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The article discusses patient de-identification practices related to clinical trials, citing challenges like sharing of clinical data, as of August 2015. Topics include a policy of the European Medicines Agency regarding clinical study reports for drugs that receives approval for marketing; data sharing models for companies undertaking clinical trials; risk associated with de-identification of data like filing complaint by participants of the trials; and personal information in the data.

Background A linear programming (LP) model was proposed to create de-identified data sets that maximally include spatial detail (e.g., geocodes such as ZIP or postal codes, census blocks, and locations on maps) while complying with the HIPAA Privacy Rule's Expert Determination method, i.e., ensuring that the risk of re-identification is very small. The LP model determines the transition probability from an original location of a patient to a new randomized location. However, it has a limitation for the cases of areas with a small population (e.g., median of 10 people in a ZIP code). Methods We extend the previous LP model to accommodate the cases of a smaller population in some locations, while creating de-identified patient spatial data sets which ensure the risk of reidentification is very small. Results Our LP model was applied to a data set of 11,740 postal codes in the City of Ottawa, Canada. On this data set we demonstrated the limitations of the previous LP model, in that it produces improbable results, and showed how our extensions to deal with small areas allows the deidentification of the whole data set. Conclusions The LP model described in this study can be used to de-identify geospatial information for areas with small populations with minimal distortion to postal codes. Our LP model can be extended to include other information, such as age and gender. [ABSTRACT FROM AUTHOR]

Two contemporary problems face public health professionals in collecting data from health care providers: the de-identification of geospatial information in a manner that still allows meaningful analysis, and ensuring that provider performance data (e.g., infection or screening rates) is complete and accurate. In this paper, we discuss new methods for de-identifying geographic information that will allow useful de-identified data to be disclosed to public health. In addition, we propose privacy preserving mechanisms that will likely encourage providers to disclose complete and accurate data. However, this must be accompanied by steps to grow trust between the providers and public health. [ABSTRACT FROM AUTHOR]

The authors discuss the special issue of the periodical on the topic of privacy rights of biomedical data, such as electronic medical records (EMRs). They present an overview of research on the data lifecycle of biomedical information, including data collection, clinical usage, and research usage. They argue that the issue of biomedical data privacy is influenced by factors such as ethical and legal boundaries, data types, and computing technology such as cloud computing.

Objective: In public health and health services research, the inclusion of geographic information in data sets is critical. Because of concerns over the re-identification of patients, data from small geographic areas are either suppressed or the geographic areas are aggregated into larger ones. Our objective is to estimate the population size cut-off at which a geographic area is sufficiently large so that no data suppression or further aggregation is necessary. Design: The 2001 Canadian census data were used to conduct a simulation to model the relationship between geographic area population size and uniqueness for some common demographic variables. Cut-offs were computed for geographic area population size, and prediction models were developed to estimate the appropriate cut-offs. Measurements: Re-identification risk was measured using uniqueness. Geographic area population size cut-offs were estimated using the maximum number of possible values in the data set and a traditional entropy measure. Results: The model that predicted population cut-offs using the maximum number of possible values in the data set had R² values around 0.9, and relative error of prediction less than 0.02 across all regions of Canada. The models were then applied to assess the appropriate geographic area size for the prescription records provided by retail and hospital pharmacies to commercial research and analysis firms. Conclusions: To manage re-identification risk, the prediction models can be used by public health professionals, health researchers, and research ethics boards to decide when the geographic area population size is sufficiently large. [ABSTRACT FROM AUTHOR]

Objective: There is increasing pressure to share health information and even make it publicly available. However, such disclosures of personal health information raise serious privacy concerns. To alleviate such concerns, it is possible to anonymize the data before disclosure. One popular anonymization approach is k-anonymity. There have been no evaluations of the actual re-identification probability of k-anonymized data sets.Design: Through a simulation, we evaluated the re-identification risk of k-anonymization and three different improvements on three large data sets.Measurement: Re-identification probability is measured under two different re-identification scenarios. Information loss is measured by the commonly used discernability metric.Results: For one of the re-identification scenarios, k-Anonymity consistently over-anonymizes data sets, with this over-anonymization being most pronounced with small sampling fractions. Over-anonymization results in excessive distortions to the data (i.e., high information loss), making the data less useful for subsequent analysis. We found that a hypothesis testing approach provided the best control over re-identification risk and reduces the extent of information loss compared to baseline k-anonymity.Conclusion: Guidelines are provided on when to use the hypothesis testing approach instead of baseline k-anonymity. [ABSTRACT FROM AUTHOR]

