Randomised clinical trial: First-line infliximab biosimilar is cost-effective compared to conventional treatment in paediatric Crohn's disease.

BACKGROUND: Data on cost-effectiveness of first-line infliximab in paediatric patients with Crohn's disease are limited. Since biologics are increasingly prescribed and accompanied by high costs, this knowledge gap needs to be addressed. AIM: To investigate the cost-effectiveness of first-line infliximab compared to conventional treatment in children with moderate-to-severe Crohn's disease. METHODS: We included patients from the Top-down Infliximab Study in Kids with Crohn's disease randomised controlled trial. Children with newly diagnosed moderate-to-severe Crohn's disease were treated with azathioprine maintenance and either five induction infliximab (biosimilar) infusions or conventional induction treatment (exclusive enteral nutrition or corticosteroids). Direct healthcare consumption and costs were obtained per patient until week 104. This included data on outpatient hospital visits, hospital admissions, drug costs, endoscopies and surgeries. The primary health outcome was the odds ratio of being in clinical remission (weighted paediatric Crohn's disease activity index<12.5) during 104 weeks. RESULTS: We included 89 patients (44 in the first-line infliximab group and 45 in the conventional treatment group). Mean direct healthcare costs per patient were €36,784 for first-line infliximab treatment and €36,874 for conventional treatment over 2 years (p = 0.981). The odds ratio of first-line infliximab versus conventional treatment to be in clinical remission over 104 weeks was 1.56 (95%CI 1.03-2.35, p = 0.036). CONCLUSIONS: First-line infliximab treatment resulted in higher odds of being in clinical remission without being more expensive, making it the dominant strategy over conventional treatment in the first 2 years after diagnosis in children with moderate-to-severe Crohn's disease. TRIAL REGISTRATION NUMBER: NCT02517684.

Validation and Reference Scores of the Transition Readiness Assessment Questionnaire in Adolescent and Young Adult IBD Patients.

OBJECTIVES: Transition readiness can predict a successful transition from pediatric to adult care. This study aimed to validate and develop age-dependent reference scores for the (Dutch version of) Transition Readiness Assessment Questionnaire (TRAQ), in adolescents and young adults (AYAs) with inflammatory bowel disease (IBD). METHODS: TRAQ has 20 items (score 1-5) distributed over 5 domains (total sum score 100) and is completed by AYAs. Following the COnsensus-based Standards for the selection of health Measurement INstruments methodology, we conducted the translation, back-to back translation, pretesting, and validation of the final Dutch version of TRAQ (TRAQ-NL) questionnaire. We used a Rasch model for structural validation, hypothesis testing for construct validity, and Cronbach alpha to demonstrate reliability. Reference scores were calculated using percentiles. RESULTS: Two hundred fifty TRAQ questionnaires were evaluated in 136 AYAs with IBD [56% Crohn disease, 58% male, median age 17.5 years (range 15.7-20.4)]. The overall mean item score was 3.87 (range 1.45-5). With good reliability (Cronbach alpha 0.87), TRAQ-NL discriminated well between knowledge levels, especially in the lower levels. Transition readiness was defined as low, moderate, adequate, or excellent in patients with TRAQ percentile scores (PC) <25th (<3.375 mean item score), 25th-50th (3.375-3.9), 50th-90th (3.91-4.7), or >90th (>4.7). Younger patients, concomitant illness, fewer visits to the transition clinic, and parental dependence were associated with significantly lower TRAQ scores. CONCLUSION: TRAQ(-NL) is reliable and valid, with age-dependent PC to identify (in)adequate transfer readiness. TRAQ can now be more easily used as a patient-reported outcome measure to monitor transition readiness longitudinally in routine care for AYAs IBD patients.

Sample size calculation for clinical trials analyzed with the meta-analytic-predictive approach.

The meta-analytic-predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the new trial when historical data is available, and the MAP approach is used in the analysis. In previous applications of the MAP approach, the prior effective sample size (ESS) acted as a metric to quantify the number of subjects the historical information is worth. However, the validity of using the prior ESS in sample size calculation (i.e., reducing the number of randomized controls by the derived prior ESS) is questionable, because different approaches may yield different values for prior ESS. In this work, we propose a straightforward Monte Carlo approach to calculate the sample size that achieves the desired power in the new trial given available historical controls. To make full use of the available historical information to simulate the new trial data, the control parameters are not taken as a point estimate but sampled from the MAP prior. These sampled control parameters and the MAP prior based on the historical data are then used to derive the statistical power for the treatment effect and the resulting required sample size. The proposed sample size calculation approach is illustrated with real-life data sets with different outcomes from three studies. The results show that this approach to calculating the required sample size for the MAP analysis is straightforward and generic.

Endovascular Revascularization Plus Supervised Exercise Versus Supervised Exercise Only for Intermittent Claudication: A Cost-Effectiveness Analysis.

