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Abstract Background The generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias. Results We briefly present bias-corrected and penalized GEE methods, along with 11 bias-adjusted covariance estimators. In addition, we focus on analyzing longitudinal or clustered data with binary outcomes using the logit link function and introduce package geessbin in R to implement conventional and modified GEE methods with bias-adjusted covariance estimators. Finally, we illustrate the implementation and detail a usage example of the package. The package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/geessbin/index.html . Conclusions The geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.

Abstract The association of physical activity with renal outcome and mortality in advanced chronic kidney disease (CKD; i.e., estimated glomerular filtration rate [eGFR]

Abstract Background Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to identify the gap between AI research and its implementation in anesthesiology via a systematic review of randomized controlled trials with meta-analysis (CRD42022353727). Methods We searched the databases of Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), Institute of Electrical and Electronics Engineers Xplore (IEEE), and Google Scholar and retrieved randomized controlled trials comparing conventional and AI-assisted anesthetic management published between the date of inception of the database and August 31, 2023. Results Eight randomized controlled trials were included in this systematic review (n = 568 patients), including 286 and 282 patients who underwent anesthetic management with and without AI-assisted interventions, respectively. AI-assisted interventions used in the studies included fuzzy logic control for gas concentrations (one study) and the Hypotension Prediction Index (seven studies; adding only one indicator). Seven studies had small sample sizes (n = 30 to 68, except for the largest), and meta-analysis including the study with the largest sample size (n = 213) showed no difference in a hypotension-related outcome (mean difference of the time-weighted average of the area under the threshold 0.22, 95% confidence interval -0.03 to 0.48, P = 0.215, I 2 93.8%). Conclusions This systematic review and meta-analysis revealed that randomized controlled trials on AI-assisted interventions in anesthesiology are in their infancy, and approaches that take into account complex clinical practice should be investigated in the future. Trial registration This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022353727).

Background: Logistic regression models are widely used to evaluate the association between a binary outcome and a set of covariates. However, when there are few study participants at the outcome and covariate levels, the models lead to bias of the odds ratio (OR) estimated using the maximum likelihood (ML) method. This bias is known as sparse data bias, and the estimated OR can yield impossibly large values because of data sparsity. However, this bias has been ignored in most epidemiological studies. Methods: We review several methods for reducing sparse data bias in logistic regression. The primary aim is to evaluate the Bayesian methods in comparison with the classical methods, such as the ML, Firth’s, and exact methods using a simulation study. We also apply these methods to a real data set. Results: Our simulation results indicate that the bias of the OR from the ML, Firth’s, and exact methods is considerable. Furthermore, the Bayesian methods with hyper-ɡ prior modeling of the prior covariance matrix for regression coefficients reduced the bias under the null hypothesis, whereas the Bayesian methods with log F-type priors reduced the bias under the alternative hypothesis. Conclusion: The Bayesian methods using log F-type priors and hyper-ɡ prior are superior to the ML, Firth’s, and exact methods when fitting logistic models to sparse data sets. The choice of a preferable method depends on the null and alternative hypothesis. Sensitivity analysis is important to understand the robustness of the results in sparse data analysis.

Introduction: For assessing drug safety using spontaneous reporting system databases, quantitative measurements, such as proportional reporting rate (PRR) and reporting odds ratio (ROR), are widely employed to assess the relationship between a drug and a suspected adverse drug reaction (ADR). The databases contain numerous ADRs, and the quantitative measurements need to be calculated by performing the analysis multiple times for each ADR. We proposed a novel, simple, and easy-to-implement method to estimate the PRR and ROR of multiple ADRs in a single analysis using a generalized mixed-effects model for signal detection.Methods: The proposed method simultaneously analyzed the association between any drug and numerous ADRs, as well as estimated the PRR and ROR for a specific combination of drugs and suspected ADRs. Furthermore, the proposed method was applied to detect drug-drug interactions associated with the concurrent use of two or more drugs.Results and discussion: In our simulation studies, the false-positive rate and sensitivity of the proposed method were similar to those of the traditional PRR and ROR. The proposed method detected known ADRs when applied to the Food and Drug Administration Adverse Event Reporting System database. As an important advantage, the proposed method allowed the simultaneous evaluation of several ADRs using multiple drugs.

