The genetic basis of dispersal in a vertebrate metapopulation.

Dispersal affects evolutionary processes by changing population size and genetic composition, influencing the viability and persistence of populations. Investigating which mechanisms underlie variation in dispersal phenotypes and whether populations harbour adaptive potential for dispersal is crucial to understanding the eco-evolutionary dynamics of this important trait. Here, we investigate the genetic architecture of dispersal among successfully recruited individuals in an insular metapopulation of house sparrows. We use an extensive long-term individual-based ecological data set and high-density single-nucleotide polymorphism (SNP) genotypes for over 2500 individuals. We conducted a genome-wide association study (GWAS), and found a relationship between dispersal probability and a SNP located near genes known to regulate circadian rhythm, glycogenesis and exercise performance, among other functions. However, this SNP only explained 3.8% of variance, suggesting that dispersal is a polygenic trait. We then used an animal model to estimate heritable genetic variation (σA 2 ), which composes 10% of the total variation in dispersal probability. Finally, we investigated differences in σA 2 across populations occupying ecologically relevant habitat types (farm vs. non-farm) using a genetic groups animal model. We found different adaptive potentials across habitats, with higher mean breeding value, σA 2 , and heritability for the habitat presenting lower dispersal rates, suggesting also different roles of environmental variation. Our results suggest a complex genetic architecture of dispersal and demonstrate that adaptive potential may be environment dependent in key eco-evolutionary traits. The eco-evolutionary implications of such environment dependence and consequent spatial variation are likely to become ever more important with the increased fragmentation and loss of suitable habitats for many natural populations.

A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations.

Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while most researchers are familiar with methods for treating missing data, accounting for ME in covariates of regression models is less common. In addition, ME and missing data are typically treated as two separate problems, despite practical and theoretical similarities. Here, we exploit the fact that missing data in a continuous covariate is an extreme case of classical ME, allowing us to use existing methodology that accounts for ME via a Bayesian framework that employs integrated nested Laplace approximations (INLA) and thus to simultaneously account for both ME and missing data in the same covariate. As a useful by-product, we present an approach to handle missing data in INLA since this corresponds to the special case when no ME is present. In addition, we show how to account for Berkson ME in the same framework. In its broadest generality, the proposed joint Bayesian framework can thus account for Berkson ME, classical ME, and missing data, or any combination of these in the same or different continuous covariates of the family of regression models that are feasible with INLA. The approach is exemplified using both simulated and real data. We provide extensive and fully reproducible Supporting Information with thoroughly documented examples using R-INLA and inlabru.

Spatial variation in the evolutionary potential and constraints of basal metabolic rate and body mass in a wild bird.

An organism's energy budget is strongly related to resource consumption, performance, and fitness. Hence, understanding the evolution of key energetic traits, such as basal metabolic rate (BMR), in natural populations is central for understanding life-history evolution and ecological processes. Here we used quantitative genetic analyses to study evolutionary potential of BMR in two insular populations of the house sparrow (Passer domesticus). We obtained measurements of BMR and body mass (Mb ) from 911 house sparrows on the islands of Leka and Vega along the coast of Norway. These two populations were the source populations for translocations to create an additional third, admixed 'common garden' population in 2012. With the use of a novel genetic group animal model concomitant with a genetically determined pedigree, we differentiate genetic and environmental sources of variation, thereby providing insight into the effects of spatial population structure on evolutionary potential. We found that the evolutionary potential of BMR was similar in the two source populations, whereas the Vega population had a somewhat higher evolutionary potential of Mb than the Leka population. BMR was genetically correlated with Mb in both populations, and the conditional evolutionary potential of BMR (independent of body mass) was 41% (Leka) and 53% (Vega) lower than unconditional estimates. Overall, our results show that there is potential for BMR to evolve independently of Mb , but that selection on BMR and/or Mb may have different evolutionary consequences in different populations of the same species.

Effectiveness of a Brief Hypnotic Induction in Third Molar Extraction: A Randomized Controlled Trial (HypMol).

