Data Representativeness: Issues and Solutions

In its control programmes on maximum residue level compliance and exposure assessments, EFSA requires the participating countries to submit results, from specific numbers of food item samples, analyzed in the countries. These data are used to obtain estimates such as the proportion of samples exceeding the maximum residue limits, and the mean and maximum residue concentration per food item to assess exposure. An important consideration is the design and analysis of the programmes. In this report, we combine elements of survey sampling methodology, and statistical modeling, as a benchmark framework for the programmes, starting from the translation of research questions into statistical problems, to the statistical analysis and interpretation. Particular focus is placed on the issues that could affect the representativeness of the data, and remedial procedures are proposed. For example, in the absence of information on the sampling design, a sensitivity analysis, across a range of designs, is proposed. On the other hand, weighted generalized linear mixed models, and generalized linear mixed models combining both conjugate and normal random effects, are proposed, to address selection bias. Likelihood-based analysis methods are also proposed to address missing and censored data problems. Suggestions for improvements in the design and analysis of the programmes are also identified and discussed. For instance, incorporation of stratified sampling methodology, in determining both the total number, and the allocation of samples to the participating countries, is proposed. All through the report, statistical analysis models which properly take into account the hierarchical (and thus correlated) structure in which the data are collected are proposed

Developing fencing policies in dryland ecosystems

The daily energy requirements of animals are determined by a combination of physical and physiological factors, but food availability may challenge the capacity to meet nutritional needs. Western gorillas (Gorilla gorilla) are an interesting model for investigating this topic because they are folivore-frugivores that adjust their diet and activities to seasonal variation in fruit availability. Observations of one habituated group of western gorillas in Bai-Hokou, Central African Republic (December 2004-December 2005) were used to examine seasonal variation in diet quality and nutritional intake. We tested if during the high fruit season the food consumed by western gorillas was higher in quality (higher in energy, sugar, fat but lower in fibre and antifeedants) than during the low fruit season. Food consumed during the high fruit season was higher in digestible energy, but not any other macronutrients. Second, we investigated whether the gorillas increased their daily intake of carbohydrates, metabolizable energy (KCal/g OM), or other nutrients during the high fruit season. Intake of dry matter, fibers, fat, protein and the majority of minerals and phenols decreased with increased frugivory and there was some indication of seasonal variation in intake of energy (KCal/g OM), tannins, protein/fiber ratio, and iron. Intake of non-structural carbohydrates and sugars was not influenced by fruit availability. Gorillas are probably able to extract large quantities of energy via fermentation since they rely on proteinaceous leaves during the low fruit season. Macronutrients and micronutrients, but not digestible energy, may be limited for them during times of low fruit availability because they are hind-gut fermenters. We discuss the advantages of seasonal frugivores having large dietary breath and flexibility, significant characteristics to consider in the conservation strategies of endangered species

Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated

A permutational-splitting sample procedure to quantify expert opinion on clusters of chemical compounds using high-dimensional data

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. We propose a method to quantify these qualitative assessments using hierarchical models. However, with the most commonly available computing resources, the high dimensionality of the vectors of fixed effects and correlated responses renders maximum likelihood unfeasible in this scenario. We devise a reliable procedure to tackle this problem and show, using theoretical arguments and simulations, that the new methodology compares favorably with maximum likelihood, when the latter option is available. The approach was motivated by a case study, which we present and analyze.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS772 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Does physical activity strengthen lungs and protect against asthma in childhood? A systematic review

BACKGROUND: Physical activity may be a potentially modifiable risk factor for asthma and driver of lung function development. This systematic review aimed to summarize the available evidence concerning the longitudinal effect of physical activity on the development of asthma, the persistence of asthma symptoms and lung function outcomes in children and adolescents. METHODS: PubMed and Embase electronic databases were searched for all original articles that investigated the longitudinal association between physical activity and asthma outcomes or lung function outcomes in children and adolescents. The search and data extraction were conducted by two independent researchers. The methodological quality of the included studies was assessed using two critical assessment tools. RESULTS: The literature search retrieved 2298 publications from the electronic databases. All articles were screened, and 2289 were subsequently excluded, resulting in nine longitudinal studies eligible for inclusion in this review. Two studies found no association with incident wheeze, and two of four found no association with various asthma outcomes. Three studies investigated the effect on lung function: one observed an association in boys only, one observed an association in girls only, and one found no associations. CONCLUSION: The evidence was highly inconsistent for the relationship between physical activity and asthma and lung function outcomes. Hence, we conclude that there is insufficient evidence to suggest that physical activity has a long-term effect on the risk of asthma development in youth. Furthermore, there is insufficient evidence to determine the longitudinal effects of physical activity on lung function in children

