New approaches for testing non-inferiority for three-arm trials with Poisson distributed outcomes.

With the availability of limited resources, innovation for improved statistical method for the design and analysis of randomized controlled trials (RCTs) is of paramount importance for newer and better treatment discovery for any therapeutic area. Although clinical efficacy is almost always the primary evaluating criteria to measure any beneficial effect of a treatment, there are several important other factors (e.g., side effects, cost burden, less debilitating, less intensive, etc.), which can permit some less efficacious treatment options favorable to a subgroup of patients. This leads to non-inferiority (NI) testing. The objective of NI trial is to show that an experimental treatment is not worse than an active reference treatment by more than a pre-specified margin. Traditional NI trials do not include a placebo arm for ethical reason; however, this necessitates stringent and often unverifiable assumptions. On the other hand, three-arm NI trials consisting of placebo, reference, and experimental treatment, can simultaneously test the superiority of the reference over placebo and NI of experimental treatment over the reference. In this article, we proposed both novel Frequentist and Bayesian procedures for testing NI in the three-arm trial with Poisson distributed count outcome. RCTs with count data as the primary outcome are quite common in various disease areas such as lesion count in cancer trials, relapses in multiple sclerosis, dermatology, neurology, cardiovascular research, adverse event count, etc. We first propose an improved Frequentist approach, which is then followed by it's Bayesian version. Bayesian methods have natural advantage in any active-control trials, including NI trial when substantial historical information is available for placebo and established reference treatment. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.

An age-dependent immuno-epidemiological model with distributed recovery and death rates.

The work is devoted to a new immuno-epidemiological model with distributed recovery and death rates considered as functions of time after the infection onset. Disease transmission rate depends on the intra-subject viral load determined from the immunological submodel. The age-dependent model includes the viral load, recovery and death rates as functions of age considered as a continuous variable. Equations for susceptible, infected, recovered and dead compartments are expressed in terms of the number of newly infected cases. The analysis of the model includes the proof of the existence and uniqueness of solution. Furthermore, it is shown how the model can be reduced to age-dependent SIR or delay model under certain assumptions on recovery and death distributions. Basic reproduction number and final size of epidemic are determined for the reduced models. The model is validated with a COVID-19 case data. Modelling results show that proportion of young age groups can influence the epidemic progression since disease transmission rate for them is higher than for other age groups.

Maintaining Program Fidelity in a Changing World: National Implementation of a School-Based HIV Prevention Program.

Large-scale, evidence-based interventions face challenges to program fidelity of implementation. We developed implementation strategies to support teachers implementing an evidence-based HIV prevention program in schools, Focus on Youth in The Caribbean (FOYC) and Caribbean Informed Parents and Children Together (CImPACT) in The Bahamas. We examined the effects of these implementation strategies on teachers' implementation in the subsequent year after the initial implementation during the COVID-19 pandemic. Data were collected from 79 Grade 6 teachers in 24 government elementary schools. Teachers completed training workshops and a pre-implementation questionnaire to record their characteristics and perceptions that might affect their program fidelity. School coordinators and peer mentors provided teachers with monitoring, feedback, and mentoring. In Year 1, teachers on average taught 79.3% of the sessions and 80.8% of core activities; teachers in Year 2 covered 84.2% of sessions and 72.9% of the core activities. Teachers with "good" or "excellent" school coordinators in the second year taught significantly more sessions on average (7.8 vs. 7.0, t = 2.04, P < 0.05) and more core activities (26.3 vs. 23.0, t = 2.41, P < 0.05) than teachers with "satisfactory" coordinators. Teachers who had a "good" or "satisfactory" mentor taught more sessions than teachers who did not have a mentor (7.9 vs. 7.3; t = 2.22; P = 0.03). Two-level mixed-effects model analysis indicated that teachers' program fidelity in Year 1, confidence in the execution of core activities, and school coordinators' performance were significantly associated with Year 2 implementation dose. Implementation of FOYC + CImPACT was significantly associated with improved student outcomes. Teachers maintained high fidelity to a comprehensive HIV prevention program over 2 years during the COVID-19 pandemic. Future program implementers should consider additional implementation support to improve the implementation of school-based programs.

