Hyperspectral Satellites, Evolution, and Development History

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract: Hyperspectral imaging has been emerged as a new generation of technology for earth observation and space exploration since the beginning of this millennium and widely used in various disciplinary and applications. The fascinating detailed spectral information acquired by hyperspectral imagers often yields results not possible with multispectral or other types of satellites. This article provides an overview of spaceborne hyperspectral imagers, technology evolution, and development history. After introduction of hyperspectral satellites, it first describes the principle of hyperspectral imaging. It then reviews the history of hyperspectral technology development, starting from the first airborne hyperspectral imager AIS built in early 1980s, the first operational airborne hyperspectral imager AVIRIS developed since middle 1980s, the first commercial airborne hyperspectral instrument CASI fabricated since late 1980s, followed by the development of hyperspectral technology in Canada between 1980s and 1990s, and the NASA planned orbiting hyperspectral imagers in 1990s, until the first spaceborne hyperspectral imager Hyperion launched in 2000. This article summarizes the survey carried


Competing risks in epidemiology: possibilities and pitfalls

P. K. Andersen
R. B. Geskus
T. de Witte
H. Putter

Abstract: BACKGROUND: In studies of all-cause mortality, the fundamental epidemiological concepts of rate and risk are connected through a well-defined one-to-one relation. An important consequence of this relation is that regression models such as the proportional hazards model that are defined through the hazard (the rate) immediately dictate how the covariates relate to the survival function (the risk). METHODS: This introductory paper reviews the concepts of rate and risk and their one-to-one relation in all-cause mortality studies and introduces the analogous concepts of rate and risk in the context of competing risks, the cause-specific hazard and the cause-specific cumulative incidence function. RESULTS: The key feature of competing risks is that the one-to-one correspondence between cause-specific hazard and cumulative incidence, between rate and risk, is lost. This fact has two important implications. First, the naïve Kaplan-Meier that takes the competing events as censored observations, is biased. Secondly, the way in which covariates are associated with the cause-specific hazards may not coincide with the way these covariates are associated with the cumulative incidence. An exampl


Doubly robust difference-in-differences estimators


Abstract: Survival analysis in the presence of competing risks imposes additional challenges for clinical investigators in that hazard function (the rate) has no one-to-one link to the cumulative incidence function (CIF, the risk). CIF is of particular interest and can be estimated non-parametrically with the use cuminc() function. This function also allows for group comparison and visualization of estimated CIF. The effect of covariates on cause-specific hazard can be explored using conventional Cox proportional hazard model by treating competing events as censoring. However, the effect on hazard cannot be directly linked to the effect on CIF because there is no one-to-one correspondence between hazard and cumulative incidence. Fine-Gray model directly models the covariate effect on CIF and it reports subdistribution hazard ratio (SHR). However, SHR only provide information on the ordering of CIF curves at different levels of covariates, it has no practical interpretation as HR in the absence of competing risks. Fine-Gray model can be fit with crr() function shipped with the cmprsk package. Time-varying covariates are allowed in the crr() function, which is specified by cov2 and tf argument


Overestimation of Cardiovascular Mortality Risk by Kaplan-Meier in Competing Risks Settings: A Web-Based Calculator and NHANES Analysis

International Journal of Statistics in Medical Research

Abstract:
Background: Traditional Kaplan-Meier (KM) event rates are widely used for cardiovascular risk prediction and tend to overestimate absolute event risk for patients by censoring competing events, such as non-cardiovascular death. Competing risks analysis (CRA), which account for such terminal events, offers more accurate estimates. However, its application in a web-based health analytics remains limited.
Methods: Using a simulated cohort (n = 2,500; 100 repetitions) and the 1999–2000 NHANES cohort (n = 2,480) with 2019 National Death Index mortality linkage, the researcher compared KM estimates to CRA’s Cumulative Incidence Function (CIF), implemented via Aalen-Johansen estimators and Fine-Gray subdistribution hazard models. We assessed relative differences (bias) in 5-, 10-, 15-, and 20-year cardiovascular mortality predictions across risk strata. Findings informed a web-based calculator prototype that dynamically estimates age-specific KM and CIF probabilities while highlighting potential misclassification risks.
Results: KM consistently overestimated cardiovascular mortality risk compared to CIF. In the NHANES cohort, KM estimated the 5-year risk to be 5.85% higher than the actual rate (4.37% vs. 4.13%) and 20-year risk by 28.3% (20.02% vs. 15.60%). In the simulated data, KM overestimated the 5-year risk by 7.63% (5.84% vs. 5.42%) and the 20-year risk by 31.17% (21.37% vs. 16.25%). KM-based models tend to misclassify a substantial portion of patients into higher-risk groups compared to CIF-adjusted models.
Conclusion: This study demonstrates that Kaplan-Meier consistently overestimates cardiovascular mortality in comparison to competing risk methods across five time points, through using both simulated and nationally representative data. We quantify this overestimation and provide an online calculator that shows differences by age. Our tool improves the usability and interpretability of competing risks analysis for older adults in digital health settings, in contrast to tools like SCORE2.


Prognostic Models With Competing Risks

Marcel Wolbers
Michael Koller
Jacqueline C.M. Witteman
Ewout W. Steyerberg

Abstract: Clinical decision-making often relies on a subject's absolute risk of a disease event of interest. However, in a frail population, competing risk events may preclude the occurrence of the event of interest. We review competing-risk regression models with a view toward predictive modeling. We show how measures of prognostic performance (such as calibration and discrimination) can be adapted to the competing-risks setting. An example of coronary heart disease (CHD) prediction in women aged 55-90 years in the Rotterdam study is used to illustrate the proposed methods, and to compare the Fine and Gray regression model to 2 alternative approaches: (1) a standard Cox survival model, which ignores the competing risk of non-CHD death, and (2) a cause-specific hazards model, which combines proportional hazards models for the event of interest and the competing event. The Fine and Gray model and the cause-specific hazards model perform similarly. However, the standard Cox model substantially overestimates 10-year risk of CHD; it classifies 18% of the individuals as high risk (>20%), compared with only 8% according to the Fine and Gray model. We conclude that competing risks have to be consid


