influenced the likelihood of speeding. For age, the odds of speeding exhibited a significant downward trend across increasing age groups; 16–24 year olds exhibited odds of speeding that were 1.5 times the odds of drivers that were 80 or more years old. For gender, males exhibited larger odds of speeding than females (1.1 times larger). In addition, the odds of speeding were larger at lower posted speed limits. The odds of speeding in 10–20 mph zones were 9.5 times the odds of zones with speed limits greater than 60 mph, implying that drivers may be unaware of the risks associated with speeding in low speed limit areas. Several questionnaire answers related to drivers’ perception of speeding were also predictive of the likelihood of speeding.

A new regression model for overdispersed binomial data accounting for outliers and an excess of zeros.

Binary outcomes are extremely common in biomedical research. Despite its popularity, binomial regression often fails to model this kind of data accurately due to the overdispersion problem. Many alternatives can be found in the literature, the beta‐binomial (BB) regression model being one of the most popular. The additional parameter of this model enables a better fit to overdispersed data. It also exhibits an attractive interpretation in terms of the intraclass correlation coefficient. Nonetheless, in many real data applications, a single additional parameter cannot handle the entire excess of variability. In this study, we propose a new finite mixture distribution with BB components, namely, the flexible beta‐binomial (FBB), which is characterized by a richer parameterization. This allows us to enhance the variance structure to account for multiple causes of overdispersion while also preserving the intraclass correlation interpretation. The novel regression model, based on the FBB distribution, exploits the flexibility and large variety of the distribution's possible shapes (which includes bimodality and various tail behaviors). Thus, it succeeds in accounting for several (possibly concomitant) sources of overdispersion stemming from the presence of latent groups in the population, outliers, and excessive zero observations. Adopting a Bayesian approach to inference, we perform an intensive simulation study that shows the superiority of the new regression model over that of the existing ones. Its better performance is also confirmed by three applications to real datasets extensively studied in the biomedical literature, namely, bacteria data, atomic bomb radiation data, and control mice data.

Gut Microbiome Composition Is Associated with Blood Pressure in Mother-Child Pairs 5 Years After Birth

Abstract Objectives Adult studies have indicated that the gut microbiome plays a role in blood pressure regulation, however, there have been no studies on this association in children or mother-child pairs. This study's aim is to determine whether gut microbiome composition is associated with systolic blood pressure (SBP) in children and their mothers 5 years after birth. Methods We obtained stool samples from Canadian mother-child pairs (n = 164 children, 168 mothers) on average 5 years after birth in the prospective Gen3G pre-birth cohort. We characterized the microbiome using 16S rRNA gene sequencing of the V4 region. We categorized SBP (measured automatically, in duplicate) into tertiles separately for mothers and children and used the lowest tertile as the reference group. We fitted beta-binomial regression models to identify differentially abundant (FDR P < 0.05) amplicon sequence variants (ASVs) of bacterial taxa, adjusting for: fruit and vegetable intake (servings/week), current smoking (mothers only), sex (children), and BMI (mothers) or BMI z-score (children). Results Mothers and children were similar across SBP tertiles with respect to all clinical characteristics, except for BMI. Mothers in the highest SBP tertile (113-166 mmHg) had a median BMI of 25.4 vs. the lowest tertile (91-106 mmHg) at 23.1, and children in the highest tertile (104-128 mmHg) had a median BMI z-score of 0.3 vs. the lowest tertile (87-98 mmHg) at -0.1. SBP was not associated with microbial diversity, but was associated with composition. After adjusting for covariates (including BMI), 4 ASVs were differentially abundant in children in the highest vs. lowest SBP tertile. Children with higher SBP had greater % of Bacteroides (sp. unknown), Collinsella (sp. unknown), and Coprococcus catus and lower % of butyrate-producing Lachnospira (sp. unknown). Among mothers, 12 ASVs were differentially abundant in the highest vs. lowest SBP tertile, with higher SBP associated with greater % of Lachnoclostridium symbiosum, a candidate-marker for colon cancer, and lower % of butyrate-producing Eubacterium eligens and Porphyromonadaceae Butyricimonas. Conclusions Altered gut microbiota composition, including lower abundance of butyrate-producing bacteria, is associated with higher blood pressure in both mothers and their children 5 years after birth. Funding Sources NHLBI.