Statistical power and sample size requirements to detect an intervention by time interaction in four-level longitudinal cluster randomized trials.

Cluster/group randomized controlled trials (CRTs) have a long history in the study of health sciences. CRT is a special type of intervention trial in which a complete group is randomly assigned to a study condition (or intervention). It is typically performed when individual randomization is difficult/impossible without substantial risk of contamination across study arms or prohibitive from the cost or group dynamics point of view. In this article, the aim is to design and analyze four-level longitudinal cluster randomized trials. The main interest here is to study the difference between treatment groups over time for such a four-level hierarchical data structure. This work is motivated by a real-life study for education based HIV prevention. Such trials are not only popular for administrative convenience, ethical considerations, subject compliance, but also help to reduce contamination bias. A random intercept mixed effects linear regression including a time by intervention interaction is used for modeling. Closed form expression of the power function to detect the interaction effect is determined. Sample size equations depend on correlation among schools but not on correlations among classes or students while, the power function depends on the product of number of units at different levels. Optimal allocation of units under a fixed cost by minimizing the expected standardized variance is also determined and are shown to be independent of correlations among units in any level. Results of detailed simulation studies find the theoretical power estimates based on the derived formulae close to the empirical estimates.

Electronic cigarette use among sexual minority and heterosexual young adults in a U.S. national sample: Exploring the modifying effects of advertisement exposure.

Sexual minorities demonstrate disparities in traditional cigarette use and nicotine-related health consequences. Electronic cigarette (e-cigarette) use is increasing, particularly among adolescents and young adults. Sexual minorities have been found to use e-cigarettes at higher rates than heterosexuals, but little is known about reasons for this disparity. The present study examined characteristics of current and lifetime e-cigarette use between sexual minority and heterosexual young adults (18-34; N = 14,174) using a U.S. national sample from the Population Assessment of Tobacco and Health (PATH) Survey-Wave 3. Sexual minority young adults were hypothesized to have higher rates of current and lifetime e-cigarette use and higher rates of exposure to e-cigarette advertisements. These exposures were hypothesized to moderate the relationship between sexual minority status and current e-cigarette use. Results revealed that sexual minority respondents demonstrated greater risk of current e-cigarette use after adjusting for several covariates (e.g., sex, age, lifetime cigarette use). However, advertisement exposures did not moderate the relationship between sexual minority status and current e-cigarette use. In contrast, sexual minority status was not associated with lifetime e-cigarette use after controlling for covariates. Post-hoc tests revealed that sexual minority status was associated with heightened risk of current and lifetime e-cigarette use only among females. This is the first study to examine the impact of e-cigarette advertising across expanded settings, including point of sale locations (e.g., retail, bars, festivals), while exploring differences in current and lifetime e-cigarette use among sexual minority and heterosexual males and females.

An Epidemic Model with Time-Distributed Recovery and Death Rates.

A compartmental epidemiological model with distributed recovery and death rates is proposed. In some particular cases, the model can be reduced to the conventional SIR model. However, in general, the dynamics of epidemic progression in this model is different. Distributed recovery and death rates are evaluated from COVID-19 data. The model is validated by the epidemiological data for different countries, and it shows better agreement with the data than the SIR model. The time-dependent disease transmission rate is estimated.

On the estimation of the incidence and prevalence in two-phase longitudinal sampling design.

Two-phase sampling design is a common practice in many medical studies. Generally, the first-phase classification is fallible but relatively cheap, while the accurate second phase state-of-the-art medical diagnosis is complex and rather expensive to perform. When constructed efficiently it offers great potential for higher true case detection as well as for higher precision at a limited cost. In this article, we consider epidemiological studies with two-phase sampling design. However, instead of a single two-phase study, we consider a scenario where a series of two-phase studies are done in a longitudinal fashion on a cohort of interest. Another major design issue is non-curable pattern of certain disease (e.g. Dementia, Alzheimer's etc.). Thus often the identified disease positive subjects are removed from the original population under observation, as they require clinical attention, which is quite different from the yet unidentified group. In this article, we motivated our methodology development from two real-life studies. We consider efficient and simultaneous estimation of prevalence as well incidence at multiple time points from a sampling design-based approach. We have explicitly shown the benefit of our developed methodology for an elderly population with significant burden of home-health care usage and at the high risk of major depressive disorder.

