Background: Knee osteoarthritis (OA) is the most common type of OA and is a major cause of pain and thus results in disability for daily activities among persons living in the community. OA currently has no cure. In addition to the conflicting recommendations from clinical guidelines, evidence about the extent to which long-term use of intra-articular injections improves patient outcomes is also lacking. Methods: Using data from the Osteoarthritis Initiative (OAI), marginal structural models (MSMs) applying inverse probability treatment weights (IPTW) were used to examine the effectiveness of intra-articular injections and changes in symptoms over time. The specific aims of this dissertation were to: 1) evaluate longitudinal use of intra-articular injections after treatment initiation among persons with radiographic knee OA; 2) quantify the extent to which intra-articular injection relieves symptoms among persons with radiographic knee OA; and 3) evaluate the performance of missing data techniques under the setting of MSMs. Results: Of those initiating injections, ~19% switched, ~21% continued injection type, and ~60% did not report any additional injections. For participants initiating corticosteroid (CO) injections, greater symptoms post-initial injection rather than changes in symptoms over time were associated with continued use compared to one-time use. Among participants with radiographic evidence of knee OA, initiating treatments with either CO or hyaluronic acid (HA) injections was not associated with reduced symptoms compared to non-users over two years. Compared to inverse probability weighting (IPW), missing data techniques such as multiple imputation (MI) produced less biased marginal causal effects (IPW: -2.33% to 15.74%; -1.88% to 4.24%). For most scenarios, estimates using MI had smaller mean square error (range: 0.013 to 0.024) than IPW (range: 0.027 to 0.22). Conclusions: Among participants with radiographic evidence of knee OA living in the community, the proportion of those switching injection use and one-time users was substantial after treatment initiation. In addition, initiating injection use was not associated with reduced symptoms over time. With respect to issues of missing data, using MI may confer an advantage over IPW in MSMs applications. The results of this work highlight the importance of using comparative effectiveness research with non-experimental data to study these commonly used injections and may help to understand the usefulness of these treatments for patients with knee OA.

U.S. state governments have implemented numerous policies to help mitigate the spread of COVID-19. While there is strong biological evidence supporting the wearing of face masks or coverings in public spaces, the impact of public masking policies remains unclear. We aimed to evaluate how early versus delayed implementation of state-level public masking orders impacted subsequent COVID-19 growth rates. We defined “early” implementation as having a state-level mandate in place before September 1, 2020, the approximate start of the school-year. We defined COVID-19 growth rates as the relative increase in confirmed cases 7, 14, 21, 30, 45, 60-days after September 1. Primary analyses used targeted maximum likelihood estimation (TMLE) with Super Learner and considered a wide range of potential confounders to account for differences between states. In secondary analyses, we took an unadjusted approach and calculated the average COVID-19 growth rate among early-implementing states divided by the average COVID-19 growth rate among late-implementing states. At a national level, the expected growth rate after 14-days was 4%lower with early vs. delayed implementation (aRR: 0.96; 95%CI: 0.95-0.98). Associations did not plateau over time, but instead grew linearly. After 60-days, the expected growth rate was 16% lower with early vs. delayed implementation (aRR:0.84; 95%CI: 0.78-0.91). Unadjusted estimates were exaggerated (e.g. 60-day RR:0.72; 95%CI: 0.60-0.84). Sensitivity analyses varying the timing of the masking order yielded similar results. In both the short and long term, state-level public masking mandates were associated with lower COVID-19 growth rates. Given their low-cost and minimal (if any) impact on the economy, masking policies are promising public health strategies to mitigate further spread of COVID-19.

