PubMed · 9060934
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M A Memon, D Rice, J H Donohue. 1997. Laparoscopic herniorrhaphy.. https://pubmed.ncbi.nlm.nih.gov/9060934/
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Assessment of quality of life is becoming standard in clinical trials. A popular method for measuring quality of life is with instruments which utilize multiple-item subscales, in which each item is scored on a Likert scale. Most statistical methods for the analysis of quality of life data in clinical trials do not explicity consider the properties and psychometric features which were of interest in scale development. In this regard, the measurement and statistical summarization of quality of life data, along with the clinical interpretation, can be somewhat disjoint from the psychometric concerns of the development process. The aim of this paper is to address the complicated issues present in analysing multiple-item ordinal quality of life data in clinical trials while maintaining fidelity to the psychometrical foundations upon which quality of life instruments are built. Accomplishing this will require the development of item response models which recognize the longitudinal aspects of clinical trial designs as well as the potential problem of informatively missing data. A general item response modeling approach is presented for longitudinal multiple-item quality of life data measured on ordinal scales with model components for missing data mechanisms and latent trait regression on treatment indicators and other covariates.
Trials in acute stroke have recruited on the basis of clinical diagnosis. Using MRI we have shown that clinical diagnosis is more limited than previously appreciated, thus trials may have been underpowered or confounded.
BACKGROUND: Intravascular ultrasound (IVUS)-guided stent implantation and the availability of a reference chart to predict the expected in-stent restenosis rate based on operator-dependent IVUS parameters may interactively facilitate optimal stent placement. The use of IVUS guidance protects against undue risks of dissection or rupture. METHODS AND RESULTS: IVUS-determined post-stent-implantation predictors of 6-month in-stent restenosis on quantitative coronary angiography (QCA) were identified by logistic regression analysis. These predictors were used to construct a reference chart that predicts the expected 6-month QCA restenosis rate. IVUS and QCA data were obtained from 3 registries (MUSIC [Multicenter Ultrasound Stenting in Coronaries study], WEST-II [West European Stent Trial II], and ESSEX [European Scimed Stent EXperience]) and 2 randomized in-stent restenosis trials (ERASER [Evaluation of ReoPro And Stenting to Eliminate Restenosis] and TRAPIST [TRApidil vs placebo to Prevent In-STent intimal hyperplasia]). In-stent restenosis was defined as luminal diameter stenosis >50% by QCA. IVUS predictors were minimum and mean in-stent area, stent length, and in-stent diameter. Multiple models were constructed with multivariate logistic regression analysis. The model containing minimum in-stent area and stent length best fit the Hosmer-Lemeshow goodness-of-fit test. This model was used to construct a reference chart to calculate the expected 6-month restenosis rate. CONCLUSIONS: The expected 6-month in-stent restenosis rate after stent implantation for short lesions in relatively large vessels can be predicted by use of in-stent minimal area (which is inversely related to restenosis) and stent length (which is directly related to restenosis), both of which can be read from a simple reference chart.