Approaches for testing noninferiority in two-arm trials for risk ratio and odds ratio.

For an existing established drug regimen, active control trials are defacto standard due to ethical reason as well as for clinical equipoise. However, when superiority claim of a new drug against the active control is unlikely to be successful, researchers often address the issue in terms of noninferiority (NI), provided the experimental drug demonstrates the evidence of other benefits beyond efficacy. Such trials aim to demonstrate that an experimental treatment is non-inferior to an existing comparator by not more than a pre-specified margin. The issue of choosing such a margin is complex. In this article, two-arm NI trials with binary outcomes are considered when margin is defined in terms of relative risk or odds ratio. A Frequentist test based on proposed NI margin is developed first. Since two-arm NI trials without placebo arm are dependent upon historical information, in order to make accurate and meaningful interpretation of their results, a Bayesian approach is developed next. Bayesian approach is flexible to incorporate the available information from the historical trial. The operating characteristics of the proposed methods are studied in terms of power and sample size for varying design factors. A clinical trial data is reanalyzed to study the properties of the proposed approach.

Non-inferiority Testing for Risk Ratio, Odds Ratio and Number Needed to Treat in Three-arm Trial.

Three-arm non-inferiority (NI) trial including the experimental treatment, an active reference treatment, and a placebo where the outcome of interest is binary are considered. While the risk difference (RD) is the most common and well explored functional form for testing efficacy (or effectiveness), however, recent FDA guideline suggested measures such as relative risk (RR), odds ratio (OR), number needed to treat (NNT) among others, on the basis of which NI can be claimed for binary outcome. Albeit, developing test based on these different functions of binary outcome are challenging. This is because the construction and interpretation of NI margin for such functions are non-trivial extensions of RD based approach. A Frequentist test based on traditional fraction margin approach for RR, OR and NNT are proposed first. Furthermore a conditional testing approach is developed by incorporating assay sensitivity (AS) condition directly into NI testing. A detailed discussion of sample size/power calculation are also put forward which could be readily used while designing such trials in practice. A clinical trial data is reanalyzed to demonstrate the presented approach.

Bayesian approach for assessing noninferiority in a three-arm trial with binary endpoint.

With the recent advancement in many therapeutic areas, quest for better and enhanced treatment options is ever increasing. While the "efficacy" metric plays the most important role in this development, emphasis on other important clinical factors such as less intensive side effects, lower toxicity, ease of delivery, and other less debilitating factors may result in the selection of treatment options, which may not beat current established treatment option in terms efficacy, yet prove to be desirable for subgroups of patients. The resultant clinical trial by means of which one establishes such slightly less efficacious treatment is known as noninferiority (NI) trial. Noninferiority trials often involve an active established comparator arm, along with a placebo and an experimental treatment arm, resulting into a 3-arm trial. Most of the past developments in a 3-arm NI trial consider defining a prespecified fraction of unknown effect size of reference drug, i.e., without directly specifying a fixed NI margin. However, in some recent developments, more direct approach is being considered with prespecified fixed margin, albeit in the frequentist setup. In this article, we consider Bayesian implementation of such trial when primary outcome of interest is binary. Bayesian paradigm is important, as it provides a path to integrate historical trials and current trial information via sequential learning. We use several approximation-based and 2 exact fully Bayesian methods to evaluate the feasibility of the proposed approach. Finally, a clinical trial example is reanalyzed to demonstrate the benefit of the proposed approach.

Bayesian approach for assessing non-inferiority in a three-arm trial with pre-specified margin.

