Estimating intraclass correlation and its confidence interval in linear mixed models2

The methodology proposed in this study is motivated by an example from the medical field. Oncologists delineate organs for radiotherapy and it is essential that the measurements agree in these procedures. To assess the consistency of measurements among oncologists, on a random sample of subjects, the intraclass correlation (ICC) would yield a suitable estimate for studying the agreement. In technical terms, the ICC is a ratio of sum of variances that are related to differences among measured subjects and the total variance. What variance is considered relevant depends on the design of agreement study; respectively, the number of variance components changes in the numerator and the denominator of the ICC. For statistical inference, it is important but challenging to determine the distribution of estimators of such ratios and to construct the confidence intervals. In most literature, the ICC has been studied for one-way and two-way analysis of variance only. Most proposed approximate methods are based on functions of the mean squares which are model-specific (e.g. two factorial) and lack generalization to higher order (e.g. three factorial) models. The objective of this study is to extend the construction of confidence intervals for the linear mixed models, but in particular to our three-way mixed models for delineation of organs. The generalization will coincide with existing methods for two-way and one-way mixed effects models. To obtain an approximate upper and lower confidence limits, we approximate the ICC with a function of F-distributed variable and a Beta distribution. Our proposed methodology is supported by simulation studies

GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN

Studies in epidemiology and social sciences are often longitudinal and outcome measures are frequently obtained by questionnaires in ordinal scales. To understand the relationship between explanatory variables and outcome measures, generalized estimating equations can be applied to provide a population-averaged interpretation and address the correlation between outcome measures. It can be performed by different software packages, but a motivating example showed differences in the output. This paper investigated the performance of GEE in R (version 3.0.2), SAS (version 9.4), and SPSS (version 22.0.0) using simulated data under default settings. Multivariate logistic distributions were used in the simulation to generate correlated ordinal data. The simulation study demonstrated substantial bias in the parameter estimates and numerical issues for data sets with relative small number of subjects. The unstructured working association matrix requires larger numbers of subjects than the independence and exchangeable working association matrices to reduce the bias and diminish numerical issues. The coverage probabilities of the confidence intervals for fixed parameters were satisfactory for the independence and exchangeable working association matrix, but they were frequently liberal for the unstructured option. Based on the performance and the available options, SPSS and multgee, and repolr in R all perform quite well for relatively large sample sizes (e.g. 300 subjects), but multgee seems to do a little better than SPSS and repolr in most settings

Shewhart’s idea of predictability and modern statistics

\u3cp\u3eShewhart’s view on statistical control as presented in his 1931 book is connected to predictability and it seems to be inspired by philosophical theories. At that time, there was no proper statistical framework available when Shewhart implemented his ideas on statistical control. This was not a problem for standard settings for which the original Shewhart control chart was developed, but there are currently several much more complicated situations where the standard tools of Shewhart do not suffice without modification. We will discuss whether current statistical notions like hypothesis testing (both the standard Neyman-Pearson theory and other forms like sequential statistics and equivalence testing), prediction intervals and tolerance intervals can be useful in these other settings. We will also discuss alternative settings of statistical control proposed in the literature including Bayesian settings.\u3c/p\u3

A comparison of spiking experiments to estimate the detection proportion of qualitative microbiological methods

\u3cp\u3eThe detection proportion of a qualitative microbiological test method is the probability to detect a single micro-organism. A general expression for the moment estimator of the detection proportion is provided. It depends on the distribution of the spikes used in a validation study through its moment-generating function. Several forms of spiking experiments are compared on their estimation performance using simulations and assuming a generalized Poisson distribution (GPD) for the spikes. The optimal design, which minimizes the mean squared error of our proposed moment estimator, depends on the dispersion parameter of the GPD. The design that uses just one spiked solution instead of multiple solutions is optimal for Poisson and overdispersed Poisson and it is robust against distributions for the spikes.\u3c/p\u3

Effectiveness of seasonal influenza vaccine in community-dwelling elderly people: a meta-analysis of test-negative design case-control studies

Background The application of test-negative design case-control studies to assess the effectiveness of influenza vaccine has increased substantially in the past few years. The validity of these studies is predicated on the assumption that confounding bias by risk factors is limited by design. We aimed to assess the effectiveness of influenza vaccine in a high-risk group of elderly people. Methods We searched the Cochrane library, Medline, and Embase up to July 13, 2014, for test-negative design case-control studies that assessed the effectiveness of seasonal influenza vaccine against laboratory confirmed influenza in community-dwelling people aged 60 years or older. We used generalised linear mixed models, adapted for test-negative design case-control studies, to estimate vaccine effectiveness according to vaccine match and epidemic conditions. Findings 35 test-negative design case-control studies with 53 datasets met inclusion criteria. Seasonal influenza vaccine was not significantly effective during local virus activity, irrespective of vaccine match or mismatch to the circulating viruses. Vaccination was significantly effective against laboratory confirmed influenza during sporadic activity (odds ratio [OR] 0·69, 95% CI 0·48–0·99) only when the vaccine matched. Additionally, vaccination was significantly effective during regional (match: OR 0·42, 95% CI 0·30–0·60; mismatch: OR 0·57, 95% CI 0·41–0·79) and widespread (match: 0·54, 0·46–0·62; mismatch: OR 0·72, 95% CI 0·60–0·85) outbreaks. Interpretation Our findings show that in elderly people, irrespective of vaccine match, seasonal influenza vaccination is effective against laboratory confirmed influenza during epidemic seasons. Efforts should be renewed worldwide to further increase uptake of the influenza vaccine in the elderly population. Funding None

