Essays on missing data problems: MSL estimation in the analysis of censored data and doubly robust estimation in the analysis of treatment effects

In Chapter 2, we consider estimation of dynamic models of recurrent events (event histories) in continuous time using censored data. We develop maximum simulated likelihood estimators where missing data are integrated out using Monte Carlo and importance sampling methods. We allow for random effects and integrate out the unobserved heterogeneity using a quadrature rule. In Monte Carlo experiments, we find that maximum simulated likelihood estimation is practically feasible and performs better than both listwise deletion and auxiliary modelling of initial conditions. In an empirical application, we study ischaemic heart disease events for male Maoris in New Zealand. Chapter 3 describes how the risk of experiencing heart attacks varies across gender and ethnicity in New Zealand. We analyse administrative data and estimate dynamic hazard models using maximum simulated likelihood methods to deal with left-censoring. The models allow risk to vary with age, previous heart attack history, and unobserved individual heterogeneity. We find that the risk of subsequent events is far higher than the risk of the first event, and particularly high within 1 year after an event. In most cases, male Maoris have the highest risk, followed by female Maoris, then male Europeans, while female Europeans have the lowest risk. Differently from the well-known propensity score (PS), the lesser known `prognostic score (PGS)' balances the potential untreated response. Chapter 4 shows that `double robustness' can be achieved by controlling both PS and PGS in various ways in a method-blind manner. In Chapter 5, we compares various treatment effect estimators through an extensive simulation study using 64 designs and two empirical examples mimicking experiments. In total, we examine 24 estimators based on matching, weighting, double robustness, regression imputation/adjustment, `complete pairing', and `propensity-score residual'. Our results show that, contrary to the common perception, doubly robust estimators are not necessarily the best. In fact, our findings recommend a couple of non-doubly-robust estimators, with a simple propensity-score-residual-based estimator being the nearly dominant best estimator

Difference in Differences and Ratio in Ratios for Limited Dependent Variables

Applying Difference in Differences (DD) to a limited dependent variable (LDV) Y has been problematic, which this paper addresses for binary, count, categorical, censored and fractional responses. The DD effect on a latent Y* can be found using a qualification dummy Q, a time dummy S and the treatment QS in the Y* model, which, however, does not satisfy the critical 'parallel trend' assumption for Y. We show that the assumption holds in different forms for LDV Y: 'ratio in ratios' or 'ratio in odds ratios'. Our simulation and empirical studies show that Poisson Quasi-MLE for non-negative Y and (multinomial) logit for binary, fractional and categorical Y work fine

Read-only Prompt Optimization for Vision-Language Few-shot Learning

In recent years, prompt tuning has proven effective in adapting pre-trained vision-language models to downstream tasks. These methods aim to adapt the pre-trained models by introducing learnable prompts while keeping pre-trained weights frozen. However, learnable prompts can affect the internal representation within the self-attention module, which may negatively impact performance variance and generalization, especially in data-deficient settings. To address these issues, we propose a novel approach, Read-only Prompt Optimization (RPO). RPO leverages masked attention to prevent the internal representation shift in the pre-trained model. Further, to facilitate the optimization of RPO, the read-only prompts are initialized based on special tokens of the pre-trained model. Our extensive experiments demonstrate that RPO outperforms CLIP and CoCoOp in base-to-new generalization and domain generalization while displaying better robustness. Also, the proposed method achieves better generalization on extremely data-deficient settings, while improving parameter efficiency and computational overhead. Code is available at https://github.com/mlvlab/RPO.Comment: Accepted at ICCV202

Self-positioning Point-based Transformer for Point Cloud Understanding

Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positioning point-based Transformer (SPoTr), which is designed to capture both local and global shape contexts with reduced complexity. Specifically, this architecture consists of local self-attention and self-positioning point-based global cross-attention. The self-positioning points, adaptively located based on the input shape, consider both spatial and semantic information with disentangled attention to improve expressive power. With the self-positioning points, we propose a novel global cross-attention mechanism for point clouds, which improves the scalability of global self-attention by allowing the attention module to compute attention weights with only a small set of self-positioning points. Experiments show the effectiveness of SPoTr on three point cloud tasks such as shape classification, part segmentation, and scene segmentation. In particular, our proposed model achieves an accuracy gain of 2.6% over the previous best models on shape classification with ScanObjectNN. We also provide qualitative analyses to demonstrate the interpretability of self-positioning points. The code of SPoTr is available at https://github.com/mlvlab/SPoTr.Comment: Accepted paper at CVPR 202

Radiofrequency ablation of very-early-stage hepatocellular carcinoma inconspicuous on fusion imaging with B-mode US: value of fusion imaging with contrast-enhanced US

