Covariate-adjusted generalized pairwise comparisons in small samples

Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds.

Performance of mixed effects models and generalized estimating equations for continuous outcomes in partially clustered trials including both independent and paired data

Many clinical trials involve partially clustered data, where some observations belong to a cluster and others can be considered independent. For example, neonatal trials may include infants from single or multiple births. Sample size and analysis methods for these trials have received limited attention. A simulation study was conducted to (1) assess whether existing power formulas based on generalized estimating equations (GEEs) provide an adequate approximation to the power achieved by mixed effects models, and (2) compare the performance of mixed models vs GEEs in estimating the effect of treatment on a continuous outcome. We considered clusters that exist prior to randomization with a maximum cluster size of 2, three methods of randomizing the clustered observations, and simulated datasets with uninformative cluster size and the sample size required to achieve 80% power according to GEE-based formulas with an independence or exchangeable working correlation structure. The empirical power of the mixed model approach was close to the nominal level when sample size was calculated using the exchangeable GEE formula, but was often too high when the sample size was based on the independence GEE formula. The independence GEE always converged and performed well in all scenarios. Performance of the exchangeable GEE and mixed model was also acceptable under cluster randomization, though under-coverage and inflated type I error rates could occur with other methods of randomization. Analysis of partially clustered trials using GEEs with an independence working correlation structure may be preferred to avoid the limitations of mixed models and exchangeable GEEs.

Bidirectional association between attitudes toward own aging and quality of life in Chinese older adults: A prospective cohort study

Although positive attitudes toward own aging (ATOA) have been shown to be associated with higher levels of quality of life (QoL) among older adults, the potential interrelationship between ATOA and QoL has not been fully explored. A sample of 2129 older adults aged 60 and above who participated in the three waves of the Chinese longitudinal healthy longevity survey was used. QoL was measured using three indicators, including self-rated health, loneliness, and life satisfaction. The cross-lagged analysis results showed that the bidirectional association between ATOA and QoL was not significant, while positive ATOA predicted better self-rated health, higher life satisfaction, and less loneliness. And there are no gender or age differences in the above relationships. In addition, economic status, sleep quality, and activity participation were common influences on self-rated health, life satisfaction, and loneliness, as well as important factors affecting ATOA. Several variables, such as demographic characteristics, health behaviors, and health status, also influenced QoL and ATOA. Measures to promote positive ATOA can help improve QoL. In addition, emphasis should be placed on improving economic status, sleep quality, and activity participation levels to enhance QoL and ATOA in older adults, with appropriate interventions targeting other factors affecting QoL and ATOA.

Optimal designs using generalized estimating equations in cluster randomized crossover and stepped wedge trials

Cluster randomized crossover and stepped wedge cluster randomized trials are two types of longitudinal cluster randomized trials that leverage both the within- and between-cluster comparisons to estimate the treatment effect and are increasingly used in healthcare delivery and implementation science research. While the variance expressions of estimated treatment effect have been previously developed from the method of generalized estimating equations for analyzing cluster randomized crossover trials and stepped wedge cluster randomized trials, little guidance has been provided for optimal designs to ensure maximum efficiency. Here, an optimal design refers to the combination of optimal cluster-period size and optimal number of clusters that provide the smallest variance of the treatment effect estimator or maximum efficiency under a fixed total budget. In this work, we develop optimal designs for multiple-period cluster randomized crossover trials and stepped wedge cluster randomized trials with continuous outcomes, including both closed-cohort and repeated cross-sectional sampling schemes. Local optimal design algorithms are proposed when the correlation parameters in the working correlation structure are known. MaxiMin optimal design algorithms are proposed when the exact values are unavailable, but investigators may specify a range of correlation values. The closed-form formulae of local optimal design and MaxiMin optimal design are derived for multiple-period cluster randomized crossover trials, where the cluster-period size and number of clusters are decimal. The decimal estimates from closed-form formulae can then be used to investigate the performances of integer estimates from local optimal design and MaxiMin optimal design algorithms. One unique contribution from this work, compared to the previous optimal design research, is that we adopt constrained optimization techniques to obtain integer estimates under the MaxiMin optimal design. To assist practical implementation, we also develop four SAS macros to find local optimal designs and MaxiMin optimal designs.

Risk factors for adverse outcomes in children with diabetic ketoacidosis

OBJECTIVE

Develop a multivariable model to identify children with diabetic ketoacidosis (DKA) and/or hyperglycemic hyperosmolar state (HHS) at increased risk of adverse outcomes, and apply it to analyze adverse outcomes during and after the COVID-19 pandemic.

