Validating a common tick survey method: cloth-dragging and line transects.

Cloth-dragging is the most widely-used method for collecting and counting ticks, but there are few studies of its reliability. By using cloth-dragging, we applied a replicated line transects survey method, in two areas in Sweden with different Ixodes ricinus tick-densities (low at Grimsö and high at Bogesund) to evaluate developmental stage specific repeatability, agreement and precision in estimates of tick abundance. 'Repeatability' was expressed as the Intraclass Correlation Coefficient (ICC), 'agreement' with the Total Deviation Index (TDI) and 'precision' by the coefficient of variation (CV) for a given dragging distance. Repeatability (ICC) and agreement (TDI) were higher for the most abundant instar (nymphs) and in the area of higher abundance. At Bogesund tick counts were higher than at Grimsö and so also repeatability, with fair to substantial ICC estimates between 0.22 and 0.75, and TDI ranged between 1 and 44.5 counts of difference (thus high to moderate agreement). At Grimsö, ICC was poor to moderate and ranged between 0 and 0.59, whereas TDI remained low with estimates lower or equal to 1 count (thus high agreement). Despite a 100-fold lower abundance at Grimsö, the same level of precision for nymphs could be achieved with a 70% increase of dragging effort. We conclude that the cloth-dragging technique is useful for surveying ticks' and primarily to estimate abundance of the nymphal stage, whereas it rarely will be recommended for larvae and adults.

Estimating marginal proportions and intraclass correlations with clustered binary data.

A logistic regression with random effects model is commonly applied to analyze clustered binary data, and every cluster is assumed to have a different proportion of success. However, it could be of interest to obtain the proportion of success over clusters (i.e. the marginal proportion of success). Furthermore, the degree of correlation among data of the same cluster (intraclass correlation) is also a relevant concept to assess, but when using logistic regression with random effects it is not possible to get an analytical expression of the estimators for marginal proportion and intraclass correlation. In our paper, we assess and compare approaches using different kinds of approximations: based on the logistic-normal mixed effects model (LN), linear mixed model (LMM), and generalized estimating equations (GEE). The comparisons are completed by using two real data examples and a simulation study. The results show the performance of the approaches strongly depends on the magnitude of the marginal proportion, the intraclass correlation, and the sample size. In general, the reliability of the approaches get worsen with low marginal proportion and large intraclass correlation. LMM and GEE approaches arises as reliable approaches when the sample size is large.

Seizure-onset zone localization by statistical parametric mapping in visually normal (18) F-FDG PET studies.

OBJECTIVE: Neuroimaging is crucial in the presurgical evaluation of patients with medically refractory epilepsy. To improve the moderate sensitivity of [(18) F]fluorodeoxyglucose-positron emission tomography ((18) F-FDG-PET), our aim was to evaluate the usefulness of statistical parametric mapping (SPM) to localize the seizure-onset zone (SOZ) in PET studies deemed normal by visual assessment. METHODS: Fifty-five patients with medically refractory epilepsy whose (18) F-FDG-PET was visually evaluated as normal were retrospectively included. Twenty of these patients had undergone surgical intervention. PET images were analyzed by SPM8 using a corrected p-value of p < 0.05 and three uncorrected p-values of p < 0.0001, p < 0.001, and p < 0.005, matched with minimum cluster sizes of k > 0, k > 20, k > 100, and k > 200, respectively. The SPM-identified potential seizure zone (SZ) was compared to the SOZ, which was determined by consensus during patient management meetings in the epilepsy unit, taking into account presurgical tests. Studies in which the SPM-identified potential SZ was concordant with the SOZ were considered "correctly localizing." RESULTS: The SPM threshold combination with the least restrictive p-value and greatest minimum cluster size achieved the highest rate of correctly localizing studies. When p < 0.005/k > 200 was used, 40% (22/55) of studies were correctly localizing, and the concordance obtained in the surgically intervened subgroup was substantial (к = 0.607, 95% confidence interval [CI] 0.258-0.957), which was comparable to the concordance obtained by magnetic resonance imaging (MRI) (к = 0.783, 95% CI 0.509-1.000). SIGNIFICANCE: SPM offers improved SOZ localization in (18) F-FDG-PET studies that are negative on visual assessment. For this purpose, statistical parametric maps could be thresholded with liberal p-values and restrictive cluster sizes.