Third molar extraction is a painful treatment for patients, and thus, it can be used to investigate the effects of analgesics on pain. Hypnosis can help to reduce pain and to decrease the intake of postoperative systemic analgesics. In this study, the effectiveness of a brief hypnotic induction for patients undergoing third molar extractions was investigated. Data were collected from 33 patients with third molar extractions on the right and left sides. Patients received 2 different types of pain interventions in this monocentric randomized crossover trial. Third molar extraction was conducted on 1 side with reduced preoperative local anesthetics and an additional brief hypnotic induction (Dave Elman technique). The other side was conducted with regular preoperative local anesthetics without a brief hypnotic induction (standard care). Intake of postoperative systemic analgesics was allowed in both treatments. Patients' expectations about hypnosis were assessed at baseline. The primary outcome was the area under the curve with respect to ground of pain intensity after the treatment. Secondary outcomes were the amount of postoperative analgesics consumed and the preferred treatment. There was no evidence that the area under the curve with respect to ground of pain differed between the 2 interventions (controlling for gender), but the patients' expectations affected the effectiveness of the brief hypnotic induction. This means that patients with high expectations about hypnosis benefit more from treatment with reduced preoperative local anesthetics and additional brief hypnotic induction. PERSPECTIVE: Hypnosis is used as a treatment to reduce pain in general and dental settings. In this study, additional a brief hypnotic induction with reduced preoperative local anesthetic use did not generally reduce posttreatment pain after third molar extraction more than regular local anesthetics. The expectation of the patients about the effectiveness of hypnosis affected the effectiveness of the brief hypnotic induction so that patients with high expectations had a larger benefit from a brief hypnotic induction than patients with low expectations.

Rewriting results sections in the language of evidence.

Despite much criticism, black-or-white null-hypothesis significance testing with an arbitrary P-value cutoff still is the standard way to report scientific findings. One obstacle to progress is likely a lack of knowledge about suitable alternatives. Here, we suggest language of evidence that allows for a more nuanced approach to communicate scientific findings as a simple and intuitive alternative to statistical significance testing. We provide examples for rewriting results sections in research papers accordingly. Language of evidence has previously been suggested in medical statistics, and it is consistent with reporting approaches of international research networks, like the Intergovernmental Panel on Climate Change, for example. Instead of re-inventing the wheel, ecology and evolution might benefit from adopting some of the 'good practices' that exist in other fields.

Effect of Briefing on Acupuncture Treatment Outcome Expectations, Pain, and Adverse Side Effects Among Patients With Chronic Low Back Pain: A Randomized Clinical Trial.

Importance: In observational studies, patients' treatment outcome expectations have been associated with better outcomes (ie, a placebo response), whereas concerns about adverse side effects have been associated with an in increase in the negative effects of treatments (ie, a nocebo response). Some randomized trials have suggested that communication from clinicians could affect the treatment outcomes by changing patients' expectations. Objective: To investigate whether treatment outcome expectations and reported adverse side effects could be affected by different briefing contents before a minimal acupuncture treatment in patients with chronic low back pain (CLBP). Design, Setting, and Participants: This randomized single-blinded clinical trial was conducted among patients with CLBP at 1 outpatient clinic in Switzerland who had a pain intensity of at least 4 on a numeric rating scale from 0 to 10. Different recruitment channels were used to enroll patients. Data were collected from May 2016 to December 2017 and were analyzed from June to November 2018. Interventions: Patients were randomized to receive either a regular expectation briefing or a high expectation briefing (effectiveness) and either a regular adverse side effect briefing or an intense adverse side effect briefing (adverse side effect) in a 2 × 2 factorial design. The intervention (briefing sessions and written materials) was standardized and delivered before the acupuncture treatment, with additional booster informative emails provided during the 4-week, 8-session acupuncture course. Main Outcomes and Measures: The primary end point was the patients' expectations regarding the effectiveness of the acupuncture treatment (Expectation for Treatment Scale [ETS]) after the briefing and the subsequent pain intensity (numeric rating scale). The primary end point for the adverse side effect briefing was the adverse side effect score at the end of the acupuncture treatment, derived from session-by-session assessments of adverse side effects. Results: A total of 152 patients with CLBP (mean [SD] age, 39.54 [12.52] years; 100 [65.8%] women) were included. The estimated group difference (regular vs high) for the ETS was -0.16 (95% CI -0.81 to 0.50, P = .64), indicating no evidence for a difference between intervention groups. There was also no evidence for a difference in pain intensity at the end of the acupuncture treatment between the groups with different expectation briefings. The adverse side effects score in the group with the intense adverse side effect briefing were estimated to be 1.31 times higher (95% CI, 0.94 to 1.82; P = .11) than after a regular adverse side effect briefing, but the finding was not statistically significant. Conclusions and Relevance: In this study, suggestions regarding treatment benefits (placebo) and adverse side effects (nocebo) did not affect treatment expectations or adverse side effects. Information regarding adverse side effects might require more research to understand nocebo responses. Trial Registration: German Clinical Trials Register Identifier: DRKS00010191.

