Verifiable identification condition for nonignorable nonresponse data with categorical instrumental variables

We consider a model identification problem in which an outcome variable contains nonignorable missing values. Statistical inference requires a guarantee of the model identifiability to obtain estimators enjoying theoretically reasonable properties such as consistency and asymptotic normality. Recently, instrumental or shadow variables, combined with the completeness condition in the outcome model, have been highlighted to make a model identifiable. However, the completeness condition may not hold even for simple models when the instrument is categorical. We propose a sufficient condition for model identifiability, which is applicable to cases where establishing the completeness condition is difficult. Using observed data, we demonstrate that the proposed conditions are easy to check for many practical models and outline their usefulness in numerical experiments and real data analysis

Value iteration with deep neural networks for optimal control of input-affine nonlinear systems

This paper proposes a new algorithm with deep neural networks to solve optimal control problems for continuous-time input nonlinear systems based on a value iteration algorithm. The proposed algorithm applies the networks to approximating the value functions and control inputs in the iterations. Consequently, the partial differential equations of the original algorithm reduce to the optimization problems for the parameters of the networks. Although the conventional algorithm can obtain the optimal control with iterative computations, each of the computations needs to be completed precisely, and it is hard to achieve sufficient precision in practice. Instead, the proposed method provides a practical method using deep neural networks and overcomes the difficulty based on a property of the networks, under which our convergence analysis shows that the proposed algorithm can achieve the minimum of the value function and the corresponding optimal controller. The effectiveness of the proposed method even with reasonable computational resources is demonstrated in two numerical simulations

Dispensabilities of Carbonic Anhydrase in Proteobacteria

Carbonic anhydrase (CA) (E.C. 4.2.1.1) is a ubiquitous enzyme catalysing interconversion between CO2 and bicarbonate. The irregular distribution of the phylogenetically distinct classes of CA in procaryotic genome suggests its complex evolutionary history in procaryotes. Genetic evidence regarding the dispensability of CA under high-CO2 air in some model organisms indicates that CA-deficient microorganisms can persist in the natural environment by choosing high-CO2 niches. In this study, we studied the distribution of CA in the genome of Proteobacteria. While a large majority of the genome-sequenced Proteobacteria retained a CA gene(s), intracellular bacterial genera such as Buchnera and Rickettsia contained CA-defective strains. Comparison between CA-retaining and CA- deficient genomes showed the absence of whole coding sequence in some strains and the presence of frameshifted coding sequence in other strains. The evidence suggests that CA is inactivated and lost in some proteobacteria during the course of evolution based on its dispensability

Efficient Multiple-Robust Estimation for Nonresponse Data Under Informative Sampling

Nonresponse after probability sampling is a universal challenge in survey sampling, often necessitating adjustments to mitigate sampling and selection bias simultaneously. This study explored the removal of bias and effective utilization of available information, not just in nonresponse but also in the scenario of data integration, where summary statistics from other data sources are accessible. We reformulate these settings within a two-step monotone missing data framework, where the first step of missingness arises from sampling and the second originates from nonresponse. Subsequently, we derive the semiparametric efficiency bound for the target parameter. We also propose adaptive estimators utilizing methods of moments and empirical likelihood approaches to attain the lower bound. The proposed estimator exhibits both efficiency and double robustness. However, attaining efficiency with an adaptive estimator requires the correct specification of certain working models. To reinforce robustness against the misspecification of working models, we extend the property of double robustness to multiple robustness by proposing a two-step empirical likelihood method that effectively leverages empirical weights. A numerical study is undertaken to investigate the finite-sample performance of the proposed methods. We further applied our methods to a dataset from the National Health and Nutrition Examination Survey data by efficiently incorporating summary statistics from the National Health Interview Survey data.Comment: 2 figure

Identification enhanced generalised linear model estimation with nonignorable missing outcomes

