Exploring the impacts of risk factors on mortality patterns of global Alzheimer's disease and related dementias from 1990 to 2021.

Alzheimer's Disease and Related Dementias (ADRD) affect millions of people worldwide, with mortality rates influenced by several risk factors and exhibiting significant heterogeneity across geographical regions. This study aimed to investigate the impact of risk factors on global ADRD mortality patterns from 1990 to 2021, utilizing clustering and modeling techniques. Data on ADRD mortality rates, cardiovascular disease, and diabetes prevalence were obtained for 204 countries from the GBD platform. Additional variables such as HDI, life expectancy, alcohol consumption, and tobacco use prevalence were sourced from the UNDP and WHO. All the data were extracted for men, women, and the overall population. Longitudinal k-means clustering and generalized estimating equations were applied for data analysis. The findings revealed that cardiovascular disease had significant positive effects of 1.84, 3.94, and 4.70 on men, women, and the overall ADRD mortality rates, respectively. Tobacco showed positive effects of 0.92, 0.13, and 0.39, while alcohol consumption had negative effects of - 0.59, - 9.92, and - 2.32, on men, women, and the overall ADRD mortality rates, respectively. The countries were classified into five distinct subgroups. Overall, cardiovascular disease and tobacco use were associated with increased ADRD mortality rates, while moderate alcohol consumption exhibited a protective effect. Notably, tobacco use showed a protective effect in cluster A, as did alcohol consumption in cluster B. The effects of risk factors on ADRD mortality rates varied among the clusters, highlighting the need for further investigation into the underlying causal factors.

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.

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.

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.

Corneal densitometry in Chinese adults with healthy corneas: associations with sex, age, ocular metrics, and optical characteristics.

BACKGROUND: Standardized corneal densitometry (CD) values in large samples of healthy Chinese individuals are scarce. Therefore, we aimed to determine the standard CD values using a Scheimpflug camera in healthy corneas, investigate the correlations of sex, age, and ocular parameters with corneal density, and explore the impact of corneal density on the forward scattering and optical quality of the eye. METHODS: This retrospective observational study involved 990 healthy Chinese individuals, including 494 males and 496 females (mean age: 23.88 ± 6.90 years). The CD values at various depths and radial areas of 0-12 mm were measured using a Scheimpflug camera. Densitometric measurements were expressed in standardized grayscale units (GSU). The optical scatter index (OSI), modulation transfer function cutoff values (MTFcutoff), and Strehl's ratio (SR) were also determined using an optical quality analysis system. RESULTS: The average CD within a 12 mm diameter area was 16.26 ± 1.35 GSU. The highest and lowest optical densities at different depths were observed in the anterior (21.41 ± 2.16 GSU) and posterior (12.00 ± 1.01 GSU) layers, respectively (P < 0.001). Similarly, the maximum and minimum optical densities at different radial areas were observed in the 10-12 mm (14.09 ± 0.93 GSU) and 2-6 mm (25.93 ± 4.77 GSU) circles, respectively (P < 0.001). There was no significant difference in the average CD within a 12 mm diameter area between males and females (P > 0.05). However, upon adjusting for age, central corneal thickness (CCT), corneal curvature, white-to-white (WTW) corneal diameter, and axial length, females exhibited a greater average CD within the 12 mm diameter and in the 6-10 mm and 10-12 mm circles than males. Age-related changes in CD were evident, except in the 2-6 mm circle. CCT, corneal curvature, WTW corneal diameter, and partial depth correlated with CD in the radial area, and CD in different areas correlated with the OSI, MTFcutoff, and SR (P < 0.05). CONCLUSIONS: This study provides the normative CD measurement data of Chinese adults with healthy corneas, emphasizing the significance of sex, age, CCT, corneal curvature, and WTW corneal diameter in CD evaluation. Notably, elevated CD can lead to increased forward scattering within the eye, thereby affecting the optical quality.

Sleeping more than 8 h: a silent factor contributing to decreased muscle mass in Chinese community-dwelling older adults.

