Biomedical subjects

Ulka B Campbell

Publications and source records attributed to Ulka B Campbell.

3 recordsLinked to original sources

PubMed2005

Distributional interaction: Interpretational problems when using incidence odds ratios to assess interaction.

It is well known that the incidence odds ratio approximates the risk ratio when the disease of interest is rare, but increasingly overestimates the risk ratio as the disease becomes more common. However when assessing interaction, incidence odds ratios may not approximate risk ratios even when the disease is rare. We use the term "distributional interaction" to refer to interaction that appears when using incidence odds ratios that does not appear, or appears to a lesser degree, when using risk ratios. The interpretational problems that arise from this discrepancy can have important implications in epidemiologic research. Therefore, quantification of the relationship between the interaction odds ratio and the interaction risk ratio is warranted. In this paper, we provide a formula to quantify the differences between incidence odds ratios and risk ratios when they are used to estimate effect modification on a multiplicative scale. Using this formula, we examine the conditions under which these two estimates diverge. Furthermore, we expand this discussion to the implications of using incidence odds ratios to assess effect modification on an additive scale. Finally, we illustrate how distributional interaction arises and the problems that it causes using an example from the literature. Whenever the risk of the outcome variable is non-negligible, distributional interaction is possible. This is true even when the disease is rare (e.g., disease risk is less than 5%). Therefore, when assessing interaction on either an additive or multiplicative scale, caution should be taken in interpreting interaction estimates based on incidence odds ratios.

Journal Article↗

PubMed2004

Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias.

BACKGROUND: The case-only study for investigating gene-environment interactions provides increased statistical efficiency over case-control analyses. This design has been criticized for being susceptible to bias arising from non-independence between the genetic and environmental factors in the population. Given that independence is critical to the validity of case-only estimates of interaction, researchers frequently use controls to evaluate whether the independence assumption is tenable, as advised in the literature. Our work investigates to what extent this approach is appropriate and how non-independence can be accounted for in case-only analyses. METHODS: We provide a formula in epidemiological terms that illustrates the relationship between the gene-environment association measured among controls and the gene-environment association in the source population. Using this formula, we conducted sensitivity analyses to describe the circumstances in which controls can be used as proxy for the source population when evaluating gene-environment independence. Lastly, we generated hypothetical cohort data to examine whether multivariable modelling approaches can be used to control for non-independence. RESULTS: Our sensitivity analyses show that controls should not be used to evaluate gene-environment independence in the population, even when the baseline risk of disease is low (i.e. 1%), and the interaction and independent effects are moderate (i.e. risk ratio = 2). When the factors are associated, it is possible to remove bias arising from non-independence using standard statistical multivariable techniques in case-only analyses. CONCLUSIONS: Even when the disease risk is low, evaluation of gene-environment independence in controls does not provide a consistent test for bias in the case-only study. Given that control for non-independence is possible when the source of the non-independence can be conceptualized, the case-only design may still be a useful epidemiological tool for examining gene-environment interactions.

Bias↗

PubMed2004

Treatment and referral patterns for colorectal cancer.

BACKGROUND: National guidelines recommend adjuvant chemotherapy for colorectal cancer stages III, and IV; however, it has been shown that only 45-55% of these patients receive chemotherapy. OBJECTIVES: We sought to describe treatment patterns for patients diagnosed with colorectal cancer and to examine the reasons why patients do not receive chemotherapy. RESEARCH DESIGN: This was a retrospective cohort study. SETTING AND PATIENTS: Patients included newly diagnosed cases of colorectal cancer at a health maintenance organization in central Massachusetts between January 1, 1997, and June 30, 1999. MAIN OUTCOME MEASURE: The main outcome measure was a referral or visit to an oncologist. RESULTS: Sixty-six percent (n=143) of the 217 colorectal cancer cases had a referral/visit to an oncologist or evidence of chemotherapy within 4 months of the index date. The referral rates by stage were: stage I, 47.7%; stage II, 59.5%; stage III, 87.1%; and stage IV, 66.7%. Of patients not referred with stage III disease, 4/8 were not referred because the treating physician did not recommend an oncology referral; patient refusal accounted for 3/8 (37.5%). The most commonly cited reason for lack of referral for stage IV patients was existing comorbidities or death. Younger age (<70 years) and stage III at diagnosis were significant predictors of oncology referral/visit. CONCLUSIONS: A substantial proportion of colorectal cancer patients are receiving appropriate referral for chemotherapy. This study is the first to elucidate reasons why patients do not receive chemotherapy and highlights both patient and physician factors.

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