[Figure: see text].

Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes.

In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects accelerated failure time model for survival outcomes, a linear mixed model for continuous longitudinal outcomes and a proportional odds mixed model for ordinal longitudinal outcomes, where these outcome processes are linked through a set of association parameters. A primary objective of this study is to examine the effects of association parameters on the estimators of joint models. The model parameters are estimated by the method of maximum likelihood. The finite-sample properties of the estimators are studied using Monte Carlo simulations. The empirical study suggests that the degree of association among the outcome processes influences the bias, efficiency, and coverage probability of the estimators. Our proposed joint model estimators are approximately unbiased and produce smaller mean squared errors as compared to the estimators obtained from separate models. This work is motivated by a large multicenter study, referred to as the Genetic and Inflammatory Markers of Sepsis (GenIMS) study. We apply our proposed method to the GenIMS data analysis.

Repeated Echocardiograms Do Not Provide Incremental Prognostic Value to Single Echocardiographic Assessment in Minimally Symptomatic Patients with Chronic Heart Failure: Results of the Bio-SHiFT Study.

BACKGROUND: We aimed to compare the prognostic value of a single "baseline" echocardiogram with repeated echocardiography in stable chronic heart failure (CHF) patients. We hypothesized that repeated echocardiograms would contain incremental prognostic information. METHODS: In the prospective Bio-SHiFT study, we performed 332 echocardiograms in 106 patients during a median follow-up of 2.3 years. The endpoint comprised HF hospitalization, left ventricular (LV) assist device implantation, heart transplantation, and cardiovascular death. We compared hazard ratios (HRs; adjusted for N-terminal pro-brain natriuretic peptide) from Cox models for the first available measurement with HRs from joint models, which model individual trajectories based on the repeated measurements and link these to the time-to-event data. RESULTS: The mean age of the patients was 58.1 years; 78.3% were male, 12.6% had New York Heart Association class >II, all had reduced ejection fraction, and the most common HF etiologies were cardiomyopathies (51%) and ischemia (40%). The endpoint occurred in 25 patients. Both the single measurements and the temporal trajectories were significantly associated with the endpoint (adjusted HR Cox model [95% CI] vs adjusted HR joint model [95% CI]): LV ejection fraction, 1.47 (0.93-2.31) vs 1.77 (1.13-2.93); diastolic LV diameter, 1.64 (1.09-2.47) vs 1.68 (1.12-2.57); systolic LV diameter, 1.72 (1.10-2.69) vs 1.68 (1.13-2.63); systolic left atrial diameter, 1.88 (1.18-3.00) vs 2.60 (1.48-4.97); E/A ratio, 2.73 (1.42-5.26) vs 3.87 (1.75-10.13); and E/e' ratio, 2.30 (1.38-3.84) vs 2.99 (1.68-6.19). None of the trajectories from the investigated parameters showed worsening prior to events. CONCLUSIONS: Although single baseline or repeatedly measured echocardiographic parameters were associated with the endpoint, all parameters remained on average stable during the 2.3 years of follow-up in this largely minimally symptomatic CHF cohort. Thus, regular echocardiographic monitoring of systolic or diastolic LV function within this time frame does not carry incremental prognostic information over a single baseline measurement.

Personalized dynamic risk assessment in nephrology is a next step in prognostic research.

In nephrology, repeated measures are frequently available (glomerular filtration rate or proteinuria) and linked to adverse outcomes. However, several features of these longitudinal data should be considered before making such inferences. These considerations are discussed, and we describe how joint modeling of repeatedly measured and time-to-event data may help to assess disease dynamics and to derive personalized prognosis. Joint modeling combines linear mixed-effects models and Cox regression model to relate patient-specific trajectory to their prognosis. We describe several aspects of the relationship between time-varying markers and the endpoint of interest that are assessed with real examples to illustrate the aforementioned aspects of the longitudinal data provided. Thus, joint models are valuable statistical tools for study purposes but also may help health care providers in making well-informed dynamic medical decisions.

Cost-effectiveness of enzyme replacement therapy with alglucosidase alfa in adult patients with Pompe disease.