DESIGN

Retrospective review of clinical data from 4565 admissions (4284 with DKA alone, 31 [0.7%] only HHS, 250 [5.4%] hyperosmolar DKA) to a large academic children's hospital from January 2010-June 2023. 2010-2019 data (N=3004) were used as a training dataset, and 2020-2021 (N=903) and 2022-2023 (N=658) data for validation. Death or intensive care unit stays >48 hours comprised a composite "Adverse Outcome" group. Risks for this composite outcome were assessed using generalized estimating equations.

RESULTS

There were 47 admissions with Adverse Outcomes (1.5%) in 2010-2019, 46 (5.0%) in 2020-2021, and 16 (2.4%) in 2022-2023. Eight patients died (0.18%). Maximum serum glucose, initial pH and diagnosis of type 2 diabetes most strongly predicted Adverse Outcomes. The proportion of patients with type 2 diabetes was highest in 2020-2021. A multivariable model incorporating these factors had excellent discrimination (area under receiver operator characteristic curve [AUC] of 0.948) for the composite outcome in the training dataset, and similar predictive power (AUC 0.960 and 0.873) in the 2020-2021 and 2022-2023 validation datasets, respectively. In the full dataset, AUC for death was 0.984.

CONCLUSIONS

Type 2 diabetes and severity of initial hyperglycemia and acidosis are independent risk factors for Adverse Outcomes, and explain the higher frequency of Adverse Outcomes during the COVID-19 pandemic. Risks decreased in January 2022-June 2023.

MIGS in Severe Glaucoma: 12-Month Retrospective Efficacy and Safety of Microinvasive Glaucoma Surgery with Cataract Extraction

Purpose

Despite holding promise, reports of using MIGS in severe glaucoma are scarce, and none has described combining multiple MIGS in this population. To the best of our knowledge, this is the largest study to report outcomes of phacoemulsification and MIGS (Phaco/MIGS) in patients with severe glaucoma.

Methods

This retrospective review comprised 327 clinical visits of 71 patients with severe glaucoma who underwent Phaco/MIGS with iStent, endocyclodestruction, Kahook Dual Blade, Hydrus Microstent, or a combination of these MIGS (cMIGS) performed between 2016 and 2021. Primary outcomes included intraocular pressure (IOP) and medication burden evaluated by Generalized Estimating Equations, as well as Kaplan-Meier Estimates. Further analyses compared the efficacy of cMIGS and single Phaco/MIGS (sMIGS), procedure duration, visual acuity, and complications.

Results

Mean preoperative IOP was 16.7 mmHg ± 5.8 (SD) on 2.3 ± 1.9 medications overall (N = 71), 16.9 ± 6.3 mmHg on 1.7 ± 1.9 medications in the sMIGS group (N = 37), and 16.4 ± 5.3 mmHg on 2.9 ± 1.6 medications in the cMIGS group (N = 34). Throughout 12 months, Phaco/MIGS led to significant reduction patterns in IOP (p < 0.001) and medications (p = 0.03). At 12 months, 47.5%, 87.5%, and 64.7% of the patients achieved IOP ≤ 12 mmHg, 17 mmHg, or predetermined goal IOP, respectively, without additional medication or procedure. Mean 12-month IOP was 13.5 ± 3.1 mmHg on 1.8 ± 1.7 medications. After adjusting for baseline medication burden, the reduction pattern in IOP (p < 0.05) was different between cMIGS and sMIGS, favoring cMIGS, and the groups had similar reduction patterns in medications (p = 0.75).

Conclusion

The use of Phaco/MIGS in patients with cataract and severe glaucoma may significantly reduce IOP and medication burden throughout 12 months and, thus, may serve as a stepping stone in severe glaucoma patients with visually significant cataract before proceeding with more invasive glaucoma surgery. This effect may be potentiated by the combination effect of cMIGS.

Does the nurse-led case management benefit rheumatoid arthritis patients in reducing distressing symptoms and C-reactive protein: a 2-year follow-up study in Taiwan

Background

Rheumatoid arthritis (RA) is a chronic disease and may worsen over time. Today, nurse-led case management (NLCM) has been recommended to improve clinical outcomes for chronic disease patients, yet little is known regarding its impact on pain, fatigue, and C-reactive protein (CRP) among RA patients. We aimed to explore this issue among such groups via a two-group pre- and post-test approach.

