Quantification of collider-stratification bias and the birthweight paradox.
The 'birthweight paradox' describes the phenomenon whereby birthweight-specific mortality curves cross when stratified on other exposures, most notably cigarette smoking. The paradox has been noted widely in the literature and numerous explanations and corrections have been suggested. Recently, causal diagrams have been used to illustrate the possibility for collider-stratification bias in models adjusting for birthweight. When two variables share a common effect, stratification on the variable representing that effect induces a statistical relation between otherwise independent factors. This bias has been proposed to explain the birthweight paradox. Causal diagrams may illustrate sources of bias, but are limited to describing qualitative effects. In this paper, we provide causal diagrams that illustrate the birthweight paradox and use a simulation study to quantify the collider-stratification bias under a range of circumstances. Considered circumstances include exposures with and without direct effects on neonatal mortality, as well as with and without indirect effects acting through birthweight on neonatal mortality. The results of these simulations illustrate that when the birthweight-mortality relation is subject to substantial uncontrolled confounding, the bias on estimates of effect adjusted for birthweight may be sufficient to yield opposite causal conclusions, i.e. a factor that poses increased risk appears protective. Effects on stratum-specific birthweight-mortality curves were considered to illustrate the connection between collider-stratification bias and the crossing of the curves. The simulations demonstrate the conditions necessary to give rise to empirical evidence of the paradox.
Whitcomb
Brian W
BW
Division of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA 01003-9304,USA. bwhitcomb@schoolph.umass.edu
Schisterman
Enrique F
EF
Perkins
Neil J
NJ
Platt
Robert W
RW
D000465:Algorithms; D015982:Bias; D001724:Birth Weight; D015984:Causality; D005260:Female; D006801:Humans; D007226:Infant Mortality; D007231:Infant, Newborn; D008297:Male; D015233:Models, Statistical; D015995:Prevalence; D012307:Risk Factors; D012907:Smoking / Q000009:adverse effects*
Environ Health Perspect. 2005 Jul;113(7):853-7
16002372
Epidemiol Perspect Innov. 2004 Oct 08;1(1):4
15507130
Epidemiology. 1999 Jan;10(1):37-48
9888278
Epidemiology. 2004 Sep;15(5):615-25
15308962
Am J Epidemiol. 1983 Dec;118(6):857-64
6650487
Am J Epidemiol. 1971 Jun;93(6):443-56
5562717
Am J Epidemiol. 2006 Aug 15;164(4):312-4; discussion 315-6
16847041
Bull World Health Organ. 1987;65(5):663-737
3322602
Int J Epidemiol. 2002 Feb;31(1):163-5
11914314
Am J Epidemiol. 2004 Aug 1;160(3):211-2; discussion 215-6
15257991
Am J Epidemiol. 2006 Dec 1;164(11):1121-3; discussion 1124-5
16931545
Int J Epidemiol. 1983 Sep;12(3):319-25
6685112
J Obstet Gynaecol Can. 2004 Nov;26(11):953-60
15560858
Am J Epidemiol. 2004 Aug 1;160(3):199-206
15257989
Stat Med. 2005 Jun 15;24(11):1683-702
15742358
Int J Epidemiol. 1983 Sep;12(3):314-8
6685111
Epidemiology. 2009 Jul;20(4):488-95
19525685
Eur J Epidemiol. 2008;23(3):163-6
18224448
Am J Epidemiol. 2006 Dec 1;164(11):1115-20
16931543
Am J Epidemiol. 2002 Jan 15;155(2):176-84
11790682
Epidemiology. 2003 May;14(3):300-6
12859030
Artif Intell Med. 1995 Dec;7(6):561-82
8963376
Paediatr Perinat Epidemiol. 2003 Apr;17(2):114-6
12675775
Am J Epidemiol. 2006 Aug 15;164(4):303-11
16847040
Int J Epidemiol. 1986 Jun;15(2):188-96
3721681
Epidemiology. 1992 Mar;3(2):143-55
1576220
Hum Reprod. 2007 Dec;22(12):3267
17940070
Int J Epidemiol. 2003 Aug;32(4):632-3
12913041
Am J Epidemiol. 2001 Feb 15;153(4):332-7
11207150
Int J Epidemiol. 2001 Dec;30(6):1233-41
11821313