Hamed Hosseini
1, Mohammad Ali Mansournia
1, Seyed Massood Nabavi
2, Ali Asghar Akhlaghi
3, Jaleh Gholami
4, Kazem Mohammad
1, Reza Majdzadeh
1,5*1 Department of Epidemiology and Biostatistics, Public Health School, Tehran University of Medical Sciences, Tehran, Iran
2 Neurology Group, Department of Regenerative Biomedicine, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
3 Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
4 Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
5 Knowledge Utilization Research Centre (KURC), Tehran University of Medical Sciences, Tehran, Iran
Abstract
Randomized clinical trials are considered the ideal source for generation of robust evidence for clinical and public health decision
making. Estimation of treatment effect in observational studies is always subject to varying degrees of bias due to lack of random
allocation, blindness, precise definition of intervention, as well as the existence of potential unknown and unmeasured confounding
variables. Unlike other conventional methods, instrumental variable analysis (IVA), as a method for controlling confounding bias
in non-randomized studies, attempts to estimate the treatment effect with the least bias even without knowing and measuring the
potential confounders in the causal pathway. In this paper, after understanding the main concepts of this approach, it has been
attempted to provide a method for analyzing and reporting the IVA for clinical researchers through a simplified example. The data
used in this paper is derived from the clinical data of the follow-up of multiple sclerosis (MS) patients treated with two class of
interferon.