| Titre : | Efficiency optimization of the selection period in therapeutic trials. (1997) |
| Auteurs : | Ckgm MOONS ; A.A.A. BAK ; D.E. GROBBEE ; J.D.F. HABBEMA ; A. HOFMAN ; J.J.C. JONKER ; T. STIJNEN ; G.A. VAN ES ; Centre For Clinical Decision Sciences. Department of Public Health. Erasmus University Medical School. Rotterdam. NLD ; Consultation Centre For Clinical Research. Academic Hospital Rotterdam. Dijkzigt. NLD ; Department of Epidemiology and Biostatistics. Erasmus University Medical School. Rotterdam. NLD |
| Type de document : | Article |
| Dans : | Journal of clinical epidemiology (vol. 50, n° 7, 1997) |
| Pagination : | 793-800 |
| Langues: | Anglais |
| Mots-clés : | Sélection ; Epidémiologie ; Méthodologie ; Analyse coût efficacité ; Evaluation ; Homme ; Europe ; Pays Bas |
| Résumé : | [BDSP. Notice produite par INIST RN9RR0xx. Diffusion soumise à autorisation]. To determine eligibility for a (randomized) clinical trial, measuring the inclusion and exclusion criteria can be extended over a period of time. During this period, known as the selection period, a patient is repeatedly examined at certain time intervals. This study describes an approach for optimizing the efficiency of the selection period. Efficiency is defined as the costs of randomizing one patient. The objective is to construct prediction models based on data obtained early in the selection period to predict subsequent exclusions. A prediction model increases the efficiency if after its application the costs per randomization are lower. The approach is illustrated using data from the selection period of the Rotterdam Cardiovascular Risk Intervention (ROCARI) trial which was composed of five consecutive patient visits. At each visit, data to determine eligibility was obtained. We found that logistic regression models based on data of the first and second visit could predict exclusions during the third visit. Application of the prediction models suggested that in this particular trial the costs per randomization would decrease by $ 52. As the initial costs per randomization were $ 1444, there would be a 3.6% (52/1444) savings in recruitment costs under the prediction models, accounting for a savings of more than $ 450,000. (...) |
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