Insights on bias and information in group-level studies.
Ecological and aggregate data studies are examples of group-level studies. Even though the link between the predictors and outcomes is not preserved in these studies, inference about individual-level exposure effects is often a goal. The disconnection between the level of inference and the level of analysis expands the array of potential biases that can invalidate the inference from group-level studies. While several sources of bias, specifically due to measurement error and confounding, may be more complex in group-level studies, two sources of bias, cross-level and model specification bias, are a direct consequence of the disconnection. With the goal of aligning inference from individual versus group-level studies, I discuss the interplay between exposure and study design. I specify the additional assumptions necessary for valid inference, specifically that the between- and within-group exposure effects are equal. Then cross-level inference is possible. However, all the information in the group-level analysis comes from between-group comparisons. Models where the group-level analysis provides even a small percentage of information about the within-group exposure effect are most susceptible to model specification bias. Model specification bias can be even more serious when the group-level model isn't derived from an individual-level model.
Sheppard
Lianne
L
Departments of Biostatistics and Environmental Health, Box 357232, University of Washington, Seattle, WA 98195-7232, USA. sheppard@u.washington.edu
D000704:Analysis of Variance; D015982:Bias; D015984:Causality; D003198:Computer Simulation; D003625:Data Collection / Q000706:statistics & numerical data; D004041:Dietary Fats / Q000009:adverse effects; D004781:Environmental Exposure; D015340:Epidemiologic Research Design; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology; D012306:Risk; D016008:Statistical Distributions
D004041:Dietary Fats