Testing for heterogeneity among the components of a binary composite outcome in a clinical trial

Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study

BACKGROUND

While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size.

METHODS

Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome.

RESULTS

6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32-1.44), and mortality (RR 0.82, 0.41-1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28-0.98, p = 0.044) and mortality (RR 0.56, 95%CI 0.33-0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control.

CONCLUSIONS

There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide.

Estimating and testing an index of bias attributable to composite outcomes in comparative studies

OBJECTIVES

This study aimed to develop an index to evaluate the bias attributable to composite outcomes (BACOs) in comparative clinical studies.

STUDY DESIGN AND SETTING

The author defined the BACO index as the ratio of the logarithm of the association measure (e.g., relative risk) of the composite outcome to that of its most relevant component endpoint (e.g., mortality). Methods to calculate the confidence intervals and test the null hypotheses (BACO index = 1) were described and applied in systematically selected clinical trials. Two other preselected trials were included as "positive controls" for being examples of primary composite outcomes disregarded because of inconsistency with the treatment effect on mortality.

RESULTS

The BACO index values different from 1 were classified according to whether the use of composite outcomes overestimated (BACO index >1), underestimated (BACO index between 0 and <1), or inverted (BACO index <0) the association between exposure and prognosis. In 3 of 23 systematically selected trials and the two positive controls, the BACO indices were significantly lower than 1 (P < 0.005).

CONCLUSION

BACO index can warn that the composite outcome association is stronger, weaker, or even opposite than that of its most critical component.

Evidence-based statistical analysis and methods in biomedical research (SAMBR) checklists according to design features

BACKGROUND

Statistical analysis according to design features and objectives is essential to ensure the validity and reliability of the study findings and conclusions in biomedical research. Heterogeneity in reporting study design elements and conducting statistical analyses is often observed for the same study design and study objective in medical literatures. Sometimes, researchers face a lot of predicaments using appropriate statistical approaches highlighted by methodologists for a specific study design either due to lack of accessibility or understanding of statistical methods or unavailability of checklists related to design and analysis in a concise format. The purpose of this review is to provide the checklist of statistical analysis and methods in biomedical research (SAMBR) to applied researchers.

RECENT FINDINGS

We initially identified the important steps of reporting design features that may influence the choice of statistical analysis in biomedical research and essential steps of data analysis of common studies. We subsequently searched for statistical approaches employed for each study design/study objective available in publications and other resources. Compilation of these steps produced SAMBR guidance document, which includes three parts. Applied researchers can use part (A) and part (B) of SAMBR to describe or evaluate research design features and quality of statistical analysis, respectively, in reviewing studies or designing protocols. Part (C) of SAMBR can be used to perform essential and preferred evidence-based data analysis specific to study design and objective.

CONCLUSIONS

We believe that the statistical methods checklists may improve reporting of research design, standardize methodological practices, and promote consistent application of statistical approaches, thus improving the quality of research studies. The checklists do not enforce the use of suggested statistical methods but rather highlight and encourage to conduct the best statistical practices. There is a need to develop an interactive web-based application of the checklists for users for its wide applications.

Clinical trial testing in-home multidisciplinary care management for older adults with cognitive vulnerability: Rationale and study design

Care management approaches are being widely tested in the Medicare-eligible population to manage chronic conditions, but few have focused on cognitive vulnerability as the pathway to optimizing independence in the community-dwelling older population. Cognitive vulnerability refers to living with dementia, depression, and/or a history of delirium. Many studies have shown that cognitive vulnerability is associated with poor health-related outcomes in community-dwelling older adults, raising the health policy importance of finding evidence-based approaches to improve outcomes for this target population. Moreover, very little is known about effects of care management approaches in the rapidly growing Medicare Advantage population. In response to these knowledge gaps, we are testing the efficacy of an in-home, nurse practitioner-led care management team for adults age ≥ 65 with cognitive vulnerability in a Medicare Advantage population. Older adults and family caregivers randomized either to this multidisciplinary care management team, or to a telephonic care management program routinely offered by our Medicare Advantage partner. The intervention period is 12 months and the primary outcome is any emergency department visit or hospitalization over the 12-month period. In this paper, we report on the rationale for testing a multidisciplinary care management intervention for this target population, and explain how a university-based research team collaborated with a Medicare Advantage insurer to conceptualize and implement the clinical trial. We also provide details on study design, and on components of the in-home and telephonic care management interventions. We conclude with a synopsis of recruitment progress along with selected baseline characteristics of the study cohort.

