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      Individual patient- versus group-level data meta-regressions for the investigation of treatment effect modifiers: ecological bias rears its ugly head

      , , , , , ANTI-LYMPHOCYTE ANTIBODY INDUCTION THERAPY STUDY GROUP
      Statistics in Medicine
      Wiley

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          Abstract

          When performing a meta-analysis, interest often centres on finding explanations for heterogeneity in the data, rather than on producing a single summary estimate. Such exploratory analyses are frequently undertaken with published, study-level data, using techniques of meta-analytic regression. Our goal was to explore a real-world example for which both published, group-level and individual patient-level data were available, and to compare the substantive conclusions reached by both methods. We studied the benefits of anti-lymphocyte antibody induction therapy among renal transplant patients in five randomized trials, focusing on whether there are subgroups of patients in whom therapy might prove particularly beneficial. Allograft failure within 5 years was the endpoint studied. We used a variety of analytic approaches to the group-level data, including weighted least-squares regression (N=5 studies), logistic regression (N=628, the total number of subjects), and a hierarchical Bayesian approach. We fit logistic regression models to the patient-level data. In the patient-level analysis, we found that treatment was significantly more effective among patients with elevated (20 per cent or more) panel reactive antibodies (PRA) than among patients without elevated PRA. These patients comprise a small (about 15 per cent of patients) subgroup of patients that benefited from therapy. The group-level analyses failed to detect this interaction. We recommend using individual patient data, when feasible, to study patient characteristics, in order to avoid the potential for ecological bias introduced by group-level analyses. Copyright 2002 John Wiley & Sons, Ltd.

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          Most cited references33

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          A random-effects regression model for meta-analysis.

          Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.
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            The fallacy of the ecological fallacy: the potential misuse of a concept and the consequences.

            Ecological studies have been evaluated in epidemiological contexts in terms of the "ecological fallacy." Although the empirical evidence for a lack of comparability between correlations derived from ecological- and individual-level analyses is compelling, the conceptual meaning of the ecological fallacy remains problematic. This paper argues that issues in cross-level inference can be usefully conceptualized as validity problems, problems not peculiar to ecological-level analyses. Such an approach increases the recognition of both potential inference problems in individual-level studies and the unique contributions of ecological variables. This, in turn, expands the terrain for the location of causes for disease and interventions to improve the public's health.
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              Uses of ecologic analysis in epidemiologic research.

              Despite the widespread use of ecologic analysis in epidemiologic research and health planning, little attention has been given by health scientists and practitioners to the methodological aspects of this approach. This paper reviews the major types of ecologic study designs, the analytic methods appropriate for each, the limitations of ecologic data for making causal inferences and what can be done to minimize these problems, and the relative advantages of ecologic analysis. Numerous examples are provided to illustrate the important principles and methods. A careful distinction is made between ecologic studies that generate or test etiologic hypotheses and those that evaluate the impact of intervention programs or policies (given adequate knowledge of disease etiology). Failure to recognize this difference in the conduct of ecologic studies can lead to results that are not very informative or that are misinterpreted by others.
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                Author and article information

                Journal
                Statistics in Medicine
                Statist. Med.
                Wiley
                0277-6715
                1097-0258
                February 15 2002
                February 15 2002
                2002
                : 21
                : 3
                : 371-387
                Article
                10.1002/sim.1023
                11813224
                1651fb55-7563-4690-b564-f58be76c7cae
                © 2002

                http://doi.wiley.com/10.1002/tdm_license_1.1

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                Self URI (article page): http://doi.wiley.com/10.1002/sim.1023

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