Unresolved issues with noninferiority pragmatic trials: Results of a literature survey.

BACKGROUND: Issues with specification of margins, adherence, and analytic population can potentially bias results toward the alternative in randomized noninferiority pragmatic trials. To investigate this potential for bias, we conducted a targeted search of the medical literature to examine how noninferiority pragmatic trials address these issues. METHODS: An Ovid MEDLINE database search was performed identifying publications in New England Journal of Medicine, Journal of the American Medical Association, Lancet, or British Medical Journal published between 2015 and 2021 that included the words "pragmatic" or "comparative effectiveness" and "noninferiority" or "non-inferiority." Our search identified 14 potential trials, 12 meeting our inclusion criteria (11 individually randomized, 1 cluster-randomized). RESULTS: Eleven trials had results that met the criteria established for noninferiority. Noninferiority margins were prespecified for all trials; all but two trials provided justification of the margin. Most trials did some monitoring of treatment adherence. All trials conducted intent-to-treat or modified intent-to-treat analyses along with per-protocol analyses and these analyses reached similar conclusions. Only two trials included all randomized participants in the primary analysis, one used multiple imputation for missing data. The percentage excluded from primary analyses ranged from ∼2% to 30%. Reasons for exclusion included randomization in error, nonadherence, not receiving assigned treatment, death, withdrawal, lost to follow-up, and incomplete data. CONCLUSION: Specification of margins, adherence, and analytic population require careful consideration to prevent bias toward the alternative in noninferiority pragmatic trials. Although separate guidance has been developed for noninferiority and pragmatic trials, it is not compatible with conducting a noninferiority pragmatic trial. Hence, these trials should probably not be done in their current format without developing new guidelines.

The dementia care study (D-CARE): Recruitment strategies and demographic characteristics of participants in a pragmatic randomized trial of dementia care.

INTRODUCTION: Pragmatic research studies that include diverse dyads of persons living with dementia (PLWD) and their family caregivers are rare. METHODS: Community-dwelling dyads were recruited for a pragmatic clinical trial evaluating three approaches to dementia care. Four clinical trial sites used shared and site-specific recruitment strategies to enroll health system patients. RESULTS: Electronic health record (EHR) queries of patients with a diagnosis of dementia and engagement of their clinicians were the main recruitment strategies. A total of 2176 dyads were enrolled, with 80% recruited after the onset of the pandemic. PLWD had a mean age of 80.6 years (SD 8.5), 58.4% were women, and 8.8% were Hispanic/Latino, and 11.9% were Black/African American. Caregivers were mostly children of the PLWD (46.5%) or spouses/partners (45.2%), 75.8% were women, 9.4% were Hispanic/Latino, and 11.6% were Black/African American. DISCUSSION: Health systems can successfully enroll diverse dyads in a pragmatic clinical trial.

Beyond In-hospital Mortality: Use of Postdischarge Quality-Metrics Provides a More Complete Picture of Older Adult Trauma Care.

OBJECTIVE: To identify the distributions of and extent of variability among 3 new sets of postdischarge quality-metrics measured within 30/90/365 days designed to better account for the unique health needs of older trauma patients: mortality (expansion of the current in-hospital standard), readmission (marker of health-system performance and care coordination), and patients' average number of healthy days at home (marker of patient functional status). BACKGROUND: Traumatic injuries are a leading cause of death and loss of independence for the increasing number of older adults living in the United States. Ongoing efforts seek to expand quality evaluation for this population. METHODS: Using 100% Medicare claims, we calculated hospital-specific reliability-adjusted postdischarge quality-metrics for older adults aged 65 years or older admitted with a primary diagnosis of trauma, older adults with hip fracture, and older adults with severe traumatic brain injury. Distributions for each quality-metric within each population were assessed and compared with results for in-hospital mortality, the current benchmarking standard. RESULTS: A total of 785,867 index admissions (305,186 hip fracture and 92,331 severe traumatic brain injury) from 3692 hospitals were included. Within each population, use of postdischarge quality-metrics yielded a broader range of outcomes compared with reliance on in-hospital mortality alone. None of the postdischarge quality-metrics consistently correlated with in-hospital mortality, including death within 1 year [ r =0.581 (95% CI, 0.554-0.608)]. Differences in quintile-rank revealed that when accounting for readmissions (8.4%, κ=0.029) and patients' average number of healthy days at home (7.1%, κ=0.020), as many as 1 in 14 hospitals changed from the best/worst performance under in-hospital mortality to the completely opposite quintile rank. CONCLUSIONS: The use of new postdischarge quality-metrics provides a more complete picture of older adult trauma care: 1 with greater room for improvement and better reflection of multiple aspects of quality important to the health and recovery of older trauma patients when compared with reliance on quality benchmarking based on in-hospital mortality alone.

