Identifying Placebo Responders in Drug Treated Subjects

确定药物治疗受试者中的安慰剂反应者

• 批准号: 7027013
• 负责人: THADDEUS TARPEY
• 金额: $ 18.55万
• 依托单位: WRIGHT STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-05 至 2007-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7027013
• 关键词: behavioral medicine; depression; human data; human therapy evaluation; longitudinal human study; mathematical model; mental disorder chemotherapy; mental disorders; model design /development; placebos; psychopharmacology; statistics /biometry

DESCRIPTION (provided by applicant): In order to determine the efficacy of pharmacological interventions in clinical trials, the placebo effect must be taken into consideration. It is important to distinguish placebo effects from the effects of the actual treatment being tested. Most studies of the placebo effect have looked at responders who have been treated with a placebo and have tried to identify factors that can predict such response. This is obviously a very limited approach because data from subjects treated with the active drug is ignored. The research proposed here is to develop statistical methodology for identifying and differentiating placebo responses from true drug responses in the treatment of mental illnesses such as depression where placebo response rates tend to be high. The initial research will focus on clinical trial data studying treatments for depression. The proposed statistical methodology will combine functional data analysis with cluster analysis and mixture models. Outcome profiles for individual subjects will be estimated using longitudinal data from clinical trials. Appropriate basis functions will be determined to estimate the profile trajectories. The profiles will then be described by a small number of estimated basis function coefficients. Depending on the distribution of estimated coefficients, representative profiles will be estimated using principal point/cluster analysis or a finite mixture analysis. Data from the placebo arm of the studies will also be used to estimate and validate the representative profiles. The representative profiles will then be used to classify future subjects as placebo responders, true drug responders or a combination of a drug-placebo responder. Further work will refine the methodology by incorporating random effects models and addressing problems such as missing data. The derived models will be cross-classified with clinician determined responder/non-responder status for validation. In addition, data from discontinuation studies are available and will be used to further validate the models for placebo response. The second phase of the study will apply the methodology to characterize classes of drugs (ssri, tricyclics and maoi) with respect to their placebo response profiles. The final phase of the research will apply the methodology of determining the placebo effect from the true drug effect in other mental illnesses such as anxiety, obsessive compulsive disorders, panic and post traumatic stress syndrome.

描述（由申请人提供）：为了确定临床试验中药物干预的有效性，必须考虑安慰剂效应。重要的是要区分安慰剂效应和实际治疗的效应。大多数关于安慰剂效应的研究都着眼于接受安慰剂治疗的反应者，并试图确定可以预测这种反应的因素。这显然是一种非常有限的方法，因为忽略了接受活性药物治疗的受试者的数据。本文提出的研究旨在开发统计方法，用于识别和区分安慰剂反应与治疗抑郁症等精神疾病的真实药物反应，其中安慰剂反应率往往很高。最初的研究将集中在研究抑郁症治疗的临床试验数据上。拟议的统计方法将联合收割机功能数据分析与聚类分析和混合模型相结合。将使用临床试验的纵向数据估计个体受试者的结局特征。将确定适当的基函数来估计轮廓轨迹。然后将通过少量估计的基函数系数来描述轮廓。根据估计系数的分布，将使用主点/聚类分析或有限混合分析来估计代表性曲线。研究安慰剂组的数据也将用于估计和验证代表性特征。然后将使用代表性特征将未来受试者分类为安慰剂应答者、真正药物应答者或药物-安慰剂应答者的组合。进一步的工作将通过纳入随机效应模型和解决数据缺失等问题来完善方法。将推导出的模型与临床医生确定的应答者/非应答者状态交叉分类，以进行验证。此外，中止研究的数据可用，并将用于进一步验证安慰剂应答模型。研究的第二阶段将应用该方法来描述药物类别（ssri，三环类药物和maoi）的安慰剂反应特征。研究的最后阶段将采用从其他精神疾病（如焦虑、强迫症、恐慌和创伤后应激综合征）的真实药物效应中确定安慰剂效应的方法。