Timing of complementary feeding is associated with gut microbiota diversity and composition and short chain fatty acid concentrations\xa0over the first year of life

BackgroundEarly introduction of complementary foods has been associated with various immune disorders, oxidative stress, and obesity in childhood. The gut microbiota and the short chain fatty acids (SCFAs) they produce are postulated to be on the causal pathway. The objective of this study was to determine if early complementary feeding (i.e. consumption of solids or non-water/formula liquids at or before 3 months) is prospectively associated with infant gut microbiota composition, diversity and SCFAs at 3 and 12 months of age in the Nurture birth cohort.ResultsMother-infant dyads in the early complementary feeding group (n = 18) had similar baseline characteristics to those in the later feeding group (n = 49). We assessed differential abundance of microbial taxa (measured by 16S rRNA gene sequencing of the V4 region) by timing of complementary feeding using beta-binomial regression models (considering a two-sided FDR corrected p-value of < 0.05 as significant), and we fittted linear regression models to assess the association between early complementary feeding and SCFA concentrations (quantified using gas chromatography). After multivariable adjustment for breastfeeding, delivery method, birth weight, and gestational age, there were 13 differentially abundant microbial amplicon sequence variants (ASVs) by timing of introduction to complementary foods at 3 months and 20 ASVs at 12 months. Infants introduced to complementary foods early (vs. later) had higher concentrations of the SCFA butyric acid (mean difference = 0.65, 95% CI: 0.27, 1.04, p < 0.01) and total SCFAs (mean difference = 38.8, 95% CI: 7.83, 69.7) at 12 months. Bilophila wadsworthia and Lachnospiraceae Roseburia were associated with early (vs. later) complementary feeding and with higher butyric acid concentrations at 3 and 12 months, respectively.ConclusionsOur findings are consistent with the hypothesis that early (vs. later) introduction to complementary foods is associated with altered gut microbiota composition and butyric acid concentrations measured in stool until at least 1 year of age. Further research is needed to determine if these changes mediate future development of metabolic and immune conditions.

Tilted Beta Binomial Linear Regression Model: A Bayesian Approach

This paper proposes new linear regression models to deal with overdispersed binomial datasets. These new models, called tilted beta binomial regression models, are defined from the tilted beta binomial distribution, proposed assuming that the parameter of the binomial distribution follows a tilted beta distribution. As a particular case of this regression models, we propose the beta rectangular binomial regression models, defined from the binomial distribution assuming that their parameters follow a beta rectangular distribution. These new linear regression models, defined assuming that the parameters of these new distributions follow regression structures, are fitted applying Bayesian methods and using the OpenBUGS software. The proposed regression models are fitted to an overdispersed binomial dataset of the number of seeds that germinate depending on the type of chosen seed and root.

Integrating psychosocial care into routine cancer care: A stepped-wedge design cluster randomized controlled trial (SWD-RCT) to evaluate effectiveness of the HuCare Quality Improvement Strategy (HQIS) on health-related quality of life (HRQoL).

6515 Background: Cancer patients (pts) often do not receive evidence-based psychosocial care. We evaluate the effects of an implementation strategy we previously demonstrated feasible, which includes communication skill training for all physicians and nurses; four support visits at the centers by an improvement team to assist staff in identifying obstacles, finding solutions, and strengthening motivation; screening for distress and social needs; individualized pts' education with a referring nurse; use of a question prompt list. Methods: Multicenter incomplete SWD-RCT with 3 clusters of 5 centers each. Consecutive outpatients requiring medical treatment and diagnosed in the previous 2 months were eligible. Primary endpoint: difference of at least one of the 2 domains of HRQoL emotional or social functions, at 3 months from baseline, in pts of the centers that implemented the HQIS vs standard of care (SoC). Secondary endpoints include: patient mood, long-term effect, overall HRQoL. Analyses were performed using a beta-binomial regression model. Results: 762 pts were enrolled. At baseline, 41% showed high anxiety (HADS-A&gt;7), and 88% had at least one psychosocial need. 299 health professionals attended 3-day courses (84% of all clinical staff). 647 pts (85%) were available for analysis. The 315 pts who received HQIS exhibited better quality of life for the emotional domain than those assigned to SOC (OR=1.115, p=0.016). Pts who showed the greatest improvement were the older (OR=1.003, p=0.035), had lower anxiety basal levels (OR=0.853, p&lt;0.001), and social needs were met (OR=1.182, p&lt;0.001). The difference was not significant for the social domain (OR=0.955, p=0.353). The HQIS’s long-term effect was confirmed for the emotional domain at 12 months. No effect on mood (HADS-D) and overall HRQoL was observed. Conclusions: To our knowledge this is the first RCT demonstrating the effectiveness of a psychosocial care implementation strategy on cancer patients’ emotional well-being. Clinical trial information: NCT03008993.

