Using disease progression models as a tool to detect drug effect
| Autor: | Stephen B. Duffull, D R Mould, N. G. Denman |
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| Rok vydání: | 2007 |
| Předmět: |
Drug
medicine.medical_specialty media_common.quotation_subject Comorbidity Models Biological Drug Therapy Dose adjustment medicine Humans Pharmacology (medical) Computer Simulation Drug Interactions Pharmacokinetics Intensive care medicine Drug effect Statistical hypothesis testing media_common Pharmacology Clinical Trials as Topic Models Statistical Dose-Response Relationship Drug business.industry Disease progression Placebo Effect Antidepressive Agents Test (assessment) Surgery Clinical trial Treatment Outcome Research Design Data Interpretation Statistical Pharmacology Clinical Disease Progression business Null hypothesis |
| Zdroj: | Clinical pharmacology and therapeutics. 82(1) |
| ISSN: | 0009-9236 |
| Popis: | Generally, information required for approval of new drugs is dichotomous in that the drug is either efficacious and safe or not. Consequently, the purpose of most confirmatory clinical trials is to test the null hypothesis. The primary reasons for designing hypothesis testing trials are to provide the information required for approval using analyses techniques that are relatively straightforward and free of apparent assumptions. However, the information required for approval is very different from that used by prescribers for decision making. In the clinic, decisions must be made about dose adjustment for individual patients in the presence of additional therapies and co-morbidities. Choice of drug and dosing regimen is therefore a classical risk to benefit decision that is often poorly informed from the results of confirmatory trials. Therefore, providing answers to the more difficult question of how to use the drug in a clinical setting is essential. |
| Databáze: | OpenAIRE |
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