Estimating the response rate in the presence of measurement error

Statistics in Medicine
Yongming QuTodd M Sanger

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.

References

Jul 16, 2002·Current Opinion in Rheumatology·Sydney Lou Bonnick
Dec 5, 2002·Journal of Clinical Densitometry : the Official Journal of the International Society for Clinical Densitometry·Leon LenchikUNKNOWN International Society for Clinical Densitometry Position Development Panel and Scientific Advisory Committee
Sep 25, 2003·Osteoporosis International : a Journal Established As Result of Cooperation Between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA·Mary L Bouxsein
Jan 13, 2004·Journal of Clinical Densitometry : the Official Journal of the International Society for Clinical Densitometry·E Michael Lewiecki

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Citations

Sep 25, 2007·Journal of Clinical Densitometry : the Official Journal of the International Society for Clinical Densitometry·William D LeslieLiqun Wang
Jul 7, 2010·Statistics in Medicine·Wen Li, Yongming Qu
Oct 5, 2007·Statistics in Medicine·Steven Snapinn, Qi Jiang

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