Improved mortality analysis in early-phase dose-ranging clinical trials for emergency medical diseases using Bayesian time-to-event models with active comparators.

Emergency medical diseases (EMDs) are the leading cause of death worldwide. A time-to-death analysis is needed to accurately identify the risks and describe the pattern of an EMD because the mortality rate can peak early and then decline. Dose-ranging Phase II clinical trials are essential for developing new therapies for EMDs. However, most dose-finding trials do not analyze mortality as a time-to-event endpoint. We propose three Bayesian dose-response time-to-event models for a secondary mortality analysis of a clinical trial: a two-group (active treatment vs control) model, a three-parameter sigmoid EMAX model, and a hierarchical EMAX model. The study also incorporates one specific active treatment as an active comparator in constructing three new models. We evaluated the performance of these six models and a very popular independent model using simulated data motivated by a randomized Phase II clinical trial focused on identifying the most effective hyperbaric oxygen dose to achieve favorable functional outcomes in patients with severe traumatic brain injury. The results show that the three-group, EMAX, and EMAX model with an active comparator produce the smallest averaged mean squared errors and smallest mean absolute biases. We provide a new approach for time-to-event analysis in early-phase dose-ranging clinical trials for EMDs. The EMAX model with an active comparator can provide valuable insights into the mortality analysis of new EMDs or other conditions that have changing risks over time. The restricted mean survival time, a function of the model's hazards, is recommended for displaying treatment effects for EMD research.

Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance. Methods: The Bayesian hierarchical model uses Poisson models to describe the accrual performance of individual cancer clinical trials and a hierarchical component to describe the variation in performance across studies. Additionally, this model evaluates the impacts of the C3OD and the COVID-19 pandemic using posterior probabilities across evaluation years. The performance metric is the ratio of the observed accrual rate to the target accrual rate. Results: Posterior medians of the annual accrual performance at the KUCC from 2018 to 2023 are 0.233, 0.246, 0.197, 0.150, 0.254, and 0.340. The COVID-19 pandemic partly explains the drop in performance in 2020 and 2021. The posterior probability that annual accrual performance is better with C3OD in 2023 than pre-pandemic (2019) is 0.935. Conclusions: This study comprehensively evaluates the annual performance of clinical trial accrual at the KUCC, revealing a negative impact of COVID-19 and an ongoing positive impact of C3OD implementation. Two sensitivity analyses further validate the robustness of our model. Evaluating annual accrual performance across clinical trials is essential for a cancer center. The performance evaluation tools described in this paper are highly recommended for monitoring clinical trial accrual.

Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.

Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.

Racial differences in serum chemokines in prostate cancer patients.

BACKGROUND: This study aimed to understand the differential levels of inflammatory chemokines in association with higher prostate cancer incidence and mortality in African American (AA) men than in Caucasians (CA). METHODS: The authors used a chemokine assay to simultaneously measure 40 chemokines and cytokines levels in the serum of preoperative prostate cancer patients and healthy controls of AA and CA races. Selected chemokines (CXCL2, CXCL5, and CCL23) serum level was validated in 211 serum samples from prostate cancer patients and healthy controls. Differential expression of CXCL5 and CCL23 was analyzed using immunohistochemistry in a representative cohort of prostate tumor tissues of AA and CA races. RESULTS: Race-specific comparisons from 211 serum samples showed significantly higher levels of CXCL2 (control: 3104.0 pg/mL vs. cancer: 2451.0 pg/mL) and CXCL5 (control: 5189.0 pg/mL vs. cancer: 5459.0 pg/mL) in AA men than in CAs (CXCL2; control: 1155.0 pg/mL vs. cancer: 889.3 pg/mL, and CXCL5; control: 1183.0 pg/mL vs. cancer: 977.5 pg/mL). CCL23 differed significantly within and between the races with a lower level in AA cancer cases (454.5 vs. 966.6 pg/mL) than healthy controls (740.5 vs. 1263.0 pg/mL). Patient age, prostate-specific antigen, or Gleason scores were not significantly associated with these chemokines. Immunostaining for CXCL5 and CCL23 in a representative cohort of archival prostate tissues displayed significantly higher CXCL5 in prostate tumors than in adjacent benign tissues, whereas CCL23 was nondetectable in most of the analyzed tumor tissues. CONCLUSION: Lower levels of CCL23 in AA prostate cancer patient sera and tumor tissues and high CXCL2 and CXCL5 may contribute to aggressive prostate cancer, as often seen in AA men. The disproportionate levels of serum chemokines associated with race warrant further exploration to improve equitability in precision oncology to benefit prostate cancer patients.

