Combining Dynamic Predictions from Joint Models for Longitudinal and Time-to-Event Data using Bayesian Model Averaging

The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive application of this type of models has been to obtain individualized predictions of survival probabilities and/or of future longitudinal responses. The advantageous feature of these predictions is that they are dynamically updated as extra longitudinal responses are collected for the subjects of interest, providing real time risk assessment using all recorded information. The aim of this paper is two-fold. First, to highlight the importance of modeling the association structure between the longitudinal and event time responses that can greatly influence the derived predictions, and second, to illustrate how we can improve the accuracy of the derived predictions by suitably combining joint models with different association structures. The second goal is achieved using Bayesian model averaging, which, in this setting, has the very intriguing feature that the model weights are not fixed but they are rather subject- and time-dependent, implying that at different follow-up times predictions for the same subject may be based on different models

Personalised biopsy schedules based on risk of Gleason upgrading for patients with low-risk prostate cancer on active surveillance.

OBJECTIVE: To develop a model and methodology for predicting the risk of Gleason upgrading in patients with prostate cancer on active surveillance (AS) and using the predicted risks to create risk-based personalised biopsy schedules as an alternative to one-size-fits-all schedules (e.g. annually). Furthermore, to assist patients and doctors in making shared decisions on biopsy schedules, by providing them quantitative estimates of the burden and benefit of opting for personalised vs any other schedule in AS. Lastly, to externally validate our model and implement it along with personalised schedules in a ready to use web-application. PATIENTS AND METHODS: Repeat prostate-specific antigen (PSA) measurements, timing and results of previous biopsies, and age at baseline from the world's largest AS study, Prostate Cancer Research International Active Surveillance (PRIAS; 7813 patients, 1134 experienced upgrading). We fitted a Bayesian joint model for time-to-event and longitudinal data to this dataset. We then validated our model externally in the largest six AS cohorts of the Movember Foundation's third Global Action Plan (GAP3) database (>20 000 patients, 27 centres worldwide). Using the model predicted upgrading risks; we scheduled biopsies whenever a patient's upgrading risk was above a certain threshold. To assist patients/doctors in the choice of this threshold, and to compare the resulting personalised schedule with currently practiced schedules, along with the timing and the total number of biopsies (burden) planned, for each schedule we provided them with the time delay expected in detecting upgrading (shorter is better). RESULTS: The cause-specific cumulative upgrading risk at the 5-year follow-up was 35% in PRIAS, and at most 50% in the GAP3 cohorts. In the PRIAS-based model, PSA velocity was a stronger predictor of upgrading (hazard ratio [HR] 2.47, 95% confidence interval [CI] 1.93-2.99) than the PSA level (HR 0.99, 95% CI 0.89-1.11). Our model had a moderate area under the receiver operating characteristic curve (0.6-0.7) in the validation cohorts. The prediction error was moderate (0.1-0.2) in theGAP3 cohorts where the impact of the PSA level and velocity on upgrading risk was similar to PRIAS, but large (0.2-0.3) otherwise. Our model required re-calibration of baseline upgrading risk in the validation cohorts. We implemented the validated models and the methodology for personalised schedules in a web-application (http://tiny.cc/biopsy). CONCLUSIONS: We successfully developed and validated a model for predicting upgrading risk, and providing risk-based personalised biopsy decisions in AS of prostate cancer. Personalised prostate biopsies are a novel alternative to fixed one-size-fits-all schedules, which may help to reduce unnecessary prostate biopsies, while maintaining cancer control. The model and schedules made available via a web-application enable shared decision-making on biopsy schedules by comparing fixed and personalised schedules on total biopsies and expected time delay in detecting upgrading

A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

According to the Threshold Theory (Hampton, 1995, 2007) semantic categorization decisions come about through the placement of a threshold criterion along a dimension that represents items' similarity to the category representation. The adequacy of this theory is assessed by applying a formalization of the theory, known as the Rasch model (Rasch, 1960; Thissen & Steinberg, 1986), to categorization data for eight natural language categories and subjecting it to a formal test. In validating the model special care is given to its ability to account for inter- and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Rasch model that can be used to uncover the nature of category representations and the sources of categorization differences are discussed

LOng-term follow-up after liVE kidney donation (LOVE) study: A longitudinal comparison study protocol

