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.

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.

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.

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/ ).

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.

Postoperative analgesia after surgical repair of distal radius fracture: a randomized comparison between distal peripheral nerve blockade and surgical site infiltration.

BACKGROUND: Pain following open reduction and internal fixation of distal radius fracture (DRF) can be significant. This study compared the intensity of pain up to 48 hours after volar plating for DRF, associated to either an ultrasound guided distal nerve block (DNB) or surgical site infiltration (SSI). METHODS: In this prospective single blind randomized study, 72 patients scheduled for DRF surgery under 1.5% lidocaine axillary block were allocated to receive, at the end of surgery, either an ultrasound-guided median and radial nerves block with ropivacaine 0.375% (DNB) performed by the anesthesiologist or a SSI with the same drug regimen, performed by the surgeon. Primary outcome was the duration between analgesic technique (H0) and pain reappearance (Numerical Rating Scale (NRS 0-10)>3). Secondary outcomes were the quality of analgesia, the quality of sleep, the magnitude of motor blockade, and the patient satisfaction. The study was built on a statistical hypothesis of equivalence. RESULTS: Fifty-nine patients were included in the final per-protocol analysis (DNB=30, SSI=29). Time to reach NRS>3 was (in median [95%CI]) 267 min [155;727] and 164 min [120;181] respectively after DNB and SSI (difference=103 min [-22;594] - rejection of equivalence hypothesis). Pain intensity throughout the 48 hours, quality of sleep, opiate consumption, motor blockade and patient satisfaction was not significantly different between groups. CONCLUSIONS: Although DNB provides a longer analgesia than SSI, both techniques gave comparable level of pain control during the first 48 hours after surgery, without any difference in the incidence of side effects or patient satisfaction.

Riluzole for treating spasticity in patients with chronic traumatic spinal cord injury: Study protocol in the phase ib/iib adaptive multicenter randomized controlled RILUSCI trial.

BACKGROUND: Satisfactory treatment is often lacking for spasticity, a highly prevalent motor disorder in patients with spinal cord injury (SCI). Low concentrations of riluzole potently reduce the persistent sodium current, the post-SCI increase in which contributes to spasticity. The repurposing of this drug may therefore constitute a useful potential therapeutic option for relieving SCI patients suffering from chronic traumatic spasticity. OBJECTIVE: RILUSCI is a phase 1b-2b trial designed to assess whether riluzole is a safe and biologically effective means of managing spasticity in adult patients with traumatic chronic SCI. METHODS: In this multicenter double-blind trial, adults (aged 18-65 years) suffering from spasticity after SCI (target enrollment: 90 participants) will be randomly assigned to be given either a placebo or a recommended daily oral dose of riluzole for two weeks. The latter dose will be previously determined in phase 1b of the study by performing double-blind dose-finding tests using a Bayesian continuous reassessment method. The primary endpoint of the trial will be an improvement in the Modified Ashworth Score (MAS) or the Numerical Rating Score (NRS) quantifying spasticity. The secondary outcomes will be based on the safety and pharmacokinetics of riluzole as well as its impact on muscle spasms, pain, bladder dysfunction and quality of life. Analyses will be performed before, during and after the treatment and the placebo-controlled period. CONCLUSION: To the best of our knowledge, this clinical trial will be the first to document the safety and efficacy of riluzole as a means of reducing spasticity in patients with chronic SCI. TRIAL REGISTRATION: The clinical trial, which is already in progress, was registered on the ClinicalTrials.gov website on August 9, 2016 under the registration number NCT02859792. TRIAL SPONSOR: Assistance Publique-Hôpitaux de Marseille.

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.

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.

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.

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.

Impact of early corticosteroids on 60-day mortality in critically ill patients with COVID-19: A multicenter cohort study of the OUTCOMEREA network.

OBJECTIVES: In severe COVID-19 pneumonia, the appropriate timing and dosing of corticosteroids (CS) is not known. Patient subgroups for which CS could be more beneficial also need appraisal. The aim of this study was to assess the effect of early CS in COVID-19 pneumonia patients admitted to the ICU on the occurrence of 60-day mortality, ICU-acquired-bloodstream infections(ICU-BSI), and hospital-acquired pneumonia and ventilator-associated pneumonia(HAP-VAP). METHODS: We included patients with COVID-19 pneumonia admitted to 11 ICUs belonging to the French OutcomeReaTM network from January to May 2020. We used survival models with ponderation with inverse probability of treatment weighting (IPTW). RESULTS: The study population comprised 303 patients having a median age of 61.6 (53-70) years of whom 78.8% were male and 58.6% had at least one comorbidity. The median SAPS II was 33 (25-44). Invasive mechanical ventilation was required in 34.8% of the patients. Sixty-six (21.8%) patients were in the Early-C subgroup. Overall, 60-day mortality was 29.4%. The risks of 60-day mortality (IPTWHR = 0.86;95% CI 0.54 to 1.35, p = 0.51), ICU-BSI and HAP-VAP were similar in the two groups. Importantly, early CS treatment was associated with a lower mortality rate in patients aged 60 years or more (IPTWHR, 0.53;95% CI, 0.3-0.93; p = 0.03). In contrast, CS was associated with an increased risk of death in patients younger than 60 years without inflammation on admission (IPTWHR = 5.01;95% CI, 1.05, 23.88; p = 0.04). CONCLUSION: For patients with COVID-19 pneumonia, early CS treatment was not associated with patient survival. Interestingly, inflammation and age can significantly influence the effect of CS.