Methods

All subjects were recruited from one hospital in Taiwan from January 2017 to June 2018 and assigned to either a 6-month NLCM program in addition to usual care or to a control group that received usual care only. All of them were followed for 2 years. Outcomes of interests were compared at four time points: baseline, the third day after NLCM completion, and at 6 and 24 months after NLCM. Effects between them were tested using the generalized estimating equations (GEE) model after adjusting for differences at baseline.

Results

A total of 50 patients in the NLCM group and 46 in the control group were recruited for data analysis. Results from the GEE model indicated that integrating NLCM into conventional care benefited patients in decreasing levels of pain and fatigue, as well as CRP value. These improvements were still observed for 2 years after NLCM.

Conclusion

NLCM was shown to be helpful in lowering pain, fatigue, and CRP, which implies that NLCM may be a reference in the provision of tailored care for those affected by rheumatism.

Multivariate analysis of the effect of keratinized mucosa on peri-implant tissues with platform switching: A retrospective study

BACKGROUND

In recent years, platform switching implant treatment has been increasing, which is believed to minimize bone loss around the implant after placement. However, there have been no reports on the relationship between keratinized mucosa width (KMW) and bone loss and soft tissue recession in platform switching implants.

OBJECTIVE

We evaluated the effect of the KMW on the amount of bone loss and soft tissue recession around a platform switching implant retrospectively using multivariate analysis.

MATERIALS AND METHODS

This one-year retrospective study included 91 implants in 48 patients. Age, sex, a history of periodontitis, implant location, oral hygiene status, and the KMW were included as explanatory variables to evaluate bone loss (BL) and buccal gingival height (GH). Generalized estimating equations (GEEs) were used to evaluate the effect of the KMW on platform switching peri-implant tissues.

RESULTS

The mean bone loss on the mesial (ΔBLm), distal (ΔBLd), and buccal (ΔBLb) sides of the implant were 0.16 ± 0.27 mm, 0.19 ± 0.34 mm, and 0.24 ± 0.50 mm, respectively, at 1 year after superstructure placement. The mean amount of change of GH (ΔGH) on the buccal side was 0.30 ± 0.47 mm. After correcting for confounders using GEEs, the results suggested that KMW <1.5 mm was a significant factor (P < 0.001) for bone loss over time in ΔBLm, ΔBLd, and ΔBLb. In addition, for soft tissues on the buccal side, KMW <1.5 mm was a significant factor for ΔGH reduction over time (P < 0.001).

CONCLUSIONS

Keratinized mucosa width ≥1.5 mm was associated with a higher probability less hard and soft tissue recession around the platform switching implant after 1 year from superstructure placement.

A study on the correlations of PRL levels with anxiety, depression, sleep, and self-efficacy in patients with prolactinoma

Purpose

The purpose of this study was to explore the factors influencing PRL levels in patients with prolactinoma and to investigate the correlations between anxiety, depression, sleep, self-efficacy, and PRL levels.

Methods

This retrospective study included 176 patients with prolactinoma who received outpatient treatment at the Affiliated Hospital of Zunyi Medical University from May 2017 to August 2022. The general information questionnaire, Hospital Anxiety and Depression Scale (HADS), Athens Insomnia Scale (AIS), and General Self-Efficacy Scale (GSES) were used for data collection. A generalized estimating equation (GEE) model was used to analyze the factors influencing PRL levels in patients with prolactinoma. GEE single-effect analysis was used to compare PRL levels at different time points between anxiety group and nonanxiety group, between insomnia group and normal group, and between low, medium, and high self-efficacy groups.

Results

The median baseline PRL level and the PRL levels at 1, 3, 6, and 12 months of follow-up were 268.50 ng/ml, 122.25 ng/ml, 21.20 ng/ml, 19.65 ng/ml, and 16.10 ng/ml, respectively. Among patients with prolactinoma, 59.10% had anxiety (HADS-A score = 7.35 ± 3.34) and 28.98% had depression (HADS-D score = 5.23 ± 3.87), 9.10% had sleep disorders (AIS score = 6.10 ± 4.31) and 54.55% had low self-efficacy (GSES score = 2.13 ± 0.83). Educational level, tumor size, number of visits, sleep quality, anxiety level, and self-efficacy level were found to be factors influencing PRL levels in patients with prolactinoma (P<0.05). Higher PRL levels were observed in the anxiety group compared to the non-anxiety group (P<0.001), in the insomnia group compared to the normal group (P<0.05), and in the low self-efficacy group compared to the medium and high self-efficacy groups (P<0.05).

