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1.
Clin Toxicol (Phila) ; 45(2): 132-5, 2007.
Artigo em Inglês | MEDLINE | ID: mdl-17364629

RESUMO

BACKGROUND: Although activated charcoal is widely used for the treatment of self-poisoning, its effectiveness is unknown. An important consideration is patient compliance since poor compliance will limit effectiveness. We aimed to describe compliance in a randomized controlled trial (RCT) performed in Sri Lanka, presuming that this would set the upper limits for compliance in routine clinical use. METHOD: 1,103 patients randomized to single or multiple (six doses q4h) 50 g doses of superactivated charcoal were prospectively observed. Charcoal was given by study doctors who recorded the amount ingested and the amount of persuasion required for the patients to drink the charcoal. RESULTS: 559 patients were randomized to receive one dose and 544 to receive six doses. Data was available for 1,071 (97%) patients. Eighty-eight were unable to complete their course; 98 required a NG tube, leaving 885 patients that received the first dose by mouth. The mean estimated amount of the prescribed dose of charcoal taken orally as a single or first dose was 83% (95% C.I. 82-84%). For patients receiving multiple doses, this amount fell over the next five doses to 66% (63-69%). While only 3.2% of patients refused the first dose, 12.3% refused the sixth. Relatively less persuasion was required for patients ingesting the first or single dose; 38% of patients required intense persuasion by the sixth dose. CONCLUSION: Compliance for a single dose of superactivated charcoal among trial patients was good. However, even in the ideal circumstances of a RCT, compliance decreased thereafter for patients taking more than one dose.


Assuntos
Carvão Vegetal/administração & dosagem , Cooperação do Paciente , Intoxicação/tratamento farmacológico , Carvão Vegetal/uso terapêutico , Relação Dose-Resposta a Droga , Esquema de Medicação , Humanos , Intubação Gastrointestinal , Estudos Prospectivos , Sri Lanka
2.
J Biopharm Stat ; 15(4): 719-38, 2005.
Artigo em Inglês | MEDLINE | ID: mdl-16022175

RESUMO

Sequential methods provide a formal framework by which clinical trial data can be monitored as they accumulate. The results from interim analyses can be used either to modify the design of the remainder of the trial or to stop the trial as soon as sufficient evidence of either the presence or absence of a treatment effect is available. The circumstances under which the trial will be stopped with a claim of superiority for the experimental treatment, must, however, be determined in advance so as to control the overall type I error rate. One approach to calculating the stopping rule is the group-sequential method. A relatively recent alternative to group-sequential approaches is the adaptive design method. This latter approach provides considerable flexibility in changes to the design of a clinical trial at an interim point. However, a criticism is that the method by which evidence from different parts of the trial is combined means that a final comparison of treatments is not based on a sufficient statistic for the treatment difference, suggesting that the method may lack power. The aim of this paper is to compare two adaptive design approaches with the group-sequential approach. We first compare the form of the stopping boundaries obtained using the different methods. We then focus on a comparison of the power of the different trials when they are designed so as to be as similar as possible. We conclude that all methods acceptably control type I error rate and power when the sample size is modified based on a variance estimate, provided no interim analysis is so small that the asymptotic properties of the test statistic no longer hold. In the latter case, the group-sequential approach is to be preferred. Provided that asymptotic assumptions hold, the adaptive design approaches control the type I error rate even if the sample size is adjusted on the basis of an estimate of the treatment effect, showing that the adaptive designs allow more modifications than the group-sequential method.


Assuntos
Interpretação Estatística de Dados , Projetos de Pesquisa/estatística & dados numéricos , Algoritmos , Simulação por Computador , Tamanho da Amostra
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