Survival without severe neonatal morbidity after antenatal betamethasone dose reduction: a post hoc analysis of a randomized non-inferiority trial.

BACKGROUND: Antenatal betamethasone is recommended before preterm delivery to accelerate fetal lung maturation. However, its optimal dose remains unknown. A 50% dose reduction was proposed to decrease the potential dose-related long-term neurodevelopmental side effects, including psychological development, sleep, and emotional disorders. Because noninferiority of the half dose in terms of the need for exogenous surfactant was not shown in the primary analysis, its impact on survival without major neonatal morbidity needs to be investigated. OBJECTIVE: This study aimed to investigate the impact of antenatal betamethasone dose reduction on survival of very preterm infants without severe neonatal morbidity, a factor known to have a strong correlation with long-term outcomes. STUDY DESIGN: We performed a post hoc secondary analysis of a randomized, multicenter, double-blind, placebo-controlled, noninferiority trial, testing half (11.4 mg once; n=1620) vs full (11.4 mg twice, 24 hours apart; n=1624) antenatal betamethasone doses in women at risk of preterm delivery. To measure survival without severe neonatal morbidity at hospital discharge among neonates born before 32 weeks of gestation, we used the definition of the French national prospective study on preterm children, EPIPAGE 2, comprising 1 of the following morbidities: grade 3 to 4 intraventricular hemorrhage, cystic periventricular leukomalacia, necrotizing enterocolitis stage ≥2, retinopathy of prematurity requiring anti-vascular endothelial growth factor therapy or laser, and moderate-to-severe bronchopulmonary dysplasia. RESULTS: After exclusion of women who withdrew consent or had pregnancy termination and of participants lost to follow-up (8 in the half-dose and 10 in the full-dose group), the rate of survival without severe neonatal morbidity among neonates born before 32 weeks of gestation was 300 of 451 (66.5%) and 304 of 462 (65.8%) in the half-dose and full-dose group, respectively (risk difference, +0.7%; 95% confidence interval, -5.6 to +7.1). There were no significant between-group differences in the cumulative number of neonatal morbidities. Results were similar when using 2 other internationally recognized definitions of severe neonatal morbidity and when considering the overall population recruited in the trial. CONCLUSION: In the BETADOSE trial, severe morbidity at discharge of newborns delivered before 32 weeks of gestation was found to be similar among those exposed to 11.4-mg and 22.8-mg antenatal betamethasone. Additional studies are needed to confirm these findings.

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas.

We congratulate the authors for the new meta-analysis model that accounts for different outcomes. We discuss the modeling choice and the Bayesian setting, specifically, we point out the connection between the Bayesian hierarchical model and a mixed-effect model formulation to subsequently discuss possible future method extensions.

Neonatal outcomes for women at risk of preterm delivery given half dose versus full dose of antenatal betamethasone: a randomised, multicentre, double-blind, placebo-controlled, non-inferiority trial.

