Obesity Epidemiology

INTERPRETING EPIDEMIOLOGIC EVIDENCE AND CAUSAL INFERENCE 39substantial selection bias because participants who stay often differ from those who dropout. Traditional methods of statistical analysis, such as last observation carried forward(LOCF) or imputation techniques, cannot easily correct for such bias. 4Noncompliance, when participants do not adhere to assigned dietary regimens orlifestyle interventions, is a related problem. The longer the follow-up period of a trial,the less likely participants are to adhere to the assigned intervention. For example, during8 years of follow-up in the Women’s Health Initiative (WHI), most of those randomized tothe low-fat group were unable to achieve the target fat reduction goal of 20%. 5 Significantreductions in fat intake are usually reflected in a decrease in high-density lipoprotein(HDL) cholesterol and an increase in triglycerides. 6 Yet in the trial, there were no appreciabledifferences in blood levels of HDL cholesterol or triglycerides between the low-fatand usual diet groups, although there was a modest reduction in low-density lipoprotein(LDL) cholesterol in the low-fat group. These outcomes called into question the degreeof fat reduction achieved in this study. Reduced compliance, which typically biases theresults toward null, complicated the interpretation of the findings.The long-term nature of RCTs puts even the best-designed interventions at riskof becoming obsolete. For example, when the WHI was designed in the early 1990s,a low-fat dietary pattern was the prevailing recommendation for weight loss andprevention of chronic diseases, such as cancer and coronary heart disease (CHD).During the course of the study, substantial clinical and epidemiologic evidenceemerged showing that type of fat was more important than total amount of fat inreducing risk of CHD, and that substitution of fat for carbohydrates was unlikely tohave appreciable effects on either weight loss or risk of CHD. 7 These new data weakenedthe original justification for the intervention, which could not be changed oncethe trial was underway.Ethical, logistical, financial, and methodological constraints put large diet and exerciseRCTs with hard disease end points such as CHD or mortality out of reach formost investigators. Thus, prospective cohort studies remain the mainstay of research onthe consequences and determinants of obesity. Growing numbers of such studies andpublicly available electronic databases offer unprecedented opportunities for obesityresearch. They also challenge investigators to apply sound epidemiologic principles andscientific rigor to study design, analyses, and interpretations of data. There are severalthreats to the internal validity of cohort studies, even those that are well powered andhave minimal loss to follow-up. These include confounding, measurement error, andreverse causation. We will discuss each of these issues in the following sections.ConfoundingConfounding in Studies on the Consequences of ObesityConfounding refers to distortion of the association between an exposure and disease,brought about by the association of a third variable that influences the outcome underinvestigation. 8 Typically, the confounder is correlated with both the exposure and theoutcome, and is not on the causal pathway between exposure and disease. Confoundingis one of the most important threats to the validity of nonrandomized studies. Althoughthere is no formal statistical test to identify confounding, there is a rule of thumb—a 10% or greater change in the magnitude of the association between exposure and diseaseafter controlling for the confounding variable. 9 Cigarette smoking is a classic exampleof confounding in studies of obesity and mortality. Smokers tend to be leaner and