Confidence intervals of prediction accuracy measures for multivariable prediction models based on the bootstrap-based optimism correction methods

In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical prediction models, the point estimates of the prediction accuracy measures are corrected by adequate bootstrap-based correction methods, but their confidence intervals are not corrected, e.g., the DeLong's confidence interval is usually used for assessing the C-statistic. These naive methods do not adjust for the optimism bias and do not account for statistical variability in the estimation of parameters in the prediction models. Therefore, their coverage probabilities of the true value of the prediction accuracy measure can be seriously below the nominal level (e.g., 95%). In this article, we provide two generic bootstrap methods, namely (1) location-shifted bootstrap confidence intervals and (2) two-stage bootstrap confidence intervals, that can be generally applied to the bootstrap-based optimism correction methods, i.e., the Harrell's bias correction, 0.632, and 0.632+ methods. In addition, they can be widely applied to various methods for prediction model development involving modern shrinkage methods such as the ridge and lasso regressions. Through numerical evaluations by simulations, the proposed confidence intervals showed favourable coverage performances. Besides, the current standard practices based on the optimism-uncorrected methods showed serious undercoverage properties. To avoid erroneous results, the optimism-uncorrected confidence intervals should not be used in practice, and the adjusted methods are recommended instead. We also developed the R package predboot for implementing these methods (https://github.com/nomahi/predboot). The effectiveness of the proposed methods are illustrated via applications to the GUSTO-I clinical trial

Causal Diagrams: Pitfalls and Tips

Graphical models are useful tools in causal inference, and causal directed acyclic graphs (DAGs) are used extensively to determine the variables for which it is sufficient to control for confounding to estimate causal effects. We discuss the following ten pitfalls and tips that are easily overlooked when using DAGs: 1) Each node on DAGs corresponds to a random variable and not its realized values; 2) The presence or absence of arrows in DAGs corresponds to the presence or absence of individual causal effect in the population; 3) "Non-manipulable" variables and their arrows should be drawn with care; 4) It is preferable to draw DAGs for the total population, rather than for the exposed or unexposed groups; 5) DAGs are primarily useful to examine the presence of confounding in distribution in the notion of confounding in expectation; 6) Although DAGs provide qualitative differences of causal structures, they cannot describe details of how to adjust for confounding; 7) DAGs can be used to illustrate the consequences of matching and the appropriate handling of matched variables in cohort and case-control studies; 8) When explicitly accounting for temporal order in DAGs, it is necessary to use separate nodes for each timing; 9) In certain cases, DAGs with signed edges can be used in drawing conclusions about the direction of bias; and 10) DAGs can be (and should be) used to describe not only confounding bias but also other forms of bias. We also discuss recent developments of graphical models and their future directions

Bias amplification in the g-computation algorithm for time-varying treatments: a case study of industry payments and prescription of opioid products

BACKGROUND: It is often challenging to determine which variables need to be included in the g-computation algorithm under the time-varying setting. Conditioning on instrumental variables (IVs) is known to introduce greater bias when there is unmeasured confounding in the point-treatment settings, and this is also true for near-IVs which are weakly associated with the outcome not through the treatment. However, it is unknown whether adjusting for (near-)IVs amplifies bias in the g-computation algorithm estimators for time-varying treatments compared to the estimators ignoring such variables. We thus aimed to compare the magnitude of bias by adjusting for (near-)IVs across their different relationships with treatments in the time-varying settings. METHODS: After showing a case study of the association between the receipt of industry payments and physicians' opioid prescribing rate in the US, we demonstrated Monte Carlo simulation to investigate the extent to which the bias due to unmeasured confounders is amplified by adjusting for (near-)IV across several g-computation algorithms. RESULTS: In our simulation study, adjusting for a perfect IV of time-varying treatments in the g-computation algorithm increased bias due to unmeasured confounding, particularly when the IV had a strong relationship with the treatment. We also found the increase in bias even adjusting for near-IV when such variable had a very weak association with unmeasured confounders between the treatment and the outcome compared to its association with the time-varying treatments. Instead, this bias amplifying feature was not observed (i.e., bias due to unmeasured confounders decreased) by adjusting for near-IV when it had a stronger association with the unmeasured confounders (≥0.1 correlation coefficient in our multivariate normal setting). CONCLUSION: It would be recommended to avoid adjusting for perfect IV in the g-computation algorithm to obtain a less biased estimate of the time-varying treatment effect. On the other hand, it may be recommended to include near-IV in the algorithm unless their association with unmeasured confounders is very weak. These findings would help researchers to consider the magnitude of bias when adjusting for (near-)IVs and select variables in the g-computation algorithm for the time-varying setting when they are aware of the presence of unmeasured confounding

治療の不遵守を伴うランダム化試験における補助変数を用いた頑健かつ効率的な構造ネスト平均モデルの推定

学位の種別: 論文博士審査委員会委員 : （主査）東京大学特任教授 森 武俊, 東京大学教授 赤林 朗, 東京大学准教授 馬淵 昭彦, 東京大学准教授 今井 健, 東京大学講師 成瀬 昂University of Tokyo(東京大学

Wide dynamic range charge sensor operation by high-speed feedback control of radio-frequency reflectometry

Semiconductor quantum dots are useful for controlling and observing quantum states and can also be used as sensors for reading out quantum bits and exploring local electronic states in nanostructures. However, challenges remain for the sensor applications, such as the trade-off between sensitivity and dynamic range and the issue of instability due to external disturbances. In this study, we demonstrate proportional-integral-differential feedback control of the radio-frequency reflectometry in GaN nanodevices using a field-programmable gate array. This technique can maintain the operating point of the charge sensor with high sensitivity. The system also realizes a wide dynamic range and high sensor sensitivity through the monitoring of the feedback signal. This method has potential applications in exploring dynamics and instability of electronic and quantum states in nanostructures.Comment: 13 pages, 5 figure

The ability to induce heat shock transcription factor-regulated genes in response to lethal heat stress is associated with thermotolerance in tomato cultivars

Heat stress is a severe challenge for plant production, and the use of thermotolerant cultivars is critical to ensure stable production in high-temperature-prone environments. However, the selection of thermotolerant cultivars is difficult due to the complex nature of heat stress and the time and space needed for evaluation. In this study, we characterized genome-wide differences in gene expression between thermotolerant and thermosensitive tomato cultivars and examined the possibility of selecting gene expression markers to estimate thermotolerance among different tomato cultivars. We selected one thermotolerant and one thermosensitive cultivar based on physiological evaluations and compared heat-responsive gene expression in these cultivars under stepwise heat stress and acute heat shock conditions. Transcriptomic analyses reveled that two heat-inducible gene expression pathways, controlled by the heat shock element (HSE) and the evening element (EE), respectively, presented different responses depending on heat stress conditions. HSE-regulated gene expression was induced under both conditions, while EE-regulated gene expression was only induced under gradual heat stress conditions in both cultivars. Furthermore, HSE-regulated genes showed higher expression in the thermotolerant cultivar than the sensitive cultivar under acute heat shock conditions. Then, candidate expression biomarker genes were selected based on the transcriptome data, and the usefulness of these candidate genes was validated in five cultivars. This study shows that the thermotolerance of tomato is correlated with its ability to maintain the heat shock response (HSR) under acute severe heat shock conditions. Furthermore, it raises the possibility that the robustness of the HSR under severe heat stress can be used as an indicator to evaluate the thermotolerance of crop cultivars

Characterization of expressed sequence tags from a full-length enriched cDNA library of Cryptomeria japonica male strobili

<p>Abstract</p> <p>Background</p> <p><it>Cryptomeria japonica </it>D. Don is one of the most commercially important conifers in Japan. However, the allergic disease caused by its pollen is a severe public health problem in Japan. Since large-scale analysis of expressed sequence tags (ESTs) in the male strobili of <it>C. japonica </it>should help us to clarify the overall expression of genes during the process of pollen development, we constructed a full-length enriched cDNA library that was derived from male strobili at various developmental stages.</p> <p>Results</p> <p>We obtained 36,011 expressed sequence tags (ESTs) from either one or both ends of 19,437 clones derived from the cDNA library of <it>C. japonica </it>male strobili at various developmental stages. The 19,437 cDNA clones corresponded to 10,463 transcripts. Approximately 80% of the transcripts resembled ESTs from <it>Pinus </it>and <it>Picea</it>, while approximately 75% had homologs in <it>Arabidopsis</it>. An analysis of homologies between ESTs from <it>C. japonica </it>male strobili and known pollen allergens in the Allergome Database revealed that products of 180 transcripts exhibited significant homology. Approximately 2% of the transcripts appeared to encode transcription factors. We identified twelve genes for MADS-box proteins among these transcription factors. The twelve MADS-box genes were classified as <it>DEF/GLO/GGM13-, AG-, AGL6-, TM3- </it>and <it>TM8</it>-like MIKC^Cgenes and type I MADS-box genes.</p> <p>Conclusion</p> <p>Our full-length enriched cDNA library derived from <it>C. japonica </it>male strobili provides information on expression of genes during the development of male reproductive organs. We provided potential allergens in <it>C. japonica</it>. We also provided new information about transcription factors including MADS-box genes expressed in male strobili of <it>C. japonica</it>. Large-scale gene discovery using full-length cDNAs is a valuable tool for studies of gymnosperm species.</p

Noise robust automatic charge state recognition in quantum dots by machine learning and pre-processing, and visual explanations of the model with Grad-CAM

Charge state recognition in quantum dot devices is important in preparation of quantum bits for quantum information processing. Towards auto-tuning of larger-scale quantum devices, automatic charge state recognition by machine learning has been demonstrated. In this work, we propose a simpler method using machine learning and pre-processing. We demonstrate the operation of the charge state recognition and evaluated an accuracy high as 96%. We also analyze the explainability of the trained machine learning model by gradient-weighted class activation mapping (Grad-CAM) which identifies class-discriminative regions for the predictions. It exhibits that the model predicts the state based on the change transition lines, indicating human-like recognition is realized.Comment: 15 pages, 6 figure