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Causal inference on networks often encounters interference problems. The potentialoutcomes of a unit depend not only on its treatment but also on the treatments of its neighbors in the network. The classic causal inference assumption of no interference among units is untenable in networks, and many fundamental results in causal inference may no longer hold in the presence of interference. To address interference problems in networks, this thesis proposes a novel Network Embedding Matching (NEM) framework for estimating causal effects under network interference. We recover causal effects based on network structure in an observed network. Furthermore, we extend the network interference from direct neighbors to k-hop neighbors. Unlike most previous studies, which had strong assumptions on interference among units in the network and did not consider network structure, our framework incorporates network structure into the estimation of causal effects. In addition, our NEM framework can be implemented in networks for randomized experiments and observational studies. Our approach is interpretable and can be easily applied to networks. We compare our approach with other existing methods in simulations and real networks, and we show that our approach outperforms other methods under linear and nonlinear network interference. / Statistics

Autistic traits are a composition of behavioral constructs that encompasses social functioning, communication, and rigid and repetitive behaviors that might impact an individual’s quality of life. The specificity of these traits is not yet fully understood, nor which traits that might be most debilitating for autistic people. We recruited 366 participants, out of which 78 were diagnosed as autistic, and measured levels of different character traits as well as their quality of life. We ran a Bayesian regression model and found extreme evidence that the behavioral constructs of prosopagnosia, social anhedonia, alexithymia and cognitive empathy contribute to autistic social functioning, while affective empathy did not seem to contribute to the same extent. To estimate the effect of each construct on quality of life we employed Causal Inference methodology and found likely effects of social anhedonia (-0.131 [-0.248, 0.00]) and alexithymia (-0.255 [-0.37, -0.154]). Therefore, both social anhedonia and alexithymia might be effective targets for intervention for autistic people struggling with social functioning.

<p>Mediation analyses play a critical role in social and personality psychology research. However, current approaches for assessing power and sample size in mediation models have limitations, particularly when dealing with complex mediation models and multiple mediator sequential models. These limitations stem from limited software options and the substantial computational time required. In this part, we address these challenges by extending the joint significance test and product of coefficients test to incorporate the fourth-pathed mediated effect and generalized kth-pathed mediated effect. Additionally, we propose a model-based bootstrap method and provide convenient R tools for estimating power in complex mediation models. Through our research, we demonstrate that power decreases as the number of mediators increases and as the influence of coefficients varies. We summarize our results and discuss the implications of power analysis in relation to mediator complexity and coefficient influence. We provide insights for researchers seeking to optimize study designs and enhance the reliability of their findings in complex mediation models. </p> <p>Matching is a crucial step in causal inference, as it allows for more robust and reasonable analyses by creating better-matched pairs. However, in real-world scenarios, data are often collected and stored by different local institutions or separate departments, posing challenges for effective matching due to data fragmentation. Additionally, the harmonization of such data needs to prioritize privacy preservation. In this part, we propose a new hierarchical framework that addresses these challenges by implementing differential privacy on raw data to protect sensitive information while maintaining data utility. We also design a data access control system with three different access levels for designers based on their roles, ensuring secure and controlled access to the matched datasets. Simulation studies and analyses of datasets from the 2017 Atlantic Causal Inference Conference Data Challenge are conducted to showcase the flexibility and utility of our framework. Through this research, we contribute to the advancement of statistical methodologies in matching and privacy-preserving data analysis, offering a practical solution for data integration and privacy protection in causal inference studies. </p> <p>Biomarker discovery is a complex and resource-intensive process, encompassing discovery, qualification, verification, and validation stages prior to clinical evaluation. Streamlining this process by efficiently identifying relevant biomarkers in the discovery phase holds immense value. In this part, we present a likelihood ratio-based approach to accurately identify truly relevant protein markers in discovery studies. Leveraging the observation of unimodal underlying distributions of expression profiles for irrelevant markers, our method demonstrates promising performance when evaluated on real experimental data. Additionally, to address non-normal scenarios, we introduce a kernel ratio-based approach, which we evaluate using non-normal simulation settings. Through extensive simulations, we observe the high effectiveness of the kernel method in discovering the set of truly relevant markers, resulting in precise biomarker identifications with elevated sensitivity and a low empirical false discovery rate.  </p>