BACKGROUND: Muscle mass loss is an age-related process that can be exacerbated by lifestyle, environmental and other factors, but can be mitigated by good sleep. The objective of this study was to investigate the correlation between varying time lags of sleep duration and the decline in muscle mass among individuals aged 60 years or older by using real-world health monitoring data obtained from wearable devices and smart home health monitoring devices. METHODS: This study included 86,037 observations from 2,869 participants in the Mobile Support System database. Missing data were supplemented by multiple imputation. The investigation utilized generalized estimating equations and restricted cubic spline curve to examine the relationship between sleep duration and low muscle mass. Various lag structures, including 0, 1, 2, 0-1, 0-2, and 1-2 months, were fitted, and the interaction effect of observation time with sleep duration was estimated for each lag structure. Additionally, subgroup analyses were conducted. The models were adjusted for various covariates, including gender, age, body mass index, footsteps, smoking status, drinking status, marital status, number of chronic diseases, number of medications, diabetes mellitus, hyperlipidemia, coronary artery disease, respiratory disease, and musculoskeletal disease and an interaction term between time and sleep duration. RESULTS: The results of the generalized estimating equation showed a significant correlation (p < 0.001) between sleep duration of 8 h or more and low muscle mass in older adults, using 6-7 h of sleep as a reference. This effect was seen over time and prolonged sleep accumulated over multiple months had a greater effect on muscle mass loss than a single month. The effect of long sleep duration on muscle mass loss was significantly greater in females than in males and greater in the over-75 than in the under-75 age group. Restricted cubic spline plots showed a non-linear relationship between sleep duration and low muscle mass (p < 0.001). CONCLUSIONS: This study found an association between sustained nighttime sleep of more than eight hours and decreased muscle mass in older adults, especially older women.

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.

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.

Bidirectional associations between dietary diversity and depressive symptoms in Chinese adult women: A retrospective cohort study.

OBJECTIVE: This study aimed to examine the bidirectional associations between dietary diversity and clinical depressive symptoms in adult women, and influencing factors of clinical depressive symptoms. METHODS: This longitudinal study included a total of 22,385 participants, each of whom underwent at least two data collections. We used convenience sampling to recruit women from a health management center of a general hospital in southern China from April 2015 to December 2021. They completed an online self-reported health questionnaire, which included demographic characteristics, lifestyle information, the Dietary Diversity Scale (DDS), and the Patient Health Questionnaire-9. RESULTS: New-onset depressive symptoms and low dietary diversity were observed in this study among 1285 and 3223 participants, respectively. Negative associations were observed between baseline low dietary diversity and new-onset depressive symptoms (P < 0.05) and between baseline depressive symptoms and low dietary diversity (P < 0.001). Cross-lagged panel analysis indicated that dietary diversity negatively and prospectively predicted depressive symptoms, but vice versa (P < 0.05). Strong evidence of a nonlinear association between DDS scores and incident depressive symptoms was found (P nonlinear < 0.05) regardless of whether the variables were adjusted. Besides, age, menarche age, physical activity, sleep duration, longer sedentary behavior and other lifestyle factors were influencing factors of depressive symptoms (P < 0.05). CONCLUSIONS: The present study identified bidirectional associations between dietary diversity and depressive symptoms, and the associations were found to have a non-linear pattern. Adherence to dietary diversity and a healthy lifestyle could be effective non-pharmacological preventive measures to reduce the incidence of depressive symptoms.

Longitudinal Quantiles of Frailty Trajectories Considering Death: New Insights into Sex and Cohort Differences in the Reference Curves for Frailty Progression of Older European.

BACKGROUND: Most previous studies of frailty trajectories in older adults focus on the average trajectory and ignore death. Longitudinal quantile analysis of frailty trajectories permits the definition of reference curves, and the application of mortal cohort inference provides more realistic estimates than models that ignore death. METHODS: Using data from individuals aged 65 or older (n = 25 446) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) from 2004 to 2020, we derived repeated values of the Frailty Index (FI) based on the accumulation of health deficits. We applied weighted Generalized Estimating Equations to estimate the quantiles of the FI trajectory, adjusting for sample attrition due to death, sex, education, and cohort. RESULTS: The FI quantiles increased with age and progressed faster for those with the highest level of frailty (ß^a0.9 = 0.0229, p < .001; ß^a0.5 = 0.0067, p < .001; H0: ßa0.5=ßa0.9, p < .001). Education was consistently associated with a slower progression of the FI in all quantiles (ß^ae0.1 = -0.0001, p < .001; ß^ae0.5 =-0.0004, p < .001; ß^ae0.9 = -0.0003, p < .001) but sex differences varied across the quantiles. Women with the highest level of frailty showed a slower progression of the FI than men when considering death. Finally, no cohort effects were observed for the FI progression. CONCLUSIONS: Quantile FI trajectories varied by age, sex, education, and cohort. These differences could inform the practice of interventions aimed at older adults with the highest level of frailty.