Risk factors for the development of chronic back pain after percutaneous vertebroplasty versus conservative treatment.

In a recent randomized controlled trial comparing vertebroplasty (VP) versus conservative treatment (CT) in patients with symptomatic vertebral fractures (VF), we observed the development of chronic back pain (CBP) in nearly one-quarter of patients. The aim of this study was to identify the risk factors related to the development of severe CBP in these subjects. We evaluated risk factors including visual analog scale (VAS) at baseline and during the 1-year follow-up, age, gender, symptom onset time, number, type and severity of VF at baseline, number of vertebral bodies treated, incident VF, and antiosteoporotic treatment, among others. CBP was considered in patients with VAS ≥ 7 at 12 months. 91/125 patients completed the 12-months follow-up. CBP was observed in 23% of VP-treated patients versus 23% receiving CT. Patients developing CBP after VP showed a longer symptom onset time (82% ≥ 4 months in VP vs. 40% in CT, P = 0.03). On univariate analysis, female gender (OR 1.52; 95% CI 1.47-1.57, P < 0.0001), multiple acute VF (OR 1.79; 95% CI 1.71-1.87, P < 0.0001), VAS ≥ 7 two months after treatment (OR 11.04; 95% CI 6.71-18.17, P < 0.0001), and type of antiosteoporotic drug (teriparatide) (OR 0.12; 95% CI 0.03-0.60, P = 0.0236) were risk factors of CBP development in both groups. In the multivariate analysis, the main risk factors were baseline and post-treatment VAS ≥ 7, longer symptom onset time, and type of antiosteoporotic treatment. In conclusion, 23% of patients with symptomatic osteoporotic VF developed severe CBP independently of the type of treatment. Symptom onset time before VP and persistence of severe CBP after treatment were the main factors related to CBP with teriparatide treatment decreasing the risk of this complication.

A non-parametric approach to estimate the total deviation index for non-normal data.

Concordance indices are used to assess the degree of agreement between different methods that measure the same characteristic. In this context, the total deviation index (TDI) is an unscaled concordance measure that quantifies to which extent the readings from the same subject obtained by different methods may differ with a certain probability. Common approaches to estimate the TDI assume data are normally distributed and linearity between response and effects (subjects, methods and random error). Here, we introduce a new non-parametric methodology for estimation and inference of the TDI that can deal with any kind of quantitative data. The present study introduces this non-parametric approach and compares it with the already established methods in two real case examples that represent situations of non-normal data (more specifically, skewed data and count data). The performance of the already established methodologies and our approach in these contexts is assessed by means of a simulation study.

Carryover negligibility and relevance in bioequivalence studies.

The carryover effect is a recurring issue in the pharmaceutical field. It may strongly influence the final outcome of an average bioequivalence study. Testing a null hypothesis of zero carryover is useless: not rejecting it does not guarantee the non-existence of carryover, and rejecting it is not informative of the true degree of carryover and its influence on the validity of the final outcome of the bioequivalence study. We propose a more consistent approach: even if some carryover is present, is it enough to seriously distort the study conclusions or is it negligible? This is the central aim of this paper, which focuses on average bioequivalence studies based on 2 × 2 crossover designs and on the main problem associated with carryover: type I error inflation. We propose an equivalence testing approach to these questions and suggest reasonable negligibility or relevance limits for carryover. Finally, we illustrate this approach on some real datasets.

Distinguishability and agreement with continuous data.