A Randomized Trial of a Multifactorial Strategy to Prevent Serious Fall Injuries.

BACKGROUND: Injuries from falls are major contributors to complications and death in older adults. Despite evidence from efficacy trials that many falls can be prevented, rates of falls resulting in injury have not declined. METHODS: We conducted a pragmatic, cluster-randomized trial to evaluate the effectiveness of a multifactorial intervention that included risk assessment and individualized plans, administered by specially trained nurses, to prevent fall injuries. A total of 86 primary care practices across 10 health care systems were randomly assigned to the intervention or to enhanced usual care (the control) (43 practices each). The participants were community-dwelling adults, 70 years of age or older, who were at increased risk for fall injuries. The primary outcome, assessed in a time-to-event analysis, was the first serious fall injury, adjudicated with the use of participant report, electronic health records, and claims data. We hypothesized that the event rate would be lower by 20% in the intervention group than in the control group. RESULTS: The demographic and baseline characteristics of the participants were similar in the intervention group (2802 participants) and the control group (2649 participants); the mean age was 80 years, and 62.0% of the participants were women. The rate of a first adjudicated serious fall injury did not differ significantly between the groups, as assessed in a time-to-first-event analysis (events per 100 person-years of follow-up, 4.9 in the intervention group and 5.3 in the control group; hazard ratio, 0.92; 95% confidence interval [CI], 0.80 to 1.06; P = 0.25). The rate of a first participant-reported fall injury was 25.6 events per 100 person-years of follow-up in the intervention group and 28.6 events per 100 person-years of follow-up in the control group (hazard ratio, 0.90; 95% CI, 0.83 to 0.99; P = 0.004). The rates of hospitalization or death were similar in the two groups. CONCLUSIONS: A multifactorial intervention, administered by nurses, did not result in a significantly lower rate of a first adjudicated serious fall injury than enhanced usual care. (Funded by the Patient-Centered Outcomes Research Institute and others; STRIDE ClinicalTrials.gov number, NCT02475850.).

Costs of fall injuries in the STRIDE study: an economic evaluation of healthcare system heterogeneity and heterogeneity of treatment effect.

OBJECTIVES: The Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) Study cluster-randomized 86 primary care practices in 10 healthcare systems to a patient-centered multifactorial fall injury prevention intervention or enhanced usual care, enrolling 5451 participants. We estimated total healthcare costs from participant-reported fall injuries receiving medical attention (FIMA) that were averted by the STRIDE intervention and tested for healthcare-system-level heterogeneity and heterogeneity of treatment effect (HTE). METHODS: Participants were community-dwelling adults age ≥ 70 at increased fall injury risk. We estimated practice-level total costs per person-year of follow-up (PYF), assigning unit costs to FIMA with and without an overnight hospital stay. Using independent variables for treatment arm, healthcare system, and their interaction, we fit a generalized linear model with log link, log follow-up time offset, and Tweedie error distribution. RESULTS: Unadjusted total costs per PYF were $2,034 (intervention) and $2,289 (control). The adjusted (intervention minus control) cost difference per PYF was -$167 (95% confidence interval (CI), -$491, $216). Cost heterogeneity by healthcare system was present (p = 0.035), as well as HTE (p = 0.090). Adjusted total costs per PYF in control practices varied from $1,529 to $3,684 for individual healthcare systems; one system with mean intervention minus control costs of -$2092 (95% CI, -$3,686 to -$944) per PYF accounted for HTE, but not healthcare system cost heterogeneity. CONCLUSIONS: We observed substantial heterogeneity of healthcare system costs in the STRIDE study, with small reductions in healthcare costs for FIMA in the STRIDE intervention accounted for by a single healthcare system. TRIAL REGISTRATION: Clinicaltrials.gov (NCT02475850).