Analysis of Competing Risks Data with Covariates Subject to Detection Limits

Yilin Wu
Rui Huang
Huixia Judy Wang
Liming Xiang

Journal of Computational And Graphical Statistics

Abstract: Abstract Competing risks data are commonly encountered in biomedical studies when subjects may experience multiple types of events and the occurrence of the primary event of interest can be precluded by a competing event. Challenges arise for regression analysis of such data with covariates subject to censoring due to detection limits. We propose a novel multiple imputation method for inference under Fine-Gray’s subdistribution hazard model with censored covariates subject to detection limits. Our proposal uses the information from the fully observed covariate values and the failure outcomes to impute the censored covariates iteratively using rejection sampling, which makes the imputation model compatible to the substantive model and the estimation efficiency improve significantly. We show the consistency and asymptotic normality of the resulting estimator and demonstrate its promising finite sample performance through simulation studies. Moreover, we extend this new proposal to assess the impacts of censored covariates on the predictive performance of the competing risks model. To illustrate its practical utility, we provide an application to the data from a study of community acquired pneumonia. Supplementary materials for this article are available online.


A proportional hazards regression model for the subdistribution with right‐censored and left‐truncated competing risks data

Abstract: With competing risks failure time data, one often needs to assess the covariate effects on the cumulative incidence probabilities. Fine and Gray proposed a proportional hazards regression model to directly model the subdistribution of a competing risk. They developed the estimating procedure for right-censored competing risks data, based on the inverse probability of censoring weighting. Right-censored and left-truncated competing risks data sometimes occur in biomedical researches. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with right-censored and left-truncated data. We adopt a new weighting technique to estimate the parameters in this model. We have derived the large sample properties of the proposed estimators. To illustrate the application of the new method, we analyze the failure time data for children with acute leukemia. In this example, the failure times for children who had bone marrow transplants were left truncated.


Rethinking construction cost overruns: cognition, learning and estimation

Journal of Financial Management of Property and Construction

Abstract: Purpose – Drawing on mainstream arguments in the literature, the paper presents a coherent and holistic view on the causes of cost overruns, and the dynamics between cognitive dispositions, learning and estimation. A cost prediction model has also been developed using data mining for estimating final cost of projects. The paper aims to discuss these issues. Design/methodology/approach – A mixed-method approach was adopted: a qualitative exploration of the causes of cost overrun followed by an empirical development of a final cost model using artificial neural networks. Findings – A conceptual model to distinguish between the often conflated causes of underestimation and cost overruns on large publicly funded projects. The empirical model developed in this paper achieved an average absolute percentage error of 3.67 percent with 87 percent of the model predictions within a range of ±5 percent of the actual final cost. Practical implications – The model developed can be converted to a desktop package for quick cost predictions and the generation of various alternative solutions for a construction project in a sort of what-if analysis for the purposes of comparison. The use of the mode


The chained difference-in-differences

Christophe Bell'ego
David Benatia
Vincent Dortet-Bernardet

Abstract: This paper studies the identification, estimation, and inference of long-term (binary) treatment effect parameters when balanced panel data is not available, or consists of only a subset of the available data. We develop a new estimator: the chained difference-in-differences, which leverages the overlapping structure of many unbalanced panel data sets. This approach consists in aggregating a collection of short-term treatment effects estimated on multiple incomplete panels. Our estimator accommodates (1) multiple time periods, (2) variation in treatment timing, (3) treatment effect heterogeneity, (4) general missing data patterns, and (5) sample selection on observables. We establish the asymptotic properties of the proposed estimator and discuss identification and efficiency gains in comparison to existing methods. Finally, we illustrate its relevance through (i) numerical simulations, and (ii) an application about the effects of an innovation policy in France.


Practical recommendations for reporting Fine-Gray model analyses for competing risk data

Abstract: In survival analysis, a competing risk is an event whose occurrence precludes the occurrence of the primary event of interest. Outcomes in medical research are frequently subject to competing risks. In survival analysis, there are 2 key questions that can be addressed using competing risk regression models: first, which covariates affect the rate at which events occur, and second, which covariates affect the probability of an event occurring over time. The cause‐specific hazard model estimates the effect of covariates on the rate at which events occur in subjects who are currently event‐free. Subdistribution hazard ratios obtained from the Fine‐Gray model describe the relative effect of covariates on the subdistribution hazard function. Hence, the covariates in this model can also be interpreted as having an effect on the cumulative incidence function or on the probability of events occurring over time. We conducted a review of the use and interpretation of the Fine‐Gray subdistribution hazard model in articles published in the medical literature in 2015. We found that many authors provided an unclear or incorrect interpretation of the regression coefficients associated with this m


Cumulative Incidence Estimation in the Presence of Competing Risks

Abstract: When competing risks are present, the appropriate estimate of the failure probabilities is the cumulative incidence. stcompet creates new variables containing the estimate of this function, its standard error, and ln(– ln) transformed confidence bounds. Two examples are presented to illustrate the use of the new command and some key features of the cumulative incidence.