Immigration counter-acts local micro-evolution of a major fitness component: Migration-selection balance in free-living song sparrows.

Ongoing adaptive evolution, and resulting "evolutionary rescue" of declining populations, requires additive genetic variation in fitness. Such variation can be increased by gene flow resulting from immigration, potentially facilitating evolution. But, gene flow could in fact constrain rather than facilitate local adaptive evolution if immigrants have low additive genetic values for local fitness. Local migration-selection balance and micro-evolutionary stasis could then result. However, key quantitative genetic effects of natural immigration, comprising the degrees to which gene flow increases the total local additive genetic variance yet counteracts local adaptive evolutionary change, have not been explicitly quantified in wild populations. Key implications of gene flow for population and evolutionary dynamics consequently remain unclear. Our quantitative genetic analyses of long-term data from free-living song sparrows (Melospiza melodia) show that mean breeding value for local juvenile survival to adulthood, a major component of fitness, increased across cohorts more than expected solely due to drift. Such micro-evolutionary change should be expected given nonzero additive genetic variance and consistent directional selection. However, this evolutionary increase was counteracted by negative additive genetic effects of recent immigrants, which increased total additive genetic variance but prevented a net directional evolutionary increase in total additive genetic value. These analyses imply an approximate quantitative genetic migration-selection balance in a major fitness component, and hence demonstrate a key mechanism by which substantial additive genetic variation can be maintained yet decoupled from local adaptive evolutionary change.

Consistent scaling of inbreeding depression in space and time in a house sparrow metapopulation.

Inbreeding may increase the extinction risk of small populations. Yet, studies using modern genomic tools to investigate inbreeding depression in nature have been limited to single populations, and little is known about the dynamics of inbreeding depression in subdivided populations over time. Natural populations often experience different environmental conditions and differ in demographic history and genetic composition, characteristics that can affect the severity of inbreeding depression. We utilized extensive long-term data on more than 3,100 individuals from eight islands in an insular house sparrow metapopulation to examine the generality of inbreeding effects. Using genomic estimates of realized inbreeding, we discovered that inbred individuals had lower survival probabilities and produced fewer recruiting offspring than noninbred individuals. Inbreeding depression, measured as the decline in fitness-related traits per unit inbreeding, did not vary appreciably among populations or with time. As a consequence, populations with more resident inbreeding (due to their demographic history) paid a higher total fitness cost, evidenced by a larger variance in fitness explained by inbreeding within these populations. Our results are in contrast to the idea that effects of inbreeding generally depend on ecological factors and genetic differences among populations, and expand the understanding of inbreeding depression in natural subdivided populations.

Resistance to gapeworm parasite has both additive and dominant genetic components in house sparrows, with evolutionary consequences for ability to respond to parasite challenge.

Host-parasite relationships are likely to change over the coming decades in response to climate change and increased anthropogenic stressors. Understanding the genetic architecture of parasite resistance will aid prediction of species' responses to intensified parasite challenge. The gapeworm "Syngamus trachea" is prevalent in natural bird populations and causes symptomatic infections ranging from mild to severe. The parasite may affect ecological processes by curtailing bird populations and is important due to its propensity to spread to commercially farmed birds. Our large-scale data set on an insular house sparrow metapopulation in northern Norway includes information on gapeworm prevalence and infection intensity, allowing assessment of the genetics of parasite resistance in a natural system. To determine whether parasite resistance has a heritable genetic component, we performed variance component analyses using animal models. Resistance to gapeworm had substantial additive genetic and dominance variance, and genome-wide association studies to identify single nucleotide polymorphisms associated with gapeworm resistance yielded multiple loci linked to immune function. Together with genome partitioning results, this indicates that resistance to gapeworm is under polygenic control in the house sparrow, and probably in other bird species. Hence, our results provide the foundation needed to study any eco-evolutionary processes related to gapeworm infection, and show that it is necessary to use methods suitable for polygenic and nonadditive genetic effects on the phenotype.

Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation.

Popular frameworks for studying habitat selection include resource-selection functions (RSFs) and step-selection functions (SSFs), estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animals. Conceptually, slopes that vary by individual, that is, random coefficient models, could be used to accommodate inter-individual heterogeneity with either approach. While fitting such models for RSFs is possible with standard software for generalized linear mixed-effects models (GLMMs), straightforward and efficient one-step procedures for fitting SSFs with random coefficients are currently lacking. To close this gap, we take advantage of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood-equivalent to a Poisson model with stratum-specific fixed intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference for random-slope models becomes feasible with standard Bayesian techniques, or with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including models without random slopes and mixed conditional regression models fit using a two-step algorithm. Using data from mountain goats (Oreamnos americanus) and Eurasian otters (Lutra lutra), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to compare different estimation approaches for SSFs and to demonstrate the importance of including individual-specific slopes when estimating individual- and population-level habitat-selection parameters. By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales, but these models had previously been very challenging to fit.

Inbreeding reduces long-term growth of Alpine ibex populations.

Many studies document negative inbreeding effects on individuals, and conservation efforts to preserve rare species routinely employ strategies to reduce inbreeding. Despite this, there are few clear examples in nature of inbreeding decreasing the growth rates of populations, and the extent of population-level effects of inbreeding in the wild remains controversial. Here, we take advantage of a long-term dataset of 26 reintroduced Alpine ibex (Capra ibex ibex) populations spanning nearly 100 years to show that inbreeding substantially reduced per capita population growth rates, particularly for populations in harsher environments. Populations with high average inbreeding (F ≈ 0.2) had population growth rates reduced by 71% compared with populations with no inbreeding. Our results show that inbreeding can have long-term demographic consequences even when environmental variation is large and deleterious alleles may have been purged during bottlenecks. Thus, efforts to guard against inbreeding effects in populations of endangered species have not been misplaced.

Endotype-Phenotype Patterns in Meniere's Disease Based on Gadolinium-Enhanced MRI of the Vestibular Aqueduct.

Two histopathological subtypes of Meniere's disease (MD) were recently described in a human post-mortem pathology study. The first subtype demonstrated a degenerating distal endolymphatic sac (ES) in the affected inner ear (subtype MD-dg); the second subtype (MD-hp) demonstrated an ES that was developmentally hypoplastic. The two subtypes were associated with different clinical disease features (phenotypes), suggesting that distinct endotype-phenotype patterns exist among MD patients. Therefore, clinical endotyping based on ES pathology may reveal clinically meaningful MD patient subgroups. Here, we retrospectively determined the ES pathologies of clinical MD patients (n = 72) who underwent intravenous delayed gadolinium-enhanced inner ear magnetic resonance imaging using previously established indirect radiographic markers for both ES pathologies. Phenotypic subgroup differences were evidenced; for example, the MD-dg group presented a higher average of vertigo attacks (ratio of vertigo patterns daily/weekly/other vs. monthly, MD-dg: 6.87: 1; MD-hp: 1.43: 1; p = 0.048) and more severely reduced vestibular function upon caloric testing (average caloric asymmetry ratio, MD-dg: 30.2% ± 30.4%; MD-hp: 13.5% ± 15.2%; p = 0.009), while the MD-hp group presented a predominantly male sex ratio (MD-hp: 0.06:1 [f/m]; MD-dg: 1.2:1 [f/m]; p = 0.0004), higher frequencies of bilateral clinical affection (MD-hp: 29.4%; MD-dg: 5.5%; p = 0.015), a positive family history for hearing loss/vertigo/MD (MD-hp: 41.2%; MD-dg: 15.7%; p = 0.028), and radiographic signs of concomitant temporal bone abnormalities, i.e., semicircular canal dehiscence (MD-hp: 29.4%; MD-dg: 3.6%; p = 0.007). In conclusion, this new endotyping approach may potentially improve the diagnosis, prognosis and clinical decision-making for individual MD patients.

