A cluster randomized controlled trial comparing relative effectiveness of two licensed influenza vaccines in US nursing homes: Design and rationale

Cluster Randomized Trials Designed to Support Generalizable Inferences

When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies, we show that all the estimators have low bias but markedly different precision. Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.

Assessing the representativeness of cluster randomized trials: Evidence from two large pragmatic trials in United States nursing homes

BACKGROUND/AIMS

When the randomized clusters in a cluster randomized trial are selected based on characteristics that influence treatment effectiveness, results from the trial may not be directly applicable to the target population. We used data from two large nursing home-based pragmatic cluster randomized trials to compare nursing home and resident characteristics in randomized facilities to eligible non-randomized and ineligible facilities.

METHODS

We linked data from the high-dose influenza vaccine trial and the Music & Memory Pragmatic TRIal for Nursing Home Residents with ALzheimer's Disease (METRICaL) to nursing home assessments and Medicare fee-for-service claims. The target population for the high-dose trial comprised Medicare-certified nursing homes; the target population for the METRICaL trial comprised nursing homes in one of four US-based nursing home chains. We used standardized mean differences to compare facility and individual characteristics across the three groups and logistic regression to model the probability of nursing home trial participation.

RESULTS

In the high-dose trial, 4476 (29%) of the 15,502 nursing homes in the target population were eligible for the trial, of which 818 (18%) were randomized. Of the 1,361,122 residents, 91,179 (6.7%) were residents of randomized facilities, 463,703 (34.0%) of eligible non-randomized facilities, and 806,205 (59.3%) of ineligible facilities. In the METRICaL trial, 160 (59%) of the 270 nursing homes in the target population were eligible for the trial, of which 80 (50%) were randomized. Of the 20,262 residents, 973 (34.4%) were residents of randomized facilities, 7431 (36.7%) of eligible non-randomized facilities, and 5858 (28.9%) of ineligible facilities. In the high-dose trial, randomized facilities differed from eligible non-randomized and ineligible facilities by the number of beds (132.5 vs 145.9 and 91.9, respectively), for-profit status (91.8% vs 66.8% and 68.8%), belonging to a nursing home chain (85.8% vs 49.9% and 54.7%), and presence of a special care unit (19.8% vs 25.9% and 14.4%). In the METRICaL trial randomized facilities differed from eligible non-randomized and ineligible facilities by the number of beds (103.7 vs 110.5 and 67.0), resource-poor status (4.6% vs 10.0% and 18.8%), and presence of a special care unit (26.3% vs 33.8% and 10.9%). In both trials, the characteristics of residents in randomized facilities were similar across the three groups.

CONCLUSION

In both trials, facility-level characteristics of randomized nursing homes differed considerably from those of eligible non-randomized and ineligible facilities, while there was little difference in resident-level characteristics across the three groups. Investigators should assess the characteristics of clusters that participate in cluster randomized trials, not just the individuals within the clusters, when examining the applicability of trial results beyond participating clusters.

An overview of optimal designs under a given budget in cluster randomized trials with a binary outcome

Cluster randomized trial design may raise financial concerns because the cost to recruit an additional cluster is much higher than to enroll an additional subject in subject-level randomized trials. Therefore, it is desirable to develop an optimal design. For local optimal designs, optimization means the minimum variance of the estimated treatment effect under the total budget. The local optimal design derived from the variance needs the input of an association parameter ρ in terms of a "working" correlation structure R ( ρ ) in the generalized estimating equation models. When the range of ρ instead of an exact value is available, the parameter space is defined as the range of ρ and the design space is defined as enrollment feasibility, for example, the number of clusters or cluster size. For any value ρ within the range, the optimal design and relative efficiency for each design in the design space is obtained. Then, for each design in the design space, the minimum relative efficiency within the parameter space is calculated. MaxiMin design is the optimal design that maximizes the minimum relative efficiency among all designs in the design space. Our contributions are threefold. First, for three common measures (risk difference, risk ratio, and odds ratio), we summarize all available local optimal designs and MaxiMin designs utilizing generalized estimating equation models when the group allocation proportion is predetermined for two-level and three-level parallel cluster randomized trials. We then propose the local optimal designs and MaxiMin designs using the same models when the group allocation proportion is undecided. Second, for partially nested designs, we develop the optimal designs for three common measures under the setting of equal number of subjects per cluster and exchangeable working correlation structure in the intervention group. Third, we create three new Statistical Analysis System (SAS) macros and update two existing SAS macros for all the optimal designs. We provide two examples to illustrate our methods.