Assessing Cardiac Risk in Pregnant Women With Heart Disease: How Risk Scores Are Created and Their Role in Clinical Practice

Pregnancy, which is associated with profound cardiovascular changes and higher risk of thrombosis, increases the risk of cardiovascular complications in women with pre-existing heart disease. A comprehensive history and physical examination, 12-lead electrocardiogram, and transthoracic echocardiogram remain the foundation of assessing cardiac risk during pregnancy in women with heart disease. These are often combined to generate risk scores, which are statistically derived. Several statistically derived risk and 1 lesion-specific classification system are currently available. A suggested clinical approach to risk stratification is first to identify pregnancies in women with cardiac lesions at risk for serious or life-threatening maternal cardiac complications and for the remainder to use the Cardiac Disease in Pregnancy II (CARPREG II) risk score, integrating additional lesion-specific and patient-specific information. Conversely, clinicians can use the modified World Health Organization (mWHO) risk classification system and integrate general risk predictors and patient-specific information. Importantly, cardiac-risk assessment should always incorporate clinical judgement in addition to the use of risk scores or risk-classification systems. As pregnant women with heart disease are also at risk for obstetric and fetoneonatal complications, risk assessment should be performed by a multidisciplinary team, preferably before conception, or as soon as conception is confirmed, and repeated at regular intervals during the course of pregnancy.

Bin-CE: A comprehensive web application to decide upon the best set of outcomes to be combined in a binary composite endpoint

The estimation of the Sample Size Requirement (SSR) when using a binary composite endpoint (i.e. two or more outcomes combined in a unique primary endpoint) is not trivial. Besides information about the rate of events for each outcome, information about the strength of association between the outcomes is crucial, since it can determine an increase or decrease of the SSR. Specifically, the greater the strength of association between outcomes the higher the SSR. We present Bin-CE, a free tool to assist clinicians for computing the SSR for binary composite endpoints. In a first step, the user enters a set of candidate outcomes, the assumed rate of events for each outcome and the assumed effect of therapy on each outcome. Since the strength of the association between outcomes is usually unknown, a semi-parametric approach linking the a priori clinical knowledge of the potential degree of association between outcomes with the exact values of these parameters was programmed with Bin-CE. Bin-CE works with a recursive algorithm to choose the best combination of outcomes that minimizes the SSR. In addition, Bin-CE computes the sample size using different algorithms and shows different figures plotting the magnitude of the sample size reduction, and the effect of different combinations of outcomes on the rate of the primary endpoint. Finally, Bin-CE is programmed to perform sensitivity analyses. This manuscript presents the mathematic bases and introduces the reader to the use of Bin-CE using a real example.

Validation of days at home as an outcome measure after surgery: a prospective cohort study in Australia

OBJECTIVE

To evaluate 'days at home up to 30 days after surgery' (DAH₃₀) as a patient-centred outcome measure.

DESIGN

Prospective cohort study.

DATA SOURCE

Using clinical trial data (seven trials, 2109 patients) we calculated DAH₃₀ from length of stay, readmission, discharge destination and death up to 30 days after surgery.

MAIN OUTCOME

The association between DAH₃₀ and serious complications after surgery.

RESULTS

One or more complications occurred in 263 of 1846 (14.2%) patients, including 19 (1.0%) deaths within 30 days of surgery; 245 (11.6%) patients were discharged to a rehabilitation facility and 150 (7.1%) were readmitted to hospital within 30 days of surgery. The median DAH₃₀ was significantly less in older patients (p<0.001), those with poorer physical functioning (p<0.001) and in those undergoing longer operations (p<0.001). Patients with serious complications had less days at home than patients without serious complications (20.5 (95% CI 19.1 to 21.9) vs 23.9 (95% CI 23.8 to 23.9) p<0.001), and had higher rates of readmission (16.0% vs 5.9%; p<0.001). After adjusting for patient age, sex, physical status and duration of surgery, the occurrence of postoperative complications was associated with fewer days at home after surgery (difference 3.0(95% CI 2.1 to 4.0) days; p<0.001).

CONCLUSIONS

DAH₃₀ has construct validity and is a readily obtainable generic patient-centred outcome measure. It is a pragmatic outcome measure for perioperative clinical trials.

Composite End Points in Clinical Trials of Heart Failure Therapy

Composite end points are popular outcomes in clinical trials of heart failure therapies. For example, a global rank composite is typically analyzed using a Mann–Whitney U test, and the results are summarized by the mean of ranks and a corresponding P value. The mean of ranks is uninformative, and a clinically meaningful estimate of the treatment effect is needed to communicate study results and facilitate an assessment of heterogeneity (the consistency of the effect across outcomes). The probability index is intuitive for clinicians, easy to calculate, and may be applied to various composites. We suggest a simple and familiar plot to assess heterogeneity across outcomes, which should be routine when analyzing composites. We think that the probability index provides an immediate and simple solution to an overt problem.