Non-inferiority trials are becoming increasingly popular for comparative effectiveness research. However, inclusion of the placebo arm, whenever possible, gives rise to a three-arm trial which has lesser burdensome assumptions than a standard two-arm non-inferiority trial. Most of the past developments in a three-arm trial consider defining a pre-specified fraction of unknown effect size of reference drug, that is, without directly specifying a fixed non-inferiority margin. However, in some recent developments, a more direct approach is being considered with pre-specified fixed margin albeit in the frequentist setup. Bayesian paradigm provides a natural path to integrate historical and current trials' information via sequential learning. In this paper, we propose a Bayesian approach for simultaneous testing of non-inferiority and assay sensitivity in a three-arm trial with normal responses. For the experimental arm, in absence of historical information, non-informative priors are assumed under two situations, namely when (i) variance is known and (ii) variance is unknown. A Bayesian decision criteria is derived and compared with the frequentist method using simulation studies. Finally, several published clinical trial examples are reanalyzed to demonstrate the benefit of the proposed procedure.

H-CLAP: hierarchical clustering within a linear array with an application in genetics.

In most cases where clustering of data is desirable, the underlying data distribution to be clustered is unconstrained. However clustering of site types in a discretely structured linear array, as is often desired in studies of linear sequences such as DNA, RNA or proteins, represents a problem where data points are not necessarily exchangeable and are directionally constrained within the array. Each position in the linear array is fixed, and could be either "marked" (i.e., of interest such as polymorphic or substitute sites) or "non-marked." Here we describe a method for clustering of those marked sites. Since the cluster-generating process is constrained by discrete locality inside such an array, traditional clustering methods need adjustment to be appropriate. We develop a hierarchical Bayesian approach. We adopt a Markov clustering algorithm, revealing any natural partitioning in the pattern of marked sites. The resulting recursive partitioning and clustering algorithm is named hierarchical clustering in a linear array (H-CLAP). It employs domain-specific directional constraints directly in the likelihood construction. Our method, being fully Bayesian, is more flexible in cluster discovery compared to a standard agglomerative hierarchical clustering algorithm. It not only provides hierarchical clustering, but also cluster boundaries, which may have their own biological significance. We have tested the efficacy of our method on data sets, including two biological and several simulated ones.

Healthcare use among older primary care patients with minor depression.

OBJECTIVE: To determine the rate of healthcare utilization for older primary care patients by depression status. DESIGN: Cross-sectional data analysis. SETTING: Primary care practices, western New York state. PARTICIPANTS: 753 patients aged 65 years and older. MEASURES: Diagnostic depression categories were determined using the Structured Clinical Interview for DSM-IV (SCID). The Cornell Services Index (CSI) measured outpatient medical visits. Demographic, clinical, and functional variables were obtained from medical records and interview data. RESULTS: 41.23% had subsyndromal or minor depression (M/SSD) and 53.15% had no depression. The unadjusted mean number of outpatient medical visits was greater in those with M/SSD (3.96 visits within 3 months) compared to those without depression (2.84), with a significant difference after adjusting for demographic, functional, and clinical factors. CONCLUSION: Those with M/SSD had higher rates of healthcare utilization compared with those without depressive symptoms. Future research should examine whether interventions for older adults with M/SSD reduce healthcare utilization.

Untangling therapeutic ingredients of a personalized intervention for patients with depression and severe COPD.

OBJECTIVE: We developed a personalized intervention for depressed patients with COPD (PID-C) aimed to mobilize patients to participate in the care of both conditions. We showed that PID-C reduced depressive symptoms and dyspnea-related disability more than usual care over 28 weeks. This study focused on untangling key therapeutic ingredients of PID-C. DESIGN: Randomized controlled trial. SETTING: Community. PARTICIPANTS: 138 patients who received the diagnoses of COPD and major depression after screening 898 consecutive admissions for acute inpatient pulmonary rehabilitation. INTERVENTION: Nine sessions of PID-C compared with usual care over 28 weeks. MEASUREMENTS: Primary outcome measures were the 17-item Hamilton Depression Rating Scale and the Pulmonary Functional Status and Dyspnea Questionnaire-Modified. Other measures were adherence to rehabilitation exercise (≥2 hours per week) and adherence to adequate antidepressant prescriptions. RESULTS: Low severity of dyspnea-related disability and adherence to antidepressants predicted subsequent improvement of depression. Exercise and low depression severity predicted improvement of dyspnea-related disability. CONCLUSIONS: PID-C led to an interacting spiral of improvement in both depression and disability in a gravely medically ill population with a 17% mortality rate over 28 weeks and an expected deterioration in disability. The interrelationship of the course of depression and dyspnea-related disability underscores the need to target adherence to both antidepressants and chronic obstructive pulmonary disease rehabilitation. PID-C may serve as a care management model for depressed persons suffering from medical illnesses with a deteriorating course.

