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.

A case study of ascertainment bias for the primary outcome in the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) trial.

BACKGROUND/AIM: In clinical trials, there is potential for bias from unblinded observers that may influence ascertainment of outcomes. This issue arose in the Strategies to Reduce Injuries and Develop Confidence in Elders trial, a cluster randomized trial to test a multicomponent intervention versus enhanced usual care (control) to prevent serious fall injuries, originally defined as a fall injury leading to medical attention. An unblinded nurse falls care manager administered the intervention, while the usual care arm did not involve contact with a falls care manager. Thus, there was an opportunity for falls care managers to refer participants reporting falls to seek medical attention. Since this type of observer bias could not occur in the usual care arm, there was potential for additional falls to be reported in the intervention arm, leading to dilution of the intervention effect and a reduction in study power. We describe the clinical basis for ascertainment bias, the statistical approach used to assess it, and its effect on study power. METHODS: The prespecified interim monitoring plan included a decision algorithm for assessing ascertainment bias and adapting (revising) the primary outcome definition, if necessary. The original definition categorized serious fall injuries requiring medical attention into Type 1 (fracture other than thoracic/lumbar vertebral, joint dislocation, cut requiring closure) and Type 2 (head injury, sprain or strain, bruising or swelling, other). The revised definition, proposed by the monitoring plan, excluded Type 2 injuries that did not necessarily require an overnight hospitalization since these would be most subject to bias. These injuries were categorized into those with (Type 2b) and without (Type 2c) medical attention. The remaining Type 2a injuries required medical attention and an overnight hospitalization. We used the ratio of 2b/(2b + 2c) in intervention versus control as a measure of ascertainment bias; ratios > 1 indicated the likelihood of falls care manager bias. We determined the effect of ascertainment bias on study power for the revised (Types 1 and 2a) versus original definition (Types 1, 2a, and 2b). RESULTS: The estimate of ascertainment bias was 1.14 (95% confidence interval: 0.98, 1.30), providing evidence of the likelihood of falls care manager bias. We estimated that this bias diluted the hazard ratio from the hypothesized 0.80 to 0.86 and reduced power to under 80% for the original primary outcome definition. In contrast, adapting the revised definition maintained study power at nearly 90%. CONCLUSION: There was evidence of ascertainment bias in the Strategies to Reduce Injuries and Develop Confidence in Elders trial. The decision to adapt the primary outcome definition reduced the likelihood of this bias while preserving the intervention effect and study power.

Two-stage randomized trial design for testing treatment, preference, and self-selection effects for count outcomes.

While the traditional clinical trial design lays emphasis on testing the treatment effect between randomly assigned groups, it ignores the role of patient preference for a particular treatment in the trial. Yet, for healthcare providers who seek to optimize the patient-centered treatment strategy, the evaluation of a patient's psychology toward each treatment could be a key consideration. The two-stage randomized trial design allows researchers to test patient's preference and selection effects, in addition to the treatment effect. The current methodology for the two-stage design is limited to continuous and binary outcomes; this article extends the model to include count outcomes. The test statistics for preference, selection, and treatment effects are derived. Closed-form sample size formulae are presented for each effect. Simulations are presented to demonstrate the properties of the unstratified and stratified designs. Finally, we apply methods to the use of antimicrobials at the end of life to demonstrate the applicability of the methods.

Designing Trials with Purpose: Pragmatic Clinical Trials of Nonpharmacological Approaches for Pain Management.

OBJECTIVE: Pain is one of the most significant causes of morbidity and disability worldwide. The efficacy of several nonpharmacological approaches for pain management has been established, but significant gaps exist between this evidence and their limited availability and use in routine clinical practice. Questions remain about their effectiveness and how best to integrate them in usual care to optimize patient-centered outcomes. Pragmatic clinical trials (PCTs) may help address this gap. Informed by the Pragmatic Explanatory Continuum Indicator Summary (PRECIS-2), we sought to describe the key features of optimized PCTs of nonpharmacological approaches for the management of pain and common co-occurring conditions. METHODS: To accomplish this objective, we searched the published literature on PCTs of nonpharmacological pain management approaches from 2010-2019 and applied the PRECIS-2 criteria. We discuss key PRECIS-2 domains of interest for designing and performing PCTs and cite specific examples from the published literature as potential models for future PCTs. RESULTS: We found 13 nonpharmacological PCTs. They were heterogeneous in size, recruitment, follow-up time, and location. The lessons learned from these studies led us to explicate key features of trials on the explanatory-pragmatic continuum across the PRECIS-2 domains that can be used by future investigators when designing their clinical trials of nonpharmacological approaches to pain management. CONCLUSIONS: We encourage the increased application of PCTs to produce timely and valuable results and products that will inform the development of safe and effective integrated pain care plans that optimize important patient-centered outcomes.

Pioglitazone after Ischemic Stroke or Transient Ischemic Attack.