BACKGROUND: Antenatal betamethasone is recommended before preterm delivery to accelerate fetal lung maturation. However, reports of growth and neurodevelopmental dose-related side-effects suggest that the current dose (12 mg plus 12 mg, 24 h apart) might be too high. We therefore investigated whether a half dose would be non-inferior to the current full dose for preventing respiratory distress syndrome. METHODS: We designed a randomised, multicentre, double-blind, placebo-controlled, non-inferiority trial in 37 level 3 referral perinatal centres in France. Eligible participants were pregnant women aged 18 years or older with a singleton fetus at risk of preterm delivery and already treated with the first injection of antenatal betamethasone (11·4 mg) before 32 weeks' gestation. We used a computer-generated code producing permuted blocks of varying sizes to randomly assign (1:1) women to receive either a placebo (half-dose group) or a second 11·4 mg betamethasone injection (full-dose group) 24 h later. Randomisation was stratified by gestational age (before or after 28 weeks). Participants, clinicians, and study staff were masked to the treatment allocation. The primary outcome was the need for exogenous intratracheal surfactant within 48 h after birth. Non-inferiority would be shown if the higher limit of the 95% CI for the between-group difference between the half-dose and full-dose groups in the primary endpoint was less than 4 percentage points (corresponding to a maximum relative risk of 1·20). Four interim analyses monitoring the primary and the secondary safety outcomes were done during the study period, using a sequential data analysis method that provided futility and non-inferiority stopping rules and checked for type I and II errors. Interim analyses were done in the intention-to-treat population. This trial was registered with ClinicalTrials.gov, NCT02897076. FINDINGS: Between Jan 2, 2017, and Oct 9, 2019, 3244 women were randomly assigned to the half-dose (n=1620 [49·9%]) or the full-dose group (n=1624 [50·1%]); 48 women withdrew consent, 30 fetuses were stillborn, 16 neonates were lost to follow-up, and 9 neonates died before evaluation, so that 3141 neonates remained for analysis. In the intention-to-treat analysis, the primary outcome occurred in 313 (20·0%) of 1567 neonates in the half-dose group and 276 (17·5%) of 1574 neonates in the full-dose group (risk difference 2·4%, 95% CI -0·3 to 5·2); thus non-inferiority was not shown. The per-protocol analysis also did not show non-inferiority (risk difference 2·2%, 95% CI -0·6 to 5·1). No between-group differences appeared in the rates of neonatal death, grade 3-4 intraventricular haemorrhage, stage ≥2 necrotising enterocolitis, severe retinopathy of prematurity, or bronchopulmonary dysplasia. INTERPRETATION: Because non-inferiority of the half-dose compared with the full-dose regimen was not shown, our results do not support practice changes towards antenatal betamethasone dose reduction. FUNDING: French Ministry of Health.

Development of consensus-driven SPIRIT and CONSORT extensions for early phase dose-finding trials: the DEFINE study.

BACKGROUND: Early phase dose-finding (EPDF) trials are crucial for the development of a new intervention and influence whether it should be investigated in further trials. Guidance exists for clinical trial protocols and completed trial reports in the SPIRIT and CONSORT guidelines, respectively. However, both guidelines and their extensions do not adequately address the characteristics of EPDF trials. Building on the SPIRIT and CONSORT checklists, the DEFINE study aims to develop international consensus-driven guidelines for EPDF trial protocols (SPIRIT-DEFINE) and reports (CONSORT-DEFINE). METHODS: The initial generation of candidate items was informed by reviewing published EPDF trial reports. The early draft items were refined further through a review of the published and grey literature, analysis of real-world examples, citation and reference searches, and expert recommendations, followed by a two-round modified Delphi process. Patient and public involvement and engagement (PPIE) was pursued concurrently with the quantitative and thematic analysis of Delphi participants' feedback. RESULTS: The Delphi survey included 79 new or modified SPIRIT-DEFINE (n = 36) and CONSORT-DEFINE (n = 43) extension candidate items. In Round One, 206 interdisciplinary stakeholders from 24 countries voted and 151 stakeholders voted in Round Two. Following Round One feedback, one item for CONSORT-DEFINE was added in Round Two. Of the 80 items, 60 met the threshold for inclusion (≥ 70% of respondents voted critical: 26 SPIRIT-DEFINE, 34 CONSORT-DEFINE), with the remaining 20 items to be further discussed at the consensus meeting. The parallel PPIE work resulted in the development of an EPDF lay summary toolkit consisting of a template with guidance notes and an exemplar. CONCLUSIONS: By detailing the development journey of the DEFINE study and the decisions undertaken, we envision that this will enhance understanding and help researchers in the development of future guidelines. The SPIRIT-DEFINE and CONSORT-DEFINE guidelines will allow investigators to effectively address essential items that should be present in EPDF trial protocols and reports, thereby promoting transparency, comprehensiveness, and reproducibility. TRIAL REGISTRATION: SPIRIT-DEFINE and CONSORT-DEFINE are registered with the EQUATOR Network ( https://www.equator-network.org/ ).

Bayesian dose regimen assessment in early phase oncology incorporating pharmacokinetics and pharmacodynamics.