Cause-Specific Cumulative Incidence Estimation and the Fine and Gray Model Under Both Left Truncation and Right Censoring

Abstract: Summary The standard estimator for the cause-specific cumulative incidence function in a competing risks setting with left truncated and/or right censored data can be written in two alternative forms. One is a weighted empirical cumulative distribution function and the other a product-limit estimator. This equivalence suggests an alternative view of the analysis of time-to-event data with left truncation and right censoring: individuals who are still at risk or experienced an earlier competing event receive weights from the censoring and truncation mechanisms. As a consequence, inference on the cumulative scale can be performed using weighted versions of standard procedures. This holds for estimation of the cause-specific cumulative incidence function as well as for estimation of the regression parameters in the Fine and Gray proportional subdistribution hazards model. We show that, with the appropriate filtration, a martingale property holds that allows deriving asymptotic results for the proportional subdistribution hazards model in the same way as for the standard Cox proportional hazards model. Estimation of the cause-specific cumulative incidence function and regression on the


Heritage and Advanced Technology Systems Engineering Lessons Learned from NASA Deep Space Missions

Bryan Barley
Allen S. Bacskay
Marilyn Newhouse

Abstract: In the design and development of complex spacecraft missions, project teams frequently assume the use of advanced technology systems or heritage systems to enable a mission or reduce the overall mission risk and cost. As projects proceed through the development life cycle, increasingly detailed knowledge of the advanced and heritage systems within the spacecraft and mission environment identifies unanticipated technical issues. Resolving these issues often results in cost overruns and schedule impacts. The National Aeronautics and Space Administration (NASA) Discovery & New Frontiers (D&NF) Program Office at Marshall Space Flight Center (MSFC) recently studied cost overruns and schedule delays for 5 missions. The goal was to identify the underlying causes for the overruns and delays, and to develop practical mitigations to assist the D&NF projects in identifying potential risks and controlling the associated impacts to proposed mission costs and schedules. The study found that optimistic hardware/software inheritance and technology readiness assumptions caused cost and schedule growth for four of the five missions studied. The cost and schedule growth was not found to r


Estimating sample size in the presence of competing risks – Cause-specific hazard or cumulative incidence approach?

Abstract:
<jats:p>
                    In designing randomised clinical trials involving competing risks endpoints, it is important to consider competing events to ensure appropriate determination of sample size. We conduct a simulation study to compare sample sizes obtained from the cause-specific hazard and cumulative incidence (CMI) approaches, by first assuming exponential event times. As the proportional subdistribution hazard assumption does not hold for the CMI exponential (CMI
                    <jats:sub>Exponential</jats:sub>
                    ) model, we further investigate the impact of violation of such an assumption by comparing the results obtained from the CMI exponential model with those of a CMI model assuming a Gompertz distribution (CMI
                    <jats:sub>Gompertz</jats:sub>
                    ) where the proportional assumption is tenable. The simulation suggests that the CMI
                    <jats:sub>Exponential</jats:sub>
                    approach requires a considerably larger sample size when treatment reduces the hazards of both the main event, A, and the competing risk, B. When treatment has a beneficial effect on A but no effect on B, the sample sizes required by both methods are largely similar, especially for large reduction in the main risk. If treatment has a protective effect on A but adversely affects B, then the sample size required by CMI
                    <jats:sub>Exponential</jats:sub>
                    is notably smaller than cause-specific hazard for small to moderate reduction in the main risk. Further, a smaller sample size is required for CMI
                    <jats:sub>Gompertz</jats:sub>
                    as compared with CMI
                    <jats:sub>Exponential</jats:sub>
                    . The choice between a cause-specific hazard or CMI model in competing risks outcomes has implications on the study design. This should be made on the basis of the clinical question of interest and the validity of the associated model assumption.
                  </jats:p>


Optimism Bias Evaluation and Decision-Making Risk Forecast on Bridge Project Cost Based on Reference Class Forecasting: Evidence from China

Huimin Liu
Canhui Jiang
Yan Liu
Marcel Hertogh
Xue Lyu

Abstract: <jats:p>The high uncertainty of megaproject results in increasing complexity in the decision-making and ultimately leads to different degrees of cost overrun and project delays. One of the critical reasons for cost overrun and delay is the optimism bias of decision makers. Although the previous literature has analyzed the cost overrun distribution of bridges, roads and other infrastructure projects, there is still a lack of research on how to make more reasonable decisions according to the cost overrun risk and cost-benefit theory by considering the expectation of cost overrun. Therefore, this paper firstly measures optimism bias by conducting the field research and interviews regarding over 30 long bridges in China. On the basis of the optimism bias measure, a decision-making risk model of bridge projects with the expectation of cost overrun has been built. Then the paper takes Hangzhou Bay Bridge as an example to discuss the influence of cost overrun predication, implicit benefits and the project’s operation time on NPV results. Moreover, the probability of project unbuildability risk under different degrees of cost optimism bias has also been discussed. Finally, suggestions for risk forecast are provided for decision-makers to make more objective and comprehensive judgments.</jats:p>


Time-dependent covariates in the proportional subdistribution hazards model for competing risks

Abstract: Separate Cox analyses of all cause-specific hazards are the standard technique of choice to study the effect of a covariate in competing risks, but a synopsis of these results in terms of cumulative event probabilities is challenging. This difficulty has led to the development of the proportional subdistribution hazards model. If the covariate is known at baseline, the model allows for a summarizing assessment in terms of the cumulative incidence function. black Mathematically, the model also allows for including random time-dependent covariates, but practical implementation has remained unclear due to a certain risk set peculiarity. We use the intimate relationship of discrete covariates and multistate models to naturally treat time-dependent covariates within the subdistribution hazards framework. The methodology then straightforwardly translates to real-valued time-dependent covariates. As with classical survival analysis, including time-dependent covariates does not result in a model for probability functions anymore. Nevertheless, the proposed methodology provides a useful synthesis of separate cause-specific hazards analyses. We illustrate this with hospital infection data, w


The number of primary events per variable affects estimation of the subdistribution hazard competing risks model.

Abstract:
OBJECTIVES
To examine the effect of the number of events per variable (EPV) on the accuracy of estimated regression coefficients, standard errors, empirical coverage rates of estimated confidence intervals, and empirical estimates of statistical power when using the Fine-Gray subdistribution hazard regression model to assess the effect of covariates on the incidence of events that occur over time in the presence of competing risks.

STUDY DESIGN AND SETTING
Monte Carlo simulations were used. We considered two different definitions of the number of EPV. One included events of any type that occurred (both primary events and competing events), whereas the other included only the number of primary events that occurred.

