Squeezing observational data for better causal inference: Methods and examples for prevention research.
Randomised controlled trials (RCTs) are typically viewed as the gold standard for causal inference. This is because effects of interest can be identified with the fewest assumptions, especially imbalance in background characteristics. Yet because conducting RCTs are expensive, time consuming and sometimes unethical, observational studies are frequently used to study causal associations. In these studies, imbalance, or confounding, is usually controlled with multiple regression, which entails strong assumptions. The purpose of this manuscript is to describe strengths and weaknesses of several methods to control for confounding in observational studies, and to demonstrate their use in cross-sectional dataset that use patient registration data from the Juan Pablo II Primary Care Clinic in La Pintana-Chile. The dataset contains responses from 5855 families who provided complete information on family socio-demographics, family functioning and health problems among their family members. We employ regression adjustment, stratification, restriction, matching, propensity score matching, standardisation and inverse probability weighting to illustrate the approaches to better causal inference in non-experimental data and compare results. By applying study design and data analysis techniques that control for confounding in different ways than regression adjustment, researchers may strengthen the scientific relevance of observational studies.
Department of Family Medicine, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile.|Department of Family Social Science, University of Minnesota, Saint Paul, MN, USA.|Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN, USA.
Diego
D
Garcia-Huidobro
Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
J
J
Michael Oakes
D000293:Adolescent; D000328:Adult; D001289:Attention Deficit Disorder with Hyperactivity / Q000517:prevention & control*; D002648:Child; D002677:Chile; D015986:Confounding Factors, Epidemiologic; D003430:Cross-Sectional Studies; D003627:Data Interpretation, Statistical; D005190:Family; D005191:Family Characteristics; D005192:Family Health; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D064887:Observational Studies as Topic / Q000379:methods*; D057216:Propensity Score; D012044:Regression Analysis; D012107:Research Design; D012959:Socioeconomic Factors; D055815:Young Adult
Causal inference; Methodology; Methods; Observational studies; Randomised trials