Traditional and new composite endpoints in heart failure clinical trials: facilitating comprehensive efficacy assessments and improving trial efficiency

Composite endpoints are commonly used as the primary measure of efficacy in heart failure clinical trials to assess the overall treatment effect and to increase the efficiency of trials. Clinical trials still must enrol large numbers of patients to accrue a sufficient number of outcome events and have adequate power to draw conclusions about the efficacy and safety of new treatments for heart failure. Additionally, the societal and health system perspectives on heart failure have raised interest in ascertaining the effects of therapy on outcomes such as repeat hospitalization and the patient's burden of disease. Thus, novel methods for using composite endpoints in clinical trials (e.g. clinical status composite endpoints, recurrent event analyses) are being applied in current and planned trials. Endpoints that measure functional status or reflect the patient experience are important but used cautiously because heart failure treatments may improve function yet have adverse effects on mortality. This paper discusses the use of traditional and new composite endpoints, identifies qualities of robust composites, and outlines opportunities for future research.

Designing and analyzing clinical trials with composite outcomes: consideration of possible treatment differences between the individual outcomes

When the individual outcomes within a composite outcome appear to have different treatment effects, either in magnitude or direction, researchers may question the validity or appropriateness of using this composite outcome as a basis for measuring overall treatment effect in a randomized controlled trial. The question remains as to how to distinguish random variation in estimated treatment effects from important heterogeneity within a composite outcome. This paper suggests there may be some utility in directly testing the assumption of homogeneity of treatment effect across the individual outcomes within a composite outcome. We describe a treatment heterogeneity test for composite outcomes based on a class of models used for the analysis of correlated data arising from the measurement of multiple outcomes for the same individuals. Such a test may be useful in planning a trial with a primary composite outcome and at trial end with final analysis and presentation. We demonstrate how to determine the statistical power to detect composite outcome treatment heterogeneity using the POISE Trial data. Then we describe how this test may be incorporated into a presentation of trial results with composite outcomes. We conclude that it may be informative for trialists to assess the consistency of treatment effects across the individual outcomes within a composite outcome using a formalized methodology and the suggested test represents one option.

Comparison of the global statistical test and composite outcome for secondary analyses of multiple coronary heart disease outcomes

Multiple outcomes (or multiple endpoints), such as mortality and recurrent myocardial infarction, are increasingly common in clinical trials and are often of interest in secondary analyses. Traditionally, a clinical trial protocol is built around a single event as its primary outcome, with several secondary outcomes specified, the analyses for which lack the same level of power. To accommodate all the relevant outcomes and to increase the power of the comparison in trials evaluating the efficacy of treatments for coronary heart disease, investigators often chose to construct a composite outcome. The more conventional composite outcome fails to account for the relative importance and the relationship (correlation) among its components. The purpose of this work is to demonstrate the usefulness of the Global Statistical Test, which considers the correlation between multiple outcomes, as an alternative strategy for these situations and to demonstrate its effect on hypothesis testing and power analysis issues in comparison with the traditional composite outcome analysis. Data from the cardiovascular clinical trial Systolic Hypertension in the Elderly Population are used as an example.

Delphi-consensus weights for ischemic and bleeding events to be included in a composite outcome for RCTs in thrombosis prevention

BACKGROUND AND OBJECTIVES

To weight ischemic and bleeding events according to their severity to be used in a composite outcome in RCTs in the field of thrombosis prevention.

METHOD

Using a Delphi consensus method, a panel of anaesthesiology and cardiology experts rated the severity of thrombotic and bleeding clinical events. The ratings were expressed on a 10-point scale. The median and quartiles of the ratings of each item were returned to the experts. Then, the panel members evaluated the events a second time with knowledge of the group responses from the first round. Cronbach's a was used as a measure of homogeneity for the ratings. The final rating for each event corresponded to the median rating obtained at the last Delphi round.

RESULTS

Of 70 experts invited, 32 (46%) accepted to participate. Consensus was reached at the second round as indicated by Cronbach's a value (0.99 (95% CI 0.98-1.00)) so the Delphi was stopped. Severity ranged from under-popliteal venous thrombosis (median = 3, Q1 = 2; Q3 = 3) to ischemic stroke or intracerebral hemorrhage with severe disability at 7 days and massive pulmonary embolism (median = 9, Q1 = 9; Q3 = 9). Ratings did not differ according to the medical specialty of experts.

CONCLUSIONS

These ratings could be used to weight ischemic and bleeding events of various severity comprising a composite outcome in the field of thrombosis prevention.