Sample size calculation in hierarchical 2×2 factorial trials with unequal cluster sizes.

Motivated by a suicide prevention trial with hierarchical treatment allocation (cluster-level and individual-level treatments), we address the sample size requirements for testing the treatment effects as well as their interaction. We assume a linear mixed model, within which two types of treatment effect estimands (controlled effect and marginal effect) are defined. For each null hypothesis corresponding to an estimand, we derive sample size formulas based on large-sample z-approximation, and provide finite-sample modifications based on a t-approximation. We relax the equal cluster size assumption and express the sample size formulas as functions of the mean and coefficient of variation of cluster sizes. We show that the sample size requirement for testing the controlled effect of the cluster-level treatment is more sensitive to cluster size variability than that for testing the controlled effect of the individual-level treatment; the same observation holds for testing the marginal effects. In addition, we show that the sample size for testing the interaction effect is proportional to that for testing the controlled or the marginal effect of the individual-level treatment. We conduct extensive simulations to validate the proposed sample size formulas, and find the empirical power agrees well with the predicted power for each test. Furthermore, the t-approximations often provide better control of type I error rate with a small number of clusters. Finally, we illustrate our sample size formulas to design the motivating suicide prevention factorial trial. The proposed methods are implemented in the R package H2x2Factorial.

Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial.

BACKGROUND/AIMS: When participants in individually randomized group treatment trials are treated by multiple clinicians or in multiple group treatment sessions throughout the trial, this induces partially nested clusters which can affect the power of a trial. We investigate this issue in the Whole Health Options and Pain Education trial, a three-arm pragmatic, individually randomized clinical trial. We evaluate whether partial clusters due to multiple visits delivered by different clinicians in the Whole Health Team arm and dynamic participant groups due to changing group leaders and/or participants across treatment sessions during treatment delivery in the Primary Care Group Education arm may impact the power of the trial. We also present a Bayesian approach to estimate the intraclass correlation coefficients. METHODS: We present statistical models for each treatment arm of Whole Health Options and Pain Education trial in which power is estimated under different intraclass correlation coefficients and mapping matrices between participants and clinicians or treatment sessions. Power calculations are based on pairwise comparisons. In practice, sample size calculations depend on estimates of the intraclass correlation coefficients at the treatment sessions and clinician levels. To accommodate such complexities, we present a Bayesian framework for the estimation of intraclass correlation coefficients under different participant-to-session and participant-to-clinician mapping scenarios. We simulated continuous outcome data based on various clinical scenarios in Whole Health Options and Pain Education trial using a range of intraclass correlation coefficients and mapping matrices and used Gibbs samplers with conjugate priors to obtain posteriors of the intraclass correlation coefficients under those different scenarios. Posterior means and medians and their biases are calculated for the intraclass correlation coefficients to evaluate the operating characteristics of the Bayesian intraclass correlation coefficient estimators. RESULTS: Power for Whole Health Team versus Primary Care Group Education is sensitive to the intraclass correlation coefficient in the Whole Health Team arm. In these two arms, an increased number of clinicians, more evenly distributed workload of clinicians, or more homogeneous treatment group sizes leads to increased power. Our simulation study for the intraclass correlation coefficient estimation indicates that the posterior mean intraclass correlation coefficient estimator has less bias when the true intraclass correlation coefficients are large (i.e. 0.10), but when the intraclass correlation coefficient is small (i.e. 0.01), the posterior median intraclass correlation coefficient estimator is less biased. CONCLUSION: Knowledge of intraclass correlation coefficients and the structure of clustering are critical to the design of individually randomized group treatment trials with partially nested clusters. We demonstrate that the intraclass correlation coefficient of the Whole Health Team arm can affect power in the Whole Health Options and Pain Education trial. A Bayesian approach provides a flexible procedure for estimating the intraclass correlation coefficients under complex scenarios. More work is needed to educate the research community about the individually randomized group treatment design and encourage publication of intraclass correlation coefficients to help inform future trial designs.

