Evolution of head-shaft angle and neck-shaft angle in childhood

The current retrospective study investigates the natural evolution of head-shaft angle (HSA) and neck-shaft angle (NSA) in childhood. It is not known if a high HSA in early childhood leads to a high HSA in adulthood. This study aims to characterize the evolution of HSA and compares it with the more commonly known NSA in healthy children. We measured radiographs of 84 different healthy hips of children between 3 and 14.5 years old who underwent at least 2 radiographs of the pelvis, corresponding to 286 measurements. We used a linear mixed model to determine the covariance between random intercept and slope while allowing each individual hip to change over time. The covariance for HSA between random intercept and random slope was -4.262 (p < 0.001), corresponding to a high negative correlation of -0.717, for NSA -2.754 (p = 0.031) or a high negative correlation of -0.779. HSA and NSA were strongly correlated, a value of 0.736 (p < 0.001) was measured. The high negative correlation for random intercept and random slope means that the higher the initial value (intercept), the steeper the decline (slope). Therefore HSA decreases faster in hips with high HSA at an early age. Hips with high HSA in early childhood do not necessarily lead to hips with high HSA in adulthood. Our results may aid in future clinical decision making in patients with developmental dysplasia of the hip (DDH) with high HSA in particular.

A Bayesian hierarchical change point model with parameter constraints

Alzheimer’s disease is the leading cause of dementia among adults aged 65 or above. Alzheimer’s disease is characterized by a change point signaling a sudden and prolonged acceleration in cognitive decline. The timing of this change point is of clinical interest because it can be used to establish optimal treatment regimens and schedules. Here, we present a Bayesian hierarchical change point model with a parameter constraint to characterize the rate and timing of cognitive decline among Alzheimer’s disease patients. We allow each patient to have a unique random intercept, random slope before the change point, random change point time, and random slope after the change point. The difference in slope before and after a change point is constrained to be nonpositive, and its parameter space is partitioned into a null region (representing normal aging) and a rejection region (representing accelerated decline). Using the change point time, the estimated slope difference, and the threshold of the null region, we are able to (1) distinguish normal aging patients from those with accelerated cognitive decline, (2) characterize the rate and timing for patients experiencing cognitive decline, and (3) predict personalized risk of progression to dementia due to Alzheimer’s disease. We apply the approach to data from the Religious Orders Study, a national cohort study of aging Catholic nuns, priests, and lay brothers.

Issues with the Random Effects Approach in Meta-Analysis of Sparse Incidence Data

The current interest in meta-analysis of count data in which some studies have zero events (sparse data) has led to re-assessment of commonly used meta-analysis methods to establish their validity in such scenarios. The general consensus is that methods which exclude studies with zero events should be avoided. In the family of parametric methods, random effects models come out highly recommended. While acknowledging the strength of this approach, one of its aspects with potentially undesirable impact on the results, is often overlooked. The random effects approach accounts for the variation in the effect measure across studies by using models with random slopes. It has been shown that parameters associated with a random structure risk being estimated with biased unless the distribution of the random effects is correctly specified. In meta-analysis the parameter of interest, average effect measure, is associated with a random structure (random slope). Information on how the effect measure point and precision estimates are affected by misspecification of random effects distribution is still lacking. To fill in the information gap, we used a simulation study to investigate the impact of misspecification of distribution of random effects in this context and provide guidelines in using the random effects approach. Our results indicated that relative bias in the estimated effect measure could be as high as 30% and 95% confidence interval coverage as low as 0%. These results send a clear message that possible effects of misspecification of the distribution of random effects should not be ignored. In light of these findings, we have proposed a sensitivity analysis that also establishes whether a random slope model is necessary.

Conditional repeatability and the variance explained by reaction norm variation in random slope models

Individuals differ in average phenotypes, but also in sensitivity to environmental variation. Such variation is biologically relevant, because it reflects variation in reaction norms. Between-individual variation in average phenotypes is typically quantified as random-intercept variation in linear mixed-effects models or as intra-class correlations (also known as repeatability). Similarly, context-sensitivity can be modelled as random-slope variation. However, random-slope variation implies that between-individual variation varies across the range of a covariate (environment, context, time or age) and has thus been called ‘conditional’ repeatability. While studies fitting random-slope models are on a rapid increase, there is a lack of a general concept for the quantification of context-sensitive between-individual variation. We here propose to put reaction-norm (random-slope) variation in perspective of the total phenotypic variance and suggest a way of standardization that we call random-slope coefficient of determination . Furthermore, we illustrate that instead of the random-intercept variance, the average repeatability across an environmental gradient will be a biologically more relevant description of between-individual variation and we call this the marginalized repeatability Rmar. We provide simple equation to calculated key descriptors of conditional repeatabilities, clarify the difference between random-intercept variation and average between-individual variation and make recommendations for comprehensive reporting. Most importantly, reporting should include means and variances of covariates. While we introduce the concept with individual-variation in mind, the framework is equally applicable to other type of between-group/cluster variation that varies across some (environmental) gradient.

Secukinumab provides sustained low rates of radiographic progression in psoriatic arthritis: 52-week results from a phase 3 study, FUTURE 5

Objective To evaluate the effect of secukinumab on radiographic progression through 52 weeks in patients with PsA from the FUTURE 5 study. Methods Patients with active PsA, stratified by prior anti-TNF use (naïve or inadequate response), were randomized to s.c. secukinumab 300 mg load (300 mg), 150 mg load (150 mg), 150 mg no load regimens or placebo at baseline, at weeks 1, 2 and 3 and every 4 weeks starting at week 4. Radiographic progression was assessed by change in van der Heijde-modified total Sharp score (vdH-mTSS; mean of two readers). Statistical analysis used a linear mixed-effects model (random slope) at weeks 24 and 52, and observed data at week 52. Assessments at week 52 included additional efficacy endpoints (non-responders imputation and mixed-effects models for repeated measures) and safety. Results The majority (86.6%) of patients completed 52 weeks of treatment. The proportion of patients with no radiographic progression (change from baseline in vdH-mTSS ⩽0.5) was 91.8, 85.2 and 87.2% in 300, 150 and 150 mg no load groups, respectively, at week 52. The change in vdH-mTSS from baseline to week 52 using random slope [mean change (s.e.)] was –0.18 (0.17), 0.11 (0.18) and –0.20 (0.18) in 300, 150 and 150 mg no load groups, respectively; the corresponding observed data [mean change (s.d.)] was –0.09 (1.02), 0.13 (1.39) and 0.21 (1.15). Clinical efficacy endpoints were sustained, and no new or unexpected safety signals were reported through 52 weeks. Conclusion Secukinumab 300 and 150 mg with or without s.c. loading regimen provided sustained low rates of radiographic progression through 52 weeks of treatment. Trial registration ClinicalTrials.gov, http://clinicaltrials.gov, NCT02404350.