RT Book, Section
A1 Reyes, Eric M.
A1 Thomas, Laine E.
A2 Lopes, Renato D.
A2 Harrington, Robert A.
SR Print(0)
ID 57836740
T1 Chapter 15. Analytical Methods of Addressing Confounding
T2 Understanding Clinical Research
YR 2013
FD 2013
PB The McGraw-Hill Companies
PP New York, NY
SN 978-0-07-174678-6
LK accessmedicine.mhmedical.com/content.aspx?aid=57836740
RD 2024/04/19
AB Chapter  13  discussed  many  of  the  obstacles  inherent  in  the  analysis  of  data  from  observational  studies,  with  bias  due  to  confounding  arguably  the  largest  of  these  concerns.  Confounding  can  be  seen  as  an  issue  of  “mistaken  identity,”  in  which  the  cause  of  an  observed  effect  is  attributed  to  the  wrong  party.  As  an  example,  consider  a  cohort  study  undertaken  to  assess  the  efficacy  of  a  treatment.  In  this  cohort,  younger  people  are  more  likely  to  receive  the  treatment  and  are  less  likely  to  experience  the  outcome  of  interest  (Figure  15–1).  If  a  treatment  effect  is  observed,  it  is  unclear  whether  the  observed  effect  is  due  to  the  treatment  or  to  the  younger  age  of  the  patients  receiving  the  treatment.  That  is,  age  and  treatment  are  said  to  be  confounded;  equivalently,  age  is  said  to  be  a  confounder.  Formally,  a  confounder  is  any  variable  related  to  both  the  outcome  of  interest  and  the  treatment  under  study.  In  our  example,  age  affects  both  the  event  rate  and  which  treatment  the  person  receives.