Ethical analysis of vulnerabilities in cluster randomized trials involving people living with dementia in long-term care homes

Cluster randomized trials (CRT) of non-pharmacological interventions are an important means of improving the quality of care and quality of life of people living with dementia (PLWD) in long-term care (LTC) homes. PLWD in LTC homes are, however, vulnerable in manifold ways. Therefore, researchers require guidance to ensure that the rights and welfare of PLWD are protected in the course of this valuable research. In this article, we introduce a framework for identifying vulnerabilities in randomized trials and apply it to three CRTs involving PLWD in LTC homes. CRTs may render PLWD in LTC homes vulnerable to three autonomy wrongs: inadequately informed consent, inadequately voluntary consent, and invasions of privacy; two welfare wrongs: risks of therapeutic procedure exceed potential benefits, and excessive risk of non-therapeutic procedures; and one justice wrong: unjust impact of research activities on care. We then discuss appropriate, feasible additional protections that can be implemented to mitigate vulnerability while preserving the scientific validity of the CRT. Corresponding additional protections that can be feasibly implemented include capacity assessments, substitute decision-makers, assent, insulation from LTC home employees during the consent process, patient advocates, utilizing LTC home employees for data collection, stakeholder engagement, additional supervision during study procedures, using caregivers to complete questionnaires by proxy, and gatekeeper permission. Reassuringly, many of these additional protections promote, rather than imperil, the scientific validity of these trials.

Pragmatic Trials in Long-Term Care: Research Challenges and Potential Solutions in Relation to Key Areas of Care

As a method of research, pragmatic trials are recommended so as to generate results that are applicable to real-world care. This intent is especially important for the millions of older adults who receive long-term care in thousands of nursing homes and assisted living communities across the country-and many millions more around the globe. This article presents key points raised by experts participating in a conference funded by the National Institute of Aging held at the 2021 conference of the Society for Post-Acute and Long-term Care Medicine. The purpose of the conference was to convene leading clinicians, researchers, and industry partners to address special considerations of pragmatic trials in long-term care. Cross-cutting and unique challenges and solutions to conducting pragmatic trials were discussed focusing on 3 areas of clinical relevance to long-term care: (1) functional care and outcomes, (2) psychosocial care and quality of life, and (3) medical care and outcomes, with a special focus on persons with dementia. Challenges and innovative solutions were organized across the 9 domains of the revised Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) Tool, and future research recommendations for pragmatic trials in long-term care were identified.

Pragmatic Trials in Long-Term Care: Research Challenges and Potential Solutions in Relation to Key Areas of Care

As a method of research, pragmatic trials are recommended so as to generate results that are applicable to real-world care. This intent is especially important for the millions of older adults who receive long-term care in thousands of nursing homes and assisted living communities across the country-and many millions more around the globe. This article presents key points raised by experts participating in a conference funded by the National Institute of Aging held at the 2021 conference of the Society for Post-Acute and Long-term Care Medicine. The purpose of the conference was to convene leading clinicians, researchers, and industry partners to address special considerations of pragmatic trials in long-term care. Cross-cutting and unique challenges and solutions to conducting pragmatic trials were discussed focusing on 3 areas of clinical relevance to long-term care: (1) functional care and outcomes, (2) psychosocial care and quality of life, and (3) medical care and outcomes, with a special focus on persons with dementia. Challenges and innovative solutions were organized across the 9 domains of the revised Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) Tool, and future research recommendations for pragmatic trials in long-term care were identified.

Pragmatic trials in long-term care: Research challenges and potential solutions in relation to key areas of care

As a method of research, pragmatic trials are recommended so as to generate results that are applicable to real-world care. This intent is especially important for the millions of older adults who receive long-term care in thousands of nursing homes and assisted living communities across the country-and many millions more around the globe. This article presents key points raised by experts participating in a conference funded by the National Institute of Aging held at the 2021 conference of the Society for Post-Acute and Long-term Care Medicine. The purpose of the conference was to convene leading clinicians, researchers, and industry partners to address special considerations of pragmatic trials in long-term care. Cross-cutting and unique challenges and solutions to conducting pragmatic trials were discussed focusing on 3 areas of clinical relevance to long-term care: (1) functional care and outcomes, (2) psychosocial care and quality of life, and (3) medical care and outcomes, with a special focus on persons with dementia. Challenges and innovative solutions were organized across the 9 domains of the revised Pragmatic-Explanatory Continuum Indicator Summary (PRECIS) Tool, and future research recommendations for pragmatic trials in long-term care were identified.