BACKGROUND: Patients with ischemic stroke or transient ischemic attack (TIA) are at increased risk for future cardiovascular events despite current preventive therapies. The identification of insulin resistance as a risk factor for stroke and myocardial infarction raised the possibility that pioglitazone, which improves insulin sensitivity, might benefit patients with cerebrovascular disease. METHODS: In this multicenter, double-blind trial, we randomly assigned 3876 patients who had had a recent ischemic stroke or TIA to receive either pioglitazone (target dose, 45 mg daily) or placebo. Eligible patients did not have diabetes but were found to have insulin resistance on the basis of a score of more than 3.0 on the homeostasis model assessment of insulin resistance (HOMA-IR) index. The primary outcome was fatal or nonfatal stroke or myocardial infarction. RESULTS: By 4.8 years, a primary outcome had occurred in 175 of 1939 patients (9.0%) in the pioglitazone group and in 228 of 1937 (11.8%) in the placebo group (hazard ratio in the pioglitazone group, 0.76; 95% confidence interval [CI], 0.62 to 0.93; P=0.007). Diabetes developed in 73 patients (3.8%) and 149 patients (7.7%), respectively (hazard ratio, 0.48; 95% CI, 0.33 to 0.69; P<0.001). There was no significant between-group difference in all-cause mortality (hazard ratio, 0.93; 95% CI, 0.73 to 1.17; P=0.52). Pioglitazone was associated with a greater frequency of weight gain exceeding 4.5 kg than was placebo (52.2% vs. 33.7%, P<0.001), edema (35.6% vs. 24.9%, P<0.001), and bone fracture requiring surgery or hospitalization (5.1% vs. 3.2%, P=0.003). CONCLUSIONS: In this trial involving patients without diabetes who had insulin resistance along with a recent history of ischemic stroke or TIA, the risk of stroke or myocardial infarction was lower among patients who received pioglitazone than among those who received placebo. Pioglitazone was also associated with a lower risk of diabetes but with higher risks of weight gain, edema, and fracture. (Funded by the National Institute of Neurological Disorders and Stroke; ClinicalTrials.gov number, NCT00091949.).

Extensions to the two-stage randomized trial design for testing treatment, self-selection, and treatment preference effects to binary outcomes.

While traditional clinical trials seek to determine treatment efficacy within a specified population, they often ignore the role of a patient's treatment preference on his or her treatment response. The two-stage (doubly) randomized preference trial design provides one approach for researchers seeking to disentangle preference effects from treatment effects. Currently, this two-stage design is limited to the design and analysis of continuous outcome variables; in this presentation, we extend this current design to include binary variables. We present test statistics for testing preference, selection, and treatment effects in a two-stage randomized design with a binary outcome measure, with and without stratification. We also derive closed-form sample size formulas to indicate the number of patients needed to detect each effect. A series of simulation studies explore the properties and efficiency of both the unstratified and stratified two-stage randomized trial designs. Finally, we demonstrate the applicability of these methods using an example of a trial of Hepatitis C treatment.

Colonoscopy vs. Fecal Immunochemical Test in Reducing Mortality From Colorectal Cancer (CONFIRM): Rationale for Study Design.

RATIONALE: Colorectal cancer (CRC) is preventable through screening, with colonoscopy and fecal occult blood testing comprising the two most commonly used screening tests. Given the differences in complexity, risk, and cost, it is important to understand these tests' comparative effectiveness. STUDY DESIGN: The CONFIRM Study is a large, pragmatic, multicenter, randomized, parallel group trial to compare screening with colonoscopy vs. the annual fecal immunochemical test (FIT) in 50,000 average risk individuals. CONFIRM examines whether screening colonoscopy will be superior to a FIT-based screening program in the prevention of CRC mortality measured over 10 years. Eligible individuals 50-75 years of age and due for CRC screening are recruited from 46 Veterans Affairs (VA) medical centers. Participants are randomized to either colonoscopy or annual FIT. Results of colonoscopy are managed as per usual care and study participants are assessed for complications. Participants testing FIT positive are referred for colonoscopy. Participants are surveyed annually to determine if they have undergone colonoscopy or been diagnosed with CRC. The primary endpoint is CRC mortality. The secondary endpoints are (1) CRC incidence (2) complications of screening colonoscopy, and (3) the association between colonoscopists' characteristics and neoplasia detection, complications and post-colonoscopy CRC. CONFIRM leverages several key characteristics of the VA's integrated healthcare system, including a shared medical record with national databases, electronic CRC screening reminders, and a robust national research infrastructure with experience in conducting large-scale clinical trials. When completed, CONFIRM will be the largest intervention trial conducted within the VA (ClinicalTrials.gov identifier: NCT01239082).

Combined angiotensin inhibition for the treatment of diabetic nephropathy.