Phase I dose-finding trials in oncology seek to find the maximum tolerated dose of a drug under a specific schedule. Evaluating drug schedules aims at improving treatment safety while maintaining efficacy. However, while we can reasonably assume that toxicity increases with the dose for cytotoxic drugs, the relationship between toxicity and multiple schedules remains elusive. We proposed a Bayesian dose regimen assessment method (DRtox) using pharmacokinetics/pharmacodynamics (PK/PD) to estimate the maximum tolerated dose regimen (MTD-regimen) at the end of the dose-escalation stage of a trial. We modeled the binary toxicity via a PD endpoint and estimated the dose regimen toxicity relationship through the integration of a dose regimen PD model and a PD toxicity model. For the first model, we considered nonlinear mixed-effects models, and for the second one, we proposed the following two Bayesian approaches: a logistic model and a hierarchical model. In an extensive simulation study, the DRtox outperformed traditional designs in terms of proportion of correctly selecting the MTD-regimen. Moreover, the inclusion of PK/PD information helped provide more precise estimates for the entire dose regimen toxicity curve; therefore the DRtox may recommend alternative untested regimens for expansion cohorts. The DRtox was developed to be applied at the end of the dose-escalation stage of an ongoing trial for patients with relapsed or refractory acute myeloid leukemia (NCT03594955) once all toxicity and PK/PD data are collected.

A straightforward meta-analysis approach for oncology phase I dose-finding studies.

Phase I early-phase clinical studies aim at investigating the safety and the underlying dose-toxicity relationship of a drug or combination. While little may still be known about the compound's properties, it is crucial to consider quantitative information available from any studies that may have been conducted previously on the same drug. A meta-analytic approach has the advantages of being able to properly account for between-study heterogeneity, and it may be readily extended to prediction or shrinkage applications. Here we propose a simple and robust two-stage approach for the estimation of maximum tolerated dose(s) utilizing penalized logistic regression and Bayesian random-effects meta-analysis methodology. Implementation is facilitated using standard R packages. The properties of the proposed methods are investigated in Monte Carlo simulations. The investigations are motivated and illustrated by two examples from oncology.

DICE: A Bayesian model for early dose finding in phase I trials with multiple treatment courses.

Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose-fInding CumulativE), for dose escalation and de-escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time-to-event continual reassessment method (TITE-CRM) and a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens.

Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose-finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLTo ) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose-allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD-regimen compared to the recommendation of the dose-allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

Conditional Non-parametric Bootstrap for Non-linear Mixed Effect Models.

PURPOSE: Non-linear mixed effect models are widely used and increasingly integrated into decision-making processes. Propagating uncertainty is an important element of this process, and while standard errors (SE) on pa- rameters are most often computed using asymptotic approaches, alternative methods such as the bootstrap are also available. In this article, we propose a modified residual parametric bootstrap taking into account the different levels of variability involved in these models. METHODS: The proposed approach uses samples from the individual conditional distribution, and was implemented in R using the saemix algorithm. We performed a simulation study to assess its performance in different scenarios, comparing it to the asymptotic approximation and to standard bootstraps in terms of coverage, also looking at bias in the parameters and their SE. RESULTS: Simulations with an Emax model with different designs and sigmoidicity factors showed a similar coverage rate to the parametric bootstrap, while requiring less hypotheses. Bootstrap improved coverage in several scenarios compared to the asymptotic method especially for the variance param-eters. However, all bootstraps were sensitive to estimation bias in the original datasets. CONCLUSIONS: The conditional bootstrap provided better coverage rate than the traditional residual bootstrap, while preserving the structure of the data generating process.

Dose-finding designs for cumulative toxicities using multiple constraints.

This article addresses the concern regarding late-onset dose-limiting toxicities (DLT), moderate toxicities below the threshold of a DLT and cumulative toxicities that may lead to a DLT, which are mostly disregarded or handled in an ad hoc manner when determining the maximum tolerated dose (MTD) in dose-finding cancer clinical trials. An extension of the Time-to-Event Continual Reassessment Method (TITE-CRM) which allows for the specification of toxicity constraints on both DLT and moderate toxicities, and can account for partial information is proposed. The method is illustrated in the context of an Erlotinib dose-finding trial with low DLT rates, but a significant number of moderate toxicities leading to treatment discontinuation in later cycles. Based on simulations, our method performs well at selecting the dose level that satisfies both the DLT and moderate-toxicity constraints. Moreover, it has similar probability of correct selection compared to the TITE-CRM when the true MTD based on DLT only and the true MTD based on grade 2 or higher toxicities alone coincide, but reduces the probability of recommending a dose above the MTD.