Sample size calculation in three-level cluster randomized trials using generalized estimating equation models

Three-level cluster randomized trials (CRTs) are increasingly used in implementation science, where 2fold-nested-correlated data arise. For example, interventions are randomly assigned to practices, and providers within the same practice who provide care to participants are trained with the assigned intervention. Teerenstra et al proposed a nested exchangeable correlation structure that accounts for two levels of clustering within the generalized estimating equations (GEE) approach. In this article, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in three-level CRTs. Given the nested exchangeable correlation structure, we derive the asymptotic variances of the estimator of the treatment effect for different types of outcomes. When the number of clusters is small, researchers have proposed bias-corrected sandwich estimators to improve performance in two-level CRTs. We extend the variances of two bias-corrected sandwich estimators to three-level CRTs. The equal provider and practice sizes were assumed to calculate number of practices for simplicity. However, they are not guaranteed in practice. Relative efficiency (RE) is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal provider and practice sizes. The expressions of REs are obtained from both asymptotic variance estimation and bias-corrected sandwich estimators. Their performances are evaluated for different scenarios of provider and practice size distributions through simulation studies. Finally, a percentage increase in the number of practices is proposed due to efficiency loss from unequal provider and/or practice sizes.

Optimal designs in three-level cluster randomized trials with a binary outcome

Cluster randomized trials (CRTs) were originally proposed for use when randomization at the subject level is practically infeasible or may lead to a severe estimation bias of the treatment effect. However, recruiting an additional cluster costs more than enrolling an additional subject in an individually randomized trial. Under budget constraints, researchers have proposed the optimal sample sizes in two-level CRTs. CRTs may have a three-level structure, in which two levels of clustering should be considered. In this paper, we propose optimal designs in three-level CRTs with a binary outcome, assuming a nested exchangeable correlation structure in generalized estimating equation models. We provide the variance of estimators of three commonly used measures: risk difference, risk ratio, and odds ratio. For a given sampling budget, we discuss how many clusters and how many subjects per cluster are necessary to minimize the variance of each measure estimator. For known association parameters, the locally optimal design is proposed. When association parameters are unknown but within predetermined ranges, the MaxiMin design is proposed to maximize the minimum of relative efficiency over the possible ranges, that is, to minimize the risk of the worst scenario.

Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models

There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency (RE) of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a set of correlated data is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which the "working correlation structure" is introduced and the association pattern depends on a vector of association parameters denoted by ρ. In this paper, we utilize GEE models to test the treatment effect in a two-group comparison for continuous, binary, or count data in CRTs. The variances of the estimator of the treatment effect are derived for the different types of outcome. RE is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal cluster sizes. We discuss a commonly used structure in CRTs-exchangeable, and derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster size distributions through simulation studies. We propose an adjusted sample size due to efficiency loss. Additionally, we also propose an optimal sample size estimation based on the GEE models under a fixed budget for known and unknown association parameter (ρ) in the working correlation structure within the cluster.

Clinical characteristics associated with the onset of delirium among long-term nursing home residents

Background

Nursing home residents are frail, have multiple medical comorbidities, and are at high risk for delirium. Most of the existing evidence base on delirium is derived from studies in the acute in-patient population. We examine the association between clinical characteristics and medication use with the incidence of delirium during the nursing home stay.

Methods

This is a retrospective cohort study of 1571 residents from 12 nursing homes operated by a single care provider in Ontario, Canada. Residents were over the age of 55 and admitted between February 2010 and December 2015 with no baseline delirium and a minimum stay of 180 days. Residents with moderate or worse cognitive impairment at baseline were excluded. The baseline and follow-up characteristics of residents were collected from the Resident Assessment Instrument-Minimal Data Set 2.0 completed at admission and repeated quarterly until death or discharge. Multivariate logistic regression was used to identify characteristics and medication use associated with the onset of delirium.

Results

The incidence of delirium was 40.4% over the nursing home stay (mean LOS: 32 months). A diagnosis of dementia (OR: 2.54, p < .001), the presence of pain (OR: 1.64, p < .001), and the use of antipsychotics (OR: 1.87, p < .001) were significantly associated with the onset of delirium. Compared to residents who did not develop delirium, residents who developed a delirium had a greater increase in the use of antipsychotics and antidepressants over the nursing home stay.