Background/AimsTo determine the value of fusion imaging with contrast-enhanced ultrasonography (CEUS) and computed tomography (CT)/magnetic resonance (MR) images for percutaneous radiofrequency ablation (RFA) of very-early-stage hepatocellular carcinomas (HCCs) that are inconspicuous on fusion imaging with B-mode ultrasound (US) and CT/MR images.MethodsThis retrospective study was approved by our institutional review board and the requirement for informed consent was waived. Fusion imaging with CEUS using Sonazoid contrast agent and CT/MR imaging was performed on HCCs (<2 cm) that were inconspicuous on fusion imaging with B-mode US. We evaluated the number of cases that became conspicuous on fusion imaging with CEUS. Percutaneous RFA was performed under the guidance of fusion imaging with CEUS. Technical success and major complication rates were assessed.ResultsIn total, 30 patients with 30 HCCs (mean, 1.2 cm; range, 0.6-1.7 cm) were included, among which 25 (83.3%) became conspicuous on fusion imaging with CEUS at the time of the planning US and/or RFA procedure. Of those 25 HCCs, RFA was considered feasible for 23 (92.0%), which were thus treated. The technical success and major complication rates were 91.3% (21/23) and 4.3% (1/23), respectively.ConclusionsFusion imaging with CEUS and CT/MR imaging is highly effective for percutaneous RFA of very-early-stage HCCs inconspicuous on fusion imaging with B-mode US and CT/MR imaging

Direct Reprogramming of Rat Neural Precursor Cells and Fibroblasts into Pluripotent Stem Cells

Background: Given the usefulness of rats as an experimental system, an efficient method for generating rat induced pluripotent stem (iPS) cells would provide researchers with a powerful tool for studying human physiology and disease. Here, we report direct reprogramming of rat neural precursor (NP) cells and rat embryonic fibroblasts (REF) into iPS cells by retroviral transduction using either three (Oct3/4, Sox2, and Klf4), four (Oct3/4, Sox2, Klf4, and c-Myc), or five (Oct3/4, Sox2, Klf4, c-Myc, and Nanog) genes. Methodology and Principal Findings: iPS cells were generated from both NP and REF using only three (Oct3/4, Sox2, and Klf4) genes without c-Myc. Two factors were found to be critical for efficient derivation and maintenance of rat iPS cells: the use of rat instead of mouse feeders, and the use of small molecules specifically inhibiting mitogen-activated protein kinase and glycogen synthase kinase 3 pathways. In contrast, introduction of embryonic stem cell (ESC) extracts induced partial reprogramming, but failed to generate iPS cells. However, when combined with retroviral transduction, this method generated iPS cells with significantly higher efficiency. Morphology, gene expression, and epigenetic status confirmed that these rat iPS cells exhibited ESC-like properties, including the ability to differentiate into all three germ layers both in vitro and in teratomas. In particular, we found that these rat iPS cells could differentiate to midbrain-like dopamine neurons with a high efficiency. Conclusions/Significance: Given the usefulness of rats as an experimental system, our optimized method would be useful for generating rat iPS cells from diverse tissues and provide researchers with a powerful tool for studying human physiology and disease

Review and comparison of treatment effect estimators using propensity and prognostic scores

In finding effects of a binary treatment, practitioners use mostly either propensity score matching (PSM) or inverse probability weighting (IPW). However, many new treatment effect estimators are available now using propensity score and prognostic score , and some of these estimators are much better than PSM and IPW in several aspects. In this paper, we review those recent treatment effect estimators to show how they are related to one another, and why they are better than PSM and IPW. We compare 26 estimators in total through extensive simulation and empirical studies. Based on these, we recommend recent treatment effect estimators using overlap weight , and targeted MLE using statistical/machine learning, as well as a simple regression imputation/adjustment estimator using linear prognostic score models

Multidisciplinary Understanding of the Urban Heating Problem and Mitigation: A Conceptual Framework for Urban Planning

With the global acceleration of urbanization, temperatures in cities are rising continuously with global climate change, creating an imminent risk of urban heat islands and urban heating. Although much research has attempted to analyze urban heating from various perspectives, a comprehensive approach to urban planning that addresses the problem is just beginning. This study suggests a conceptual framework for multidisciplinary understanding of urban heating by reviewing 147 selected articles from various fields, published between 2007 and 2021, that discuss urban heating mitigation. From these, we identified several outdoor and indoor temperature-reduction factors and proposed area-based, zoning-based, and point-based approaches to mitigate urban heating

Factors Affecting Crash Involvement of Commercial Vehicle Drivers: Evaluation of Commercial Vehicle Drivers’ Characteristics in South Korea

The aim of this study was to evaluate the effects of driver-related factors on crash involvement of four different types of commercial vehicles—express buses, local buses, taxis, and trucks—and to compare outcomes across types. Previous studies on commercial vehicle crashes have generally been focused on a single type of commercial vehicle; however, the characteristics of drivers as factors affecting crashes vary widely across types of commercial vehicles as well as across study sites. This underscores the need for comparative analysis between different types of commercial vehicles that operate in similar environments. Toward these ends, we analyzed 627,594 commercial vehicle driver records in South Korea using a mixed logit model able to address unobserved heterogeneity in crash-related data. The estimated outcomes showed that driver-related factors have common effects on crash involvement: greater experience had a positive effect (diminished driver crash involvement), while traffic violations, job change, and previous crash involvement had negative effects. However, the magnitude of the effects and heterogeneity varied across different types of commercial vehicles. The findings support the contention that the safety management policy of commercial drivers needs to be set differently according to the vehicle type. Furthermore, the variables in this study can be used as promising predictors to quantify potential crash involvement of commercial vehicles. Using these variables, it is possible to proactively identify groups of accident-prone commercial vehicle drivers and to implement effective measures to reduce their involvement in crashes