The analysis of concordance among repeated measures has received a huge amount of attention in the statistical literature leading to a range of different approaches. However, because all the approaches are able to assess the closeness among the readings taken on the same subject, the conclusions about the degree of concordance should be similar regardless the approach applied. Here, two indices to assess the concordance among continuous repeated measures, the intraclass correlation coefficient and the total deviation index, are applied and compared in two case examples. The first example concerns the repeatability of individual nutrient allocation strategy assessed by stable isotope analysis. The second example dealt with the assessment of the concordance of functional magnetic resonance imaging data that shows spatial correlation. The results differ depending upon the approach applied leading to contradictory conclusions about the degree of concordance. The reason behind these results is discussed reaching the conclusion that the total deviation index is just assessing agreement among repeated measurements, whereas the intraclass correlation coefficient assesses the concept of distinguishability among subjects that involves agreement among repeated measurements and spread of subjects at once. Therefore, the best way to select the right approach is to understand the right question behind the research hypothesis.

Spatio-temporal analysis of mortality among children under the age of five in Manhiça (Mozambique) during the period 1997-2005.

BACKGROUND: Reducing childhood mortality is the fourth goal of the Millennium Development Goals agreed at the United Nations Millennium Summit in September 2000. However, childhood mortality in developing countries remains high. Providing an accurate picture of space and time-trend variations in child mortality in a region might generate further ideas for health planning actions to achieve such a reduction. The purpose of this study was to examine the spatio-temporal variation for child mortality rates in Manhiça, a district within the Maputo province of southern rural Mozambique during the period 1997-2005 using a proper generalized linear mixed model. RESULTS: The results showed that childhood mortality in all the area was modified from year to year describing a convex time-trend but the spatial pattern described by the neighbourhood-specific underlying mortality rates did not change during the entire period from 1997 to 2005, where neighbourhoods with highest risks are situated in the peripheral side of the district. The spatial distribution, though more blurred here, was similar to the spatial distribution of child malaria incidence in the same area. The peak in mortality rates observed in 2001 could have been caused by the precipitation system that started in early February 2000, following which heavy rains flooded parts of Mozambique's southern provinces. However, the mortality rates at the end of the period returned to initial values. CONCLUSIONS: The results of this study suggest that the health intervention programmes established in Manhiça to alleviate the effects of flooding on child mortality should cover a period of around five years and that special attention might be focused on eradicating malaria transmission. These outcomes also suggest the utility of suitably modelling space-time trend variations in a region when a point effect of an environmental factor affects all the study area.

The effect of variability and carryover on average bioequivalence assessment: a simulation study.

The purpose of this study was to evaluate the effect of residual variability and carryover on average bioequivalence (ABE) studies performed under a 22 crossover design. ABE is usually assessed by means of the confidence interval inclusion principle. Here, the interval under consideration was the standard 'shortest' interval, which is the mainstream approach in practice. The evaluation was performed by means of a simulation study under different combinations of carryover and residual variability besides of formulation effect and sample size. The evaluation was made in terms of percentage of ABE declaration, coverage and interval precision. As is well known, high levels of variability distort the ABE procedures, particularly its type II error control (i.e. high variabilities make difficult to declare bioequivalence when it holds). The effect of carryover is modulated by variability and is especially disturbing for the type I error control. In the presence of carryover, the risk of erroneously declaring bioequivalence may become high, especially for low variabilities and large sample sizes. We end up with some hints concerning the controversy about pretesting for carryover before performing ABE analysis.

A generalized concordance correlation coefficient based on the variance components generalized linear mixed models for overdispersed count data.

The classical concordance correlation coefficient (CCC) to measure agreement among a set of observers assumes data to be distributed as normal and a linear relationship between the mean and the subject and observer effects. Here, the CCC is generalized to afford any distribution from the exponential family by means of the generalized linear mixed models (GLMMs) theory and applied to the case of overdispersed count data. An example of CD34+ cell count data is provided to show the applicability of the procedure. In the latter case, different CCCs are defined and applied to the data by changing the GLMM that fits the data. A simulation study is carried out to explore the behavior of the procedure with a small and moderate sample size.

The total deviation index estimated by tolerance intervals to evaluate the concordance of measurement devices.