Aquatic Viruses and Climate Change.

The viral component in aquatic systems clearly needs to be incorporated into future ocean and inland water climate models. Viruses have the potential to influence carbon and nutrient cycling in aquatic ecosystems significantly. Changing climate likely has both direct and indirect influence on virus-mediated processes, among them an impact on food webs, biogeochemical cycles and on the overall metabolic performance of whole ecosystems. Here we synthesise current knowledge on potential climate-related consequences for viral assemblages, virus-host interactions and virus functions, and in turn, viral processes contributing to climate change. There is a need to increase the accuracy of predictions of climate change impacts on virus- driven processes, particularly of those linked to biological production and biogeochemical cycles. Comprehension of the relationships between microbial/viral processes and global phenomena is essential to predict the influence on as well as the response of the biosphere to global change.

Estimation of ascertainment bias and its effect on power in clinical trials with time-to-event outcomes.

While the gold standard for clinical trials is to blind all parties-participants, researchers, and evaluators-to treatment assignment, this is not always a possibility. When some or all of the above individuals know the treatment assignment, this leaves the study open to the introduction of postrandomization biases. In the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial, we were presented with the potential for the unblinded clinicians administering the treatment, as well as the individuals enrolled in the study, to introduce ascertainment bias into some but not all events comprising the primary outcome. In this article, we present ways to estimate the ascertainment bias for a time-to-event outcome, and discuss its impact on the overall power of a trial vs changing of the outcome definition to a more stringent unbiased definition that restricts attention to measurements less subject to potentially differential assessment. We found that for the majority of situations, it is better to revise the definition to a more stringent definition, as was done in STRIDE, even though fewer events may be observed.

Soil microbial inoculation during flood events shapes headwater stream microbial communities and diversity.

Flood events are now recognized as potentially important occasions for the transfer of soil microbes to stream ecosystems. Yet, little is known about these "dynamic pulses of microbial life" for stream bacterial community composition (BCC) and diversity. In this study, we explored the potential alteration of stream BCC by soil inoculation during high flow events in six pre-alpine first order streams and the larger Oberer Seebach. During 1 year, we compared variations of BCC in soil water, stream water and in benthic biofilms at different flow conditions (low to intermediate flows versus high flow). Bacterial diversity was lowest in biofilms, followed by soils and highest in headwater streams and the Oberer Seebach. In headwater streams, bacterial diversity was significantly higher during high flow, as compared to low flow (Shannon diversity: 7.6 versus 7.9 at low versus high flow, respectively, p < 0.001). Approximately 70% of the bacterial operational taxonomic units (OTUs) from streams and stream biofilms were the same as in soil water, while in the latter one third of the OTUs were specific to high flow conditions. These soil high-flow OTUs were also found in streams and biofilms at other times of the year. These results demonstrate the relevance of floods in generating short and reoccurring inoculation events for flowing waters. Moreover, they show that soil microbial inoculation during high flow enhances microbial diversity and shapes fluvial BCC even during low flow. Hence, soil microbial inoculation during floods could act as a previously overlooked driver of microbial diversity in headwater streams.