Approaches for estimating prevalence ratios

Deddens, James A.; Petersen, Martin R.

doi:10.1136/oem.2007.034777

Cited by 322 publications

(273 citation statements)

References 21 publications

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“…As mentioned above, a problem of nonconvergence of the log-binomial model did not occur in our simulations, something which it is known does not always occur in practice 5,6,7,21 . We believe that this occurred due to the design of our simulation, with only one binary factor, and to the discarding of the contingency table with null cells.…”

Section: Discussionmentioning

confidence: 63%

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Bias of using odds ratio estimates in multinomial logistic regressions to estimate relative risk or prevalence ratio and alternatives

et al. 2014

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Bias of using odds ratio estimates in multinomial logistic regressions to estimate relative risk or prevalence ratio and alternatives Viés da razão de chances estimada pela regressão logística multinomial para estimar o risco relativo ou a razão de prevalência e alternativas El sesgo del odds ratio estimado por la regresión logística multinomial para estimar el riesgo relativo o la razón de prevalencia y alternativas

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Section: Discussionmentioning

confidence: 63%

“…Several authors have shown that the odds ratio (OR) obtained by the logistic model can underestimate or overestimate the RR or the PR 1,2,3,4 . Alternatives have also been suggested and discussed, such as the use of the Poisson regression with robust variance and the log-binomial model 1,3,4,5,6,7,8,9,10 .…”

Section: Introductionmentioning

confidence: 99%

Bias of using odds ratio estimates in multinomial logistic regressions to estimate relative risk or prevalence ratio and alternatives

et al. 2014

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“…Because >10% of the population had the outcome, robust Poisson regression was used instead of logistic regression to avoid overestimation of risk ratios. 28,29 Reporting both Cox and robust Poisson regression (at 2 and 3 years) alongside univariate data is justified on the grounds of establishing parameter stability. Two-and 3-year survival times were chosen based on the number of events (ie, events were too infrequent at 1 year) and the finding that this period is clinically relevant to this population.…”

Section: Discussionmentioning

confidence: 99%

A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

MSc

Baracos

Winget

et al. 2010

Cancer

138

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BACKGROUND: Cancer survival is related to features of the primary malignancy and concurrent presence of nonmalignant diseases (comorbidities), including weight-related conditions (obesity, weight loss). The Charlson and Elixhauser methods are 2 well-known methods that take comorbidities into account when explaining survival. They differ in both the number and categorization of comorbidities. METHODS: Cancer, comorbidity, and survival data were acquired from inpatient administrative hospital records in 574 colorectal cancer patients. Robust Poisson regression was used to analyze 2-and 3-year survival according to cancer features and comorbidities classified by the Charlson and Elixhauser methods. Data for weight-related conditions (body mass index, weight loss) and performance status were acquired upon a new patient visit to the regional cancer center. Discrimination was assessed with the concordance Overall, the Elixhauser comorbidities outperformed the Charlson comorbidities (P < .05). The use of self-reported weight and performance status data significantly increased discrimination by the Elixhauser method in 2-year but not 3-year survival. CONCLUSIONS: The Elixhauser method is a superior comorbidity risk-adjustment model for colorectal cancer survival prediction.

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“…To summarize the independent associations of multiple explanatory variables simultaneously, log-binomial regression analysis with backward selection was conducted. For convergence issues in the log-binomial model, the COPY method 19 was used with 999 copies. To facilitate interpretation, this method, which provides relative risk estimates, was selected for use in the current analysis over the more popular logistic regression, which provides odds ratio estimates.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of patients with stage III colon adenocarcinoma who fail to receive guideline‐recommended treatment

PhD

Hossain

Yasui

et al. 2010

Cancer

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BACKGROUND: Many patients with stage III colon adenocarcinoma do not receive adjuvant chemotherapy despite the proven survival advantage it offers. To enhance the provision of optimal cancer care, patient characteristics associated with not receiving guideline-adherent treatment must be identified among patients with operable, stage III colon adenocarcinoma. METHODS: This was a population-based, retrospective study of all patients who underwent surgery for stage III colon adenocarcinoma diagnosed from 2002 through 2005 in Alberta, Canada. Demographic and treatment information captured in the Alberta Cancer Registry were linked to: 1) hospital discharge data to determine comorbidities, 2) electronic medical records to identify consults with oncologists, and 3) the 2001 Canadian census for neighborhood-level socioeconomic data. Multivariate log-binomial regression models were used to identify patient characteristics that were associated with not having a consultation with a medical oncologist and not receiving adjuvant chemotherapy. RESULTS: Of the 772 patients who underwent surgery for stage III colon adenocarcinoma and met the eligibility criteria, 618 patients (80%) had a consultation with an oncologist. Of those, 388 patients (63%) initiated adjuvant chemotherapy within 84 days of their surgery. Patient characteristics that were associated with not having a consultation with an oncologist were neighborhood income, geography, age, and comorbidities. Of those patients who had a consultation, after adjusting for comorbidities, only older age was related to not receiving adjuvant chemotherapy. CONCLUSIONS: The current results indicated that the proportion of patients with stage III colon adenocarcinoma who did not receive treatment according to evidence-based guidelines was appreciable. The authors concluded that the association of this failure with patient age, geography, and income is concerning and that evaluation of referral patterns and interventions are needed. Cancer 2010;116:4849-56.

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Approaches for estimating prevalence ratios

Cited by 322 publications

References 21 publications

Bias of using odds ratio estimates in multinomial logistic regressions to estimate relative risk or prevalence ratio and alternatives

Bias of using odds ratio estimates in multinomial logistic regressions to estimate relative risk or prevalence ratio and alternatives

A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

Characteristics of patients with stage III colon adenocarcinoma who fail to receive guideline‐recommended treatment

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