BACKGROUND: Combination therapy with angiotensin-converting-enzyme (ACE) inhibitors and angiotensin-receptor blockers (ARBs) decreases proteinuria; however, its safety and effect on the progression of kidney disease are uncertain. Methods We provided losartan (at a dose of 100 mg per day) to patients with type 2 diabetes, a urinary albumin-to-creatinine ratio (with albumin measured in milligrams and creatinine measured in grams) of at least 300, and an estimated glomerular filtration rate (GFR) of 30.0 to 89.9 ml per minute per 1.73 m(2) of body-surface area and then randomly assigned them to receive lisinopril (at a dose of 10 to 40 mg per day) or placebo. The primary end point was the first occurrence of a change in the estimated GFR (a decline of ≥ 30 ml per minute per 1.73 m(2) if the initial estimated GFR was ≥ 60 ml per minute per 1.73 m(2) or a decline of ≥ 50% if the initial estimated GFR was <60 ml per minute per 1.73 m(2)), end-stage renal disease (ESRD), or death. The secondary renal end point was the first occurrence of a decline in the estimated GFR or ESRD. Safety outcomes included mortality, hyperkalemia, and acute kidney injury. Results The study was stopped early owing to safety concerns. Among 1448 randomly assigned patients with a median follow-up of 2.2 years, there were 152 primary end-point events in the monotherapy group and 132 in the combination-therapy group (hazard ratio with combination therapy, 0.88; 95% confidence interval [CI], 0.70 to 1.12; P=0.30). A trend toward a benefit from combination therapy with respect to the secondary end point (hazard ratio, 0.78; 95% CI, 0.58 to 1.05; P=0.10) decreased with time (P=0.02 for nonproportionality). There was no benefit with respect to mortality (hazard ratio for death, 1.04; 95% CI, 0.73 to 1.49; P=0.75) or cardiovascular events. Combination therapy increased the risk of hyperkalemia (6.3 events per 100 person-years, vs. 2.6 events per 100 person-years with monotherapy; P<0.001) and acute kidney injury (12.2 vs. 6.7 events per 100 person-years, P<0.001). Conclusions Combination therapy with an ACE inhibitor and an ARB was associated with an increased risk of adverse events among patients with diabetic nephropathy. (Funded by the Cooperative Studies Program of the Department of Veterans Affairs Office of Research and Development; VA NEPHRON-D ClinicalTrials.gov number, NCT00555217.).

Aquatic methane dynamics in a human-impacted river-floodplain of the Danube.

River-floodplain systems are characterized by changing hydrological connectivity and variability of resources delivered to floodplain water bodies. Although the importance of hydrological events has been recognized, the effect of flooding on CH4 concentrations and emissions from European, human-impacted river-floodplains is largely unknown. This study evaluates aquatic concentrations and emissions of CH4 from a highly modified, yet partly restored river-floodplain system of the Danube near Vienna (Austria). We covered a broad range of hydrological conditions, including a 1-yr flood event in 2012 and a 100-yr flood in 2013. Our findings demonstrate that river-floodplain waters were supersaturated with CH4, hence always serving as a source of CH4 to the atmosphere. Hydrologically isolated habitats in general have higher concentrations and produce higher fluxes despite lower physically defined velocities. During surface connection, however, CH4 is exported from the floodplain to the river, suggesting that the main channel serves as an "exhaust pipe" for the floodplain. This mechanism was especially important during the 100-yr flood, when a clear pulse of CH4 was flushed from the floodplain with surface floodwaters. Our results emphasize the importance of floods differing in magnitude for methane evasion from river-floodplains; 34% more CH4 was emitted from the entire system during the year with the 100-yr flood compared to a hydrologically "normal" year. Compared to the main river channel, semiisolated floodplain waters were particularly strong sources of CH4. Our findings also imply that the predicted increased frequency of extreme flooding events will have significant consequences for methane emission from river-floodplain systems.

Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure.

BACKGROUND: When designing studies that have a continuous outcome as the primary endpoint, the hypothesized effect size ([Formula: see text]), that is, the hypothesized difference in means ([Formula: see text]) relative to the assumed variability of the endpoint ([Formula: see text]), plays an important role in sample size and power calculations. Point estimates for [Formula: see text] and [Formula: see text] are often calculated using historical data. However, the uncertainty in these estimates is rarely addressed. METHODS: This article presents a hybrid classical and Bayesian procedure that formally integrates prior information on the distributions of [Formula: see text] and [Formula: see text] into the study's power calculation. Conditional expected power, which averages the traditional power curve using the prior distributions of [Formula: see text] and [Formula: see text] as the averaging weight, is used, and the value of [Formula: see text] is found that equates the prespecified frequentist power ([Formula: see text]) and the conditional expected power of the trial. This hypothesized effect size is then used in traditional sample size calculations when determining sample size for the study. RESULTS: The value of [Formula: see text] found using this method may be expressed as a function of the prior means of [Formula: see text] and [Formula: see text], [Formula: see text], and their prior standard deviations, [Formula: see text]. We show that the "naïve" estimate of the effect size, that is, the ratio of prior means, should be down-weighted to account for the variability in the parameters. An example is presented for designing a placebo-controlled clinical trial testing the antidepressant effect of alprazolam as monotherapy for major depression. CONCLUSION: Through this method, we are able to formally integrate prior information on the uncertainty and variability of both the treatment effect and the common standard deviation into the design of the study while maintaining a frequentist framework for the final analysis. Solving for the effect size which the study has a high probability of correctly detecting based on the available prior information on the difference [Formula: see text] and the standard deviation [Formula: see text] provides a valuable, substantiated estimate that can form the basis for discussion about the study's feasibility during the design phase.