Conclusion

PRL levels in patients with prolactinoma are related to education level, tumor size, number of visits, anxiety, self-efficacy, and sleep but not depression. PRL levels were higher in patients with anxiety, low self-efficacy, and sleep disorders.

Estimating average treatment effects for clustered RCTs with recruitment bias

In clustered randomized controlled trials (RCTs), sample recruitment is often conducted after cluster randomization. This timing can lead to recruitment bias if access to the intervention affects the composition of study-eligible cluster entrants and study consenters. This article develops a potential outcomes framework in such settings that yields a causal estimand that pertains to the always-recruited in either research condition. A consistent inverse probability weighting (IPW) estimator is developed using data on recruits only, and a generalized estimating equations approach is used to obtain robust clustered SE estimators that adjust for estimation error in the IPW weights. A simple data collection strategy is discussed to improve the predictive accuracy of the logit propensity score models. Simulations show that the IPW estimator achieves nominal confidence interval coverage under the assumed identification conditions. An empirical application demonstrates the methods using data from an RCT testing the effects of a behavioral health intervention in schools. An R program for estimation is available for download.

Homogeneity pursuit and variable selection in regression models for multivariate abundance data

When building regression models for multivariate abundance data in ecology, it is important to allow for the fact that the species are correlated with each other. Moreover, there is often evidence species exhibit some degree of homogeneity in their responses to each environmental predictor, and that most species are informed by only a subset of predictors. We propose a generalized estimating equation (GEE) approach for simultaneous homogeneity pursuit (ie, grouping species with similar coefficient values while allowing differing groups for different covariates) and variable selection in regression models for multivariate abundance data. Using GEEs allows us to straightforwardly account for between-response correlations through a (reduced-rank) working correlation matrix. We augment the GEE with both adaptive fused lasso- and adaptive lasso-type penalties, which aim to cluster the species-specific coefficients within each covariate and encourage differing levels of sparsity across the covariates, respectively. Numerical studies demonstrate the strong finite sample performance of the proposed method relative to several existing approaches for modeling multivariate abundance data. Applying the proposed method to presence-absence records collected along the Great Barrier Reef in Australia reveals both a substantial degree of homogeneity and sparsity in species-environmental relationships. We show this leads to a more parsimonious model for understanding the environmental drivers of seabed biodiversity, and results in stronger out-of-sample predictive performance relative to methods that do not accommodate such features.

Longitudinal varying coefficient single-index model with censored covariates

It is of interest to health policy research to estimate the population-averaged longitudinal medical cost trajectory from initial cancer diagnosis to death, and understand how the trajectory curve is affected by patient characteristics. This research question leads to a number of statistical challenges because the longitudinal cost data are often non-normally distributed with skewness, zero-inflation, and heteroscedasticity. The trajectory is nonlinear, and its length and shape depend on survival, which are subject to censoring. Modeling the association between multiple patient characteristics and nonlinear cost trajectory curves of varying lengths should take into consideration parsimony, flexibility, and interpretation. We propose a novel longitudinal varying coefficient single-index model. Multiple patient characteristics are summarized in a single-index, representing a patient's overall propensity for healthcare use. The effects of this index on various segments of the cost trajectory depend on both time and survival, which is flexibly modeled by a bivariate varying coefficient function. The model is estimated by generalized estimating equations with an extended marginal mean structure to accommodate censored survival time as a covariate. We established the pointwise confidence interval of the varying coefficient and a test for the covariate effect. The numerical performance was extensively studied in simulations. We applied the proposed methodology to medical cost data of prostate cancer patients from the Surveillance, Epidemiology, and End Results-Medicare-Linked Database.

Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome

Cluster-randomized trials (CRTs) involve randomizing entire groups of participants-called clusters-to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units.

Trajectories of fear of cancer recurrence and its influence factors: A longitudinal study on Chinese newly diagnosed cancer patients

OBJECTIVE

The fear of cancer recurrence (FCR) is an ongoing and common psychological problem faced by cancer patients. The objective of this study was to explore the variation trend of FCR and its influencing factors in Chinese newly diagnosed cancer patients from admission to 2 months after discharge. Demographic and tumor characteristics, as well as experiential avoidance (EA), were used as predictors.