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The international SPICE (Software Process Improvement and Capability dEtermination) project was set up to support the development of the ISO/IEC 15504 standard for software process assessment (SPA). The project mounted a set of trials to validate the emerging standard against the goals and requirements defined at the start of the SPICE project and to verify the consistency and usability of its component parts. A considerable number of empirical evaluation studies have been conducted during the Phase 2 SPICE Trials based on ISO/IEC PDTR 15504 (between September 1996 and June 1998). Such an exercise is unprecedented in the software engineering standards community and it provides a unique opportunity for empirical validation. The purpose of this paper is to present major parts of the findings of the empirical studies conducted as part of the SPICE Project during Phase 2 of the SPICE Trials. The topics covered in this paper include (i) investigation into reasons for performing SPAs, (ii) evaluation of the internal consistency of the capability dimension, (iii) use of interrater agreement as a measure of the reliability of assessments, (iv) evaluation of the predictive validity of process capability, (v) evaluation of an exemplar model (Part 5), (vi) identification of factors influencing assessor effort, and (vii) empirical comparison between ISO/IEC PDTR 15504 and ISO 9001. Major lessons learned as well as future research directions are summarized on the strengths and weaknesses of ISO/IEC 15505. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Much effort has been devoted to the development and empirical validation of object-oriented metrics. The empirical validations performed thus far would suggest that a core set of validated metrics is close to being identified. However, none of these studies allow for the potentially confounding effect of class size. In this paper, we demonstrate a strong size confounding effect and question the results of previous object-oriented metrics validation studies. We first investigated whether there is a confounding effect of class size in validation studies of object-oriented metrics and show that, based on previous work, there is reason to believe that such an effect exists. We then describe a detailed empirical methodology for identifying those effects. Finally, we perform a study on a large C++ telecommunications framework to examine if size is really a confounder. This study considered the Chidamber and Kemerer metrics and a subset of the Lorenz and Kidd metrics. The dependent variable was the incidence of a fault attributable to a field failure (fault-proneness of a class). Our findings indicate that, before controlling for size, the results are very similar to previous studies: The metrics that are expected to be validated are indeed associated with fault-proneness. After controlling for size, none of the metrics we studied were associated with fault-proneness anymore. This demonstrates a strong size confounding effect and casts doubt on the results of previous object-oriented metrics validation studies. It is recommended that previous validation studies be reexamined to determine whether their conclusions would still hold after controlling for size and that future validation studies should always control for size. [ABSTRACT FROM AUTHOR]

The basic premise of software inspections is that they detect and remove defects before they propagate to subsequent development phases where their detection and correction cost escalates. To exploit their full potential, software inspections must call for a close and strict examination of the inspected artifact. For this, reading techniques for detect detection may be helpful since these techniques tell inspection participants what to look for and, more importantly, how to scrutinize a software artifact in a systematic manner. Recent research efforts investigated the benefits of scenario-based reading techniques. A major finding has been that these techniques help inspection teams find more defects than existing state-of-the-practice approaches, such as, ad-hoc or checklist-based reading (CBR). In this paper, we experimentally compare one scenario-based reading technique, namely, perspective-based reading (PBR), for defect detection in code documents with the more traditional CBR approach. The comparison was performed in a series of three studies, as a quasi experiment and two internal replications, with a total of 60 professional software developers at Bosch Telecom GmbH. Meta-analytic techniques were applied to analyze the data. Our results indicate that PBR is more effective than CBR (i.e., it resulted in inspection teams detecting more unique defects than CBR) and that the cost of defect detection using PBR is significantly lower than CBR. Therefore, this study provides evidence demonstrating the efficacy of PBR scenarios for code documents in an industrial setting. [ABSTRACT FROM AUTHOR]

