Approach to Decision Rule Development
By: Dr. Venkatesh Thiruganasambandamoorthy
Methodological Standards for Clinical Decision Rules
By: Dr. Lisa Calder & Ian Stiell October-November 2014 Clinical Decision Rules require 4 key stages of development prior to adoption in to clinical practice: derivation, prospective validation, evaluation of implementation and knowledge translation. The first step entails a derivation study that ideally is conducted prospectively and has a large number of outcome cases. The second step is a prospective validation study that explicitly evaluates the new rule for accuracy, physician acceptability and potential impact. The third step is an implementation trial to evaluate the actual impact of the rule on patient outcomes in real clinical practice. Be very cautious incorporating any decision rule into your practice which has not been through at least the first two steps. Examples of such rigorous decision rules include the Canadian CT head rule, Canadian C-spine rule and Ottawa Ankle rule.
Validation of clinical decision rules By: Dr. Ian Stiell November 2012
Critical appraisal criteria for a paper that validates an existing decision rule are different than those for a study that derives or creates the rule. Most important is that the study evaluates the existing rule accurately and explicitly such that the physicians using it are adequately instructed. Some studies do a validation from an existing database but we believe that it is far better to conduct a prospective real-time validation by clinicians.
By: Dr. Christian Vaillancourt
Collinearity means that two of the predictors entered in a regression analysis model correlate with each other (they measure almost the same thing, e.g. %body fat and total body weight). When more than two predictors interact with each other, it is called multicollinearity.Collinearity can be a problem, especially when very high, since the software will simply not be able to perform the regression analyses, or will provide unreliable results. The degree of collinearity can be estimated using the Variance Inflation Factor (VIF) which should be <5-10.