The effect of type 2 diabetes and diabetic peripheral neuropathy on predictive grip force control.

This study investigated the impact of type 2 diabetes and diabetic peripheral neuropathy on grip force control during object manipulation. The study included three age-matched groups: type 2 diabetes alone (n = 11), type 2 diabetes with neuropathy (n = 13), and healthy controls (n = 12). Grip force control variables derived from lifting and holding an experimental cup were the ratio between grip force and load forces during lifting (GFR), latency 1 and latency 2, which represented the time between the object's grip and its lift-off from the table, and the period between object's lift-off and the grip force peak, respectively; time lag, which denoted the time difference between the grip and load force peaks during the lifting phase, and finally static force, which was the grip force average during the holding phase. Grip force control variables were compared between groups using one-way ANOVA and Kruskal-Wallis test. Post-hoc analysis was used to compare differences between groups. GFR and latency 1 showed significant differences between groups; the type 2 diabetes with neuropathy group showed larger GFR than the type 2 diabetes alone and healthy control groups. The latency 1was longer for the group with neuropathy in comparison with the health control group. There were no significant differences between groups for latency 2, time lag, and static force. Our results showed impaired GFR and latency 1 in participants with type 2 diabetes with neuropathy while the time lag was preserved. People with type 2 diabetes alone might not have any deficits in grip force control. Higher grip forces might expose people with type 2 diabetes and diabetic peripheral neuropathy to the risk of fatigue and injuring their hands. Future studies should investigate strategies to help people with type 2 diabetes with neuropathy adjust grip forces during object manipulation.

Using a Bayesian model of the joint distribution of pain and time on medication to decide on pain medication for neuropathy.

The PAIN-CONTRoLS trial compared four medications in treating Cryptogenic sensory polyneuropathy. The primary outcome was a utility function that combined two outcomes, patients' pain score reduction and patients' quit rate. However, additional analysis of the individual outcomes could also be leveraged to inform selecting an optimal medication for future patients. We demonstrate how joint modeling of longitudinal and time-to-event data from PAIN-CONTRoLS can be used to predict the effects of medication in a patient-specific manner and helps to make patient-focused decisions. A joint model was used to evaluate the two outcomes while accounting for the association between the longitudinal process and the time-to-event processes. Results suggested no significant association between the patients' pain scores and time to the medication quit in the PAIN-CONTRoLS study, but the joint model still provided robust estimates and a better model fit. Using the model estimates, given patients' baseline characteristics, a drug profile on both the pain reduction and medication time could be obtained for each drug, providing information on how likely they would quit and how much pain reduction they should expect. Our analysis suggested that drugs viable for one patient may not be beneficial for others.

Evaluating Work Impairment as a Source of Financial Toxicity in Cancer Healthcare and Negative Impacts on Health Status.

How the socioeconomic factors intersect for a particular patient can determine their susceptibility to financial toxicity, what costs they will encounter during treatment, the type and quality of their care, and the potential work impairments they face. The primary goal of this study was to evaluate financial factors leading to worsening health outcomes by the cancer subtype. A logistic model predicting worsening health outcomes while assessing the most influential economic factors was constructed by the University of Michigan Health and Retirement Study. A forward stepwise regression procedure was implemented to identify the social risk factors that impact health status. Stepwise regression was done on data subsets based on the cancer types of lung, breast, prostate, and colon cancer to determine whether significant predictors of worsening health status were different or the same across cancer types. Independent covariate analysis was also conducted to cross-validate our model. On the basis of the model fit statistics, the two-factor model has the best fit, that is, the lowest AIC among potential models of 3270.56, percent concordance of 64.7, and a C-statistics of 0.65. The two-factor model used work impairment and out-of-pocket costs, significantly contributing to worsening health outcomes. Covariate analysis demonstrated that younger patients with cancer experienced more financial burdens leading to worsening health outcomes than elderly patients aged 65 years and above. Work impairment and high out-of-pocket costs were significantly associated with worsening health outcomes among cancer patients. Matching the participants who need the most financial help with appropriate resources is essential to mitigate the financial burden. Significance: Among patients with cancer, work impairment and out-of-pocket are the two primary factors contributing to adverse health outcomes. Women, African American or other races, the Hispanic population, and younger individuals have encountered higher work impairment and out-of-pocket costs due to cancer than their counterparts.

