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Probability sampling in matched case-control study in drug abuse | 34737
Journal of Proteomics & Bioinformatics

Journal of Proteomics & Bioinformatics
Open Access

ISSN: 0974-276X

+44 1223 790975

Probability sampling in matched case-control study in drug abuse


7th International Conference on Proteomics & Bioinformatics

October 24-26, 2016 Rome, Italy

Surya Raj Niraula, Devendra B Chhetry, Girish K Singh and S Nagesh

B P Koirala Institute of Health Sciences, Nepal

Scientific Tracks Abstracts: J Proteomics Bioinform

Abstract :

Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. We compared the statistical properties of two surveys of drug abuse in the same community: One using snowball sampling of drug users who then identified â�?�?friend controlsâ�? and the other using a random sample of non-drug users (controls) who then identified â�?�?friend casesâ�?. Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. On the other hand, bootstrap analysis of the random-sample data set showed less variation and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93 for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p<.001). The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Biography :

Surya Raj Niraula has completed his PhD from Tribhuvan University and Post-doctoral from University of Washington, USA. He is the Professor of Biostatistics at B P Koirala Institute of Health Sciences, Nepal. He has published more than five dozen of papers in reputed journals and has been serving as a Statistical Reviewer in many national and international journals. He was awarded with ‘Young Scientist Award’ in 2009, Nepal Bidhyabhusan KA in 2010 and Honored by the President - Constitutional Assembly, 2011. He has presented many conference papers in USA, UK, Korea, Singapore, Thailand, India and Nepal.

Email: sniraula@yahoo.com

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