RESULTS
The definition of EPV that included only the number of primary events was preferable to the alternative definition, as the number of competing events had minimal impact on estimation. In general, 40-50 EPV were necessary to ensure accurate estimation of regression coefficients and associated quantities. However, if all of the covariates are continuous or are binary with moderate prevalence, then 10 EPV are sufficient to ensure accurate estimation.

CONCLUSION
Analysts must base the number of EPV on the number of primary events that occurred.


stpm2cr: A flexible parametric competing risks model using a direct likelihood approach for the cause-specific cumulative incidence function

Abstract: In competing-risks analysis, the cause-specific cumulative incidence function (CIF) is usually obtained in a modeling framework by either 1) transforming on all cause-specific hazards or 2) transforming by using a direct relationship with the subdistribution hazard function. We expand on current competing-risks methodology from within the flexible parametric survival modeling framework and focus on the second approach. This approach models all cause-specific CIFs simultaneously and is more useful for answering prognostic-related questions. We propose the direct flexible parametric survival modeling approach for the cause-specific CIF. This approach models the (log cumulative) baseline hazard without requiring numerical integration, which leads to benefits in computational time. It is also easy to make out-of-sample predictions to estimate more useful measures and incorporate alternative link functions, for example, logit links. To implement these methods, we introduce a new estimation command, stpm2cr, and demonstrate useful predictions from the model through an illustrative melanoma dataset.


A review of the use of time‐varying covariates in the Fine‐Gray subdistribution hazard competing risk regression model

Peter C. Austin
Aurélien Latouche
Jason P. Fine

Abstract: In survival analysis, time-varying covariates are covariates whose value can change during follow-up. Outcomes in medical research are frequently subject to competing risks (events precluding the occurrence of the primary outcome). We review the types of time-varying covariates and highlight the effect of their inclusion in the subdistribution hazard model. External time-dependent covariates are external to the subject, can effect the failure process, but are not otherwise involved in the failure mechanism. Internal time-varying covariates are measured on the subject, can effect the failure process directly, and may also be impacted by the failure mechanism. In the absence of competing risks, a consequence of including internal time-dependent covariates in the Cox model is that one cannot estimate the survival function or the effect of covariates on the survival function. In the presence of competing risks, the inclusion of internal time-varying covariates in a subdistribution hazard model results in the loss of the ability to estimate the cumulative incidence function (CIF) or the effect of covariates on the CIF. Furthermore, the definition of the risk set for the subdistribution


Semiparametric regression on cumulative incidence function with interval-censored competing risks data

G. Bakoyannis
Menggang Yu
C. Yiannoutsos

Abstract: Many biomedical and clinical studies with time‐to‐event outcomes involve competing risks data. These data are frequently subject to interval censoring. This means that the failure time is not precisely observed but is only known to lie between two observation times such as clinical visits in a cohort study. Not taking into account the interval censoring may result in biased estimation of the cause‐specific cumulative incidence function, an important quantity in the competing risks framework, used for evaluating interventions in populations, for studying the prognosis of various diseases, and for prediction and implementation science purposes. In this work, we consider the class of semiparametric generalized odds rate transformation models in the context of sieve maximum likelihood estimation based on B‐splines. This large class of models includes both the proportional odds and the proportional subdistribution hazard models (i.e., the Fine–Gray model) as special cases. The estimator for the regression parameter is shown to be consistent, asymptotically normal and semiparametrically efficient. Simulation studies suggest that the method performs well even with small sample sizes. As an illustration, we use the proposed method to analyze data from HIV‐infected individuals obtained from a large cohort study in sub‐Saharan Africa. We also provide the R function ciregic that implements the proposed method and present an illustrative example. Copyright © 2017 John Wiley & Sons, Ltd.


FORECASTING AND FINANCIAL PREDICTABILITY IN COMPLEX INFRASTRUCTURE AND ENERGY PROJECTS

Abstract: Complex infrastructure and energy projects are highly exposed to cost overruns, delays, contractual changes, macroeconomic variations, and scope revisions. In this context, this article analyzes how financial forecasting can enhance economic predictability and improve executive decision-making in capital-intensive projects. The research is qualitative and theoretical-analytical in nature, developed through a narrative literature review. The analysis articulates contributions on cost overrun, optimism bias, strategic misrepresentation, reference class forecasting, project controls, and the finance function. The findings indicate that forecasting should not be treated as a simple spreadsheet update or reporting routine, but as a mechanism for continuously revising the assumptions that support the project’s economic trajectory. When integrated with risk analysis, estimate at completion, contingency management, and comparison with historical reference classes, financial forecasting strengthens the ability to anticipate deviations, revise scenarios, and support decisions regarding contracts, cash flow, schedule, margin, and economic viability. The article concludes that financial predictability in complex projects does not depend on eliminating uncertainty, but on the institutional capacity to recognize, measure, and translate it into decision-oriented information.


Competing risks regression for clustered data

Bo Zhou
Jason P. Fine
Aurélien Latouche
Myriam Labopin

Abstract: A population average regression model is proposed to assess the marginal effects of covariates on the cumulative incidence function when there is dependence across individuals within a cluster in the competing risks setting. This method extends the Fine-Gray proportional hazards model for the subdistribution to situations, where individuals within a cluster may be correlated due to unobserved shared factors. Estimators of the regression parameters in the marginal model are developed under an independence working assumption where the correlation across individuals within a cluster is completely unspecified. The estimators are consistent and asymptotically normal, and variance estimation may be achieved without specifying the form of the dependence across individuals. A simulation study evidences that the inferential procedures perform well with realistic sample sizes. The practical utility of the methods is illustrated with data from the European Bone Marrow Transplant Registry.