Background: The benefits of live donor kidney transplantation must be balanced against the potential harm to the donor. Well-designed prospective studies are needed to study the long-term consequences of kidney donation. Methods: The "LOng-term follow-up after liVE kidney donation" (LOVE) study is a single center longitudinal cohort study on long-term consequences after living kidney donation. We will study individuals who have donated a kidney from 1981 through 2010 in the Erasmus University Medical Center in Rotterdam, The Netherlands. In this time period, 1092 individuals donated a kidney and contact information is available for all individuals. Each participating donor will be matched (1:4) to non-donors derived from the population-based cohort studies of the Rotterdam Study and the Study of Health in Pomerania. Matching will be based on baseline age, gender, BMI, ethnicity, kidney function, blood pressure, pre-existing co-morbidity, smoking, the use of alcohol and highest education degree. Follow-up data is collected on kidney function, kidney-related comorbidity, mortality, quality of life and psychological outcomes in all participants. Discussion: This study will provide evidence on the long-term consequences of live kidney donation for the donor compared to matched non-donors and evaluate the current donor eligibility criteria. Trial registration: Dutch Trial Register NTR3795

Personalized schedules for surveillance of low-risk prostate cancer patients

Summary. Low-risk prostate cancer patients enrolled in active surveillance (AS) programs commonly undergo biopsies on a frequent basis for examination of cancer progression. AS programs employ a fixed schedule of biopsies for all patients. Such fixed and frequent schedules may schedule unnecessary biopsies. Since biopsies are burdensome, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Motivated by the world’s largest AS program, Prostate Cancer Research International Active Surveillance (PRIAS), we present personalized schedules for biopsies to counter these problems. Using joint models for time-to-event and longitudinal data, our methods combine information from historical prostate-specific antigen levels and repeat biopsy results of a patient, to schedule the next biopsy. We also present methods to compare personalized schedules with existing biopsy schedules

Positive association between physical outcomes and patient-reported outcomes in late-onset Pompe disease: a cross sectional study

BACKGROUND: Pompe disease is a rare, progressive metabolic myopathy. The aim of this study is to investigate the associations of physical outcomes with patient-reported outcome measures (PROMs) in late-onset Pompe disease. METHODS: We included 121 Dutch adult patients with Pompe disease. Physical outcomes comprised muscle strength (manual muscle testing using Medical Research Council [MRC] grading, hand-held dynamometry [HHD]), walking ability (6-min walk test [6MWT]), and pulmonary function (forced vital capacity [FVC] in upright and supine positions). PROMs comprised quality of life (Short Form 36 health survey [SF-36]), participation (Rotterdam Handicap Scale [RHS]) and daily-life activities (Rasch-Built Pompe-Specific Activity [R-PAct] Scale). Analyses were cross-sectional: the time-point before, and closest to, start of Enzyme Replacement Therapy was chosen. Associations between PROMs and physical outcomes were investigated using linear regression models. RESULTS: RHS and R-PAct scores were better in patients with higher FVC supine and upright, HHD, MRC and 6MWT scores, accounting for the effect of sex, disease duration, use of wheelchair and ventilator support. While the SF-36 Physical Component Summary (PCS) was correlated positively with FVC upright, HHD, MRC and 6MWT scores, there was no significant relationship between the SF-36 Mental Component Summary (MCS) and any of the physical outcomes. CONCLUSIONS: Participation, daily-life activities, and the physical component of quality of life of adult Pompe patients are positively correlated to physical outcomes. This work serves as a first step towards assessing how changes over time in physical outcomes are related to changes in PROMs, and to define the minimal change in physical outcomes required to make an important difference for the patient

Performance of classification systems for age-related macular degeneration in the rotterdam study

Purpose: To compare frequently used classification systems for age-related macular degeneration(AMD) in their abilty to predictlate AMD. Methods:Intotal,9066participantsfromthepopulation-basedRotterdamStudywere followedupforprogressionofAMDduringastudyperiodupto30years.AMDlesions weregradedoncolorfundusphotographsafterconfirmationonotherimagemodalities andgroupedatbaselineaccordingtosixclassificationsystems.LateAMDwasdefinedas geographicatrophyorchoroidalneovascularization.Incidencerate(IR)andcumulative incidence(CuI)oflateAMDwerecalculated,andKaplan-Meierplotsandareaunderthe operating characteristics curves(AUCs)wereconstructed. Results: A total of 186 persons developed incident late AMD during a mean follow-up timeof8.7years.TheAREDSsimplifiedscaleshowedthehighestIRforlateAMDat104 cases/1000 py for ages 75 years. The 3-Continent harmonization classification provided the most stable progression. Drusen area >10% ETDRS grid (hazard ratio 30.05, 95% confidence interval [CI] 19.25–46.91) was most prognostic of progression. The highest AUC of late AMD (0.8372, 95% CI: 0.8070-0.8673) was achieved when all AMD features present at base line were included. Conclusions: Highest turnover rates from intermediate to late AMD were provided by the AREDS simplified scale and the Rotterdam classification. The 3-Continent harmonization classification showed the most stable progression. All features, especially drusenarea,contribute to late AMD prediction. Translational Relevance: Findings will help stakeholders select appropriate classification systems for screening,deep learning algorithms, or trials