Strategies for handling missing data in longitudinal studies with questionnaires

\u3cp\u3eMissing data methods, maximum likelihood estimation (MLE) and multiple imputation (MI), for longitudinal questionnaire data were investigated via simulation. Predictive mean matching (PMM) was applied at both item and scale levels, logistic regression at item level and multivariate normal imputation at scale level. We investigated a hybrid approach which is combination of MLE and MI, i.e. scales from the imputed data are eliminated if all underlying items were originally missing. Bias and mean square error (MSE) for parameter estimates were examined. ML seemed to provide occasionally the best results in terms of bias, but hardly ever on MSE. All imputation methods at the scale level and logistic regression at item level hardly ever showed the best performance. The hybrid approach is similar or better than its original MI. The PMM-hybrid approach at item level demonstrated the best MSE for most settings and in some cases also the smallest bias.\u3c/p\u3

Cognitive subtypes in non-affected siblings of schizophrenia patients: characteristics and profile congruency with affected family members

Background Although cognitive subtypes have been suggested in schizophrenia patients, similar analyses have not been carried out in their non-affected siblings. Subtype classification may provide more insight into genetically driven variation in cognitive function. We investigated cognitive subtypes in siblings. Method Cluster analyses were performed in 654 non-affected siblings, on a cognitive battery that included tests of attention, intellectual function and episodic memory. Resulting subtypes in the siblings were analyzed for cognitive, demographic and clinical characteristics and compared with those of their probands. Results Three sibling subtypes of cognitive function were distinguished: ‘normal’, ‘mixed’ and ‘impaired’. Normal profile siblings (n = 192) were unimpaired on cognitive tests, in contrast to their proband (n = 184). Mixed profile siblings (n = 228) and their probands (n = 222) had a more similar performance pattern. Impaired profile siblings had poorer functional outcomes (n = 234) and their profile was almost identical to that of their proband (n = 223). Probands with cognitively impaired siblings could be distinguished from other schizophrenia patients by their own cognitive performance. They also had poorer clinical characteristics, including achievement of symptomatic remission. Conclusions Unaffected siblings of patients with schizophrenia are heterogeneous with respect to cognitive function. The poorer the cognitive profile of the sibling, the higher the level of correspondence with the proband. The sibling's cognitive subtype was predictive for disease course in the proband. Distinguishing cognitive subtypes of unaffected siblings may be of relevance for genetic studies

Optimal unidirectional switch designs

\u3cp\u3eStepped wedge designs and delayed start designs can all be considered as special cases of the so-called unidirectional switch design. This paper provides optimal proportions of clusters that are allocated to switch patterns in a unidirectional switch design to minimize the asymptotic variance of the treatment effect estimator. This unique optimal design applies to certain cross-sectional and longitudinal variance component models. When the intraclass correlation coefficient is zero, the optimal unidirectional switch design coincides with the classic (cluster) parallel group design. The optimal unidirectional switch design is more efficient than the optimal stepped wedge design and delayed start designs. Compared with the uniform unidirectional switch design, the efficiency gain of the optimal unidirectional switch design can be substantial, but it depends on the intraclass correlation and the cluster size. We also showed that augmenting the optimal stepped wedge design with pure control pattern is more efficient than the optimal stepped wedge design. In addition, robust minimax design for unidirectional switch design, delayed start design, and stepped wedge design are provided.\u3c/p\u3

Bayesian modeling of Dupuytren disease using copula Gaussian graphical models

Dupuytren disease is a fibroproliferative disorder with unknown etiology that often progresses and eventually can cause permanent contractures of the affected fingers. Most of the researches on severity of the disease and the phenotype of this disease are observational studies without concrete statistical analyses. There is a lack of multivariate analysis for the disease taking into account potential risk factors. In this paper, we provide a novel Bayesian framework to discover potential risk factors and which fingers are jointly affected. Copula Gaussian graphical modeling is one potential way to discover the underlying conditional independence of variables in mixed data. Our Bayesian approach is based on copula Gaussian graphical models. We embed a graph selection procedure inside a semiparametric Gaussian copula. We carry out the posterior inference by using an efficient sampling scheme which is a trans-dimensional MCMC approach based on birth-death process. We implemented the method as a general purpose in the R package BDgraph. Keywords: Dupuytren disease; Risk factors; Bayesian inference; Copula Gaussian graphical models; Bayesian model selection; Latent variable models; Birth-death process; Markov chain Monte Carl

Cubital tunnel syndrome : a comparison of an endoscopic technique with a minimal invasive open technique

Both open and endoscopic methods for ulnar nerve decompression have been described. The purpose of this study is to compare the 6-month results of a minimal invasive open technique with an endoscopic technique. We treated 60 patients with unilateral ulnar neuropathy at the elbow, employing both techniques. Six months postoperative we found no differences in treatment effect on pain and disability scores between both groups, but both techniques resulted in an early postoperative relief of symptoms and good patient satisfaction