Background: The aim of the trial is to evaluate the effectiveness of interventions provided by online support program apps, adopting health-related quality of life (HR-QOL) scores as indicators. Methods: The design is as an open, randomized, parallel-group trial with longitudinal data collection. The subjects will be female cancer patients receiving treatment in a Japanese National Cancer Hospital. Patients assigned to the experimental group will use three apps: an app for them to monitor their own health (monitoring app), an app to assess their understanding of their diagnosis and treatment and their readiness to receive treatment (confirmation app), and an app to address mental health issues (writing app); patients assigned to the control group will use only the monitoring app. At baseline (before patients undergo cancer treatment) and three other times during the study, evaluation indicators will be obtained from three different standardized HR-QOL scales that are incorporated in the monitoring app. The study hypothesis is that at 6 months after patients’ baseline health monitoring, patients in the experimental group will have improved HR-QOL as compared with patients in the control group. Conclusion: This study is based on self-regulation theory, so it is important that the online support program works in an efficient way with respect to patients finding and setting their own health-related goals and adapting their behaviors to achieve those goals. Verifying the effectiveness of the combination of the three apps will show that it is a scientifically valid approach to maintaining or improving the HR-QOL of cancer patients.

Mobile applications are increasingly used in healthcare. We have developed a smartphone healthcare application, CALO mama Plus, that can register daily diet, exercise, mood, and sleep quality, calculate dietary intake, and provide advice using artificial intelligence technology. This 3-month randomized controlled trial tested the hypothesis that CALO mama Plus could promote body weight reduction in Japanese adults with overweight or obesity. We recruited office workers as participants. The key eligibility criteria were an age of 20–65 years and a body mass index of 23–40 kg/m2. The primary outcome was body weight change over 3 months. We enrolled 141 participants and randomly assigned them to the intervention (n = 72) and control (n = 69) groups. The intervention group used CALO mama Plus, and the control group did not receive any intervention. The change in body weight was −2.4 ± 4.0 kg and −0.7 ± 3.3 kg in the intervention and control groups, respectively. An analysis of covariance adjusted for related variables showed a significant between-group difference in body weight change (−1.60 kg; 95% confidence interval −2.83 to −0.38; p = 0.011). The present study suggests that CALO mama Plus effectively promotes weight loss.

Background: For effective screening in urinalysis, information on high-risk groups is needed; however, there is a lack of evidence in young adults in particular. The aim of this study was to provide information on urinalysis in young adults and to identify high-risk groups of urinalyses using multi-year data obtained from annual large-scale check-ups. Method: We used annual health check-up data collected from 2011 to 2016 at Kyoto University in Japan. Eligible participants were those aged 18–39 years who underwent annual health check-ups for four consecutive years between 2011 and 2016. We conducted descriptive analyses and calculated the risk ratios (RRs) for urinary abnormalities in the fourth year of urinalysis. Results: In total, 13,640 participants (10,877 men, 79.7%) met the eligibility criteria. The mean prevalence rates of proteinuria, haematuria and glucosuria were 1.61% (men: 1.63%; women: 1.53%), 1.48% (men: 0.53%; women: 5.22%) and 0.46% (men: 0.52%; women: 0.25%), respectively. Participants with urinary abnormalities at least once in the initial 3 years had a higher risk of urinary abnormalities in the fourth year than participants with no abnormal findings in the initial 3 years; the risk ratios (RRs) of proteinuria, haematuria and glucosuria were 3.5 (95% confidence interval (CI) = 3.2–3.7), 12.2 (95% CI = 11.7–12.7) and 42.6 (95% CI = 37.7–48.1), respectively. The RRs of all urinary abnormalities in the fourth year increased as the frequency of urinary abnormalities over the preceding 3 years increased. In haematuria, differences of the RR were observed between men and women. Conclusion: We clarified the prevalence of urinary abnormalities in young adults and high-risk groups of urinary abnormalities. Our findings support the need for multi-year annual urinalysis.

Statistical methods for detecting adverse drug reactions (ADRs) resulting from drug-drug interactions (DDIs) have been used in recent years to analyze the datasets in spontaneous reporting systems. We provide the SignalDetDDI macro in SAS to calculate the criteria for detecting ADRs resulting from the concomitant use of two drugs. We outline two criteria for detecting DDIs with the combination of two drugs and illustrate the implementation of the macro by way of an example. To implement the macro, a user specifies the target ADR and the two drugs to be evaluated. The SignalDetDDI macro outputs a table showing the number of reports on ADRs, the values of the two criteria for detecting ADRs, and the presence of DDIs. This macro enables users to easily and automatically assess the clinical DDIs that result from ADRs. The SignalDetDDI macro is freely available in the Supporting Information.