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23542号 / 情博第772号 / 新制||情||132(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 鹿島 久嗣, 教授 山本 章博, 教授 下平 英寿 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Depression increases the risk of suicide death and non-fatal suicide attempt. Between 2 - 6% of persons with depression will die by suicide1 and 25 - 31% of persons with depression will make a non-fatal suicide attempt during their lifetime.2,3 Despite the strong association between depression and suicidal behavior, the vast majority of persons with depression will not engage in suicidal behavior, making it difficult to accurately predict who is at risk for suicide and non-fatal suicide attempt. Identifying high risk persons who should be connected to suicide prevention interventions is an important public health goal. Furthermore, depression often co-occurs with other mental disorders, which may exert an interactive influence on the risk of suicide and suicide attempt. Understanding the joint influence of depression and other mental disorders on suicide outcomes may inform prevention strategies. The goals of this dissertation were to predict suicide and non-fatal suicide attempt among persons with depression and to quantify the causal joint effect of depression and comorbid psychiatric disorders on suicide and suicide attempt. For all three studies, we used data from Danish registries, which routinely collect high-quality data in a setting of universal health care with long-term follow-up and registration of most health and life events.4 In Study 1, we predicted suicide deaths among men and women diagnosed with depression using a case-cohort design (n = 14,737). Approximately 800 predictors were included in the machine learning models (classification trees and random forests), spanning demographic characteristics, income, employment, immigrant status, citizenship, family suicidal history (parent or spouse), previous suicide attempts, mental disorders, physical health disorders, surgeries, prescription drugs, and psychotherapy. In depressed men, we found interactions between hypnotics and sedatives, analgesics and antipyretics, and previous poisonings that were associated with a high risk of suicide. In depressed women, there were interactions between poisoning and anxiolytics and between anxiolytics and hypnotics and sedatives that were associated with suicide risk. The variables in the random forests that contributed the most to prediction accuracy in depressed men were previous poisoning diagnoses and prescriptions of hypnotics and sedatives and anxiolytics. In depressed women, the most important predictors of suicide were receipt of state pension, prescriptions for psychiatric medications (anxiolytics and antipsychotics) and diagnoses of poisoning, alcohol related disorders, and reaction to severe stress and adjustment disorders. Prescriptions of analgesics and antipyretics (e.g., acetaminophen) and antithrombotic agents (e.g., aspirin) emerged as important predictors for both depressed men and women. Study 2 predicted non-fatal suicide attempts among men and women diagnosed with depression using a case-cohort design (n = 17,995). Among depressed men, there was a high risk of suicide attempt among those who received a state pension and were diagnosed with toxic effects of substances. There was also an interaction between reaction to severe stress and adjustment disorder and not receiving psychological help that was associated with suicide attempt risk among depressed men. In depressed women, suicide attempt risk was high in those who were prescribed antipsychotics, diagnosed with specific personality disorders, did not have a poisoning diagnosis, and were not receiving a state pension. For both men and women, the random forest results showed that the strongest contributors to prediction accuracy of suicide attempts were poisonings, alcohol related disorders, reaction to severe stress and adjustment disorders, drugs used to treat psychiatric disorders (e.g., drugs used in addictive disorders, anxiolytics, hypnotics and sedatives), anti-inflammatory medications, receipt of state pension, and remaining single. Study 3 examined the joint effect of depression and other mental disorders on suicide and non-fatal suicide attempts using a case-cohort design (suicide death analysis n = 279,286; suicide attempt analysis n = 288,157). We examined pairwise combinations of depression with: 1) organic disorders, 2) substance use disorders, 3) schizophrenia, 4) bipolar disorder, 5) neurotic disorders, 6) eating disorders, 7) personality disorders, 8) intellectual disabilities, 9) developmental disorders, and 10) behavioral disorders. We fit sex-stratified joint marginal structural Cox models to account for time-varying confounding. We observed large hazard ratios for the joint effect of depression and comorbid mental disorders on suicide and suicide attempts, the effect of depression in the absence of comorbid mental disorders, and for the effect of comorbid mental disorders in the absence of depression. We observed positive and negative interdependence between different combinations of depression and comorbid mental disorders on the rate of suicide and suicide attempt, with variation by sex. Overall, depression and comorbid mental disorders are harmful exposures, both independently and jointly. All of the studies in this dissertation highlight the important role of interactions between risk factors in suicidal behavior among persons with depression. Depression is one of the most commonly assessed risk factors for suicide,5,6 and our findings underscore the value of considering additional risk factors such as other psychiatric disorders, psychiatric medications, and social factors in combination with depression. The results of this dissertation may help inform potential risk identification strategies which may facilitate the targeting of suicide prevention interventions to those most vulnerable.