An important requirement to control the inspection of software artifacts is to be able to decide, based on more objective information, whether the inspection can stop or whether it should continue to achieve a suitable level of artifact quality. A prediction of the number of remaining defects in an inspected artifact can be used for decision making. Several studies in software engineering have considered capture-recapture models, originally proposed by biologists to estimate animal populations, to make a prediction. However, few studies compare the actual number of remaining defects to the one predicted by a capture-recapture model on real software engineering artifacts. Thus, there is little work looking at the robustness of capture-recapture models under realistic software engineering conditions, where it is expected that some of their assumptions will be violated. Simulations have been performed, but no definite conclusions can be drawn regarding the degree of accuracy of such models under realistic inspection conditions and the factors affecting this accuracy. Furthermore, the existing studies focused on a subset of the existing capture-recapture models. Thus, a more exhaustive comparison is still missing. In this study, we focus on traditional inspections and estimate, based on actual inspections data, the degree of accuracy of relevant, state-of-the-art capture-recapture models as they have been proposed in biology and for which statistical estimators exist. In order to assess their robustness, we look at the impact of the number of inspectors and the number of actual defects on the estimators' accuracy based on actual inspection data. Our results show that models are strongly affected by the number of inspectors and, therefore, one must consider this factor before using capture-recapture models. When the number of inspectors is too small, no model is sufficiently accurate and underestimation may be substantial. In addition, some models perform better than others in a large number of conditions and plausible reasons are discussed. Based on our analyses, we recommend using a model taking into account that defects have different probabilities of being detected and the corresponding Jackknife Estimator. Furthermore, we attempt to calibrate the prediction models based on their relative error, as previously computed on other Inspections. Although intuitive and straightforward, we identified theoretical limitations to this approach which were then confirmed by the data. [ABSTRACT FROM AUTHOR]

One of the enduring issues being evaluated during the SPICE trials is the reliability of assessments. One type of reliability is the extent to which different assessors produce similar ratings when assessing the same organization and presented with the same evidence. In this paper we report on a study that was conducted to start answering this question. Data was collected from an assessment of 21 process instances covering 15 processes. In each of these assessments two independent assessors performed the ratings. We found that six of the fifteen processes do not meet our minimal benchmark for interrater agreement. Three of these were due to systematic biases by either an internal or external assessor. Furthermore, for eight processes specific rating scale adjustments were identified that could improve its reliability. The findings reported in this paper provide guidance for assessors using the SPICE framework. [ABSTRACT FROM AUTHOR]

The article presents an overview of a pilot study involving Centre de Recherche Informatique de Montreal, CAE Electronics and McGill University that was conducted to implement and evaluate concepts from Humphrey's Personal Software Process (PSP). The lessons learned from the research are summarized. A framework for evaluating PSP is described.

The SPICE project aims to deliver an international standard on software process assessment. Unique among software engineering standards efforts, there is an empirical trials activity as part of the SPICE project. The first phase of these trials was completed in calendar year 1995. During phase 1 data was collected to evaluate design decisions of the SPICE framework and the usability of the core SPICE documents. Part of this data was collected using questionnaires. The respondents were both assessors and assessees. In this paper we present an analysis of the responses to these questionnaires based on 35 assessments conducted during phase 1 of the SPICE trials. The results indicate that the SPICE framework is in general sound, but they also highlight some potential weaknesses. Recommendations to the SPICE project based on these results are presented. [ABSTRACT FROM AUTHOR]

Background: There has been growing interest in data synthesis for enabling the sharing of data for secondary analysis; however, there is a need for a comprehensive privacy risk model for fully synthetic data: If the generative models have been overfit, then it is possible to identify individuals from synthetic data and learn something new about them.Objective: The purpose of this study is to develop and apply a methodology for evaluating the identity disclosure risks of fully synthetic data.Methods: A full risk model is presented, which evaluates both identity disclosure and the ability of an adversary to learn something new if there is a match between a synthetic record and a real person. We term this "meaningful identity disclosure risk." The model is applied on samples from the Washington State Hospital discharge database (2007) and the Canadian COVID-19 cases database. Both of these datasets were synthesized using a sequential decision tree process commonly used to synthesize health and social science data.Results: The meaningful identity disclosure risk for both of these synthesized samples was below the commonly used 0.09 risk threshold (0.0198 and 0.0086, respectively), and 4 times and 5 times lower than the risk values for the original datasets, respectively.Conclusions: We have presented a comprehensive identity disclosure risk model for fully synthetic data. The results for this synthesis method on 2 datasets demonstrate that synthesis can reduce meaningful identity disclosure risks considerably. The risk model can be applied in the future to evaluate the privacy of fully synthetic data. [ABSTRACT FROM AUTHOR]

The article reports on the integration of mobile electronic devices in clinical trial settings. The deployment of these devices should undergo some considerations like clock synchronization of the mobile instrument to the central database, performance potentials, connectivity, budget, data conflict management and battery duration. The application of these technologies could be an effective way to adapt an electronic data capture system for research development.

