Non-compliance with randomised allocation and missing outcome data in randomised controlled trials evaluating surgical interventions : a systematic review

Acknowledgements JAC held MRC training (reference number: G0601938) and methodology (reference number: G1002292) fellowships while this research was undertaken. The Health Services Research Unit is core funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. The authors accept full responsibility for this manuscript.Peer reviewedPublisher PD

Software Process Validation: Quantitatively Measuring the Correspondence of a Process to a Model ; CU-CS-840-97

this article

Discovering Models of Software Processes from Event-Based Data ; CU-CS-819-96

Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be dicult, costly, and error prone. This presents a practical barrier to the adoption of process technologies, which would be lowered by automated assistance in creating formal models. To this end, we have developed a data analysis technique that we term process discovery. Under this technique, data describing process events are rst captured from an on-going process and then used to generate a formal model of the behavior of that process. In this paper we describe a Markov method that we developed specically for process discovery, as well as describe two additional methods that we adopted from other domains and augmented for our purposes. The three methods range from the purely algorithmic to the purely statistical. We compare the methods and discuss their application in an industrial case study

The Intersections of Race, Gender, Age, and Socioeconomic Status: Implications for Reporting Discrimination and Attributions to Discrimination.

This study employed an intersectional approach (operationalized as the combination of more than one social identity) to examine the relationship between aspects of social identity (i.e., race, gender, age, SES), self-reported level of mistreatment, and attributions for discrimination. Self-reported discrimination has been researched extensively and there is substantial evidence of its association with adverse physical and psychological health outcomes. Few studies, however, have examined the relationship of multiple demographic variables (including social identities) to overall levels self-reported mistreatment as well the selection of attributions for discrimination. A diverse community sample (N = 292; 42.12% Black; 47.26% male) reported on experiences of discrimination using the Everyday Discrimination Scale. General linear models were used to test the effect of sociodemographic characteristics (i.e., race, gender, age, SES) on total discrimination score and on attributions for discrimination. To test for intersectional relationships, we tested the effect of two-way interactions of sociodemographic characteristics on total discrimination score and attributions for discrimination. We found preliminary support for intersectional effects, as indicated by a significant race by age interaction on the selection of the race attribution for discrimination; gender by SES on the age attribution; age by gender on the education attribution; and race by SES on the economic situation attribution. Our study extends prior work by highlighting the importance of testing more than one factor as contributing to discrimination, particularly when examining to what sources individuals attribute discrimination