Exploratory time varying lagged regression: modeling association of cognitive and functional trajectories with expected clinic visits in older adults.

Motivated by a longitudinal study on factors affecting the frequency of clinic visits of older adults, an exploratory time varying lagged regression analysis is proposed to relate a longitudinal response to multiple cross-sectional and longitudinal predictors from time varying lags. Regression relations are allowed to vary with time through smooth varying coefficient functions. The main goal of the proposal is to detect deviations from a concurrent varying coefficient model potentially in a subset of the longitudinal predictors with nonzero estimated lags. The proposed methodology is geared towards irregular and infrequent data where different longitudinal variables may be observed at different frequencies, possibly at unsynchronized time points and contaminated with additive measurement error. Furthermore, to cope with the curse of dimensionality which limits related current modeling approaches, a sequential model building procedure is proposed to explore and select the time varying lags of the longitudinal predictors. The estimation procedure is based on estimation of the moments of the predictor and response trajectories by pooling information from all subjects. The finite sample properties of the proposed estimation algorithm are studied under various lag structures and correlation levels among the predictor processes in simulation studies. Application to the clinic visits data show the effect of cognitive and functional impairment scores from varying lags on the frequency of the clinic visits throughout the study.

Anticonvulsant medications for panic disorder: a review and synthesis of the evidence.

OBJECTIVES: Currently, there are no guidelines for when to use an antiepileptic drug (AED) in nonepileptic panic disorder (PD) patients. We conducted this review to ascertain what guidance available literature can provide as to when to consider AEDs for PD patients. METHODS: The primary data sources were PubMed and Google-Scholars. Search was limited to "English" and "Humans". Only papers addressing use of nonbenzodiazepine AEDs in PD were included. Data regarding study subjects, the AED utilized, and clinical responses were collected. EEG data were used to classify reports of patients with abnormal versus those with normal and/or no EEG work-up. RESULTS: Ten reports were identified for use of AEDs in PD patients with abnormal EEGs with a total of 20 patients (17 responders). None of the 10 reports were controlled studies. Eighteen reports were identified for use of AEDs in panic patients with either normal EEGs or unselected groups (no EEG work-up). Out of the 18 reports, three were controlled studies. Included in the 18 studies were 253 patients (137 responders). CONCLUSIONS: We preliminary concluded that EEG work-up could be useful in guiding the treatment in PD as an abnormal EEG may be indicative of a higher likelihood of a positive response to an AED.

A framework for testing non-inferiority in a three-arm, sequential, multiple assignment randomized trial.

Sequential multiple assignment randomized trial design is becoming increasingly used in the field of precision medicine. This design allows comparisons of sequences of adaptive interventions tailored to the individual patient. Superiority testing is usually the initial goal in order to determine which embedded adaptive intervention yields the best primary outcome on average. When direct superiority is not evident, yet an adaptive intervention poses other benefits, then non-inferiority testing is warranted. Non-inferiority testing in the sequential multiple assignment randomized trial setup is rather new and involves the specification of non-inferiority margin and other important assumptions that are often unverifiable internally. These challenges are not specific to sequential multiple assignment randomized trial and apply to two-arm non-inferiority trials that do not include a standard-of-care (or placebo) arm. To address some of these challenges, three-arm non-inferiority trials that include the standard-of-care arm are proposed. However, methods developed so far for three-arm non-inferiority trials are not sequential multiple assignment randomized trial-specific. This is because apart from embedded adaptive interventions, sequential multiple assignment randomized trial typically does not include a third standard-of-care arm. In this article, we consider a three-arm sequential multiple assignment randomized trial from an National Institutes of Health-funded study of symptom management strategies among people undergoing cancer treatment. Motivated by that example, we propose a novel data analytic method for non-inferiority testing in the framework of three-arm sequential multiple assignment randomized trial for the first time. Sample size and power considerations are discussed through extensive simulation studies to elucidate our method.

