Two-stage model for multivariate longitudinal and survival data with application to nephrology research

In many follow‐up studies different types of outcomes are collected including longitudinal measurements and time‐to‐event outcomes. Commonly, it is of interest to study the association between them. Joint modeling approaches of a single longitudinal outcome and survival process have recently gained increasing attention from both frequentist and Bayesian perspective. However, in many studies several longitudinal biomarkers are of interest and instead of selecting one single biomarker, the relationships between all these outcomes and their association with survival needs to be investigated. Our motivating study comes from Peritoneal Dialysis Programme in Nephrology research from Nephrology Unit, CHP (Hospital de Santo António), Porto, Portugal in which the interest relies on the possible association between various biomarkers (calcium, phosphate, parathormone, and creatinine) and the patients' survival. To this aim, we propose a two‐stage model‐based approach for multivariate longitudinal and survival data that allowed us to study such complex association structure. The multivariate model suggested in this paper provided new insights in the area of nephrology research showing valid results in comparison with those models studying each longitudinal biomarker with survival separately

Dental age estimation in Somali children using the Willems et al. model

AimThe aim of the current study was to retrospectively collect dental panoramic radiographs from Somali children living in Finland, to use the radiographic data to develop a new age estimation model based on the model established by Willems et al. (J Forensic Sci 46(4):893-895, 2001), and to compare the age prediction performances of the Willems et al. model (WM) and the newly developed model.Material and methodsDental panoramic radiographs from 808 healthy Somalis born in Finland were selected. The development of the seven left mandibular permanent teeth, from the central incisor to the second molar, was staged according to Demirjian et al. (Hum Biol 45(2):211-227, 1973). Radiographs with all listed permanent teeth completely developed were excluded. The studied sample consisted of 635 subjects (311 females, 324 males) ranging in age from 4 to 18years. Kappa and weighted Kappa statistics were used to quantify intra- and inter-observer agreement in stage allocation. The collected dataset was used to validate the WM, constructed on a Belgian Caucasian reference sample, and to establish a Somali-specific age estimation model (SM) based on the WM. Both models were validated and their age prediction performances quantified using mean error (ME), mean absolute error (MAE) and root mean squared error (RMSE).ResultsThe SM resulted in a slight underestimation of age when the sex groups were analysed separately or combined, with ME varying between 0.04 (standard deviation (SD) 1.01) and 0.05 (SD 1.04) years, MAE between 0.77 and 0.80years and RMSE between 1.01 and 1.04years. The WM statistically significantly underestimated the age of females, with an ME of 0.20 (SD 1.01) years (p=0.0006). For males, and for females and males combined, no statistically significant ME was observed.ConclusionThe WM and SM were similar in their age prediction performances, and the use of the WM in dental age assessment in the Somali population is justified.Peer reviewe

The effect of continuous liver normothermic machine perfusion on the severity of histological bile duct injury

Static Cold Storage (SCS) injures the bile duct, while the effect of Normothermic Machine Perfusion (NMP) is unknown. In a sub-study of the COPE trial on liver NMP, we investigated the impact of preservation type on histological bile duct injury score (BDIS). Transplants with at least one bile duct biopsy, either at end of preservation or 1 h post-reperfusion, were considered. BDIS was determined by assessing peribiliary glands injury, stromal and mural loss, haemorrhage, and thrombosis. A bivariate linear model compared BDIS (estimate, CI) between groups. Sixty-five transplants and 85 biopsies were analysed. Twenty-three grafts were preserved with SCS and 42 with NMP, with comparable baseline characteristics except for a shorter cold ischemic time in NMP. The BDIS increased over time regardless of preservation type (p = 0.04). The BDIS estimate was higher in NMP [8.02 (7.40–8.65)] than in SCS [5.39 (4.52–6.26), p < 0.0001] regardless of time. One patient in each group developed ischemic cholangiopathy, with a BDIS of 6 for the NMP-preserved liver. In six other NMP grafts, BDIS ranged 7–12 without development of ischemic cholangiopathy. In conclusion, BDIS increases over time, and the higher BDIS in NMP did not increase ischemic cholangiopathy. Thus, BDIS may overestimate this risk after liver NMP

Inverse association between atopy and melanoma: A case-control study

Heightened cutaneous immune surveillance in atopic patients may inhibit development of melanoma. The aim of this study was to analyse the association between atopy and melanoma (development and outcome). A total of 188 cases of melanoma and 596 healthy controls were interviewed by telephone with a standardized questionnaire on atopic, demographic and melanoma characteristics. Cases were matched with controls on important confounders (age, sex, sunburn sensitivity, hair colour, number of moles, sunburn as juvenile, ever sunbed use, familial melanoma). Melanoma outcome data (disease relapse and death) within cases were retrieved. Analysis showed a general inverse association between atopy and melanoma development, but this was statistically significant only for a history of personal atopy (odds ratio 0.53, 95% confidence interval: 0.30-0.96, p-value = 0.04). Among melanoma patients, atopy did not affect survival or progression. In conclusion, this study suggests an inverse association between a history of atopy and melanoma development, but not with disease progression