Bayesian variable selection based on clinical relevance weights in small sample studies-Application to colon cancer.

Using clinical data to model the medical decisions behind sequential treatment actions raises methodological challenges. Physicians often have access to many covariates that may be used when making sequential treatment decisions for individual patients. Statistical variable selection methods may help finding which of these variables are used for this decision in everyday practice. When the sample size is not large, Bayesian variable selection methods can address this setting and allow for expert information to be incorporated into prior distributions. Motivated by clinical practice data involving repeated dose adaptation for Irinotecan in colorectal metastatic cancer, we propose a modification of the stochastic search variable selection (SSVS) method, which we call weight-based SSVS (WBS). We use clinical relevance weights elicited from physician experts to construct prior distributions, with the goal to identify the most influential toxicities and other covariates used for dose adjustment. We evaluate and compare the WBS model performance to the Lasso and SSVS through an extensive simulation study. The simulations show that WBS has better performance and lower rates of false positives and false negatives than the other methods but depends strongly on the covariate weights.

A Bayesian non-inferiority approach using experts' margin elicitation - application to the monitoring of safety events.

BACKGROUND: When conducing Phase-III trial, regulatory agencies and investigators might want to get reliable information about rare but serious safety outcomes during the trial. Bayesian non-inferiority approaches have been developed, but commonly utilize historical placebo-controlled data to define the margin, depend on a single final analysis, and no recommendation is provided to define the prespecified decision threshold. In this study, we propose a non-inferiority Bayesian approach for sequential monitoring of rare dichotomous safety events incorporating experts' opinions on margins. METHODS: A Bayesian decision criterion was constructed to monitor four safety events during a non-inferiority trial conducted on pregnant women at risk for premature delivery. Based on experts' elicitation, margins were built using mixtures of beta distributions that preserve experts' variability. Non-informative and informative prior distributions and several decision thresholds were evaluated through an extensive sensitivity analysis. The parameters were selected in order to maintain two rates of misclassifications under prespecified rates, that is, trials that wrongly concluded an unacceptable excess in the experimental arm, or otherwise. RESULTS: The opinions of 44 experts were elicited about each event non-inferiority margins and its relative severity. In the illustrative trial, the maximal misclassification rates were adapted to events' severity. Using those maximal rates, several priors gave good results and one of them was retained for all events. Each event was associated with a specific decision threshold choice, allowing for the consideration of some differences in their prevalence, margins and severity. Our decision rule has been applied to a simulated dataset. CONCLUSIONS: In settings where evidence is lacking and where some rare but serious safety events have to be monitored during non-inferiority trials, we propose a methodology that avoids an arbitrary margin choice and helps in the decision making at each interim analysis. This decision rule is parametrized to consider the rarity and the relative severity of the events and requires a strong collaboration between physicians and the trial statisticians for the benefit of all. This Bayesian approach could be applied as a complement to the frequentist analysis, so both Data Safety Monitoring Boards and investigators can benefit from such an approach.

Full versus half dose of antenatal betamethasone to prevent severe neonatal respiratory distress syndrome associated with preterm birth: study protocol for a randomised, multicenter, double blind, placebo-controlled, non-inferiority trial (BETADOSE).

BACKGROUND: Although antenatal betamethasone is recommended worldwide for women at risk of preterm delivery, concerns persist regarding the long-term effects associated with this treatment. Indeed, adverse events, mainly dose-related, have been reported. The current recommended dose of antenatal betamethasone directly derives from sheep experiments performed in the late 60's and has not been challenged in 45 years. Therefore, randomized trials evaluating novel dose regimens are urgently needed. METHODS: A randomised, double blind, placebo-controlled, non-inferiority trial will be performed in 37 French level 3 maternity units. Women with a singleton pregnancy at risk of preterm delivery before 32 weeks of gestation having already received a first 11.4 mg injection of betamethasone will be randomised to receive either a second injection of 11.4 mg betamethasone (full dose arm) or placebo (half dose arm) administered intramuscularly 24 h after the first injection. The primary binary outcome will be the occurrence of severe respiratory distress syndrome (RDS), defined as the need for exogenous intra-tracheal surfactant in the first 48 h of life. Considering that 20% of the pregnant women receiving the full dose regimen would have a neonate with severe RDS, 1571 patients in each treatment group are required to show that the half dose regimen is not inferior to the full dose, that is the difference in severe RDS rate do not exceed 4% (corresponding to a Relative Risk of 20%), with a 1-sided 2.5% type-1 error and a 80% power. Interim analyses will be done after every 300 neonates who reach the primary outcome on the basis of intention-to-treat, using a group-sequential non-inferiority design. DISCUSSION: If the 50% reduced antenatal betamethasone dose is shown to be non-inferior to the full dose to prevent severe RDS associated with preterm birth, then it should be used consistently in women at risk of preterm delivery and would be of great importance to their children. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT 02897076 (registration date 09/13/2016).