BACKGROUND: In an agreement assay, it is of interest to evaluate the degree of agreement between the different methods (devices, instruments or observers) used to measure the same characteristic. We propose in this study a technical simplification for inference about the total deviation index (TDI) estimate to assess agreement between two devices of normally-distributed measurements and describe its utility to evaluate inter- and intra-rater agreement if more than one reading per subject is available for each device. METHODS: We propose to estimate the TDI by constructing a probability interval of the difference in paired measurements between devices, and thereafter, we derive a tolerance interval (TI) procedure as a natural way to make inferences about probability limit estimates. We also describe how the proposed method can be used to compute bounds of the coverage probability. RESULTS: The approach is illustrated in a real case example where the agreement between two instruments, a handle mercury sphygmomanometer device and an OMRON 711 automatic device, is assessed in a sample of 384 subjects where measures of systolic blood pressure were taken twice by each device. A simulation study procedure is implemented to evaluate and compare the accuracy of the approach to two already established methods, showing that the TI approximation produces accurate empirical confidence levels which are reasonably close to the nominal confidence level. CONCLUSIONS: The method proposed is straightforward since the TDI estimate is derived directly from a probability interval of a normally-distributed variable in its original scale, without further transformations. Thereafter, a natural way of making inferences about this estimate is to derive the appropriate TI. Constructions of TI based on normal populations are implemented in most standard statistical packages, thus making it simpler for any practitioner to implement our proposal to assess agreement.

The concordance correlation coefficient for repeated measures estimated by variance components.

The concordance correlation coefficient (CCC) is an index that is commonly used to assess the degree of agreement between observers on measuring a continuous characteristic. Here, a CCC for longitudinal repeated measurements is developed through the appropriate specification of the intraclass correlation coefficient from a variance components linear mixed model. A case example and the results of a simulation study are provided.

Geographical variability of the incidence of Type 1 diabetes in subjects younger than 30 years in Catalonia, Spain.

BACKGROUND AND OBJECTIVE: We decided to assess the geographical variability of the incidence of Type 1 diabetes in Catalonia (Spain) in subjects younger than 30 years at onset during the period 1989-1998. The effect of sex, age at onset, periods of years, and population density was also analyzed. MATERIAL AND METHODS: Data were obtained from the prospective Catalan Registry of Diabetes Mellitus. Generalized linear mixed models were used to determine the effects of the risk factors and to find out the geographical distribution. The best model was selected by the AKAIKE information criterion. RESULTS: The crude incidence of type 1 diabetes in subjects younger than 30 years was 11.8/100,000/year (95% CI 11.4-12.3). The incidence was similar between males and females in the 0-14 age group. However, there was a male preponderance in young adults. The incidence did not vary annually and was not associated with population density. The incidence did not present a spatial pattern around Catalonia. There was an unstructured geographical variability. CONCLUSIONS: Some regions of Catalonia displayed values of type I diabetes higher or lower than the expected incidence. Counties with extreme values of incidence were specific for each demographic group and in no case did these counties make up clusters, suggesting that there are explanatory factors with patterns of geographic distribution. The incidence of diabetes in young male adults in some counties was similar to that of European countries with a high incidence.

Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA).

BACKGROUND: MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample. RESULTS: Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace. CONCLUSION: Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.

Detection of significant disease risks using a spatial conditional autoregressive model.

SUMMARY: The conditional autoregressive (CAR) model is widely used to describe the geographical distribution of a specific disease risk in lattice mapping. Successful developments based on frequentist and Bayesian procedures have been extensively applied to obtain two-stage disease risk predictions at the subregional level. Bayesian procedures are preferred for making inferences, as the posterior standard errors (SE) of the two-stage prediction account for the variability in the variance component estimates; however, some recent work based on frequentist procedures and the use of bootstrap adjustments for the SE has been undertaken. In this article we investigate the suitability of an analytical adjustment for disease risk inference that provides accurate interval predictions by using the penalized quasilikelihood (PQL) technique to obtain model parameter estimates. The method is a first-order approximation of the naive SE based on a Taylor expansion and is interpreted as a conditional measure of variability providing conditional calibrated prediction intervals, given the data. We conduct a simulation study to demonstrate how the method can be used to estimate the specific subregion risk by interval. We evaluate the proposed methodology by analyzing the commonly used example data set of lip cancer incidence in the 56 counties of Scotland for the period 1975-1980. This evaluation reveals a close similarity between the solutions provided by the method proposed here and those of its fully Bayesian counterpart.