Staging clavicular development on MRI : pitfalls and suggestions for age estimation

Background MRI of the clavicle's sternal end has been studied for age estimation. Several pitfalls have been noted, but how they affect age estimation performance remains unclear. Purpose/Hypothesis To further study these pitfalls and to make suggestions for a proper use of clavicle MRI for forensic age estimation. Our hypotheses were that age estimation would benefit from 1) discarding stages 1 and 4/5; 2) including advanced substages 3aa, 3ab, and 3ac; 3) taking both clavicles into account; and 4) excluding morphological variants. Study Type Prospective cross-sectional. Population Healthy Caucasian volunteers between 11 and 30 years old (524; 277 females, 247 males). Field Strength/Sequence 3T, T-1-weighted gradient echo volumetric interpolated breath-hold examination (VIBE) MR-sequence. Assessment Four observers applied the most elaborate staging technique for long bone development that has been described in the current literature (including stages, substages, and advanced substages). One of the observers repeated a random selection of the assessments in 110 participants after a 2-week interval. Furthermore, all observers documented morphological variants. Statistical Tests Weighted kappa quantified reproducibility of staging. Bayes' rule was applied for age estimation with a continuation ratio model for the distribution of the stages. According to the hypotheses, different models were tested. Mean absolute error (MAE) differences between models were compared, as were MAEs between cases with and without morphological variants. Results Weighted kappa equaled 0.82 for intraobserver and ranged between 0.60 and 0.64 for interobserver agreement. Stages 1 and 4/5 were allocated interchangeably in 4.3% (54/1258). Age increased steadily in advanced substages of stage 3, but improvement in age estimation was not significant (right P = 0.596; left P = 0.313). The model that included both clavicles and discarded stages 1 and 4/5 yielded an MAE of 1.97 years, a root mean squared error of 2.60 years, and 69% correctly classified minors. Morphological variants rendered significantly higher MAEs (right 3.84 years, P = 0.015; left 2.93 years, P = 0.022). Data Conclusion Our results confirmed hypotheses 3) and 4), while hypotheses 1) and 2) remain to be investigated in larger studies. Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019

Dynamic classification using credible intervals in longitudinal discriminant analysis

Recently developed methods of longitudinal discriminant analysis allow for classification of subjects into prespecified prognostic groups using longitudinal history of both continuous and discrete biomarkers. The classification uses Bayesian estimates of the group membership probabilities for each prognostic group. These estimates are derived from a multivariate generalised linear mixed model of the biomarker's longitudinal evolution in each of the groups and can be updated each time new data is available for a patient, providing a dynamic (over time) allocation scheme. However, the precision of the estimated group probabilities differs for each patient and also over time. This precision can be assessed by looking at credible intervals for the group membership probabilities. In this paper, we propose a new allocation rule that incorporates credible intervals for use in context of a dynamic longitudinal discriminant analysis and show that this can decrease the number of false positives in a prognostic test, improving the positive predictive value. We also establish that by leaving some patients unclassified for a certain period, the classification accuracy of those patients who are classified can be improved, giving increased confidence to clinicians in their decision making. Finally, we show that determining a stopping rule dynamically can be more accurate than specifying a set time point at which to decide on a patient's status. We illustrate our methodology using data from patients with epilepsy and show how patients who fail to achieve adequate seizure control are more accurately identified using credible intervals compared to existing methods

A graphical vector autoregressive modelling approach to the analysis of electronic diary data

<p>Abstract</p> <p>Background</p> <p>In recent years, electronic diaries are increasingly used in medical research and practice to investigate patients' processes and fluctuations in symptoms over time. To model dynamic dependence structures and feedback mechanisms between symptom-relevant variables, a multivariate time series method has to be applied.</p> <p>Methods</p> <p>We propose to analyse the temporal interrelationships among the variables by a structural modelling approach based on graphical vector autoregressive (VAR) models. We give a comprehensive description of the underlying concepts and explain how the dependence structure can be recovered from electronic diary data by a search over suitable constrained (graphical) VAR models.</p> <p>Results</p> <p>The graphical VAR approach is applied to the electronic diary data of 35 obese patients with and without binge eating disorder (BED). The dynamic relationships for the two subgroups between eating behaviour, depression, anxiety and eating control are visualized in two path diagrams. Results show that the two subgroups of obese patients with and without BED are distinguishable by the temporal patterns which influence their respective eating behaviours.</p> <p>Conclusion</p> <p>The use of the graphical VAR approach for the analysis of electronic diary data leads to a deeper insight into patient's dynamics and dependence structures. An increasing use of this modelling approach could lead to a better understanding of complex psychological and physiological mechanisms in different areas of medical care and research.</p

Boosting joint models for longitudinal and time-to-event data

Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modelling. Commonly, joint models are estimated in likelihood based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and which do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyse the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry which collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models