METHOD

A longitudinal design and a consecutive sampling method were used to select 266 newly diagnosed cancer patients admitted to a tertiary cancer hospital in China from July to December 2022. Measurements of FCR and EA were obtained at admission (T1), 1 month after discharge (T2), and 2 months post-discharge (T3). Generalized estimating equations were used to identify factors associated with FCR for longitudinal data analysis.

RESULTS

A total of 266 participants completed the follow-up. Both FCR and EA scores of patients with newly diagnosed cancer showed a significant trend of first increasing and then decreasing at baseline and follow-up (p < 0.001). The junior secondary and less education level, rural residence, advanced tumor and high EA level were risk factors for higher FCR.

CONCLUSIONS

Our findings suggest that the FCR levels of most newly diagnosed cancer patients in China are different at the three time points and affected by different factors, with the highest level at 1 month after discharge. These results have significant implications for future identifying populations in need of targeted intervention based on their FCR trajectories.

Analysis of Adverse Drug Reactions in Pediatric Patients with Epilepsy: An Intensive Pharmacovigilance Study

Epilepsy is a chronic neurological disease characterized by the presence of spontaneous seizures, with a higher incidence in the pediatric population. Anti-seizure medication (ASM) may produce adverse drug reactions (ADRs) with an elevated frequency and a high severity. Thus, the objective of the present study was to analyze, through intensive pharmacovigilance over 112 months, the ADRs produced by valproic acid (VPA), oxcarbazepine (OXC), phenytoin (PHT), and levetiracetam (LEV), among others, administered to monotherapy or polytherapy for Mexican hospitalized pediatric epilepsy patients. A total of 1034 patients were interviewed; 315 met the inclusion criteria, 211 patients presented ADRs, and 104 did not. A total of 548 ASM-ADRs were identified, and VPA, LEV, and PHT were the main culprit drugs. The most frequent ADRs were drowsiness, irritability, and thrombocytopenia, and the main systems affected were hematologic, nervous, and dermatologic. LEV and OXC caused more nonsevere ADRs, and PHT caused more severe ADRs. The risk analysis showed an association between belonging to the younger groups and polytherapy with ADR presence and between polytherapy and malnutrition with severe ADRs. In addition, most of the severe ADRs were preventable, and most of the nonsevere ADRs were nonpreventable.

Patterns of site-level periodontal disease and within-mouth correlation among older adults in the Hispanic Community Health Study/Study of Latinos

OBJECTIVES

Clinical measures of periodontal disease such as attachment loss (CAL) and probing depth (PD) vary considerably between and within individuals with periodontitis and are known to be influenced by person-level factors (e.g. age and race/ethnicity) as well as intraoral characteristics (e.g. tooth type and location). This study sought to characterize site-level disease patterns and correlations using both person-level and intraoral factors through a model-based approach.

METHODS

This study used full-mouth, six sites per tooth, periodontal examination data collected from 2301 Hispanic/Latino adults aged 60-74 years in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The presence of site-level CAL ≥3 mm and PD ≥4 mm was estimated using generalized estimating equations (GEE), explicitly modelling pairwise periodontal site correlations, while adjusting for number of teeth, sex and Hispanic/Latino background. Subsequently tooth- and tooth-site patterns of intraoral CAL ≥3 mm and PD ≥4 mm were estimated and visualized in the HCHS/SOL population.

RESULTS

The findings showed that posterior sites had the highest odds of CAL ≥3 mm and PD ≥4 mm. Sites located in the interproximal space had higher odds of PD ≥4 mm but lower odds of CAL ≥3 mm than non-interproximal sites. Mexicans had the lowest odds of CAL ≥3 mm among all Hispanic/Latino backgrounds. While Mexicans had lower odds of PD ≥4 mm than Central Americans and Cubans, they had higher odds than Dominicans and Puerto Ricans. Site-level proportions and pairwise correlations of PD ≥4 mm were generally smaller than those of CAL ≥3 mm.

CONCLUSIONS

The patterns of site-level probabilities of clinical measures of periodontal disease can be defined based on tooth, site and individual-level characteristics. Intraoral correlation patterns, while complex, are quantifiable. The risk factors for site-level CAL ≥3 mm may differ from those of PD ≥4 mm. Likewise, participant risk factors for site-level clinical measures of periodontal disease are distinct from those that affect individual-level periodontitis prevalence. Future epidemiological investigations should consider model-based approaches when examining site-level disease probabilities to identify intra-oral patterns of periodontal disease and make inferences about the larger population.