Animal models with group-specific additive genetic variances: extending genetic group models.

BACKGROUND: The animal model is a key tool in quantitative genetics and has been used extensively to estimate fundamental parameters, such as additive genetic variance or heritability. An implicit assumption of animal models is that all founder individuals derive from a single population. This assumption is commonly violated, for instance in crossbred livestock or when a meta-population is split into genetically differentiated subpopulations. Ignoring that base populations are genetically heterogeneous and thus split into different 'genetic groups' may lead to biased parameter estimates, especially for additive genetic variance. To avoid such biases, genetic group animal models, which account for the presence of more than one genetic group, have been proposed. Unfortunately, the method to date is only computationally feasible when the breeding values of the groups are allowed to differ in their means, but not in their variances. RESULTS: We present an extension of the animal model that permits estimation of group-specific additive genetic variances. This is achieved by employing group-specific relatedness matrices for the breeding value components to different genetic groups. We derive these matrices by decomposing the full relatedness matrix via the generalized Cholesky decomposition, and by scaling the respective matrix components for each group. We propose a computationally convenient approximation for the matrix component that encodes for the Mendelian sampling variance, and show that this approximation is not critical. In addition, we explain why segregation variances are often negligible when analyzing the complex polygenic traits that are frequently the focus of evolutionary ecologists and animal breeders. Simulations and an example from an insular meta-population of house sparrows in Norway with three distinct genetic groups illustrate that the method is successful in estimating group-specific additive genetic variances, and that segregation variances are indeed negligible in the empirical example. CONCLUSIONS: Quantifying differences in additive genetic variance within and among populations is of major biological interest in ecology, evolution, and animal and plant breeding. The proposed method allows to estimate such differences for subpopulations that form a connected set of populations, and may thus also be useful to study temporal or spatial variation of additive genetic variances.

Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load.

Inbreeding depression, the deterioration in mean trait value in progeny of related parents, is a fundamental quantity in genetics, evolutionary biology, animal and plant breeding, and conservation biology. The magnitude of inbreeding depression can be quantified by the inbreeding load, typically measured in numbers of lethal equivalents, a population genetic quantity that allows for comparisons between environments, populations or species. However, there is as yet no quantitative assessment of which combinations of statistical models and metrics of inbreeding can yield such estimates. Here, we review statistical models that have been used to estimate inbreeding load and use population genetic simulations to investigate how unbiased estimates can be obtained using genomic and pedigree-based metrics of inbreeding. We use simulated binary viability data (i.e., dead versus alive) as our example, but the concepts apply to any trait that exhibits inbreeding depression. We show that the increasingly popular generalized linear models with logit link do not provide comparable and unbiased population genetic measures of inbreeding load, independent of the metric of inbreeding used. Runs of homozygosity result in unbiased estimates of inbreeding load, whereas inbreeding measured from pedigrees results in slight overestimates. Due to widespread use of models that do not yield unbiased measures of the inbreeding load, some estimates in the literature cannot be compared meaningfully. We surveyed the literature for reliable estimates of the mean inbreeding load from wild vertebrate populations and found an average of 3.5 haploid lethal equivalents for survival to sexual maturity. To obtain comparable estimates, we encourage researchers to use generalized linear models with logarithmic links or maximum-likelihood estimation of the exponential equation, and inbreeding coefficients calculated from runs of homozygosity, provided an assembled reference genome of sufficient quality and enough genetic marker data are available.

Heritability, selection, and the response to selection in the presence of phenotypic measurement error: Effects, cures, and the role of repeated measurements.

