Clinical Decision Science Research Articles

Proportionate responses to life events influence clinicians' judgments of psychological abnormality.

Psychological abnormality is a fundamental concept in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association, 2000) and in all clinical evaluations. How do practicing clinical psychologists use the context of life events to judge the abnormality of a person's current behaviors? The appropriate role of life-event context in assessment has long been the subject of intense debate and scrutiny among clinical theorists, yet relatively little is known about clinicians' own judgments in practice. The authors propose a proportionate-response hypothesis, such that judgments of abnormality are influenced by whether the behaviors are a disproportionate response to past events, rendering them difficult to understand or explain. Licensed, practicing clinical psychologists (N = 77) were presented with vignettes describing hypothetical people's behaviors (disordered, mildly distressed, or unaffected) that had been preceded by either traumatic or mildly distressing events. Experts' judgments of abnormality were strongly and systematically influenced by the degree of mismatch between the past event and current behaviors in strength and valence, such that the greater the mismatch, the more abnormal the person seemed. A separate, additional group of clinical psychologists (N = 20) further confirmed that the greater the degree of mismatch, the greater the perceived difficulty in understanding the patient. These findings held true across clinicians of different theoretical orientations and in disorders for which these patterns of judgments ran contrary to formal recommendations in the DSM-IV-TR (American Psychiatric Association, 2000). The rationality of these effects and implications for clinical decision science are discussed.

Statistical aspects in comparative benefit-risk assessment: challenges and opportunities for pharmaceutical statisticians

Benefit-risk assessment is a fundamental element of drug development with the aim to strengthen decision making for the benefit of public health. Appropriate benefit-risk assessment can provide useful information for proactive intervention in health care settings, which could save lives, reduce litigation, improve patient safety and health care outcomes, and furthermore, lower overall health care costs. Recent development in this area presents challenges and opportunities to statisticians in the pharmaceutical industry. We review the development and examine statistical issues in comparative benefit-risk assessment. We argue that a structured benefit-risk assessment should be a multi-disciplinary effort involving experts in clinical science, safety assessment, decision science, health economics, epidemiology and statistics. Well planned and conducted analyses with clear consideration on benefit and risk are critical for appropriate benefit-risk assessment. Pharmaceutical statisticians should extend their knowledge to relevant areas such as pharmaco-epidemiology, decision analysis, modeling, and simulation to play an increasingly important role in comparative benefit-risk assessment.

Medical Informatics Training at Stanford University School of Medicine

Stanford University School of Medicine has offered graduate degrees in medical informatics since 1982. Located approximately 50 kilometers south of San Francisco near the city of Palo Alto, the university offers both MS and PhD degrees, combining research training with formal course requirements in clinical information sciences, bioinformatics, computer science, decision science, basic biomedicine, health economics, and social and ethical issues. Requirements are adapted to the varying backgrounds of trainees. Graduates of the program work in a variety of capacities, although the majority have sought careers in academia or in industrial research settings.

The marriage of clinical trials and clinical decision science

Clinical decision science is concerned with rational clinical decisions. All branches of medical research contribute here, but controlled clinical trials of the pragmatic variety carry a particular responsibility. Usually, however, they are not conducted and reported so that they can be used directly as input to a decision analysis. We suggest that the forces of the two methodologies should be united, and point out some areas where this 'marriage' will have a non-trivial impact: choice of end points, style of outcome recording, adaptive designs, and style of result presentation. Special attention is given to the decision-analytic setting of research priorities, the role of utility calculus in quantifying the ethical dilemmas that surround clinical trials, and the use of patient attitude towards outcomes of treatment as a covariate in its own right.