Bayesian sequential integration within a preclinical pharmacokinetic and pharmacodynamic modeling framework: Lessons learned.

The present manuscript aims to discuss the implications of sequential knowledge integration of small preclinical trials in a Bayesian pharmacokinetic and pharmacodynamic (PK-PD) framework. While, at first sight, a Bayesian PK-PD framework seems to be a natural framework to allow for sequential knowledge integration, the scope of this paper is to highlight some often-overlooked challenges while at the same time providing some guidances in the many and overwhelming choices that need to be made. Challenges as well as opportunities will be discussed that are related to the impact of (1) the prior specification, (2) the choice of random effects, (3) the type of sequential integration method. In addition, it will be shown how the success of a sequential integration strategy is highly dependent on a carefully chosen experimental design when small trials are analyzed.

Bayesian knowledge integration for an in vitro-in vivo correlation model.

The primary goal of "in vitro-in vivo correlation" (IVIVC) is the reliable prediction of the in vivo serum concentration-time course, based on the in vitro drug dissolution or release profiles. IVIVC methods are particularly appropriate for formulations that are released over an extended period of time or with a lag in absorption and may support approving a change in formulation of a drug without additional bioequivalence trials in human subjects. Most of the current IVIVC models are assessed using frequentist methods, such as linear regression, based on averaged data and entail complex and potentially unstable mathematical deconvolution. The proposed IVIVC approach includes (a) a nonlinear-mixed effects model for the in vitro release data; (b) a population pharmacokinetic (PK) compartment model for the in vivo immediate release (IR) data; and (c) a system of ordinal differential equations (ODEs), containing the submodels (a) and (b), which approximates and predicts the in vivo controlled release (CR) data. The innovation in this paper consists of splitting the parameter space between submodels (a) and (b) versus (c). Subsequently, the uncertainty on these parameters is accounted for using a Bayesian framework, that is estimates from the first two submodels serve as priors for the Bayesian hierarchical third submodel. As such, the Bayesian method explained ensures a natural integration and transfer of knowledge between various sources of information, balancing possible differences in sample size and parameter uncertainty of in vitro and in vivo studies. Consequently, it is a very flexible approach yielding results for a broad range of data situations. The application of the method is demonstrated for a transdermal patch (TD).

Dose-finding methods for Phase I clinical trials using pharmacokinetics in small populations.

The aim of phase I clinical trials is to obtain reliable information on safety, tolerability, pharmacokinetics (PK), and mechanism of action of drugs with the objective of determining the maximum tolerated dose (MTD). In most phase I studies, dose-finding and PK analysis are done separately and no attempt is made to combine them during dose allocation. In cases such as rare diseases, paediatrics, and studies in a biomarker-defined subgroup of a defined population, the available population size will limit the number of possible clinical trials that can be conducted. Combining dose-finding and PK analyses to allow better estimation of the dose-toxicity curve should then be considered. In this work, we propose, study, and compare methods to incorporate PK measures in the dose allocation process during a phase I clinical trial. These methods do this in different ways, including using PK observations as a covariate, as the dependent variable or in a hierarchical model. We conducted a large simulation study that showed that adding PK measurements as a covariate only does not improve the efficiency of dose-finding trials either in terms of the number of observed dose limiting toxicities or the probability of correct dose selection. However, incorporating PK measures does allow better estimation of the dose-toxicity curve while maintaining the performance in terms of MTD selection compared to dose-finding designs that do not incorporate PK information. In conclusion, using PK information in the dose allocation process enriches the knowledge of the dose-toxicity relationship, facilitating better dose recommendation for subsequent trials.