Estimation After a Group Sequential Trial

Group sequential trials are one important instance of studies for which the sample size is not fixed a priori but rather takes one of a finite set of pre-specified values, dependent on the observed data. Much work has been devoted to the inferential consequences of this design feature. Molenberghs et al. (Statistical Methods in Medical Research, 2012) and Milanzi et al. (Properties of estimators in exponential family settings with observation-based stopping rules, 2012) reviewed and extended the existing literature, focusing on a collection of seemingly disparate, but related, settings, namely completely random sample sizes, group sequential studies with deterministic and random stopping rules, incomplete data, and random cluster sizes. They showed that the ordinary sample average is a viable option for estimation following a group sequential trial, for a wide class of stopping rules and for random outcomes with a distribution in the exponential family. Their results are somewhat surprising in the sense that the sample average is not optimal, and further, there does not exist an optimal, or even, unbiased linear estimator. However, the sample average is asymptotically unbiased, both conditionally upon the observed sample size as well as marginalized over it. By exploiting ignorability they showed that the sample average is the conventional maximum likelihood estimator. They also showed that a conditional maximum likelihood estimator is finite sample unbiased, but is less efficient than the sample average and has the larger mean squared error. Asymptotically, the sample average and the conditional maximum likelihood estimator are equivalent. This previous work is restricted, however, to the situation in which the the random sample size can take only two values, N = n or N = 2n. In this paper, we consider the more practically useful setting of sample sizes in a the finite set {n(1), n(2) , . . . , nL}. It is shown that the sample average is then a justifiable estimator, in the sense that it follows from joint likelihood estimation, and it is consistent and asymptotically unbiased. We also show why simulations can give the false impression of bias in the sample average when considered conditional upon the sample size. The consequence is that no corrections need to be made to estimators following sequential trials. When small-sample bias is of concern, the conditional likelihood estimator (CLE) provides a relatively straightforward modification to the sample average. Finally, it is shown that classical likelihood-based standard errors and confidence intervals can be applied, obviating the need for technical corrections

Estimation after a group sequential trial

Group sequential trials are one important instance of studies for which the sample size is not fixed a priori but rather takes one of a finite set of pre-specified values, dependent on the observed data. Much work has been devoted to the inferential consequences of this design feature. Molenberghs et al (2012) and Milanzi et al (2012) reviewed and extended the existing literature, focusing on a collection of seemingly disparate, but related, settings, namely completely random sample sizes, group sequential studies with deterministic and random stopping rules, incomplete data, and random cluster sizes. They showed that the ordinary sample average is a viable option for estimation following a group sequential trial, for a wide class of stopping rules and for random outcomes with a distribution in the exponential family. Their results are somewhat surprising in the sense that the sample average is not optimal, and further, there does not exist an optimal, or even, unbiased linear estimator. However, the sample average is asymptotically unbiased, both conditionally upon the observed sample size as well as marginalized over it. By exploiting ignorability they showed that the sample average is the conventional maximum likelihood estimator. They also showed that a conditional maximum likelihood estimator is finite sample unbiased, but is less efficient than the sample average and has the larger mean squared error. Asymptotically, the sample average and the conditional maximum likelihood estimator are equivalent. This previous work is restricted, however, to the situation in which the the random sample size can take only two values, N = n or N = 2n. In this paper, we consider the more practically useful setting of sample sizes in a the finite set {n1, n2, …, nL }. It is shown that the sample average is then a justifiable estimator , in the sense that it follows from joint likelihood estimation, and it is consistent and asymptotically unbiased. We also show why simulations can give the false impression of bias in the sample average when considered conditional upon the sample size. The consequence is that no corrections need to be made to estimators following sequential trials. When small-sample bias is of concern, the conditional likelihood estimator provides a relatively straightforward modification to the sample average. Finally, it is shown that classical likelihood-based standard errors and confidence intervals can be applied, obviating the need for technical corrections.status: publishe

Receipt of infant HIV DNA PCR test results is associated with a reduction in retention of HIV-exposed infants in integrated HIV care and healthcare services: a quantitative sub-study nested within a cluster randomised trial in rural Malawi

BACKGROUND: Retention of HIV-infected mothers in integrated HIV and healthcare facilities is effective at reducing mother-to-child-transmission (MTCT) of HIV. In the context of Option B+, we examined maternal and HIV-exposed infant retention across three study arms to 18 months postpartum: mother-and-infant clinics (MIP), MIP with short-messaging service (MIP + SMS) and standard of care (SOC). In particular, we focused on the impact of mothers receiving an infant's HIV PCR test result on maternal and infant study retention. METHODS: A quantitative sub-study nested within a cluster randomised trial undertaken between May 2013 and August 2016 across 30 healthcare facilities in rural Malawi enrolling HIV-infected pregnant mothers and HIV-exposed infants on delivery, was performed. Survival probabilities of maternal and HIV-exposed infant study retention was estimated using Kaplan-Meier curves. Associations between mother's receiving an infant's HIV test result and in particular, an infant's HIV-positive result on maternal and infant study retention were modelled using time-varying multivariate Cox regression. RESULTS: Four hundred sixty-one, 493, and 396 HIV-infected women and 386, 399, and 300 HIV-exposed infants were enrolled across study arms; MIP, MIP + SMS and SOC, respectively. A total of 47.5% of mothers received their infant's HIV test results < 5 months postpartum. Receiving an infant's HIV result by mothers was associated with a 70% increase in infant non-retention in the study compared with not receiving an infant's result (HR = 1.70; P-value< 0.001). Receiving a HIV-positive result was associated with 3.12 times reduced infant retention compared with a HIV-negative result (P-value< 0.001). Of the infants with a HIV-negative test result, 87% were breastfed at their final study follow-up. CONCLUSIONS: Receiving an infant's HIV test result was a driving factor for reduced infant study retention, especially an infant's HIV-positive test result. As most HIV-negative infants were still breastfed at their last follow-up, this indicates a large proportion of HIV-exposed infants were potentially at future risk of MTCT of HIV via breastfeeding but were unlikely to undergo follow-up HIV testing after breastfeeding cessation. Future studies to identify and address underlying factors associated with infant HIV testing and reduced infant retention could potentially improve infant retention in HIV/healthcare facilities. TRIAL REGISTRATION: Pan African Clinical Trial Registry: PACTR201312000678196