Missing data often result in undesirable bias and loss of efficiency. These become substantial problems when the response mechanism is nonignorable, such that the response model depends on unobserved variables. It is necessary to estimate the joint distribution of unobserved variables and response indicators to manage nonignorable nonresponse. However, model misspecification and identification issues prevent robust estimates despite careful estimation of the target joint distribution. In this study, we modelled the distribution of the observed parts and derived sufficient conditions for model identifiability, assuming a logistic regression model as the response mechanism and generalised linear models as the main outcome model of interest. More importantly, the derived sufficient conditions are testable with the observed data and do not require any instrumental variables, which are often assumed to guarantee model identifiability but cannot be practically determined beforehand. To analyse missing data, we propose a new imputation method which incorporates verifiable identifiability using only observed data. Furthermore, we present the performance of the proposed estimators in numerical studies and apply the proposed method to two sets of real data: exit polls for the 19th South Korean election data and public data collected from the Korean Survey of Household Finances and Living Conditions

Shoulder and elbow pain in elementary school baseball players : The results from a nation-wide survey in Japan

Background: Despite recommendations on how to prevent baseball injuries in youths by the Japanese Society of Clinical Sports Medicine, shoulder and elbow pain still frequently occurs in young baseball players. We conducted a questionnaire survey among baseball players at elementary schools across the country to understand the practice conditions of players, examining the risk factors of shoulder and elbow pain in baseball players. Methods: The questionnaire survey was conducted among elementary school baseball players as members of the Baseball Federation of Japan in September 2015. Results: A total of 8354 players belonging to 412 teams (average age: 8.9) responded to the survey. Among 7894 players who did not have any shoulder and/or elbow pain in September 2014, elbow pain was experienced in 12.3% of them, shoulder pain in 8.0% and shoulder and/or elbow pain in 17.4% during the previous one year. A total of 2835 (39.9% of the total) practiced four days or more per week and 97.6% practiced 3 h or more per day on Saturdays and Sundays. The risk factors associated shoulder and elbow pain included a male sex, older age, pitchers and catchers, and players throwing more than 50 balls per day. Conclusions: It has been revealed that Japanese elementary school baseball players train too much. Coaches should pay attention to older players, male players, pitchers and catchers in order to prevent shoulder and elbow pain. Furthermore, elementary school baseball players should not be allowed to throw more than 50 balls per day. Study design: Retrospective cohort study

Impact of Anatomical Resection for Hepatocellular Carcinoma With Microportal Invasion (vp1): A Multi-institutional Study by the Kyushu Study Group of Liver Surgery

Objective: The aim of the present study was to evaluate the value of anatomical resectionfor HCC with micro-portal vascular invasion (vp1) between 2000 and 2010. Summaryof Background: Vascular invasion has been reported as a prognostic factor of liverresection for hepatocellular carcinoma (HCC). Anatomical resection for HCC has resulted in optimum outcomes of eradicating intrahepatic micrometastases through the portal vein, but opposite results have also been reported. Methods: A clinical chart review was performed for 546 HCC patients with vp1. We retrospectively evaluated the recurrence-free survival (RFS) between anatomical (AR)and non-anatomical resection (NAR). The site of recurrence was also compared between these groups. The influence of AR on the overall survival (OS) and RFS rates was analyzed in patients selected by propensity score matching, and the prognostic factors were identified.Results: A total of 546 patients were enrolled, including 422 in the AR group and 124 in the NAR group. There was no difference in the 5-year OS and RFS rates between the two groups. Local recurrence was significantly more frequent in the NAR group than in the AR group. In a multivariate analysis, hepatitis C (HCV), PIVKAII ?380 mAU/ml, tumor diameter ?5 cm and ?70 years of age were significant predictors of a poor RFS after liverresection. There were no significant differences in the OS or RFS between the AR and NAR groups by a propensity score-matched analysis. Conclusion: Although local recurrence around the resection site was suppressed by AR, AR for HCC with vp1 did not influence the RFS or OS rates after hepatectomy in the modern era

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection