One of the hot topics currently in manufacturing domain is direct digital manufacturing. With introduction of cheap three-dimensional printers, the direct digital manufacturing seems to become a new ...manufacturing paradigm with an entirely different impact on society; nevertheless how this will impact the society and the differences between the paradigms are unclear. According to this background, this paper presents a comprehensive analysis of direct digital manufacturing from different perspectives in comparison to various traditional manufacturing paradigms. Authors are using a societal viewpoint to see, describe and analyse the subject instead of traditional manufacturing viewpoint. For the better understanding of direct digital manufacturing origins, a classification and historical background about available techniques are described. Furthermore, direct digital manufacturing as a paradigm is analysed and compared with craft production, mass production and mass customisation. Direct digital manufacturing's sustainability aspects related to social, economical and environmental dimensions are gathered and analysed for a better insight of this technique. A detailed case study demonstrates the energy use differences of direct digital manufacturing and mass production in depth. According to the present work, direct digital manufacturing has the possibility of combining the advantages of the other production paradigms and can have a positive impact on sustainable development; yet, there are several challenges to overcome both in technical and sociality aspects. A challenge within the social aspects can be the life style changes which can impact the job market, working environment, waste management and more.
•An overview of direct digital manufacturing is presented from different perspectives.•Direct digital manufacturing paradigm as an evolution of traditional manufacturing.•Direct digital manufacturing discussed and analysed under sustainability prism.•Energy consumption comparison for manufacturing using SLS and injection moulding.
To facilitate the transition towards sustainable manufacturing, current practices and mechanisms for value creation need to be reconsidered along the whole product lifecycle. However, academic ...research on sustainability is still bound to narrow fields of applications. In this study, a multi-disciplinary research project is presented that focuses on the development of a sustainable pedal electric cycle (Pedelec) from a first idea to a ready-to-use prototype. The results of the project show how different scientific approaches for bottom-up improvement can be applied together in a concrete case. A holistic view on the product lifecycle proved as a meaningful framework for that purpose.
Sustainable manufacturing considers the economic, environmental and social dimensions as equally important. For any product, like the common bicycles, a holistic view on the different life cycle ...phases has to be taken in order to ensure that resources are utilised adequately. Preferences on the three dimensions might lead to different selections of materials, used equipment or required education for fulfilling the considered objectives.
In a first approach, bicycle manufacturing alternatives are identified and modelled via bi-criteria mixed integer programming. The material usage is used to represent the economic dimension and the carbon dioxide equivalent is used to represent the environmental dimension. The computed supported efficient solutions provide reasonable trade-off solutions for the considered bicycle manufacturing problem.
Summary
Prediction models are often built and evaluated using data from a population that differs from the target population where model-derived predictions are intended to be used in. In this ...article, we present methods for evaluating model performance in the target population when some observations are right censored. The methods assume that outcome and covariate data are available from a source population used for model development and covariates, but no outcome data, are available from the target population. We evaluate the finite sample performance of the proposed estimators using simulations and apply the methods to transport a prediction model built using data from a lung cancer screening trial to a nationally representative population of participants eligible for lung cancer screening.
Abstract
We considered methods for transporting a prediction model for use in a new target population, both when outcome and covariate data for model development are available from a source ...population that has a different covariate distribution compared with the target population and when covariate data (but not outcome data) are available from the target population. We discuss how to tailor the prediction model to account for differences in the data distribution between the source population and the target population. We also discuss how to assess the model’s performance (e.g., by estimating the mean squared prediction error) in the target population. We provide identifiability results for measures of model performance in the target population for a potentially misspecified prediction model under a sampling design where the source and the target population samples are obtained separately. We introduce the concept of prediction error modifiers that can be used to reason about tailoring measures of model performance to the target population. We illustrate the methods in simulated data and apply them to transport a prediction model for lung cancer diagnosis from the National Lung Screening Trial to the nationally representative target population of trial-eligible individuals in the National Health and Nutrition Examination Survey.
When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from ...the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time‐fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial‐eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.
We propose methods for estimating the area under the receiver operating characteristic (ROC) curve (AUC) of a prediction model in a target population that differs from the source population that ...provided the data used for original model development. If covariates that are associated with model performance, as measured by the AUC, have a different distribution in the source and target populations, then AUC estimators that only use data from the source population will not reflect model performance in the target population. Here, we provide identification results for the AUC in the target population when outcome and covariate data are available from the sample of the source population, but only covariate data are available from the sample of the target population. In this setting, we propose three estimators for the AUC in the target population and show that they are consistent and asymptotically normal. We evaluate the finite-sample performance of the estimators using simulations and use them to estimate the AUC in a nationally representative target population from the National Health and Nutrition Examination Survey for a lung cancer risk prediction model developed using source population data from the National Lung Screening Trial.
We introduce causal interaction tree (CIT) algorithms for finding subgroups of individuals with heterogeneous treatment effects in observational data. The CIT algorithms are extensions of the ...classification and regression tree algorithm that use splitting criteria based on subgroup‐specific treatment effect estimators appropriate for observational data. We describe inverse probability weighting, g‐formula, and doubly robust estimators of subgroup‐specific treatment effects, derive their asymptotic properties, and use them to construct splitting criteria for the CIT algorithms. We study the performance of the algorithms in simulations and implement them to analyze data from an observational study that evaluated the effectiveness of right heart catheterization for critically ill patients.
Deep learning for survival outcomes Steingrimsson, Jon Arni; Morrison, Samantha
Statistics in medicine,
30 July 2020, Letnik:
39, Številka:
17
Journal Article
Recenzirano
Odprti dostop
Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard ...deep learning algorithms cannot be directly applied. We develop a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the absence of censoring is replaced by a censoring unbiased transformation. The resulting class of algorithms can be used to estimate both survival probabilities and restricted mean survival. We show how the deep learning algorithms can be implemented by adapting software for uncensored data by using a form of response transformation. We provide comparisons of the proposed deep learning algorithms to existing risk prediction algorithms for predicting survival probabilities and restricted mean survival through both simulated datasets and analysis of data from breast cancer patients.
Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often heterogeneity among trials in sample characteristics, ...treatment implementation, study design, and assessment of covariates. Stitching together this patchwork of evidence requires subject-matter knowledge, a clearly defined target population, and guidance on how to weigh evidence from different trials. Transportability analysis has provided formal identifiability conditions required to make unbiased causal inference in the target population. In this manuscript, we review these conditions along with an additional assumption required to address systematic missing data. The identifiability conditions highlight the importance of accounting for differences in treatment effect modifiers between the populations underlying the trials and the target population. We perform simulations to evaluate the bias of conventional random effect models and multiply imputed estimates using the pooled trials sample and describe causal estimators that explicitly address trial-to-target differences in key covariates in the context of systematic missing data. Results indicate that the causal transportability estimators are unbiased when treatment effect modifiers are accounted for in the analyses. Results also highlight the importance of carefully evaluating identifiability conditions for each trial to reduce bias due to differences in participant characteristics between trials and the target population. Bias can be limited by adjusting for covariates that are strongly correlated with missing treatment effect modifiers, including data from trials that do not differ from the target on treatment modifiers, and removing trials that do differ from the target and did not assess a modifier.
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