Bronchiolitis Score of Sant Joan de Déu: BROSJOD Score, validation and usefulness.

OBJECTIVE: To validate the bronchiolitis score of Sant Joan de Déu (BROSJOD) and to examine the previously defined scoring cutoff. PATIENTS AND METHODS: Prospective, observational study. BROSJOD scoring was done by two independent physicians (at admission, 24 and 48 hr). Internal consistency of the score was assessed using Cronbach's α. To determine inter-rater reliability, the concordance correlation coefficient estimated as an intraclass correlation coefficient (CCC) and limits of agreement estimated as the 90% total deviation index (TDI) were estimated. An expert opinion was used to classify patients according to clinical severity. A validity analysis was conducted comparing the 3-level classification score to that expert opinion. Volume under the surface (VUS), predictive values, and probability of correct classification (PCC) were measured to assess discriminant validity. RESULTS: About 112 patients were recruited, 62 of them (55.4%) males. Median age: 52.5 days (IQR: 32.75-115.25). The admission Cronbach's α was 0.77 (CI95%: 0.71; 0.82) and at 24 hr it was 0.65 (CI95%: 0.48; 0.7). The inter-rater reliability analysis was: CCC at admission 0.96 (95%CI 0.94-0.97), at 24 h 0.77 (95%CI 0.65-0.86), and at 48 hr 0.94 (95%CI 0.94-0.97); TDI 90%: 1.6, 2.9, and 1.57, respectively. The discriminant validity at admission: VUS of 0.8 (95%CI 0.70-0.90), at 24 h 0.92 (95%CI 0.85-0.99), and at 48 hr 0.93 (95%CI 0.87-0.99). The predictive values and PCC values were within 38-100% depending on the level of clinical severity. CONCLUSION: There is a high inter-rater reliability, showing the BROSJOD score to be reliable and valid, even when different observers apply it. Pediatr Pulmonol. 2017;52:533-539. © 2016 Wiley Periodicals, Inc.

Concordance between direct and indirect measurements of free 25-OH vitamin D.

At present, data comparing the quantification methods for measurement of free vitamin D (direct assay [direct 25-OHDF] and estimated by calculation [calculated 25-OHDF]), are scarce. The aim of this study was to analyse the concordance between these two methods of 25-OHDF analysis (direct vs. calculated). METHODS: Serum values of total 25-OHD (25-OHDT), vitamin D binding protein (DBP) (by R&D Systems ELISA), calculated 25-OHDF (by DBP, albumin and 25-OHDT) and direct 25-OHDF (by DIAsource ELISA) were analysed in 173 healthy women (aged 35-45years). Assessment of concordance was evaluated by the Bland-Altman plot and the total deviation index (TDI). RESULTS: The mean values of calculated and direct 25-OHDF in these subjects were 5.27±2.5 and 3.83±1.01pg/mL, respectively. We found significantly lower values of 25-OHDF on comparing subjects with and without vitamin D deficiency, independently of the method used. The total deviation index evaluated by the Bland-Altman plot showed low concordance for both measurements. Only low 25-OHDF levels were concordant. CONCLUSIONS: This study shows that the concordance between these two methods of 25-OHDF analysis is low and has a concentration dependent bias. Further studies are necessary to clarify the reference values and the indications for 25-OHDF measurement.

Effect of recent spinal cord injury on wnt signaling antagonists (sclerostin and dkk-1) and their relationship with bone loss. A 12-month prospective study.