Meta-analyses of phase I dose-finding studies: Application for the development of protein kinase inhibitors in oncology.

This study aimed to assess the feasibility of applying two recent phase I meta-analyses methods to protein kinase inhibitors (PKIs) developed in oncology and to identify situations where these methods could be both feasible and useful. This ancillary study used data from a systematic review conducted to identify dose-finding studies for PKIs. PKIs selected for meta-analyses were required to have at least five completed dose-finding studies involving cancer patients, with available results, and dose escalation guided by toxicity assessment. To account for heterogeneity caused by various administration schedules, some studies were divided into study parts, considered as separate entities in the meta-analyses. For each PKI, two Bayesian random-effects meta-analysis methods were applied to model the toxicity probability distribution of the recommended dose and to estimate the maximum tolerated dose (MTD). Meta-analyses were performed for 20 PKIs including 96 studies corresponding to 115 study parts. The median posterior probability of toxicity probability was below the toxicity thresholds of 0.20 for 70% of the PKIs, even if the resulting credible intervals were very wide. All approved doses were below the MTD estimated for the minimum toxicity threshold, except for one, for which the approved dose was above the MTD estimated for the maximal threshold. The application of phase I meta-analysis methods has been feasible for the majority of PKI; nevertheless, their implementation requires multiple conditions. However, meta-analyses resulted in estimates with large uncertainty, probably due to limited patient numbers and/or between-study variability. This calls into question the reliability of the recommended doses.

Bayesian framework for multi-source data integration-Application to human extrapolation from preclinical studies.

In preclinical investigations, for example, in in vitro, in vivo, and in silico studies, the pharmacokinetic, pharmacodynamic, and toxicological characteristics of a drug are evaluated before advancing to first-in-man trial. Usually, each study is analyzed independently and the human dose range does not leverage the knowledge gained from all studies. Taking into account all preclinical data through inferential procedures can be particularly interesting in obtaining a more precise and reliable starting dose and dose range. Our objective is to propose a Bayesian framework for multi-source data integration, customizable, and tailored to the specific research question. We focused on preclinical results extrapolated to humans, which allowed us to predict the quantities of interest (e.g. maximum tolerated dose, etc.) in humans. We build an approach, divided into four steps, based on a sequential parameter estimation for each study, extrapolation to human, commensurability checking between posterior distributions and final information merging to increase the precision of estimation. The new framework is evaluated via an extensive simulation study, based on a real-life example in oncology. Our approach allows us to better use all the information compared to a standard framework, reducing uncertainty in the predictions and potentially leading to a more efficient dose selection.

Home-Based Connected Devices Combined With Statistical Process Control for the Early Detection of Respiratory Exacerbations by Patients With Cystic Fibrosis: Pilot Interventional Study With a Pre-Post Design.

BACKGROUND: Currently, patients with cystic fibrosis do not routinely monitor their respiratory function at home. OBJECTIVE: This study aims to assess the clinical validity of using different connected health devices at home to measure 5 physiological parameters to help prevent exacerbations on a personalized basis from the perspective of patient empowerment. METHODS: A multicenter interventional pilot study including 36 patients was conducted. Statistical process control-the cumulative sum control chart (CUSUM)-was used with connected health device measures with the objective of sending patients alerts at a relevant time in order to identify their individual risk of exacerbations. Associated patient education was delivered. Quantitative and qualitative data were collected. RESULTS: One-half (18/36) of the patients completed the protocol through the end of the study. During the 12-month intervention, 6162 measures were collected with connected health devices, 387 alerts were sent, and 33 exacerbations were reported. The precision of alerts to detect exacerbations was weak for all parameters, which may be partly related to the low compliance of patients with the measurements. However, a decrease in the median number of exacerbations from 12 months before the study to after the 12-month intervention was observed for patients. CONCLUSIONS: The use of connected health devices associated with statistical process control showed that it was not acceptable for all patients, especially because of the burden related to measurements. However, the results suggest that it may be promising, after adaptations, for early identification and better management of exacerbations. TRIAL REGISTRATION: ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/study/NCT03304028.

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial.

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.