PubMed · 15165629
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
Garrett Fitzmaurice. 2004. Adjusting for confounding.. https://doi.org/10.1016/j.nut.2004.03.001
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
PURPOSE: Propensity scores are used in observational studies to adjust for confounding, although they do not provide control for confounders omitted from the propensity score model. We sought to determine if tests used to evaluate logistic model fit and discrimination would be helpful in detecting the omission of an important confounder in the propensity score. METHODS: Using simulated data, we estimated propensity scores under two scenarios: (1) including all confounders and (2) omitting the binary confounder. We compared the propensity score model fit and discrimination under each scenario, using the Hosmer-Lemeshow goodness-of-fit (GOF) test and the c-statistic. We measured residual confounding in treatment effect estimates adjusted by the propensity score omitting the confounder. RESULTS: The GOF statistic and discrimination of propensity score models were the same for models excluding an important predictor of treatment compared to the full propensity score model. The GOF test failed to detect poor model fit for the propensity score model omitting the confounder. C-statistics under both scenarios were similar. Residual confounding was observed from using the propensity score excluding the confounder (range: 1-30%). CONCLUSIONS: Omission of important confounders from the propensity score leads to residual confounding in estimates of treatment effect. However, tests of GOF and discrimination do not provide information to detect missing confounders in propensity score models. Our findings suggest that it may not be necessary to compute GOF statistics or model discrimination when developing propensity score models.
Explore the source record for details and available documents.
OBJECTIVES: To review current practice in the analysis and reporting of epidemiological research and to identify limitations. DESIGN: Examination of articles published in January 2001 that investigated associations between risk factors/exposure variables and disease events/measures in individuals. SETTING: Eligible English language journals including all major epidemiological journals, all major general medical journals, and the two leading journals in cardiovascular disease and cancer. MAIN OUTCOME MEASURE: Each article was evaluated with a standard proforma. RESULTS: We found 73 articles in observational epidemiology; most were either cohort or case-control studies. Most studies looked at cancer and cardiovascular disease, even after we excluded specialty journals. Quantitative exposure variables predominated, which were mostly analysed as ordered categories but with little consistency or explanation regarding choice of categories. Sample selection, participant refusal, and data quality received insufficient attention in many articles. Statistical analyses commonly used odds ratios (38 articles) and hazard/rate ratios (23), with some inconsistent use of terminology. Confidence intervals were reported in most studies (68), though use of P values was less common (38). Few articles explained their choice of confounding variables; many performed subgroup analyses claiming an effect modifier, though interaction tests were rare. Several investigated multiple associations between exposure and outcome, increasing the likelihood of false positive claims. There was evidence of publication bias. CONCLUSIONS: This survey raises concerns regarding inadequacies in the analysis and reporting of epidemiological publications in mainstream journals.