Spinal cord injury (SCI) has been associated with a marked increase in bone loss and bone remodeling, especially short-term after injury. The absence of mechanical load, mediated by osteocyte mechanosensory function, seems to be a causative factor related to bone loss in this condition. However, the pathogenesis and clinical management of this process remain unclear. Therefore, the aim of the study was to analyze the effect of recent SCI on the Wnt pathway antagonists, sclerostin and Dickkopf (Dkk-1), and their relationship with bone turnover and bone mineral density (BMD) evolution. Forty-two patients (aged 35 ± 14yrs) with a recent (<6months) complete SCI were prospectively included. Sclerostin and Dkk-1, bone turnover markers (bone formation: PINP, bone ALP; resorption: sCTx) and BMD (lumbar spine, proximal femur, total body and lower extremities [DXA]) were assessed at baseline and at 6 and 12 months. The results were compared with a healthy control group. 22/42 patients completed the 12-month follow-up. At baseline, SCI patients showed a marked increase in bone markers (PINP and sCTx), remaining significantly increased at up to 6 months of follow-up. Additionally, they presented significantly increased Dkk-1 values throughout the study, whereas sclerostin values did not significantly change. BMD markedly decreased at the proximal femur (-20.2 ± 5.4%, p < 0.01), total body (-5.7 ± 2.2%, p = 0.02) and lower extremities (-13.1 ± 4.5%, p = 0.01) at 12 months. Consequently, 59% of patients developed densitometric osteoporosis at 12 months. Patients with higher Dkk-1 values (>58 pmol/L) at baseline showed higher sublesional BMD loss. In conclusion, this study shows that short-term after SCI there is a marked increase in bone turnover and bone loss, the latter associated with an increase in Dkk-1 serum levels. The persistence of increased levels of this Wnt antagonist throughout the study and their relationship with the magnitude of bone loss suggests a contributory role of this mediator in this process.

Methodological quality and reporting of generalized linear mixed models in clinical medicine (2000-2012): a systematic review.

BACKGROUND: Modeling count and binary data collected in hierarchical designs have increased the use of Generalized Linear Mixed Models (GLMMs) in medicine. This article presents a systematic review of the application and quality of results and information reported from GLMMs in the field of clinical medicine. METHODS: A search using the Web of Science database was performed for published original articles in medical journals from 2000 to 2012. The search strategy included the topic "generalized linear mixed models","hierarchical generalized linear models", "multilevel generalized linear model" and as a research domain we refined by science technology. Papers reporting methodological considerations without application, and those that were not involved in clinical medicine or written in English were excluded. RESULTS: A total of 443 articles were detected, with an increase over time in the number of articles. In total, 108 articles fit the inclusion criteria. Of these, 54.6% were declared to be longitudinal studies, whereas 58.3% and 26.9% were defined as repeated measurements and multilevel design, respectively. Twenty-two articles belonged to environmental and occupational public health, 10 articles to clinical neurology, 8 to oncology, and 7 to infectious diseases and pediatrics. The distribution of the response variable was reported in 88% of the articles, predominantly Binomial (n = 64) or Poisson (n = 22). Most of the useful information about GLMMs was not reported in most cases. Variance estimates of random effects were described in only 8 articles (9.2%). The model validation, the method of covariate selection and the method of goodness of fit were only reported in 8.0%, 36.8% and 14.9% of the articles, respectively. CONCLUSIONS: During recent years, the use of GLMMs in medical literature has increased to take into account the correlation of data when modeling qualitative data or counts. According to the current recommendations, the quality of reporting has room for improvement regarding the characteristics of the analysis, estimation method, validation, and selection of the model.

Estimation of the concordance correlation coefficient for repeated measures using SAS and R.

The concordance correlation coefficient is one of the most common approaches used to assess agreement among different observers or instruments when the outcome of interest is a continuous variable. A SAS macro and R package are provided here to estimate the concordance correlation coefficient (CCC) where the design of the data involves repeated measurements by subject and observer. The CCC is estimated using U-statistics (UST) and variance components (VC) approaches. Confidence intervals and standard errors are reported along with the point estimate of the CCC. In the case of the VC approach, the linear mixed model output and variance components estimates are also provided. The performance of each function is shown by means of some examples with real data sets.