The Dosing of Mobile-Based Just-in-Time Adaptive Self-Management Prompts for Caregivers: Preliminary Findings From a Pilot Microrandomized Study

BACKGROUND

Caregivers of people with chronic illnesses often face negative stress-related health outcomes and are unavailable for traditional face-to-face interventions due to the intensity and constraints of their caregiver role. Just-in-time adaptive interventions (JITAIs) have emerged as a design framework that is particularly suited for interventional mobile health studies that deliver in-the-moment prompts that aim to promote healthy behavioral and psychological changes while minimizing user burden and expense. While JITAIs have the potential to improve caregivers' health-related quality of life (HRQOL), their effectiveness for caregivers remains poorly understood.

OBJECTIVE

The primary objective of this study is to evaluate the dose-response relationship of a fully automated JITAI-based self-management intervention involving personalized mobile app notifications targeted at decreasing the level of caregiver strain, anxiety, and depression. The secondary objective is to investigate whether the effectiveness of this mobile health intervention was moderated by the caregiver group. We also explored whether the effectiveness of this intervention was moderated by (1) previous HRQOL measures, (2) the number of weeks in the study, (3) step count, and (4) minutes of sleep.

METHODS

We examined 36 caregivers from 3 disease groups (10 from spinal cord injury, 11 from Huntington disease, and 25 from allogeneic hematopoietic cell transplantation) in the intervention arm of a larger randomized controlled trial (subjects in the other arm received no prompts from the mobile app) designed to examine the acceptability and feasibility of this intensive type of trial design. A series of multivariate linear models implementing a weighted and centered least squares estimator were used to assess the JITAI efficacy and effect.

RESULTS

We found preliminary support for a positive dose-response relationship between the number of administered JITAI messages and JITAI efficacy in improving caregiver strain, anxiety, and depression; while most of these associations did not meet conventional levels of significance, there was a significant association between high-frequency JITAI and caregiver strain. Specifically, administering 5-6 messages per week as opposed to no messages resulted in a significant decrease in the HRQOL score of caregiver strain with an estimate of -6.31 (95% CI -11.76 to -0.12; P=.046). In addition, we found that the caregiver groups and the participants' levels of depression in the previous week moderated JITAI efficacy.

CONCLUSIONS

This study provides preliminary evidence to support the effectiveness of the self-management JITAI and offers practical guidance for designing future personalized JITAI strategies for diverse caregiver groups.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04556591; https://clinicaltrials.gov/ct2/show/NCT04556591.

Generalizing the information content for stepped wedge designs: A marginal modeling approach

Stepped wedge trials are increasingly adopted because practical constraints necessitate staggered roll-out. While a complete design requires clusters to collect data in all periods, resource and patient-centered considerations may call for an incomplete stepped wedge design to minimize data collection burden. To study incomplete designs, we expand the metric of information content to discrete outcomes. We operate under a marginal model with general link and variance functions, and derive information content expressions when data elements (cells, sequences, periods) are omitted. We show that the centrosymmetric patterns of information content can hold for discrete outcomes with the variance-stabilizing link function. We perform numerical studies under the canonical link function, and find that while the patterns of information content for cells are approximately centrosymmetric for all examined underlying secular trends, the patterns of information content for sequences or periods are more sensitive to the secular trend, and may be far from centrosymmetric.

Combined MIGS: Comparing Additive Effects of Phacoemulsification, Endocyclophotocoagulation, and Kahook Dual Blade

Purpose

Combining two or more MIGS (cMIGS) promises to be more efficacious than single MIGS (sMIGS). This study compared the efficacy of PEcK, which combines Phacoemulsification (Phaco), Endocyclophotocoagulation (ECP), and Kahook dual blade (KDB), relative to both of its constituent sMIGS, Phaco/ECP (Endo Optiks, NJ) and Phaco/KDB (New World Medical, CA) for the first time.

Patients and methods

Data was collected retrospectively from 1833 visits of 271 patients who underwent PEcK, Phaco/ECP, or Phaco/KDB from 2016-2021 at Massachusetts Eye and Ear. Primary outcomes included Generalized Estimating Equations (GEE) of intraocular pressure (IOP) and medication burden, as well as survival models.