BACKGROUND: Pompe disease is a rare, progressive, metabolic disease, and the first treatable inheritable muscle disorder. Enzyme replacement therapy (ERT) with alglucosidase alfa is disease specific and the only medicinal product authorized for the treatment of Pompe disease. Costs of ERT are very high as for most orphan drugs. This study investigates the cost-effectiveness of ERT compared to supportive treatment in adult patients with Pompe disease. METHODS: Survival probabilities were estimated from an international observational dataset (n = 283) using a time-dependent Cox model. Quality of life was estimated on a Dutch observational dataset using a previously developed conceptual model which links clinical factors to quality of life. Costs included costs of ERT, costs of drug administration and other healthcare costs. Cost-effectiveness was estimated using a patient-level simulation model (n = 90), synthesising the information from underlying models for survival, quality of life and costs. The cost-effectiveness model estimated the (difference in) lifetime effects and costs for both treatments. Two scenarios were modelled: (1) a worse case scenario with no extrapolation of the survival gain due to ERT beyond the observed period (i.e. from 10 years onwards); and (2) a best case scenario with lifetime extrapolation of the survival gain due to ERT. Effects were expressed in (quality adjusted) life years (QALYs). Costs were discounted at 4.0% and effects at 1.5%. RESULTS: Substantial increases in survival were estimated - discounted incremental life years of ERT ranged from 1.9 years to 5.4 years in the scenarios without and with extrapolation of survival gains beyond the observed period. Quality of life was also significantly better for patients receiving ERT. Incremental costs were considerable and primarily consisted of the costs of ERT. Incremental costs per QALY were €3.2 million for scenario 1 and €1.8 million for scenario 2. CONCLUSIONS: The availability of extended, prospectively collected, longitudinal observational data on the most important input parameters required to construct a cost-effectiveness model is quite exceptional for orphan diseases. The cost-effectiveness model showed substantial survival gains from ERT. Despite these substantial gains, ERT was not cost-effective in the treatment of adult Pompe disease because of the high cost of treatment.

Extension of the association structure in joint models to include weighted cumulative effects.

Motivated by a study measuring diabetes-related risk factors and complications, we postulate an extension to the standard formulation of joint models for longitudinal and survival outcomes, wherein the longitudinal outcome has a cumulative effect on the hazard of the event, weighted by recency. We focus on the relationship between the biomarker HbA1c and the development of sight threatening retinopathy, since the impact of the HbA1c marker on the risk of sight threatening retinopathy is expected to be cumulative, with the evolution of the HbA1c marker over time contributing to progressively greater damage to the vascular structure of the retina. Opting for a parametric approach, we propose the use of the normal and skewed normal probability density functions as weight functions, estimating the relevant parameters directly from the data. The use of the recency-weighted cumulative effect specification allows us to incorporate differences in the development of the longitudinal profile over time in the calculation of hazard ratios between subjects. The proposed functions provide us with parameters with clinically relevant interpretations while retaining a degree of flexibility. In addition, they also allow answering of important clinical questions regarding the relative importance of various segments of the biomarkers history in the estimation of the risk of the event. Copyright © 2017 John Wiley & Sons, Ltd.

Bayesian hierarchical modeling of longitudinal glaucomatous visual fields using a two-stage approach.

The Bayesian approach has become increasingly popular because it allows to fit quite complex models to data via Markov chain Monte Carlo sampling. However, it is also recognized nowadays that Markov chain Monte Carlo sampling can become computationally prohibitive when applied to a large data set. We encountered serious computational difficulties when fitting an hierarchical model to longitudinal glaucoma data of patients who participate in an ongoing Dutch study. To overcome this problem, we applied and extended a recently proposed two-stage approach to model these data. Glaucoma is one of the leading causes of blindness in the world. In order to detect deterioration at an early stage, a model for predicting visual fields (VFs) in time is needed. Hence, the true underlying VF progression can be determined, and treatment strategies can then be optimized to prevent further VF loss. Because we were unable to fit these data with the classical one-stage approach upon which the current popular Bayesian software is based, we made use of the two-stage Bayesian approach. The considered hierarchical longitudinal model involves estimating a large number of random effects and deals with censoring and high measurement variability. In addition, we extended the approach with tools for model evaluation. Copyright © 2017 John Wiley & Sons, Ltd.

Prevalence and determinants of declining versus stable hemoglobin levels in whole blood donors.

BACKGROUND: A too short recovery time after blood donation results in a gradual depletion of iron stores and a subsequent decline in hemoglobin (Hb) levels over time. This decline in Hb levels may depend on individual, unobserved characteristics of the donor. STUDY DESIGN AND METHODS: We used a data set of 5388 Dutch blood donors from the Donor InSight study. The statistical analysis is based on a Bayesian growth mixture model, which assumes that each donor belongs to one of several groups. Each group implies a different Hb trajectory, and donors with similar longitudinal trajectories belong to the same group. Analyses were performed for male and female donors separately. RESULTS: For both sexes the model identified four groups of donors. Stable Hb trajectories were found among 14% of male donors and 15% of female donors; declining Hb trajectories were observed in the remaining groups of donors. The percentage of donor deferrals differed strongly between groups. CONCLUSION: The model can be used to predict to which group a donor belongs, and this prediction can be updated after each donation. This is of high practical importance because early identification of donors with declining Hb levels could help to tailor donation intervals and to prevent iron deficiency and donor deferrals.

A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event.