Conclusions

Dementia, the presence of pain, and the use of antipsychotics were associated with the onset of delirium. Pain monitoring and treatment may be important to decrease delirium in nursing homes. Future studies are necessary to examine the prescribing patterns in nursing homes and their association with delirium.

Electronic supplementary material

The online version of this article (10.1186/s12877-018-0733-3) contains supplementary material, which is available to authorized users.

Pragmatic trials may help to identify effective strategies to reduce nursing home antipsychotic medication use

Despite widespread agreement that nursing homes’ use of antipsychotic medications for residents without specific psychiatric diagnoses is a marker of poor quality of care, prevalence remains high. Additionally, variation suggests continued opportunity to improve care even in countries, like the United States, that have long-standing policies designed to decrease antipsychotic medication use. In a recent Israel Journal of Health Policy Research article, Frankenthal et al. presented results linking increased antipsychotic medication use prevalence in Tel Aviv nursing homes with facility characteristics, including some that “undermine quality of care,” and called for increased national focus on this area. While we agree with the authors that government focus can help to decrease antipsychotic medication use, experience in the United States shows that such efforts may not be sufficient: we present data showing significant variation among United States nursing homes’ antipsychotic medication use prevalence after more than ten years of national warnings and programs. This suggests that United States nursing home clinicians and caregivers continue to need effective non-pharmacologic interventions to substitute for antipsychotic medications. We suggest expanded use of cluster-randomized trials to test strategies to withdraw residents from antipsychotic medications and to implement alternate, non-pharmacological approaches for addressing the behavioral and psychological symptoms of dementia.

Optimal design of longitudinal data analysis using generalized estimating equation models

Longitudinal studies are often applied in biomedical research and clinical trials to evaluate the treatment effect. The association pattern within the subject must be considered in both sample size calculation and the analysis. One of the most important approaches to analyze such a study is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which "working correlation structure" is introduced and the association pattern within the subject depends on a vector of association parameters denoted by ρ. The explicit sample size formulas for two-group comparison in linear and logistic regression models are obtained based on the GEE method by Liu and Liang. For cluster randomized trials (CRTs), researchers proposed the optimal sample sizes at both the cluster and individual level as a function of sampling costs and the intracluster correlation coefficient (ICC). In these approaches, the optimal sample sizes depend strongly on the ICC. However, the ICC is usually unknown for CRTs and multicenter trials. To overcome this shortcoming, Van Breukelen et al. consider a range of possible ICC values identified from literature reviews and present Maximin designs (MMDs) based on relative efficiency (RE) and efficiency under budget and cost constraints. In this paper, the optimal sample size and number of repeated measurements using GEE models with an exchangeable working correlation matrix is proposed under the considerations of fixed budget, where "optimal" refers to maximum power for a given sampling budget. The equations of sample size and number of repeated measurements for a known parameter value ρ are derived and a straightforward algorithm for unknown ρ is developed. Applications in practice are discussed. We also discuss the existence of the optimal design when an AR(1) working correlation matrix is assumed. Our proposed method can be extended under the scenarios when the true and working correlation matrix are different.

Fluzone® High-Dose Influenza Vaccine

INTRODUCTION

Fluzone® High-Dose (IIV3-HD) is a trivalent, inactivated, split-virus influenza vaccine indicated for use in older adults (≥65 years of age). It contains 60 µg hemagglutinin of each influenza strain, which is four times the hemagglutinin content of standard-dose influenza vaccines, including Fluzone (IIV3-SD). IIV3-HD has been licensed for use in older adults in the US since December 2009 and in Canada since February 2016. Areas covered: In this review, we summarize postlicensure studies on the immunogenicity, safety, and effectiveness of IIV3-HD and estimates of its cost-effectiveness in older adults. We also discuss the potential application of IIV3-HD in adults 50-64 years of age and in individuals who may respond poorly to standard-dose influenza vaccines. Expert commentary: Multiple studies conducted since 2004 have consistently shown that, in older adults, IIV3-HD induces substantially greater antibody responses and better protection against influenza and influenza-associated hospitalization than IIV3-SD. Health economic analyses suggest that IIV3-HD can be a cost-effective alternative to standard-dose trivalent or quadrivalent inactivated influenza vaccines and can even be cost-saving compared to IIV3-SD in older adults. Further investigation of IIV3-HD vaccination as a way to improve immune responses and protection against influenza in immunocompromised individuals is warranted.