A copula model for marked point processes

The final publication (Diao, Liqun, Richard J. Cook, and Ker-Ai Lee. (2013) A copula model for marked point processes. Lifetime Data Analysis, 19(4): 463-489) is available at Springer via http://dx.doi.org/10.1007/s10985-013-9259-3Many chronic diseases feature recurring clinically important events. In addition, however, there often exists a random variable which is realized upon the occurrence of each event reflecting the severity of the event, a cost associated with it, or possibly a short term response indicating the effect of a therapeutic intervention. We describe a novel model for a marked point process which incorporates a dependence between continuous marks and the event process through the use of a copula function. The copula formulation ensures that event times can be modeled by any intensity function for point processes, and any multivariate model can be specified for the continuous marks. The relative efficiency of joint versus separate analyses of the event times and the marks is examined through simulation under random censoring. An application to data from a recent trial in transfusion medicine is given for illustration.Natural Sciences and Engineering Research Council of Canada (RGPIN 155849); Canadian Institutes for Health Research (FRN 13887); Canada Research Chair (Tier 1) – CIHR funded (950-226626

Measurement issues in quantitative research

Measurement is central to empirical research whether observational or experimental. Common to all measurements is the systematic application of numerical value (scale) to a variable or a factor we wish to quantify. Measurement can be applied to physical, biological, or chemical attribute or to more complex factors such as human behaviors, attitudes, physical, social or psychological characteristics or the combination of several characteristics that denote a concept. There are many reasons for the act of measurement that are relevant to health and social science disciplines: understanding aetiology of disease or developmental processes, evaluation of programs, for monitoring progress and for decision-making. Regardless of the specific purpose, we should aspire that our measurement be adequate. In this chapter, we review the properties that determine the adequacy of our measurement: reliability, validity and sensitivity and provide examples of statistical methods that are used to quantify these properties. At the concluding section, we provide examples from the physical activity and public health field in the four areas for which precise measurement are necessary illustrating how imprecise or biased scoring procedure can lead to erroneous decisions across the four major purposes of measurement

Increasing the Sensitivity of Measures to Change

Little attention is paid in prevention research to the ability of measures to accurately assess change, termed “responsiveness” or “sensitivity to change.” This paper reviews definitions and measures of responsiveness, and suggests five strategies for increasing sensitivity to change, with central focus on prevention research with small samples: (a) Improving understandability and cultural validity, (b) assuring that the measure covers the full range of the latent construct being measured, (c) eliminating redundant items, (d) maximizing sensitivity of the device used to collect responses; and (e) asking directly about change. Examples of the application of each strategy are provided. Discussion focuses on using the issues as a checklist for improving measures and the implications of sensitivity to change for prevention research with small samples