Sufficient-cause modeling with matched data using SAS
Journal
Epidemiology
Journal Volume
24
Journal Issue
6
Pages
936-937
Date Issued
2013
Author(s)
Liao S.-F.
Abstract
To the Editor: Rothman��s sufficient-cause model is a useful construct for disease causation.1 It provides a synthesis of multiple interacting risk factors, jointly and collectively.1,2 It also helps to evaluate the impact of public-health interventions.1�V5 Sufficient-cause modeling for unmatched case-control data2,5 and person-time data3,4 is simple and can be implemented through the generalized linear model (GENMOD) procedure in SAS (SAS Institute, Cary, NC); this approach has been applied successfully in cardiovascular2,5 and cancer epidemiology.3,4 For matched data, extra programming is needed. Here we present simple SAS codes (eAppendix, https://links.lww.com/EDE/A716) illustrated with two examples: a matched case-control study6 and a survival dataset7 requiring a time-matched risk-set analysis. The unit of analysis is the matching set either confounder-matched (for matched case-control data) or time-matched (for survival data).5,8 For the former, the model is where is the disease odds for individuals at the th matching set who have a risk-factor profile of For the latter, the model is where is the disease rate of individuals at time who have a risk-factor profile of The ��intercepts�� of the models, the and the , are treated as nuisance variables and will be eliminated in the model-fitting process (conditional likelihood for matched case-control data; partial likelihood for survival data). Under the assumptions of no confounding, monotonicity, and independent competing causes, the �]-coefficients of the models correspond directly to the completion-potential indices for the various classes of sufficient causes (one completion-potential index for one class of sufficient causes; the completion-potential index for the all-unknown class is 1.0 by definition).5 A small-scale simulation study (eAppendix, https://links.lww.com/EDE/A716) shows that the completion-potential estimates are approximately unbiased. With additional algebra, other sufficient-cause�Vrelated indices (such as the individual-based and the population-wide causal-pie weights) and the attributable-fraction indices (such as the population attributable fraction and the attributable fraction among the exposed) can all be calculated from these completion-potential indices (see eAppendix, https://links.lww.com/EDE/A716, for the definitions of these indices).5 Confidence intervals (CIs) for all estimates are based on the bootstrap method.2,5,8 The first example is Leisure World Study of Endometrial Cancer,6 a 1:4 matched case-control study with 63 matching sets. After model fitting, the main effect of estrogen use has a �]-coefficient (which is also the completion-potential value for the class of sufficient causes containing estrogen use) of 7.0 (95% CI = 2.7�V18). This implies that this particular class of sufficient causes is seven times as likely to cause the disease as the all-unknown class. The second example is the Bone Marrow Transplant Patients Study,7 which followed 137 subjects for adverse outcomes after transplant surgery (leukemia relapse or death). The mean follow-up duration is 782 days with a total of 83 observed failures. The model shows a main effect of the French-American-British disease classification grade and an interactive effect of cytomegalovirus infection and methotrexate use. The �]-coefficients are approximately the same for the completion-potential index for the French-American-British class (1.4 [95% CI = 0.6�V3.0]) and for the interactive class between cytomegalovirus infection and methotrexate use (1.6 [0.6�V3.8]). However, the population-wide causal-pie weights for these two are quite different (23% vs. 14%) (Figure). Other sufficient-cause�Vrelated indices and the attributable-fraction indices for these two examples are presented in eAppendix (https://links.lww.com/EDE/A716).FIGURE: Estimates and 95% CIs of sufficient-cause�Vrelated indices for the Bone Marrow Transplant Patients Study.7 CMV indicates cytomegalovirus infection; CP, completion potential; CPW, causal-pie weight; FAB, French-American-British disease classification grade; MTX, methotrexate use.These easy-to-use SAS codes for sufficient-cause modeling with matched case-control and survival data should facilitate the use of sufficient-cause modeling. Shu-Fen Liao Wen-Chung Lee Research Center for Genes, Environment and Human Health, Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan, [email protected]
SDGs
Other Subjects
bootstrapping; cancer epidemiology; case control study; completion potential index; disease classification; letter; mathematical model; priority journal; risk factor; statistical model; sufficient cause model; survival; Case-Control Studies; Causality; Epidemiologic Research Design; Humans; Linear Models; Survival Analysis
Type
letter