Results

Mean preoperative IOP was 17.6 ± 5.0 (SD) mmHg on 3.0 ± 1.4 medications in the PEcK group (n = 128), 17.9 ± 5.1 mmHg on 2.2 ± 1.5 medications in the Phaco/ECP group (n = 78), and 16.1 ± 4.3 mmHg on 0.4 ± 1.0 medications in the Phaco/KDB group (n = 65). For more than 36 months, all procedures resulted in significant patterns of IOP and medication reduction (all p < 0.001), before and after statistical adjustment. The reduction pattern in IOP was significantly different when comparing all groups over time and favored PEcK (p = 0.04), but the reduction pattern in medications was not significantly different (p = 0.11). Procedures did not differ in procedural time (p = 0.18) or in survival to maintain ≥20% IOP reduction (p = 0.43) without additional medication or procedure. There was a trend toward significant difference in maintaining IOP ≤ goal IOP that favored PEcK over Phaco/ECP after adjustment (p = 0.09).

Conclusion

PEcK may confer greater IOP reduction without added procedural time compared to Phaco/ECP and Phaco/KDB in predominantly mild or moderate glaucoma. Further research on cMIGS may benefit from adopting this comparative analysis to constituent MIGS.

The impact of correlated exposures and missing data on multiple informant models used to identify critical exposure windows

There has been heightened interest in identifying critical windows of exposure for adverse health outcomes; that is, time points during which exposures have the greatest impact on a person's health. Multiple informant models implemented using generalized estimating equations (MIM GEEs) have been applied to address this research question because they enable statistical comparisons of differences in associations across exposure windows. As interest rises in using MIMs, the feasibility and appropriateness of their application under settings of correlated exposures and partially missing exposure measurements requires further examination. We evaluated the impact of correlation between exposure measurements and missing exposure data on the power and differences in association estimated by the MIM GEE and an inverse probability weighted extension to account for informatively missing exposures. We assessed these operating characteristics under a variety of correlation structures, sample sizes, and missing data mechanisms considering various exposure-outcome scenarios. We showed that applying MIM GEEs maintains higher power when there is a single critical window of exposure and exposure measures are not highly correlated, but may result in low power and bias under other settings. We applied these methods to a study of pregnant women living with HIV to explore differences in association between trimester-specific viral load and infant neurodevelopment.

Practical strategies for operationalizing optimal allocation in stratified cluster-based outcome-dependent sampling designs

Cluster-based outcome-dependent sampling (ODS) has the potential to yield efficiency gains when the outcome of interest is relatively rare, and resource constraints allow only a certain number of clusters to be visited for data collection. Previous research has shown that when the intended analysis is inverse-probability weighted generalized estimating equations, and the number of clusters that can be sampled is fixed, optimal allocation of the (cluster-level) sample size across strata defined by auxiliary variables readily available at the design stage has the potential to increase efficiency in the estimation of the parameter(s) of interest. In such a setting, the optimal allocation formulae depend on quantities that are unknown in practice, currently making such designs difficult to implement. In this paper, we consider a two-wave adaptive sampling approach, in which data is collected from a first wave sample, and subsequently used to compute the optimal second wave stratum-specific sample sizes. We consider two strategies for estimating the necessary components using the first wave data: an inverse-probability weighting (IPW) approach and a multiple imputation (MI) approach. In a comprehensive simulation study, we show that the adaptive sampling approach performs well, and that the MI approach yields designs that are very near-optimal, regardless of the covariate type. The IPW approach, on the other hand, has mixed results. Finally, we illustrate the proposed adaptive sampling procedures with data on maternal characteristics and birth outcomes among women enrolled in the Safer Deliveries program in Zanzibar, Tanzania.

Improving sandwich variance estimation for marginal Cox analysis of cluster randomized trials

Cluster randomized trials (CRTs) frequently recruit a small number of clusters, therefore necessitating the application of small-sample corrections for valid inference. A recent systematic review indicated that CRTs reporting right-censored, time-to-event outcomes are not uncommon and that the marginal Cox proportional hazards model is one of the common approaches used for primary analysis. While small-sample corrections have been studied under marginal models with continuous, binary, and count outcomes, no prior research has been devoted to the development and evaluation of bias-corrected sandwich variance estimators when clustered time-to-event outcomes are analyzed by the marginal Cox model. To improve current practice, we propose nine bias-corrected sandwich variance estimators for the analysis of CRTs using the marginal Cox model and report on a simulation study to evaluate their small-sample properties. Our results indicate that the optimal choice of bias-corrected sandwich variance estimator for CRTs with survival outcomes can depend on the variability of cluster sizes and can also slightly differ whether it is evaluated according to relative bias or type I error rate. Finally, we illustrate the new variance estimators in a real-world CRT where the conclusion about intervention effectiveness differs depending on the use of small-sample bias corrections. The proposed sandwich variance estimators are implemented in an R package CoxBcv.