Mario A. Cleves
William Gould
Roberto G. Gutierrez
Yulia Marchenko

Abstract: An Introduction to Survival Analysis Using Stata, Third Edition is the ideal tutorial for professional data analysts who want to learn survival analysis for the first time or who are well versed in survival analysis but are not as dexterous in using Stata to analyze survival data. This text also serves as a valuable reference to those readers who already have experience using Stata’s survival analysis routines. The third edition has been updated for Stata 11, and it includes a new chapter on competing-risks analysis. This chapter describes the problems posed by competing events (events that impede the failure event of interest), and covers estimation of cause-specific hazards and cumulative incidence functions. Other enhancements include the handling of missing values by multiple imputation in Cox regression, a new-to-Stata-11 system for specifying categorical (factor) variables and their interactions, three additional diagnostic measures for Cox regression, and a more efficient syntax for obtaining predictions and diagnostics after Cox regression.


Calibration of cause-specific absolute risk for external validation using each cause-specific hazards model in the presence of competing events

S. I. Mozumder
Sarah Booth
Richard D Riley
M. Rutherford
Paul C Lambert

Abstract: When developing/validating prognostic models, it is typical to assess calibration between predicted and observed risks — either in the development dataset or in an external sample. For competing risks data, correct specification of more than one model may be required to ensure well-calibrated predicted risks for the event of interest. Furthermore, interest may be in the predicted risks of the event of interest, competing events and all-causes. Therefore, calibration must be assessed simultaneously using various measures. We focus on the calibration of prediction models for external validation using a cause-specific hazards approach. We propose that miscalibration for cause-specific hazard models be assessed using components specific to each model through the complement of the cause-specific survival alongside the assessment of the calibration of the cause-specific absolute risks. We simulated a range of scenarios to illustrate how to identify which model(s) are mis-specified in an external validation setting. Calibration plots and calibration statistics (calibration slope, calibration-in-the-large) are presented alongside performance measures such as the Brier score and Index of Prediction Accuracy. We use pseudo-observations to calculate observed risks and generate a smooth calibration curve with restricted cubic splines. We fitted flexible parametric survival models to the simulated data to flexibly estimate baseline cause-specific hazards for the prediction of individual cause-specific absolute risks. Our simulations illustrate that miscalibration due to changes in the baseline cause-specific hazards in external validation data is better identified using components from each cause-specific model. A mis-calibrated model on one cause could lead to poor calibration of the predicted absolute risks for each cause of interest, including the all-cause absolute risk. This is because prediction of a single cause-specific absolute risk is impacted by effects of variables on the cause of interest and competing events. If accurate predictions for both all-cause and each cause-specific absolute risks are of interest, this is best achieved by developing and validating models via the cause-specific hazards approach. For each cause-specific model, researchers should evaluate calibration plots separately using the complement of the cause-specific survival function to reveal the cause of any miscalibration. However, this also requires careful consideration of dependent censoring which must be sufficiently accounted for.


Artificial intelligence in construction project management: Trends, challenges and future directions

Journal of Design for Resilience in Architecture and Planning

Abstract: Contemporary construction projects are characterized by escalating complexity, voluminous data flows, and stringent sustainability requirements, rendering conventional project management methods increasingly inadequate. In response, artificial intelligence (AI) has emerged as a transformative enabler in construction project management, offering advanced capabilities in predictive analytics, process automation, and intelligent decision support. This paper explores the role of AI in the identified principal functions of construction project management, including time management, cost estimation, quality assurance, occupational health and safety, risk mitigation, resource optimization, and design management through a narrative literature review. Analysis demonstrates that AI-driven approaches significantly enhance operational efficiency and system resilience by enabling proactive identification of schedule delays, cost overruns, and safety hazards. For example, image-recognition systems integrated with Internet-of-Things sensors facilitate real-time monitoring of site conditions and adaptive response to disruptions, while neural-network models trained on historical project data yield more accurate cost forecasts than traditional estimation techniques. In the design management domain, generative design algorithms and AI-enhanced BIM integration have the potential to automate clash detection, optimize form and function, and generate innovative design alternatives that align with cost, energy, and sustainability objectives. Beyond efficiency gains, AI fosters a paradigm shift toward predictive, data-driven, and adaptive management practices that strengthen project resilience, enabling teams to anticipate, absorb, and recover from unforeseen challenges while improving project performance and sustainability. Critical barriers to widespread AI adoption are also identified in this study. Fragmented and non-standardized data ecosystems impede model training and interoperability with legacy systems, while organizational resistance and a shortage of professionals skilled in both AI and construction hinder implementation. Ethical and legal concerns—stemming from the “black-box” nature of many AI algorithms—further complicate accountability in safety-critical decisions. By synthesizing these challenges, the strategic role of AI is highlighted not only as a technological innovation but also as a catalyst for cultural and organizational transformation toward more resilient project delivery. Targeted future research directions include empirical validation of AI tools in live project environments, development of sector-specific AI frameworks tailored to the peculiarities of the construction industry, interdisciplinary collaboration among engineers, data scientists, and managers, and educational initiatives to upskill the workforce. Collectively, these steps will help bridge the gap between theoretical potential and real-world impact, positioning AI as a cornerstone of intelligent, resilient, sustainable, and high-performing construction project management.


Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): summary of findings and assessment of existing guidelines

K. Rufibach
J. Beyersmann
T. Friede
Claudia Schmoor
Regina Stegherr

Abstract: Background The SAVVY project aims to improve the analyses of adverse events (AEs) in clinical trials through the use of survival techniques appropriately dealing with varying follow-up times and competing events (CEs). This paper summarizes key features and conclusions from the various SAVVY papers. Methods Summarizing several papers reporting theoretical investigations using simulations and an empirical study including randomized clinical trials from several sponsor organizations, biases from ignoring varying follow-up times or CEs are investigated. The bias of commonly used estimators of the absolute (incidence proportion and one minus Kaplan-Meier) and relative (risk and hazard ratio) AE risk is quantified. Furthermore, we provide a cursory assessment of how pertinent guidelines for the analysis of safety data deal with the features of varying follow-up time and CEs. Results SAVVY finds that for both, avoiding bias and categorization of evidence with respect to treatment effect on AE risk into categories, the choice of the estimator is key and more important than features of the underlying data such as percentage of censoring, CEs, amount of follow-up, or value of the gold-standard. Conclusions The choice of the estimator of the cumulative AE probability and the definition of CEs are crucial. Whenever varying follow-up times and/or CEs are present in the assessment of AEs, SAVVY recommends using the Aalen-Johansen estimator (AJE) with an appropriate definition of CEs to quantify AE risk. There is an urgent need to improve pertinent clinical trial reporting guidelines for reporting AEs so that incidence proportions or one minus Kaplan-Meier estimators are finally replaced by the AJE with appropriate definition of CEs.