Factors associated with viral load non-suppression among treatment-experienced pre-teenage children living with HIV in Kenya: a nationwide population-based cohort study, 2015-2021.

Background: Viral load non-suppression (VLNS) in children is a major public health concern because of attendant HIV disease progression and risk of morbidity and mortality. Based on a nationally representative database we present estimates of the prevalence, trends and factors associated with VLNS in Kenyan pre-teenage children between 2015 and 2021. Methods: Kenya National AIDS & STI Control Program's (NASCOP) maintains an early infant diagnosis and viral load (EID/VL) database for all persons living with HIV who are enrolled in the country's primary care clinics for purposes of monitoring progress towards achievement of the 95% viral suppression goals. Participants were eligible if they were children living with HIV (CLHIV), on combination ART (cART) treatment, and ≤12 years old. The modified Mann-Kendall trend test for serially correlated data was used to identify VLNS trends. Generalized estimating equations (GEE) with a logit link was used to assess the effects of covariates on the odds of VLNS (VL ≥1,000 copies/mL) over repeated points in time, allowing for the correlation among the repeated measures. Findings: Between January 2015 and December 2021, 508,743 viral load tests were performed on samples collected from 109,682 pre-teenage children. The prevalence of VLNS decreased from 22.9% (95% CI 22.4-23.3) to 12.5% (95% CI 12.1-12.9), p < 0.0001, and mean age increased from 3.1 (4.2) to 8.0 (3.2) years in 2015 and 2021 respectively. A modified Mann-Kendall trend test for serially correlated data denotes a statistically significant decreasing trend (τ = -0.300, p < 0.0001) over the study period. In the multivariable GEE analysis adjusted for covariates, the odds of VLNS decreased by 11% per year during the study period, (GEE-aOR 0.89, 95% CI 0.88-0.90; p < 0.0001). Factors positively associated with VLNS were EFV/NVP-based first-line cART regimen (GEE-aOR 1.74, 95% CI 1.65-1.84, p < 0.0001), PI-based cART regimen (GEE-aOR 1.82, 95% CI 1.72-1.92, p < 0.0001), and children aged 1-3 years (toddlers) (GEE-aOR: 1.84, 95% CI 1.79-1.90, p < 0.0001). On the contrary, DTG-based cART regimen, were negatively associated with VLNS (GEE-aOR 0.70, 95% CI 0.65-0.75, p < 0.0001). Interpretation: There is a strong evidence of decreasing viremia between 2015 and 2021. To sustain the decreasing trend, accelerating the switch from the suboptimal EVP/NVP first-line regimen to optimised DTG regimen is warranted. Funding: U.S. President's Emergency Plan for AIDS Relief (PEPFAR) and Clinton Health Access Initiative (CHAI).

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.

Co-delivery of HIV pre-exposure prophylaxis (PrEP) and HIV testing among publicly insured adolescents and young adults (AYA) receiving medication for opioid use disorder (MOUD).

BACKGROUND: Low rates of HIV pre-exposure prophylaxis (PrEP) prescribing contribute to the disproportionate burden of HIV in the United States. Among adolescent and young adults (AYA) with opioid use disorder, HIV testing and PrEP co-prescription rates are poorly characterized. METHODS: We performed a retrospective analysis involving deidentified data from Philadelphia's Medicaid beneficiaries ages 16-29 years who were prescribed medication for opioid use disorder (MOUD) from 2015 to 2020 and continuously Medicaid-enrolled for ≥6 months prior to that prescription. After identifying the presence of a qualifying diagnosis signifying a PrEP indication, we examined the outcome of appropriate PrEP co-prescriptions and HIV testing using generalized estimating equations (GEE) modeling. RESULTS: We identified 795 AYA Medicaid beneficiaries with 1269 qualified treatment episodes. We calculated a PrEP prescribing rate of 29.47 per 1000 person-years among AYA receiving MOUD. The HIV testing rate was 63.47 per 1000 person-years among AYA receiving MOUD. GEE modeling revealed that individuals receiving methadone were more likely (aOR=2.62, 95% CI=1.06-6.49) to receive HIV testing within 6 months after a PrEP-qualifying diagnosis compared to those receiving other MOUD medications. Those who only saw outpatient behavioral health providers were less likely (aOR=0.48, 95% CI=0.24-0.99) to have received an HIV test within 6 months after the PrEP-qualifying diagnosis compared to those receiving inpatient behavioral health services. CONCLUSIONS: Co-prescription of PrEP and HIV testing among AYA receiving MOUD was rare in this large urban publicly insured population. Interventions are needed to increase HIV prevention services for this key population of AYA at risk for HIV infection.

