This report describes the development of the Perceived Involvement in Care Scale (PICS), a self-report questionnaire for patients, and its relation to primary care patients' attitudes regarding their illnesses and the management of them. The questionnaire was administered to three independent samples of adult primary care patients. Patients' satisfaction and their attitudes regarding their illnesses are evaluated after their medical visits. This instrument is designed to examine three relatively distinct factors: 1) doctor facilitation of patient involvement, 2) level of information exchange, and 3) patient participation in decision making. Of these factors, doctor facilitation and patient decision making were related significantly to patients' satisfaction with care. Doctor facilitation and information exchange related consistently to patients' perceptions of post-visit changes in their understanding, reassurance, perceived control over illness, and expectations for improvement in functioning. The role of physicians in enhancing patient involvement in care and the potential therapeutic benefits of physician facilitative behavior are addressed.
The psychometric properties of the Spanish version of the McGill Pain Questionnaire assessed in different Latin-American countries suggest that the questionnaire may be used to evaluate Spanish-speaking patients. The validity of this test should be extended with reliability studies to further establish its usefulness in the evaluation of pain.
Multivariate data are difficult to analyse partly because of difficulty in looking at the data. Repeated measures data have a special structure that makes the parallel plot particularly effective for viewing the data. In this paper we use parallel plots to display not just raw data but also residuals from standard models fit to repeated measures data. The plots are useful for determining how well a particular model fits the data, identifying outlying observations and suggesting terms missing from the linear predictor.
Complete (or balanced) repeated measures data arise when all subjects in a study are measured at the same set of time points. Data are often incomplete, because measurements are missed, or the design of the study results in subjects being measured at different sets of time points. This article reviews methods of analysis for incomplete repeated-measures data of this form, from an applied statistician's perspective. Limitations of approaches that (a) ignore between-subject variation, or (b) impute for missing values are discussed. Two methods are advocated that are relatively easy to implement using existing software, namely between-subject analysis of within-subject summary measures, and maximum likelihood based on a model for the data. Methods are applied and compared on four real-data examples with varied analytical objectives.
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