Semiparametric regression on cumulative incidence function with interval-censored competing risks data and missing event types

Jun Park
G. Bakoyannis
Ying Zhang
C. Yiannoutsos

Abstract: Summary Competing risk data are frequently interval-censored, that is, the exact event time is not observed but only known to lie between two examination time points such as clinic visits. In addition to interval censoring, another common complication is that the event type is missing for some study participants. In this article, we propose an augmented inverse probability weighted sieve maximum likelihood estimator for the analysis of interval-censored competing risk data in the presence of missing event types. The estimator imposes weaker than usual missing at random assumptions by allowing for the inclusion of auxiliary variables that are potentially associated with the probability of missingness. The proposed estimator is shown to be doubly robust, in the sense that it is consistent even if either the model for the probability of missingness or the model for the probability of the event type is misspecified. Extensive Monte Carlo simulation studies show good performance of the proposed method even under a large amount of missing event types. The method is illustrated using data from an HIV cohort study in sub-Saharan Africa, where a significant portion of events types is missing. The proposed method can be readily implemented using the new function ciregic_aipw in the R package intccr.


Rare Event Approximation Between Subdistribution Hazard Ratio and Cause-specific Hazard Ratio in Survival Analysis With Competing Risks

Abstract: Background Despite the fact that competing risks are inevitable in epidemiological and clinical studies, distinctions between the hazard ratio estimated by handling competing risks as censoring and the subditribution hazard ratio are often overlooked. Methods We derived quantitative relationships between subdistribution hazard ratio and cause-specific hazard ratio and derive an approximate calculation method to transform the two into each other. Numerical examinations of hypothetical six scenarios and published information of a randomized clinical trial of cholesterol-lowering therapy and a registry of acute myeloid leukemia were provided. Results General and approximate relationships under rare event assumptions between the two types of hazard ratio were given. The approximation formula is based on a survival ratio and has two possible applications. First, one can calculate a subdistribution hazard ratio from published information. Second, this formula allows sample size estimation that takes the presence of competing risks into account. Conclusion The distinction between the two types of hazard ratio can be addressed by focusing on two quantities. One is how the event of interest and competing risk is rare, and the other is the survival ratio.


Flexible parametric modelling of cause-specific hazards to estimate cumulative incidence functions

Abstract: BACKGROUND: Competing risks are a common occurrence in survival analysis. They arise when a patient is at risk of more than one mutually exclusive event, such as death from different causes, and the occurrence of one of these may prevent any other event from ever happening. METHODS: There are two main approaches to modelling competing risks: the first is to model the cause-specific hazards and transform these to the cumulative incidence function; the second is to model directly on a transformation of the cumulative incidence function. We focus on the first approach in this paper. This paper advocates the use of the flexible parametric survival model in this competing risk framework. RESULTS: An illustrative example on the survival of breast cancer patients has shown that the flexible parametric proportional hazards model has almost perfect agreement with the Cox proportional hazards model. However, the large epidemiological data set used here shows clear evidence of non-proportional hazards. The flexible parametric model is able to adequately account for these through the incorporation of time-dependent effects. CONCLUSION: A key advantage of using this approach is that smooth esti


Improved confidence intervals for a difference of two cause‐specific cumulative incidence functions estimated in the presence of competing risks and random censoring

Abstract: <jats:title>Abstract</jats:title><jats:p>A cause‐specific cumulative incidence function (CIF) is the probability of failure from a specific cause as a function of time. In randomized trials, a difference of cause‐specific CIFs (treatment minus control) represents a treatment effect. Cause‐specific CIF in each intervention arm can be estimated based on the usual non‐parametric Aalen–Johansen estimator which generalizes the Kaplan–Meier estimator of CIF in the presence of competing risks. Under random censoring, asymptotically valid Wald‐type confidence intervals (CIs) for a difference of cause‐specific CIFs at a specific time point can be constructed using one of the published variance estimators. Unfortunately, these intervals can suffer from substantial under‐coverage when the outcome of interest is a rare event, as may be the case for example in the analysis of uncommon adverse events. We propose two new approximate interval estimators for a difference of cause‐specific CIFs estimated in the presence of competing risks and random censoring. Theoretical analysis and simulations indicate that the new interval estimators are superior to the Wald CIs in the sense of avoiding substantial under‐coverage with rare events, while being equivalent to the Wald CIs asymptotically. In the absence of censoring, one of the two proposed interval estimators reduces to the well‐known Agresti–Caffo CI for a difference of two binomial parameters. The new methods can be easily implemented with any software package producing point and variance estimates for the Aalen–Johansen estimator, as illustrated in a real data example.</jats:p>


Analyses of cumulative incidence functions via non‐parametric multiple imputation

Abstract: <jats:title>Abstract</jats:title><jats:p>We describe a non‐parametric multiple imputation method that recovers the missing potential censoring information from competing risks failure times for the analysis of cumulative incidence functions. The method can be applied in the settings of stratified analyses, time‐varying covariates, weighted analysis of case‐cohort samples and clustered survival data analysis, where no current available methods can be readily implemented. The method uses a Kaplan–Meier imputation method for the censoring times to form an imputed data set, so cumulative incidence can be analyzed using techniques and software developed for ordinary right censored survival data. We discuss the methodology and show from both simulations and real data examples that the method yields valid estimates and performs well. The method can be easily implemented via available software with a minor programming requirement (for the imputation step). It provides a practical, alternative analysis tool for otherwise complicated analyses of cumulative incidence of competing risks data. Copyright © 2008 John Wiley & Sons, Ltd.</jats:p>