Career disruption and limitation of financial earnings due to cancer.

PURPOSE: This study investigated how cancer diagnosis and treatment lead to career disruption and, consequently, loss of income and depletion of savings. DESIGN: This study followed a qualitative descriptive design that allowed us to understand the characteristics and trends of the participants. METHOD: Patients recruited (n = 20) for this study were part of the University of Kansas Cancer Center patient advocacy research group (Patient and Investigator Voices Organizing Together). The inclusion criteria were that participants must be cancer survivors or co-survivors, be aged 18 years or older, be either employed or a student at the time of cancer diagnosis, have completed their cancer treatment, and be in remission. The responses were transcribed and coded inductively to identify themes. A thematic network was constructed based on those themes, allowing us to explore and describe the intricacies of the various themes and their impacts. RESULTS: Most patients had to quit their jobs or take extended absences from work to handle treatment challenges. Patients employed by the same employer for longer durations had the most flexibility to balance their time between cancer treatment and work. Essential, actionable items suggested by the cancer survivors included disseminating information about coping with financial burdens and ensuring that a nurse and financial navigator were assigned to every cancer patient. CONCLUSIONS: Career disruption is common among cancer patients, and the financial burden due to their career trajectory is irreparable. The financial burden is more prominent in younger cancer patients and creates a cascading effect that financially affects close family members.

Online Infectious Diseases Subspecialty Supplementary Curriculum for Medical Students and Residents: Moving Beyond "You Get What You Get".

OBJECTIVES: Students and residents rotating through infectious diseases (ID) electives are instructed primarily by participation in rounds and clinics, with teaching focused on diseases encountered. This "you get what you get" approach allows learners to apply knowledge directly to patient care, however, may miss topics encountered in standardized testing. This multisite study investigates the use of asynchronous web-based learning modules and its impact on student and resident knowledge. METHODS: Students and residents rotating through an ID elective were assigned to their standard elective (old) or asked to complete asynchronous web-based learning modules in addition to the standard curriculum (new). Learners submitted pre- and post-tests and scores were tabulated. In the following academic year, learners at the host site were provided the learning modules and a post-elective survey. RESULTS: Nine learners (100%) completed the pre-test and 5 (55.6%) completed the post-test in the standard (old) curriculum group, while 15 (100%) completed the pre-test and post-test in the new curriculum group. The mean percentage change in accuracy was 9% and 5.3% in the old and new curricula, respectively. Most (94%) survey respondents recommended continued use of the curriculum and expansion to other subspecialty electives. CONCLUSIONS: Subspecialty electives have multiple purposes including assisting learners in exploring careers, providing a well-rounded medical experience, or preparing learners for content in standardized tests. Consistent curricula are not always provided with electives to supplement the clinical experience. Our web curriculum was well-received with perceived knowledge gain, though with very small pre-post-test groups a score improvement could not be determined. An asynchronous online curriculum for learners in ID was feasible and well-received among faculty, and learners felt their knowledge was enhanced. Content areas supplemented those encountered during the ID elective. While an improvement in post-test scores was not demonstrated, learners and faculty felt modules were beneficial.

Financial burden among cancer patients: A national-level perspective.

BACKGROUND: This research study aimed to evaluate the financial burden among older cancer patients and its corresponding risk factors. Factors such as increasing treatment costs and work limitations often lead cancer patients to bankruptcy and poor quality of life. These consequences, in turn, can cause higher mortality rates among these patients. METHODS: This retrospective cohort study utilized data from the Health Retirement Study (HRS), conducted by the University of Michigan (N = 18,109). Eligible participants had responses captured from years 2002 to 2016. Participants were classified according to any self-reported cancer diagnosis (yes or no) and were compared on the basis of financial, work, and health-related outcomes. Propensity score (PS) matching was applied to reduce the effects of potential confounding factors. Also only, individuals with an age ≥50 and ≤85 during Wave 6 were retained. RESULTS: Multivariate analysis with random effects revealed several indicators of financial burden when comparing participants with a cancer diagnosis to those with no history of cancer. Mean out-of-pocket costs associated with a cancer diagnosis were $1058 higher when compared to participants with no history of cancer, suggesting that even cancer patients with insurance coverage faced out-of-pocket costs. Respondents with cancer patients had higher odds of encountering financial hardship if they are facing Work Limitations (OR = 2.714), Regular use of Medications (OR = 2.518), Hospital Stays (OR = 2.858), Declining Health (OR = 2.349), or were being covered under government health insurance (OR = 5.803) than respondents who did not have cancer, or suffered from mental health issues such as Depression (OR = 0.901). CONCLUSION: Cancer patients contend with increasing financial costs during their treatment. However, most newly diagnosed patients are not aware of these costs and are given few resources to handle them.

Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients.

BACKGROUND: Platform trials are well-known for their ability to investigate multiple arms on heterogeneous patient populations and their flexibility to add/drop treatment arms due to efficacy/lack of efficacy. Because of their complexity, it is important to develop highly optimized, transparent, and rigorous designs that are cost-efficient, offer high statistical power, maximize patient benefit, and are robust to changes over time. METHODS: To address these needs, we present a Bayesian platform trial design based on a beta-binomial model for binary outcomes that uses three key strategies: (1) hierarchical modeling of subgroups within treatment arms that allows for borrowing of information across subgroups, (2) utilization of response-adaptive randomization (RAR) schemes that seek a tradeoff between statistical power and patient benefit, and (3) adjustment for potential drift over time. Motivated by a proposed clinical trial that aims to find the appropriate treatment for different subgroup populations of ischemic stroke patients, extensive simulation studies were performed to validate the approach, compare different allocation rules, and study the model operating characteristics. RESULTS AND CONCLUSIONS: Our proposed approach achieved high statistical power and good patient benefit and was also robust against population drift over time. Our design provided a good balance between the strengths of both the traditional RAR scheme and fixed 1:1 allocation and may be a promising choice for dichotomous outcomes trials investigating multiple subgroups.

Unifying the analysis of continuous and categorical measures of weight loss and incorporating group effect: a secondary re-analysis of a large cluster randomized clinical trial using Bayesian approach.

BACKGROUND: Although frequentist paradigm has been the predominant approach to clinical studies for decades, some limitations associated with the frequentist null hypothesis significance testing have been recognized. Bayesian approaches can provide additional insights into data interpretation and inference by deriving posterior distributions of model parameters reflecting the clinical interest. In this article, we sought to demonstrate how Bayesian approaches can improve the data interpretation by reanalyzing the Rural Engagement in Primary Care for Optimizing Weight Reduction (REPOWER). METHODS: REPOWER is a cluster randomized clinical trial comparing three care delivery models: in-clinic individual visits, in-clinic group visits, and phone-based group visits. The primary endpoint was weight loss at 24 months and the secondary endpoints included the proportions of achieving 5 and 10% weight loss at 24 months. We reanalyzed the data using a three-level Bayesian hierarchical model. The posterior distributions of weight loss at 24 months for each arm were obtained using Hamiltonian Monte Carlo. We then estimated the probability of having a higher weight loss and the probability of having greater proportion achieving 5 and 10% weight loss between groups. Additionally, a four-level hierarchical model was used to assess the partially nested intervention group effect which was not investigated in the original REPOWER analyses. RESULTS: The Bayesian analyses estimated 99.5% probability that in-clinic group visits, compared with in-clinic individual visits, resulted in a higher percent weight loss (posterior mean difference: 1.8%[95% CrI: 0.5,3.2%]), a greater probability of achieving 5% threshold (posterior mean difference: 9.2% [95% CrI: 2.4, 16.0%]) and 10% threshold (posterior mean difference: 6.6% [95% CrI: 1.7, 11.5%]). The phone-based group visits had similar result. We also concluded that including intervention group did not impact model fit significantly. CONCLUSIONS: We unified the analyses of continuous (the primary endpoint) and categorical measures (the secondary endpoints) of weight loss with one single Bayesian hierarchical model. This approach gained statistical power for the dichotomized endpoints by leveraging the information in the continuous data. Furthermore, the Bayesian analysis enabled additional insights into data interpretation and inference by providing posterior distributions for parameters of interest and posterior probabilities of different hypotheses that were not available with the frequentist approach. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT02456636 ; date of registry: May 28, 2015.

Bayesian EMAX model with a mixture of normal distributions for dose-response in clinical trials.