Delay epidemic models determined by latency, infection, and immunity duration.

We propose new single and two-strain epidemic models represented by systems of delay differential equations and based on the number of newly exposed individuals. Transitions between exposed, infectious, recovered, and back to susceptible compartments are determined by the corresponding time delays. Existence and positiveness of solutions are proved. Reduction of delay differential equations to integral equations allows the analysis of stationary solutions and their stability. In the case of two strains, they compete with each other, and the strain with a larger individual basic reproduction number dominates the other one. However, if the basic reproduction number exceeds some critical values, stationary solution loses its stability resulting in periodic time oscillations. In this case, both strains are present and their dynamics is not completely determined by the basic reproduction numbers but also by other parameters. The results of the work are illustrated by comparison with data on seasonal influenza.

Effect of vaccine dose intervals: Considering immunity levels, vaccine efficacy, and strain variants for disease control strategy.

In this study, we present an immuno-epidemic model to understand mitigation options during an epidemic break. The model incorporates comorbidity and multiple-vaccine doses through a system of coupled integro-differential equations to analyze the epidemic rate and intensity from a knowledge of the basic reproduction number and time-distributed rate functions. Our modeling results show that the interval between vaccine doses is a key control parameter that can be tuned to significantly influence disease spread. We show that multiple doses induce a hysteresis effect in immunity levels that offers a better mitigation alternative compared to frequent vaccination which is less cost-effective while being more intrusive. Optimal dosing intervals, emphasizing the cost-effectiveness of each vaccination effort, and determined by various factors such as the level of immunity and efficacy of vaccines against different strains, appear to be crucial in disease management. The model is sufficiently generic that can be extended to accommodate specific disease forms.

Survivorship Care for Women Living With Ovarian Cancer: Protocol for a Randomized Controlled Trial.

BACKGROUND: Ovarian cancer ranks 12th in cancer incidence among women in the United States and 5th among causes of cancer-related death. The typical treatment of ovarian cancer focuses on disease management, with little attention given to the survivorship needs of the patient. Qualitative work alludes to a gap in survivorship care; yet, evidence is lacking to support the delivery of survivorship care for individuals living with ovarian cancer. We developed the POSTCare survivorship platform with input from survivors of ovarian cancer and care partners as a means of delivering patient-centered survivorship care. This process is framed by the chronic care model and relevant behavioral theory. OBJECTIVE: The overall goal of this study is to test processes of care that support quality of life (QOL) in survivorship. The specific aims are threefold: first, to test the efficacy of the POSTCare platform in supporting QOL, reducing depressive symptom burden, and reducing recurrence worry. In our second aim, we will examine factors that mediate the effect of the intervention. Our final aim focuses on understanding aspects of care platform design and delivery that may affect the potential for dissemination. METHODS: We will enroll 120 survivors of ovarian cancer in a randomized controlled trial and collect data at 12 and 24 weeks. Each participant will be randomized to either the POSTCare platform or the standard of care process for survivorship. Our population will be derived from 3 clinics in Texas; each participant will have received some combination of treatment modalities; continued maintenance therapy is not exclusionary. RESULTS: We will examine the impact of the POSTCare-O platform on QOL at 12 weeks after intervention as the primary end point. We will look at secondary outcomes, including depressive symptom burden, recurrence anxiety, and physical symptom burden. We will identify mediators important to the impact of the intervention to inform revisions of the intervention for subsequent studies. Data collection was initiated in November 2023 and will continue for approximately 2 years. We expect results from this study to be published in early 2026. CONCLUSIONS: This study will contribute to the body of survivorship science by testing a flexible platform for survivorship care delivery adapted for the specific survivorship needs of patients with ovarian cancer. The completion of this project will contribute to the growing body of science to guide survivorship care for persons living with cancer. TRIAL REGISTRATION: ClinicalTrials.gov NCT05752448; https://clinicaltrials.gov/study/NCT05752448. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/48069.