روش هاي وزن دهي احتمال معكوس و جانهي چندگانه براي تحليل پاسخ در حالت گمشدگي

Inverse-Probability Weighting and Multiple Imputation Methods for Analyzing Missing in the Response

رﯾﺰش داده ﻫﺎ و ﻣﻘﺎدﯾﺮ ﮔﻤﺸﺪه از ﻣﺸﮑﻼت ﻣﻌﻤﻮل در ﺗﺤﻠﯿﻞ داده ﻫﺎ ﻣﺤﺴﻮب ﻣﯽ ﺷﻮد. ﻟﺬا اﻫﻤﯿﺖ دارد ﮐﻪ ﺑﺎ ﺑﺮآورد ﻣﻘﺎدﯾﺮ ﮔﻤﺸﺪه، داده ﻫﺎ ﮐﺎﻣﻞ ﺷﺪه و در ﻣﺴﯿﺮي ﻣﻨﺎﺳﺐ و ﺻﺤﯿﺢ ﺑﺮاي ﺗﺤﻠﯿﻞ ﻗﺮار داده ﺷﻮﻧﺪ. دو روش ﻣﻌﻤﻮل ﺑﺮاي ﻣﻘﺎﺑﻠﻪ ﺑﺎ داده ﻫﺎي ﮔﻤﺸﺪه »ﺟﺎﻧﻬﯽ ﭼﻨﺪﮔﺎﻧﻪ« و »وزن دﻫﯽ اﺣﺘﻤﺎل ﻣﻌﮑﻮس« ﻫﺴﺘﻨﺪ. در اﯾﻦ ﻣﻘﺎﻟﻪ، روﯾﮑﺮد ﺳﻮﻣﯽ ﻣﻌﺮﻓﯽ ﺧﻮاﻫﺪ ﺷﺪ ﮐﻪ ﺗﺮﮐﯿﺒﯽ از دو روش ﺟﺎﻧﻬﯽ ﭼﻨﺪﮔﺎﻧﻪ و وزن دﻫﯽ اﺣﺘﻤﺎل ﻣﻌﮑﻮس اﺳﺖ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﻧﺘﺎﯾﺞ ﺣﺎﺻﻞ از ﻣﻄﺎﻟﻌﻪ ﺷﺒﯿﻪ ﺳﺎزي روش ﺗﺮﮐﯿﺒﯽ ﻣﺰاﯾﺎي ﺑﯿﺸﺘﺮي ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﯾﺮ ﮔﺰﯾﻨﻪ ﻫﺎ دارد. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ وﺟﻮد ﻣﻘﺎدﯾﺮ ﮔﻤﺸﺪه در اﮐﺜﺮ ﻣﻄﺎﻟﻌﺎت ﺑﻪ ﺧﺼﻮص در ﺣﻮزه ﭘﺰﺷﮑﯽ، ﻧﺎدﯾﺪه ﮔﺮﻓﺘﻦ آن ﻫﺎ ﺳﺒﺐ ﺗﺤﻠﯿﻞ اﺷﺘﺒﺎه ﺷﺪه و اﺳﺘﻔﺎده از روش ﻫﺎي ﻧﯿﺮوﻣﻨﺪ، ﺑﺮاي ﺗﺤﻠﯿﻞ ﺻﺤﯿﺢ، ﺑﺎ اﻫﻤﯿﺖ ﺗﻠﻘﯽ ﻣﯽ ﺷﻮد.

Data loss and missing values is a common problem in data analysis. Therefore, it is important that by estimating missing values, the data was completed and placed in the proper path. Two approaches commonly used to deal with missing data are multiple imputation (MI) and inverse-probability weighting (IPW). In this study, a third approach which is a combination of MI and IPW will be introduced. It can be said by results of the simulation study that IPW/MI can have advantages over alternatives. Regarding the missing values in most studies, especially in the medical field, ignoring them leads to wrong analysis. So, using of robust methods to proper analysis of missing values is essential.