Time-dependent predictors of loss to follow-up in HIV care in low-resource settings: A competing risks approach

Tamrat Endebu Gebre
Girma Taye
W. Deressa

Abstract: Loss to follow-up (LTFU) remains a major challenge in HIV care, particularly in resource-limited settings. While several studies have identified its predictors, many have neglected the competing risks of transfer out and death, as well as the dynamic influence of these predictors over time. A retrospective cohort study was conducted among adult HIV patients who initiated antiretroviral therapy (ART) between 2019 and 2024. LTFU was a primary outcome, whereas transfer out and death were competing risks. A Fine‒Gray subdistribution hazard ratio (SHR) regression analysis identified LTFU predictors within a competing risk framework. An extended SHR model with a time‒covariate interaction term was used to examine the predictors’ time‒varying effects on LTFU risk. Data analysis was performed via STATA 17 and Python 3.9. In a cohort of 4,135 HIV patients (8,521.54 person-years of follow-up), the overall incidence of LTFU was 13.10 per 100 person-years (95% CI: 12.35–13.89), with cumulative risks of 15%, 25%, and 35% at 1, 3, and 5 years post-ART, respectively. The predictors of LTFU included younger age (15–24 years: aSHR = 1.51), male sex (aSHR = 1.24), incomplete address details (aSHR = 1.72), noninitiation/noncompletion of TPT (aSHR = 2.16), poor adherence (aSHR = 2.54), and undernutrition (aSHR = 2.03). While younger age (e.g., 15–24 years) was associated with an increased risk of LTFU at baseline (baseline aSHR = 1.36, p = 0.014), this association diminished over time (interaction aSHR = 0.54, p = 0.001). Undernutrition consistently predicted LTFU (baseline aSHR = 1.64, p < 0.001), with no significant time-dependent effect (interaction aSHR = 1.01, p = 0.903). In conclusion, this study highlights the high incidence of LTFU among HIV patients and its key predictors. Notably, age has a significant time-dependent effect, with its influence on the risk of LTFU being most pronounced during the early stage of ART initiation, whereas nutritional status remains a consistent predictor of LTFU over time.


Competing Risks: A Practical Perspective

Abstract: Preface. Acknowledgements. 1. Introduction. 1.1 Historical notes. 1.2 Defining competing risks. 1.3 Use of the Kaplan-Meier method in the presence of competing risks. 1.4 Testing in the competing risk framework. 1.5 Sample size calculation. 1.6 Examples. 1.6.1 Tamoxifen trial. 1.6.2 Hypoxia study. 1.6.3 Follicular cell lymphoma study. 1.6.4 Bone marrow transplant study. 1.6.5 Hodgkin's disease study. 2. Survival - basic concepts. 2.1 Introduction. 2.2 Definitions and background formulae. 2.2.1 Introduction. 2.2.2 Basic mathematical formulae. 2.2.3 Common parametric distributions. 2.2.4 Censoring and assumptions. 2.3 Estimation and hypothesis testing. 2.3.1 Estimating the hazard and survivor functions. 2.3.2 Nonparametric testing: log-rank and Wilcoxon tests. 2.3.3 Proportional hazards model. 2.4 Software for survival analysis. 2.5 Closing remarks. 3. Competing risks - definitions. 3.1 Recognizing competing risks. 3.1.1 Practical approaches. 3.1.2 Common endpoints in medical research. 3.2 Two mathematical definitions. 3.2.1 Competing risks as bivariate random variable. 3.2.2 Competing risks as latent failure times. 3.3 Fundamental concepts. 3.3.1 Competing risks as bivariate random


DISE: A Dynamic Integrated Space-Economy Model for Orbital Debris Mitigation Policy Evaluation (2025)


Evolving governance in the space sector: From Legacy Space to New Space models (Zancan, Paravano, Locatelli & Trucco 2024)

E. Zancan
A. Paravano
G. Locatelli
P. Trucco

Abstract: Governance evolution from legacy to new-space delivery models, relevant to launch-availability and acquisition-era effects on schedule.


A Review of the Application of Decision Tree Analysis and Artificial Neural Networks in Project Management

Olusina Temidayo Akinyokun
Onifade. Morakinyo Kehinde
Adegoke Michael Abejide

Jurnal Teknik Industri: Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri

Abstract: The advancement of computing and communication technologies has fueled the growth of Information Technology (IT), with Artificial Intelligence (AI) emerging as a transformative force in modernizing project management practices. This study explores the application of two prominent AI techniques—Decision Tree Analysis (DTA) and Artificial Neural Networks (ANN)—in improving project planning and control. A review of empirical studies highlights the limitations of conventional tools such as Gantt charts and the Critical Path Method (CPM) in managing complex project variables, often resulting in cost overruns and schedule delays. In contrast, DTA and ANN demonstrate superior predictive accuracy, decision support, and adaptability capabilities. DTA offers transparent and structured decision-making models, while ANN excels in pattern recognition and outcome forecasting. The findings underscore that integrating these AI tools enhances project efficiency, cost estimation, and time management, establishing AI as a critical asset for future project success.Keywords: Artificial Intelligence, Decision Tree Analysis, Artificial Neural Networks, Project Planning, Project Control


The Oxford Olympics Study 2024: Are Cost and Cost Overrun at the Games Coming Down?

Abstract: The present paper is an update of the"Oxford Olympics Study 2016"(Flyvbjerg et al. 2016). We document that the Games remain costly and continue to have large cost overruns, to a degree that threatens their viability. The IOC is aware of the problem and has initiated reform. We assess the reforms and find: (a) Olympic costs are statistically significantly increasing; prior analysis did not show this trend; it is a step in the wrong direction. (b) Cost overruns were decreasing until 2008, but have increased since then; again a step in the wrong direction. (c) At present, the cost of Paris 2024 is USD 8.7 billion (2022 level) and cost overruns is 115% in real terms; this is not the cheap Games that were promised. (d) Cost overruns are the norm for the Games, past, present, and future; they are the only project type that never delivered on budget. We assess a new IOC policy of reducing cost by reusing existing venues instead of building new ones. We find that reuse did not have the desired effect for Tokyo 2020 and also look ineffective for Paris 2024. Finally, we recommend that the Games look to other types of megaprojects for better data, better forecasting, and how to generate the positive learning curves that are necessary for bringing costs and overrun down. Only if this happens are Los Angeles 2028 and Brisbane 2032 likely to live up to the IOC's intentions of a more affordable Games that more cities will want to host.