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.

The Use of Translational Modelling and Simulation to Develop Immunomodulatory Therapy as an Adjunct to Antibiotic Treatment in the Context of Pneumonia.

The treatment of respiratory tract infections is threatened by the emergence of bacterial resistance. Immunomodulatory drugs, which enhance airway innate immune defenses, may improve therapeutic outcome. In this concept paper, we aim to highlight the utility of pharmacometrics and Bayesian inference in the development of immunomodulatory therapeutic agents as an adjunct to antibiotics in the context of pneumonia. For this, two case studies of translational modelling and simulation frameworks are introduced for these types of drugs up to clinical use. First, we evaluate the pharmacokinetic/pharmacodynamic relationship of an experimental combination of amoxicillin and a TLR4 agonist, monophosphoryl lipid A, by developing a pharmacometric model accounting for interaction and potential translation to humans. Capitalizing on this knowledge and associating clinical trial extrapolation and statistical modelling approaches, we then investigate the TLR5 agonist flagellin. The resulting workflow combines expert and prior knowledge on the compound with the in vitro and in vivo data generated during exploratory studies in order to construct high-dimensional models considering the pharmacokinetics and pharmacodynamics of the compound. This workflow can be used to refine preclinical experiments, estimate the best doses for human studies, and create an adaptive knowledge-based design for the next phases of clinical development.

Multistate Modeling of COVID-19 Patients Using a Large Multicentric Prospective Cohort of Critically Ill Patients.

The mortality of COVID-19 patients in the intensive care unit (ICU) is influenced by their state at admission. We aimed to model COVID-19 acute respiratory distress syndrome state transitions from ICU admission to day 60 outcome and to evaluate possible prognostic factors. We analyzed a prospective French database that includes critically ill COVID-19 patients. A six-state multistate model was built and 17 transitions were analyzed either using a non-parametric approach or a Cox proportional hazard model. Corticosteroids and IL-antagonists (tocilizumab and anakinra) effects were evaluated using G-computation. We included 382 patients in the analysis: 243 patients were admitted to the ICU with non-invasive ventilation, 116 with invasive mechanical ventilation, and 23 with extracorporeal membrane oxygenation. The predicted 60-day mortality was 25.9% (95% CI: 21.8%-30.0%), 44.7% (95% CI: 48.8%-50.6%), and 59.2% (95% CI: 49.4%-69.0%) for a patient admitted in these three states, respectively. Corticosteroids decreased the risk of being invasively ventilated (hazard ratio (HR) 0.59, 95% CI: 0.39-0.90) and IL-antagonists increased the probability of being successfully extubated (HR 1.8, 95% CI: 1.02-3.17). Antiviral drugs did not impact any transition. In conclusion, we observed that the day-60 outcome in COVID-19 patients is highly dependent on the first ventilation state upon ICU admission. Moreover, we illustrated that corticosteroid and IL-antagonists may influence the intubation duration.

Association Between Early Invasive Mechanical Ventilation and Day-60 Mortality in Acute Hypoxemic Respiratory Failure Related to Coronavirus Disease-2019 Pneumonia.

OBJECTIVES: About 5% of patients with coronavirus disease-2019 are admitted to the ICU for acute hypoxemic respiratory failure. Opinions differ on whether invasive mechanical ventilation should be used as first-line therapy over noninvasive oxygen support. The aim of the study was to assess the effect of early invasive mechanical ventilation in coronavirus disease-2019 with acute hypoxemic respiratory failure on day-60 mortality. DESIGN: Multicenter prospective French observational study. SETTING: Eleven ICUs of the French OutcomeRea network. PATIENTS: Coronavirus disease-2019 patients with acute hypoxemic respiratory failure (Pao2/Fio2 ≤ 300 mm Hg), without shock or neurologic failure on ICU admission, and not referred from another ICU or intermediate care unit were included. INTERVENTION: We compared day-60 mortality in patients who were on invasive mechanical ventilation within the first 2 calendar days of the ICU stay (early invasive mechanical ventilation group) and those who were not (nonearly invasive mechanical ventilation group). We used a Cox proportional-hazard model weighted by inverse probability of early invasive mechanical ventilation to determine the risk of death at day 60. MEASUREMENT AND MAIN RESULTS: The 245 patients included had a median (interquartile range) age of 61 years (52-69 yr), a Simplified Acute Physiology Score II score of 34 mm Hg (26-44 mm Hg), and a Pao2/Fio2 of 121 mm Hg (90-174 mm Hg). The rates of ICU-acquired pneumonia, bacteremia, and the ICU length of stay were significantly higher in the early (n = 117 [48%]) than in the nonearly invasive mechanical ventilation group (n = 128 [52%]), p < 0.01. Day-60 mortality was 42.7% and 21.9% in the early and nonearly invasive mechanical ventilation groups, respectively. The weighted model showed that early invasive mechanical ventilation increased the risk for day-60 mortality (weighted hazard ratio =1.74; 95% CI, 1.07-2.83, p=0.03). CONCLUSIONS: In ICU patients admitted with coronavirus disease-2019-induced acute hypoxemic respiratory failure, early invasive mechanical ventilation was associated with an increased risk of day-60 mortality. This result needs to be confirmed.

Estimating Similarity of Dose-Response Relationships in Phase I Clinical Trials-Case Study in Bridging Data Package.

Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose-reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose-response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose-toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.