This article provides a state-of-the-art summary of location privacy issues and geoprivacy-preserving methods in public health interventions and health research involving disaggregate geographic data about individuals. Synthetic data generation (from real data using machine learning) is discussed in detail as a promising privacy-preserving approach. To fully achieve their goals, privacy-preserving methods should form part of a wider comprehensive socio-technical framework for the appropriate disclosure, use and dissemination of data containing personal identifiable information. Select highlights are also presented from a related December 2021 AAG (American Association of Geographers) webinar that explored ethical and other issues surrounding the use of geospatial data to address public health issues during challenging crises, such as the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Summarizes research on the reliability of software process assessments conducted by the Fraunhofer Institute for Experimental Software Engineering. Background on the institute; Information on the reliability of software measurement procedure; Types of reliability estimation methods.

Patients, families, healthcare providers and funders face multiple comparable treatment options without knowing which provides the best quality of care. As a step towards improving this, the REthinking Clinical Trials (REaCT) pragmatic trials program started in 2014 to break down many of the traditional barriers to performing clinical trials. However, until other innovative methodologies become widely used, the impact of this program will remain limited. These innovations include the incorporation of near equivalence analyses and the incorporation of artificial intelligence (AI) into clinical trial design. Near equivalence analyses allow for the comparison of different treatments (drug and non-drug) using quality of life, toxicity, cost-effectiveness, and pharmacokinetic/pharmacodynamic data. AI offers unique opportunities to maximize the information gleaned from clinical trials, reduces sample size estimates, and can potentially "rescue" poorly accruing trials. On 2 May 2023, the first REaCT international symposium took place to connect clinicians and scientists, set goals and identify future avenues for investigator-led clinical trials. Here, we summarize the topics presented at this meeting to promote sharing and support other similarly motivated groups to learn and share their experiences. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article “Unique in the Shopping Mall: On the Reidentifiability of Credit Card Metadata” by Y.-A. de Montjoye et al. in the January 30, 2015 issue.

PURPOSE: There is strong interest from patients, researchers, the pharmaceutical industry, medical journal editors, funders of research, and regulators in sharing clinical trial data for secondary analysis. However, data access remains a challenge because of concerns about patient privacy. It has been argued that synthetic data generation (SDG) is an effective way to address these privacy concerns. There is a dearth of evidence supporting this on oncology clinical trial data sets, and on the utility of privacy-preserving synthetic data. The objective of the proposed study is to validate the utility and privacy risks of synthetic clinical trial data sets across multiple SDG techniques. METHODS: We synthesized data sets from eight breast cancer clinical trial data sets using three types of generative models: sequential synthesis, conditional generative adversarial network, and variational autoencoder. Synthetic data utility was evaluated by replicating the published analyses on the synthetic data and assessing concordance of effect estimates and CIs between real and synthetic data. Privacy was evaluated by measuring attribution disclosure risk and membership disclosure risk. RESULTS: Utility was highest using the sequential synthesis method where all results were replicable and the CI overlap most similar or higher for seven of eight data sets. Both types of privacy risks were low across all three types of generative models. DISCUSSION: Synthetic data using sequential synthesis methods can act as a proxy for real clinical trial data sets, and simultaneously have low privacy risks. This type of generative model can be one way to enable broader sharing of clinical trial data. Synthetic versions of clinical trial data sets were shown to have low privacy risks and high utility. [ABSTRACT FROM AUTHOR]

It is evident that COVID-19 will remain a public health concern in the coming years, largely driven by variants of concern (VOC). It is critical to continuously monitor vaccine effectiveness as new variants emerge and new vaccines and/or boosters are developed. Systematic surveillance of the scientific evidence base is necessary to inform public health action and identify key uncertainties. Evidence syntheses may also be used to populate models to fill in research gaps and help to prepare for future public health crises. This protocol outlines the rationale and methods for a living evidence synthesis of the effectiveness of COVID-19 vaccines in reducing the morbidity and mortality associated with, and transmission of, VOC of SARS-CoV-2. Living evidence syntheses of vaccine effectiveness will be carried out over one year for (1) a range of potential outcomes in the index individual associated with VOC (pathogenesis); and (2) transmission of VOC. The literature search will be conducted up to May 2023. Observational and database-linkage primary studies will be included, as well as RCTs. Information sources include electronic databases (MEDLINE; Embase; Cochrane, L*OVE; the CNKI and Wangfang platforms), pre-print servers (medRxiv, BiorXiv), and online repositories of grey literature. Title and abstract and full-text screening will be performed by two reviewers using a liberal accelerated method. Data extraction and risk of bias assessment will be completed by one reviewer with verification of the assessment by a second reviewer. Results from included studies will be pooled via random effects meta -analysis when appropriate, or otherwise summarized narratively. Evidence generated from our living evidence synthesis will be used to inform policy making, modelling, and prioritization of future research on the effectiveness of COVID-19 vaccines against VOC. [ABSTRACT FROM AUTHOR]