Motivated by a real data example on renal graft failure, we propose a new semiparametric multivariate joint model that relates multiple longitudinal outcomes to a time-to-event. To allow for greater flexibility, key components of the model are modelled nonparametrically. In particular, for the subject-specific longitudinal evolutions we use a spline-based approach, the baseline risk function is assumed piecewise constant, and the distribution of the latent terms is modelled using a Dirichlet Process prior formulation. Additionally, we discuss the choice of a suitable parameterization, from a practitioner's point of view, to relate the longitudinal process to the survival outcome. Specifically, we present three main families of parameterizations, discuss their features, and present tools to choose between them.

The long-term outcome after severe trauma of children in Flanders (Belgium): a population-based cohort study using the International Classification of Functioning--related outcome score.

Important long-term health problems have been described after severe paediatric trauma. The International Classification of Functioning (ICF) was developed as a universal framework to describe that health. We evaluated outcome in children after 'severe' trauma (defined as: hospitalised >48 h) by means of a questionnaire based on this ICF construct (IROS). Questionnaires were sent to children; one year after this trauma and to 'control' children without any previous 'severe' trauma. We created propensity score-matched pairs (n = 133) and evaluated differences in health perception. IROS characteristics were investigated by means of Item Response Theory models. We then estimated the health state of each individual based on his/her response pattern (factor score z01) and investigated the effect of selected covariates with simple linear regression. Significant odds ratios for differences between matched groups (p < 0.05) were observed for among others emotional problems, mobility, societal life and family burden, but not for chronic pain. Children in the trauma group showed, e.g. significant more physician (estimated relative risk R' 1.7) and psychologist (R' 3.5) visits. IROS primarily provides information from medium to high health burden levels and factor scores ranged from 0.41 (lowest) to 0.967 (highest burden). A significant impact on health burden could only be proven for the 'state at discharge' (p = 0.015), although there was a tendency towards worse factor scores for children that were older, had a higher Injury Severity Score or after traffic injury. In conclusion, we showed that the burden of health problems for children and families after severe trauma is still high and physical, as well as psychosocial in nature. The health state at discharge seems to predict long-term outcome, which might be of importance in view of, e.g. trajectory assistance. IROS may provide an improved scoring system to evaluate outcome after (paediatric) injury or critical illness.

The logistic transform for bounded outcome scores.

The logistic transformation, originally suggested by Johnson (1949), is applied to analyze responses that are restricted to a finite interval (e.g. (0,1)), so-called bounded outcome scores. Bounded outcome scores often have a non-standard distribution, e.g. J- or U-shaped, precluding classical parametric statistical approaches for analysis. Applying the logistic transformation on a normally distributed random variable, gives rise to a logit-normal (LN) distribution. This distribution can take a variety of shapes on (0,1). Further, the model can be extended to correct for (baseline) covariates. Therefore, the method could be useful for comparative clinical trials. Bounded outcomes can be found in many research areas, e.g. drug compliance research, quality-of-life studies, and pain (and pain relief) studies using visual analog scores, but all these scores can attain the boundary values 0 or 1. A natural extension of the above approach is therefore to assume a latent score on 0,1) having a LN distribution. Two cases are considered: (a) the bounded outcome score is a proportion where the true probabilities have a LN distribution on (0,1) and (b) the bounded outcome score on [0,1] is a coarsened version of a latent score with a LN distribution on (0,1). We also allow the variance (on the transformed scale) to depend on treatment. The usefulness of our approach for comparative clinical trials will be assessed in this paper. It turns out to be important to distinguish the case of equal and unequal variances. For a bounded outcome score of the second type and with equal variances, our approach comes close to ordinal probit (OP) regression. However, ignoring the inequality of variances can lead to highly biased parameter estimates. A simulation study compares the performance of our approach with the two-sample Wilcoxon test and with OP regression. Finally, the different methods are illustrated on two data sets.

Power and sample size calculations for discrete bounded outcome scores.

We consider power and sample size calculations for randomized trials with a bounded outcome score (BOS) as primary response adjusted for a priori chosen covariates. We define BOS to be a random variable restricted to a finite interval. Typically, a BOS has a J- or U-shaped distribution hindering traditional parametric methods of analysis. When no adjustment for covariates is needed, a non-parametric test could be chosen. However, there is still a problem with calculating the power since the common location-shift alternative does not hold in general for a BOS. In this paper, we consider a parametric approach and assume that the observed BOS is a coarsened version of a true BOS, which has a logit-normal distribution in each treatment group allowing correction for covariates. A two-step procedure is used to calculate the power. Firstly, the power function is defined conditionally on the covariate values. Secondly, the marginal power is obtained by averaging the conditional power with respect to an assumed distribution for the covariates using Monte Carlo integration. A simulation study evaluates the performance of our method which is also applied to the ECASS-1 stroke study.