Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification
Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspe...
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| Vydané v: | BMC medical research methodology Ročník 18; číslo 1; s. 63 - 12 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
BioMed Central
22.06.2018
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1471-2288, 1471-2288 |
| On-line prístup: | Získať plný text |
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| Abstract | Background
Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood.
Methods
In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response).
Results
Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased.
Conclusion
Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. |
|---|---|
| AbstractList | Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood.BACKGROUNDLog-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood.In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response).METHODSIn this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response).Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased.RESULTSPoint estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased.Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios.CONCLUSIONUnder model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. [...]we summarize the findings and provide recommendations for the use of these models in future studies in the “Discussion” section. The MSE was calculated by taking the sum of the squared bias in log scale and the variances, in which the bias was specified as \( \frac{1}{1,000}\sum \limits_{m=1}^{1,000}{\hat{\theta}}_m-\log (trueRR) \). Because both SE and MSE depended on the sample size, the process described above was repeated for sample of size 500 for all scenarios with RR = 3. The intercepts and the thresholds were generated using the same approach as described in the previous section. Because Z2 follows the uniform distribution, the thresholds increase proportionally with the beta coefficients. [...]in a previous examination (Additional file 3), we found when an important explanatory variable was omitted, a higher order term of non-linear explanatory variable was ignored, or an interaction term was overlooked, the two models produced comparable results regardless of the outcome rate, risk ratio or the strength of association between the exposure and the confounder or between the outcome and the confounder. Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. Methods In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Results Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Conclusion Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. Keywords: Log-binomial regression, Robust (modified) Poisson regression, Model misspecification, Risk ratio, Link function misspecification Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. Abstract Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. Methods In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Results Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Conclusion Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood. Methods In this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response). Results Point estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased. Conclusion Under model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios. |
| ArticleNumber | 63 |
| Audience | Academic |
| Author | Qian, Lei Chen, Wansu Franklin, Meredith Shi, Jiaxiao |
| Author_xml | – sequence: 1 givenname: Wansu surname: Chen fullname: Chen, Wansu email: Wansu.Chen@KP.org organization: Kaiser Permanente Southern California, Department of Research and Evaluation – sequence: 2 givenname: Lei surname: Qian fullname: Qian, Lei organization: Kaiser Permanente Southern California, Department of Research and Evaluation – sequence: 3 givenname: Jiaxiao surname: Shi fullname: Shi, Jiaxiao organization: Kaiser Permanente Southern California, Department of Research and Evaluation – sequence: 4 givenname: Meredith surname: Franklin fullname: Franklin, Meredith organization: Department of Preventive Medicine, Keck School of Medicine, University of Southern California |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29929477$$D View this record in MEDLINE/PubMed |
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| Snippet | Background
Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous... Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies... Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous... [...]we summarize the findings and provide recommendations for the use of these models in future studies in the “Discussion” section. The MSE was calculated by... Abstract Background Log-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables.... |
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| SubjectTerms | Asthma Bias Data analysis Economic models Estimates Health Sciences Link function misspecification Log-binomial regression Medicine Medicine & Public Health Methods Model misspecification Parameter estimation Patients Ratios Regression analysis Research Article Risk ratio Robust (modified) Poisson regression Statistical Theory and Methods statistics and modelling Statistics for Life Sciences Theory of Medicine/Bioethics |
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| Title | Comparing performance between log-binomial and robust Poisson regression models for estimating risk ratios under model misspecification |
| URI | https://link.springer.com/article/10.1186/s12874-018-0519-5 https://www.ncbi.nlm.nih.gov/pubmed/29929477 https://www.proquest.com/docview/2071652120 https://www.proquest.com/docview/2058501978 https://pubmed.ncbi.nlm.nih.gov/PMC6013902 https://doaj.org/article/3669b0e4339a453d8f22be1bb58c74ec |
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