Determinants of bed net use conditional on access in population surveys in Ghana

BackgroundInsecticide-treated nets (ITNs) are one of the most effective and widely available methods for preventing malaria, and there is interest in understanding the complexities of behavioural drivers of non-use among those with access. This analysis evaluated net use behaviour in Ghana by exploring how several household and environmental variables relate to use among Ghanaians with access to a net.MethodsSurvey data from the Ghana 2014 Demographic and Health Survey and the 2016 Malaria Indicator Survey were used to calculate household members’ access to space under a net as well as the proportion of net use conditional on access (NUCA). Geospatial information on cluster location was obtained, as well as average humidex, a measure of how hot it feels, for the month each cluster was surveyed. The relationship between independent variables and net use was assessed via beta-binomial regression models that controlled for spatially correlated random effects using non-Gaussian kriging.ResultsIn both surveys, increasing wealth was associated with decreased net use among those with access in households when compared to the poorest category. In 2014, exposure to messages about bed net use for malaria prevention was associated with increased net use (OR 2.5, 95% CrI 1.5–4.2), as was living in a rural area in both 2014 (OR 2.5, 95% CrI 1.5–4.3) and 2016 (OR 1.6, 95% CrI 1.1–2.3). The number of nets per person was not associated with net use in either survey. Model fit was improved for both surveys by including a spatial random effect for cluster, demonstrating some spatial autocorrelation in the proportion of people using a net. Humidex, electricity in the household and IRS were not associated with NUCA.ConclusionNet use conditional on access is affected by household characteristics and is also spatially-dependent in Ghana. Setting (whether the household was urban or rural) plays a role, with wealthier and more urban households less likely to use nets when they are available. It will likely be necessary in the future to focus on rural settings, urban settings, and wealth status independently, both to better understand predictors of household net use in these areas and to design more targeted interventions to ensure consistent use of vector control interventions that meet specific needs of the population.

Optimal experimental design for predator-prey functional response experiments.

Functional response models are important in understanding predator-prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller sample sizes to achieve the same statistical outcomes as non-optimally designed experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs; these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a beta-binomial functional response model for two published datasets: an experiment involving the freshwater predator Notonecta glauca (an aquatic insect) preying on Asellus aquaticus (a small crustacean), and another experiment involving a ladybird beetle (Propylea quatuordecimpunctata L.) preying on the black bean aphid (Aphis fabae Scopoli). As a by-product, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications.

Characteristics of patients with missing information on stage: a population-based study of patients diagnosed with colon, lung or breast cancer in England in 2013

BackgroundStage is a key predictor of cancer survival. Complete cancer staging is vital for understanding outcomes at population level and monitoring the efficacy of early diagnosis initiatives. Cancer registries usually collect details of the disease extent but staging information may be missing because a stage was never assigned to a patient or because it was not included in cancer registration records. Missing stage information introduce methodological difficulties for analysis and interpretation of results. We describe the associations between missing stage and socio-demographic and clinical characteristics of patients diagnosed with colon, lung or breast cancer in England in 2013. We assess how these associations change when completeness is high, and administrative issues are assumed to be minimal. We estimate the amount of avoidable missing stage data if high levels of completeness reached by some Clinical Commissioning Groups (CCGs), were achieved nationally.MethodsIndividual cancer records were retrieved from the National Cancer Registration and linked to the Routes to Diagnosis and Hospital Episode Statistics datasets to obtain additional clinical information. We used multivariable beta binomial regression models to estimate the strength of the association between socio-demographic and clinical characteristics of patients and missing stage and to derive the amount of avoidable missing stage.ResultsMultivariable modelling showed that old age was associated with missing stage irrespective of the cancer site and independent of comorbidity score, short-term mortality and patient characteristics. This remained true for patients in the CCGs with high completeness. Applying the results from these CCGs to the whole cohort showed that approximately 70% of missing stage information was potentially avoidable.ConclusionsMissing stage was more frequent in older patients, including those residing in CCGs with high completeness. This disadvantage for older patients was not explained fully by the presence of comorbidity. A substantial gain in completeness could have been achieved if administrative practices were improved to the level of the highest performing areas. Reasons for missing stage information should be carefully assessed before any study, and potential distortions introduced by how missing stage is handled should be considered in order to draw the most correct inference from available statistics.

Validation of the 'United Registries for Clinical Assessment and Research' [UR-CARE], a European Online Registry for Clinical Care and Research in Inflammatory Bowel Disease.

The 'United Registries for Clinical Assessment and Research' [UR-CARE] database is an initiative of the European Crohn's and Colitis Organisation [ECCO] to facilitate daily patient care and research studies in inflammatory bowel disease [IBD]. Herein, we sought to validate the database by using fictional case histories of patients with IBD that were to be entered by observers of varying experience in IBD. Nineteen observers entered five patient case histories into the database. After 6 weeks, all observers entered the same case histories again. For each case history, 20 key variables were selected to calculate the accuracy for each observer. We assumed that the database was such that ≥ 90% of the entered data would be correct. The overall proportion of correctly entered data was calculated using a beta-binomial regression model to account for inter-observer variation and compared to the expected level of validity. Re-test reliability was assessed using McNemar's test. For all case histories, the overall proportion of correctly entered items and their confidence intervals included the target of 90% (Case 1: 92% [88-94%]; Case 2: 87% [83-91%]; Case 3: 93% [90-95%]; Case 4: 97% [94-99%]; Case 5: 91% [87-93%]). These numbers did not differ significantly from those found 6 weeks later [NcNemar's test p > 0.05]. The UR-CARE database appears to be feasible, valid and reliable as a tool and easy to use regardless of prior user experience and level of clinical IBD experience. UR-CARE has the potential to enhance future European collaborations regarding clinical research in IBD.