Quantitative genetic analyses require extensive measurements of phenotypic traits, a task that is often not trivial, especially in wild populations. On top of instrumental measurement error, some traits may undergo transient (i.e., nonpersistent) fluctuations that are biologically irrelevant for selection processes. These two sources of variability, which we denote here as measurement error in a broad sense, are possible causes for bias in the estimation of quantitative genetic parameters. We illustrate how in a continuous trait transient effects with a classical measurement error structure may bias estimates of heritability, selection gradients, and the predicted response to selection. We propose strategies to obtain unbiased estimates with the help of repeated measurements taken at an appropriate temporal scale. However, the fact that in quantitative genetic analyses repeated measurements are also used to isolate permanent environmental instead of transient effects requires that the information content of repeated measurements is carefully assessed. To this end, we propose to distinguish "short-term" from "long-term" repeats, where the former capture transient variability and the latter help isolate permanent effects. We show how the inclusion of the corresponding variance components in quantitative genetic models yields unbiased estimates of all quantities of interest, and we illustrate the application of the method to data from a Swiss snow vole population.

Human-biomonitoring and individual soil measurements for children and mothers in an area with recently detected mercury-contaminations and public health concerns: a cross-sectional study.

In this study, we assessed intracorporal mercury concentrations in subjects living on partially mercury-contaminated soils in a defined area in Switzerland. We assessed 64 mothers and 107 children who resided in a defined area for at least 3 months. Mercury in biological samples (urine and hair) was measured, a detailed questionnaire was administered for each individual, and individual mercury soil values were obtained. Human biomonitoring results were compared with health-related and reference values. Mothers and children in our study had geometric means (GMs) of 0.22 µg Hg/g creatinine in urine (95th percentile (P95) = 0.85 µg Hg/g) and 0.16 µg Hg/g (P95 = 0.56 µg Hg/g), respectively. In hair, mothers and children had GMs of 0.21 µg Hg/g (P95 = 0.94 µg/g) and 0.18 µg/g (P95 = 0.60 µg/g), respectively. We found no evidence for an association between mercury values in soil and those in human specimens nor for a health threat in residential mothers and children.

Predicted Mercury Soil Concentrations from a Kriging Approach for Improved Human Health Risk Assessment.

Health-risks from contaminated soils are assessed all over the world. An aspect that many risk assessments share is the heterogeneity in the distribution of contaminants. In a preceding study, we assessed potential health-risks for mothers and children living on mercury-contaminated soils in Switzerland using human biomonitoring-values (HBM) and soil samples. We assessed 64 mothers and 107 children who had resided in a defined area for at least 3 months. HBM-concentrations for mercury in urine and hair were measured, a detailed questionnaire was administered for each individual, and more than 4000 individual mercury soil values were obtained in 2015. In this study, we aimed at investigating possible associations of mercury soil- and HBM-values by re-analyzing our data, using predictions of the mercury concentrations at the exact location of the participant’s homes with a kriging approach. Although kriging proved to be a useful method to predict mercury soil concentrations, we did not detect an association between mercury soil- and HBM-values, in agreement with earlier findings. Benefits of geostatistical methods seem to be limited in the context of our study. Conclusions made in our preceding study about potential health risks for the residential population are robust and not altered by the current study.

Bias away from the null due to miscounted outcomes? A case study on the TORCH trial.

Count outcomes occur in virtually all disciplines, such as medicine, epidemiology or biology, but they often contain error. Recently, it has been shown that self-reported numbers of exacerbations of Chronic Obstructive Pulmonary Disease patients can be considerably miscounted. Motivated by this result, we reanalysed data from the Towards a Revolution in Chronic Obstructive Pulmonary Disease Health trial, a large randomized controlled trial with the self-reported number of exacerbations of Chronic Obstructive Pulmonary Disease patients as outcome. To adjust for miscounting error in the response of Poisson and (zero-inflated) negative binomial models, we introduce novel, general methodology. The key idea is to formulate a zero-inflated negative binomial model to capture the error mechanism. This parametric approach automatically circumvents drawbacks of previously suggested methodology that treats miscounted outcomes in the misclassification framework. Prior information for the response error model parameters was elicited from validation data of an external study and adaptively weighted to account for potential prior-data conflict. The results of the Bayesian hierarchical modelling approach indicated that the treatment effect has been overestimated in the original study. However, closer inspection revealed that this unexpected result was an artefact of an unaccounted time dependency of the treatment effect.