title: Estimating the response rate in the presence of measurement error.
abstract: In clinical research, it is often of interest to estimate the response rate (i.e. the proportion of subjects who achieve a clinically meaningful threshold) for a particular variable. The standard estimator of the response rate is generally biased in the presence of measurement error. The estimation accounting for the measurement error utilizing fully nonparametric (NP) methods is complicated and may not be efficient. Therefore, we propose a model-based approach assuming a parametric model for the true value and only the first few moments for the measurement error. The estimator for the true response rate and the variance for the estimator are derived. An innovative method using bootstrap simulation is proposed to check the model assumption. Simulations show that the proposed estimator outperforms a fully NP estimator if the model assumption for X holds. This method is applied to address a commonly occurring question in osteoporosis regarding response to treatment in terms of longitudinal changes in bone mineral density (BMD). Bootstrap simulations showed that the model utilized is appropriate. The proposed method can also be applied in other fields of clinical research.
authors.affiliation: Eli Lilly and Company, Indianapolis, IN 46285, USA. qu_yongming@lilly.com
authors.forename: Yongming
authors.initials: Y
authors.lastname: Qu
authors.forename: Pandurang M
authors.initials: PM
authors.lastname: Kulkarni
authors.forename: Todd M
authors.initials: TM
authors.lastname: Sanger
mesh_terms: D015502:Absorptiometry, Photon / Q000706:statistics & numerical data; D015982:Bias; D001699:Biometry; D015519:Bone Density; D016001:Confidence Intervals; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010024:Osteoporosis / Q000188:drug therapy / Q000378:metabolism; D016896:Treatment Outcome