Factors Affecting Schedule Delay and Cost Overrun in Egyptian Construction Projects


Infrastructure project cost overrun and schedule delay in Ghana: Is it an issue of resource misallocation or financial constraints?


The AI and Quantum Era: Transforming Project Management Practices

Journal of Future Artificial Intelligence and Technologies

Abstract: Project management is changing drastically due to the integration of artificial intelligence (AI) and quantum computing (QC), redefining traditional methods. This study explores Quantum AI (QAI) and AI-driven solutions to tackle enduring issues, including resource inefficiencies, schedule delays, and budget overruns. These technologies significantly enhance project outcomes by leveraging predictive analytics, dynamic scheduling, and high-dimensional optimization. A comparative analysis of prominent case studies, including the Crossrail Project, East Side Access, and the Montreal Olympics, highlights the superior performance of AI and QAI techniques compared to conventional methods. The study shows that QAI can cut delays by 60%, optimize resource allocation with 83% efficiency, and eliminate cost overruns by up to 40% using Monte Carlo simulations and Failure Mode Effects Analysis. These results demonstrate that quantum artificial intelligence is a ground-breaking tool for handling intricate, interconnected project settings. Additionally, this study emphasizes how QAI is scalable and applicable across industries, especially in fields that need real-time optimization and high-dimensional data processing. The proposed hybrid quantum-classical paradigm provides practical solutions and sets a benchmark for efficiency, scalability, and risk mitigation in project management.


Megaproject policy and planning: Problems, causes, cures

VBN Forskningsportal (Aalborg Universitet)

Abstract: This paper focuses on problems in megaproject policy and planning and their causes and possible cures.After considerations of methodology, the paper first identifies as a main problem in megaproject development pervasive misinformation about the costs, benefits, and risks involved.A consequence of misinformation is cost overruns, benefit shortfalls, and waste.Second, the paper explores the causes of misinformation and finds that political-economic explanations best account for the available evidence: planners and promoters deliberately misrepresent costs, benefits, and risks in order to increase the likelihood that it is their projects, and not the competition's, that gain approval and funding.This results in the "survival of the unfittest," where often it is not the best projects that are built, but the most misrepresented ones.Finally, the paper presents measures for reforming megaproject policy and planning with a focus on better planning methods and improved governance structures, the latter being more important.


Advancing Digital Project Management Through AI: An Interpretable POA-LightGBM Framework for Cost Overrun Prediction

Jalal Meftah Mohamed Lekraik
Opeoluwaseun Ojekemi

Abstract: Cost overruns remain one of the most persistent challenges in construction and infrastructure project management, often undermining efficiency, sustainability, and stakeholder trust. With the rise of digital transformation, artificial intelligence (AI) and machine learning (ML) provide new opportunities to enhance predictive decision-making and strengthen project control. This study introduces a digital project management framework that integrates the Pelican Optimization Algorithm (POA) with Light Gradient Boosting Machine (LGBM) to deliver reliable and interpretable cost overrun forecasting. The proposed POA-LightGBM model leverages metaheuristic-driven hyperparameter optimization to improve predictive performance and generalization. A comprehensive evaluation using multiple error metrics Coefficient of Determination (R2), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) demonstrates that POA-LGBM significantly outperformed baseline LGBM and alternative metaheuristic configurations, achieving an average R2 of 0.9786. To support transparency in digital project environments, SHapley Additive exPlanations (SHAPs) were employed to identify dominant drivers of cost overruns, including actual project cost, energy consumption, schedule deviation, and material usage. By embedding AI-enabled predictive analytics into digital project management practices, this study contributes to advancing digital transformation in project delivery, offering actionable insights for cost control, risk management, and sustainable infrastructure development.


Evaluating Cost Estimation and Budgeting Challenges in Public Health and Education Infrastructure Projects

Dr. Muhammad Imran Majeed
Dr. Abdullah Hammad
Dr Sahar Munir
Rida Maqsood
Isha Safdar

Abstract: Persistent cost overruns and budgetary deviations continue to undermine the delivery of public infrastructure projects worldwide, despite decades of reform in project management and public financial management systems. While extensive scholarship has examined cost estimation failures in large transportation and energy megaprojects, comparatively limited attention has been paid to public health and education infrastructure, sectors that are central to human capital development and social welfare. This paper addresses this gap by critically evaluating cost estimation and budgeting challenges in public health and education infrastructure projects from a global perspective.Drawing on a comprehensive synthesis of international fiscal data, infrastructure performance studies, and institutional analyses, the study examines how macro-fiscal constraints, budget allocation frameworks, and governance arrangements shape cost estimation practices and project cost performance in hospitals, schools, and higher education facilities. The paper develops an integrated conceptual framework linking fiscal structures, budget rigidity, behavioral biases, and institutional capacity to systematic underestimation and subsequent cost escalation. Empirical evidence from global datasets and sector-specific studies reveals that cost overruns in health and education infrastructure are pervasive across income groups, with average overruns frequently exceeding 20–40%, reflecting structural rather than context-specific failures.The findings demonstrate that cost estimation inaccuracies are not merely technical errors but are deeply embedded in political and institutional incentive systems that prioritize project approval over delivery realism. By explicitly connecting cost estimation processes to public budgeting mechanisms, the study advances infrastructure governance theory beyond project-level explanations. The paper concludes by outlining policy-relevant implications for improving estimation accuracy, strengthening budget credibility, and enhancing value for money in public health and education infrastructure investment.