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.

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.

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.

Using bilateral data in controls and patients with bilateral and unilateral pathology requires increased scrutiny.

In many aspects of human research, capturing multiple measures from the same participant is common due to the symmetric nature of the human body (e.g., two eyes, ten fingers, two legs, etc.). This has established a concerning paradox in biomedical and clinical research. When the same condition exist bilaterally (controls or bilateral pathology), researchers often blindly include both (or multiple) measures into the statistical analysis. This assumes that measures between the two sides are statistically independent (uncorrelated). However, there are certain inherent factors within an individual (e.g., age, sex, physical activity, gait pattern, tissue characteristics, hormonal status, pain thresholds, etc.) that would point to a statistical dependence between bilateral measures. Conversely, in unilateral pathology, it is common practice to use the contralateral side as the comparator. This assumes the exact opposite, that sans pathology, bilateral measures are perfectly correlated without bias. Both of these assumptions can lead to errors in the study conclusions. Few studies have explored the statistical dependence between multiple measures from the same participant. Thus, the purpose of this perspective is to explore the statistical considerations associated with analyzing multiple measures from the same participant and provide recommendations for navigating the use of multiple, non-temporal, data points from the same participant. To give context for these recommendations, an example dataset involving patellofemoral kinematics is provided. Due to the prevalent use of bilateral data in the current literature and the resulting potential for invalid study conclusions, we recommend that future research use caution when using multiple measures from the same participant and apply proper statistical analysis (e.g., generalized estimating equations) when these measures are not independent. If the contralateral limb is used as a comparator in unilateral pathology, strong evidence must exist that the underlying pathology has not altered the measures of interest in this contralateral limb.

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.

Information content of stepped wedge designs under the working independence assumption.

The stepped wedge design is increasingly popular in pragmatic trials and implementation science research studies for evaluating system-level interventions that are perceived to be beneficial to patient populations. An important step in planning a stepped wedge design is to understand the efficiency of the treatment effect estimator and hence the power of the study. We develop several novel analytical results for designing stepped wedge cluster randomized trials analyzed through generalized estimating equations under a misspecified working independence correlation structure. We first contribute a general variance expression of the treatment effect estimator when data collection is scheduled for each cluster-period. Because resource and patient-centered considerations may intentionally call for an incomplete design with outcome data being omitted for certain cluster-periods, we further derive the information content based on the robust sandwich variance to identify data elements that may be preferentially omitted with minimum loss of precision in estimating the treatment effect. We prove that centrosymmetric pairs of cluster-periods, treatment sequences and periods have identical information content and thus contribute equally to the treatment effect estimation, as long as the true covariance structure for the cluster-period means remains centrosymmetric. Finally, we provide an example of how to obtain an incomplete stepped wedge design that admits a more efficient independence GEE estimator but requires less data collection effort. Our results elegantly extend existing ones from linear mixed models coupled with model-based variances to accommodate a misspecified independence working correlation structure through the robust sandwich variances.

Evaluation of factors associated with bulk milk somatic cell count and total plate count in Indonesian smallholder dairy farms.

Increasing milk quality in smallholder dairy farms will result in a greater quantity of milk being delivered to milk collection centers, an increased milk price for farmers and consequently an improved farmers' livelihood. However, little research on milk quality has been performed on smallholder farms in Southeast Asia. The objective of this study was to identify risk factors associated with somatic cell count (SCC) and total plate count (TPC) in Indonesian smallholder dairy farms. One dairy cooperative in West Java, Indonesia was selected based on its willingness to participate. All 119 member farmers in the cooperative, clustered in six groups, were interviewed and a bulk milk sample from all farms was collected in April 2022. Risk factors associated with dairy farms' SCC and TPC were investigated using multivariable population-averaged generalized estimating equations (GEE) models. The mean geometric SCC and TPC from these farms were 529,665 cells/mL of milk and 474,492 cfu/mL of milk, respectively. Five risk factors including manure removal frequency, receiving mastitis treatment training, washing the udder using soap, number of workers, and ownership of the pasture area were associated with SCC. Two risk factors, manure removal frequency and dairy income contribution, were associated with TPC. These findings can therefore be used as a starting point to improve udder health and milk quality in Indonesia and other countries where smallholder farmers play a significant role in milk production.