When a dose-response relationship is monotonic, the EMAX model has been shown to provide a good empirical fit for designing and analyzing dose-response data across a wide range of pharmaceutical studies. However, the EMAX model has never been applied to a finite mixture distribution. Motivated by a proposal investigating DHA dose effect on preterm birth (PTB, <37 weeks gestation) rate, we developed a Bayesian EMAX mixture model incorporating the three normal components finite mixture model into the EMAX framework. The proposed Bayesian EMAX mixture model analyzes gestational age as a continuous variable, which allows for statistically efficient estimates of PTB rate using various cut point with the same parsimonious model. For example, we can estimate the rate of early PTB (ePTB, <34 weeks gestation), PTB (<37 weeks gestation), and late-term birth (>41 weeks gestation) using the same model. We compared our proposed EMAX mixture model with an EMAX logistic model and an independent doses logistic model for a dichotomized endpoint using extensive simulations. Across the scenarios under consideration, the EMAX mixture model achieved higher power than the EMAX logistic model and the independent doses logistic model in detecting the effect of DHA supplementation on the PTB rate. The EMAX mixture model also resulted in smaller mean squared errors (MSE) in PTB rate estimates.

A prenatal group based phone counseling intervention to improve breastfeeding rates and complementary feeding: a randomized, controlled pilot and feasibility trial.

BACKGROUND: Despite numerous benefits for both mom and baby, few infants are exclusively breastfed for the recommended first six months. Additionally, infants are given solids too early. Prenatal education increases rates of breastfeeding initiation and we hypothesize it can also improve exclusive breastfeeding rates and prevent the early introduction of solids. We conducted a randomized controlled pilot and feasibility trial to understand the feasibility and maternal acceptance of a prenatal behavioral lifestyle intervention (PBLI) delivered via group based phone counseling (GBPC) and its effectiveness on rates of exclusive breastfeeding up to six months postpartum. Secondary aims included rates of any breastfeeding up to six months, rates of early introduction of solids, and infant feeding progression. METHODS: Forty-one pregnant women were recruited from a Kansas City Metropolitan Obstetrics and Gynecology office and randomly assigned to a usual care group or a PBLI. Women in the PBLI participated in six GBPC sessions where they learned about breastfeeding and introducing solids. Feeding questionnaires to assess breastfeeding and introduction of solids were sent at two weeks, two months, four months, and six months postpartum. Structured interviews were also conducted after the intervention and at six months postpartum to assess maternal acceptance and intervention feasibility. RESULTS: Participants overwhelmingly found the intervention acceptable and beneficial. Rates of exclusive breastfeeding and any breastfeeding did not differ between groups at any time point. No between group differences were found for early introduction of solids or infant feeding progression. CONCLUSIONS: Mothers discontinue breastfeeding earlier than recommended despite high rates of initiation. A PBLI delivered via GBP is feasible, acceptable to participants, and showed positive impacts such as maternal empowerment for both breastfeeding and introducing solids. Future interventions should incorporate both prenatal and postpartum components. TRIAL REGISTRATION: Study protocols were approved by the University of Kansas Medical Center's Human Subjects Committee (STUDY00140506) and registered at ClinicalTrials.gov on 02/22/2018 ( NCT03442517 , retrospectively registered). All participants gave written informed consent prior to data collection.

A framework for personalized mammogram screening.

Breast cancer screening guidelines serve as crucial evidence-based recommendations in deciding when to begin regular screenings. However, due to developments in breast cancer research and differences in research interpretation, screening guidelines can vary between organizations and within organizations over time. This leads to significant lapses in adopting updated guidelines, variable decision making between physicians, and unnecessary screening for low to moderate risk patients (Jacobson and Kadiyala, 2017; Corbelli et al., 2014). For analysis, risk factors were assessed for patient screening behaviors and results. The outcome variable for the first analysis was whether the patient had undergone screening. The risk factors considered were age, marital status, education level, rural versus urban residence, and family history of breast cancer. The outcome variable for the second analysis was whether patients who had undergone breast cancer screening presented abnormal results. The risk factors considered were age, Body Mass Index, family history, smoking and alcohol status, hormonal contraceptive use, Hormone Replacement Therapy use, age of first pregnancy, number of pregnancies (parity), age of first menses, rural versus urban residence, and whether or not patients had at least one child. Logistic regression analysis displayed strong associations for both outcome variables. Risk of screening nonattendance was negatively associated with age as a continuous variable, age as a dichotomous variable, being married, any college education, and family history. Risk of one or more abnormal mammogram findings was positively associated with family history, and hormonal contraceptive use. This procedure will be further developed to incorporate additional risk factors and refine the analysis of currently implemented risk factors.