Analysis of the efficacy and safety of inpatient and outpatient initiation of KD for the treatment of pediatric refractory epilepsy using generalized estimating equations

Objective

To compare the efficacy and safety of inpatient and outpatient initiation ketogenic diet (KD) protocol of pediatric refractory epilepsy.

Methods

Eligible children with refractory epilepsy were randomly assigned to receive KD with inpatient and outpatient initiation. The generalized estimation equation (GEE) model was used to analyze the longitudinal variables of seizure reduction, ketone body, weight, height, body mass index (BMI), and BMI Z-score at different follow-up times between the two groups.

Results

Between January 2013 and December 2021, 78 and 112 patients were assigned to outpatient and inpatient KD initiation groups, respectively. There were no statistical differences between the two groups based on baseline demographics and clinical characteristics (all Ps > 0.05). The GEE model indicated that the rate of reduction of seizures≥50% in the outpatient initiation group was higher than that of the inpatient initiation group (p = 0.049). A negative correlation was observed between the seizure reduction and blood ketone body at 1, 6, and 12 months (all Ps < 0.05). There were no significant differences in height, weight, BMI, and BMI Z-score between the two groups over the 12-month period by the GEE models (all Ps > 0.05). Adverse events were reported by 31 patients (43.05%) in the outpatient KD initiation group and 46 patients (42.20%) in the inpatient KD initiation group, but these differences were not statistically significant (p = 0.909).

Conclusion

Our study shows that outpatient KD initiation is a safe and effective treatment for children with refractory epilepsy.

Beyond fear: A longitudinal investigation of emotions and risk of a vasovagal reaction in first-time whole-blood donors

BACKGROUND

Fear is a recognized predictor of vasovagal reactions (VVRs) in blood donors. However, less is known about the role of other emotions, including positive emotions, that donors might experience. The aim of this study was to identify the emotions experienced in center that predict onsite VVRs, and to determine at what point during the donation appointment, the experience of these emotions is most influential.

STUDY DESIGN AND METHODS

A sample of 442 first-time whole-blood donors (57.7% female; mean ± SD age 30.7 ± 11.7 years) completed a survey in the waiting area and before venepuncture in the donation chair to assess their current emotional experience. The survey data were matched with routinely-collected demographic, donation, and donor adverse event information. A generalized estimating equations model was used to identify emotions associated with the occurrence of a VVR.

RESULTS

A total of 56 (12.7%) participants experienced a VVR. The occurrence of a VVR was significantly associated with lower love/closeness/trust (OR: 0.53, 95%CI: 0.34-0.82) and higher scared/fearful/afraid (OR: 1.96, 95%CI: 1.18-3.25) states. Significant interaction effects suggested that the effect of scared/fearful/afraid decreased while stressed/nervous/overwhelmed increased from the waiting area to before venepuncture on the likelihood of a VVR.

DISCUSSION

To effectively reduce donor VVR risk, blood collection agencies need to address a broader range of emotions at different points during the donation process.

On modelling relative risks for longitudinal binomial responses: implications from two dueling paradigms

Although logistic regression is the most popular for modelling regression relationships with binary responses, many find relative risk (RR), or risk ratio, easier to interpret and prefer to use this measure of risk in regression analysis. Indeed, since Zou published his modified Poisson regression approach for modelling RR for cross-sectional data, his paper has been cited over 7 000 times, demonstrating the popularity of this alternative measure of risk in regression analysis involving binary responses. As longitudinal studies have become increasingly popular in clinical trials and observational studies, it is imperative to extend Zou's approach for longitudinal data. The two most popular approaches for longitudinal data analysis are the generalised linear mixed-effects model (GLMM) and generalised estimating equations (GEE). However, the parametric GLMM cannot be used for the extension within the current context, because Zou's approach treats the binary response as a Poisson variable, which is at odds with the Bernoulli distribution for the binary response. On the other hand, as it imposes no mathematical model on data distributions, the semiparametric GEE is coherent with Zou's modified Poisson regression. In this paper, we develop a GEE-based longitudinal model for binary responses to provide inference about RR.