Non-cancer clinical trials start-up metrics at an academic medical center: Implications for advancing research.

The primary goal for any clinical trial after it receives a funding notification is to receive regulatory approval and initiate the trial for recruitment. Every trial must go through documentation and regulatory process before it can start recruiting participants and collecting data; this initial process of review and approval is known as the study start-up process (SSU). We evaluated the average time taken for studies to receive approvals. Using data from clinical trials conducted at the University of Kansas Medical Center, various times to reach the start of the study were calculated based on the dates of individual study. The results of this analysis showed that chart review studies and investigator-initiated trials had a shorter time to activation than other types of studies. Additionally, single-center studies had a shorter activation time than multi-center studies. The analysis also demonstrated that the overall processing time consistently had been reduced over time.

Restart TICrH: An Adaptive Randomized Trial of Time Intervals to Restart Direct Oral Anticoagulants after Traumatic Intracranial Hemorrhage.

Anticoagulants prevent thrombosis and death in patients with atrial fibrillation and venous thromboembolism (VTE) but also increase bleeding risk. The benefit/risk ratio favors anticoagulation in most of these patients. However, some will have a bleeding complication, such as the common trip-and-fall brain injury in elderly patients that results in traumatic intracranial hemorrhage. Clinicians must then make the difficult decision about when to restart the anticoagulant. Restarting too early risks making the bleeding worse. Restarting too late risks thrombotic events such as ischemic stroke and VTE, the indications for anticoagulation in the first place. There are more data on restarting patients with spontaneous intracranial hemorrhage, which is very different than traumatic intracranial hemorrhage. Spontaneous intracranial hemorrhage increases the risk of rebleeding because intrinsic vascular changes are widespread and irreversible. In contrast, traumatic cases are caused by a blow to the head, usually an isolated event portending less future risk. Clinicians generally agree that anticoagulation should be restarted but disagree about when. This uncertainty leads to long restart delays causing a large, potentially preventable burden of strokes and VTE, which has been unaddressed because of the absence of high quality evidence. Restart Traumatic Intracranial Hemorrhage (the "r" distinguished intracranial from intracerebral) (TICrH) is a prospective randomized open label blinded end-point response-adaptive clinical trial that will evaluate the impact of delays to restarting direct oral anticoagulation (1, 2, or 4 weeks) on the composite of thrombotic events and bleeding in patients presenting after traumatic intracranial hemorrhage.

Designing and analyzing clinical trials for personalized medicine via Bayesian models.

Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.

Restarting and timing of oral anticoagulation after traumatic intracranial hemorrhage: a review and summary of ongoing and planned prospective randomized clinical trials.

Anticoagulant-associated traumatic intracranial hemorrhage (tICrH) is a devastating injury with high morbidity and mortality. For survivors, treating clinicians face the dilemma of restarting oral anticoagulation with scarce evidence to guide them. Thromboembolic risk is high from the bleeding event, patients' high baseline risks, that is, the pre-existing indication for anticoagulation, and the risk of immobility after the bleeding episode. This must be balanced with potentially devastating hematoma expansion or new hemorrhagic lesions. Retrospective evidence and expert opinion support restarting oral anticoagulants in most patients with tICrH, but timing is uncertain. Researchers have failed to make clear distinctions between tICrH and spontaneous intracranial hemorrhage (sICrH), which have differing natural histories. While both appear to benefit from restarting, sICrH has a higher rebleeding risk and similar or lower thrombotic risk. Clinical equipoise on restarting is also divergent. In sICrH, equipoise is centered on whether to restart. In tICrH, it is centered on when. Several prospective randomized clinical trials are ongoing or about to start to examine the risk-benefit of restarting. Most of them are restricted to patients with sICrH, with antiplatelet control groups. Most are also restricted to direct oral anticoagulants (DOACs), as they are associated with a lower overall risk of ICrH. There is some overlap with tICrH via subdural hematoma, and one trial is specific to restart timing with DOACs in only traumatic cases. This is a narrative review of the current evidence for restarting anticoagulation and restart timing after tICrH along with a summary of the ongoing and planned clinical trials.