Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation
Background: Risk Difference (RD) is becoming the measure of choice for estimating effect size in antimalarial drug efficacy trials. Calculating RD using binomial regression is prone to model nonconvergence. Cheung's modified ordinary least squares (OLS) method is a proven technique for handling...
| Үндсэн зохиолчид: | , , , , , |
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| Формат: | Journal article |
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Herbert Publications Limited
2016
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| _version_ | 1826296878053982208 |
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| author | Mukaka, M White, SA Mwapasa, V Kalilani-Phiri, L Terlouw, D Faragher, E |
| author_facet | Mukaka, M White, SA Mwapasa, V Kalilani-Phiri, L Terlouw, D Faragher, E |
| author_sort | Mukaka, M |
| collection | OXFORD |
| description | Background: Risk Difference (RD) is becoming the measure of choice for estimating effect size in antimalarial drug efficacy trials. Calculating RD using binomial regression is prone to model nonconvergence. Cheung's modified ordinary least squares (OLS) method is a proven technique for handling non-convergence when estimating RD. Other promising methods include the Poison, Additive Binomial Regression and binary regression models fitted using the statistical package R. (Deddens') Copy method that was primarily developed to overcome non-convergence of log-binomial regression models when estimating risk ratios is another potential method. Simulations were conducted to compare the performance of the Copy method against four alternatives (Cheung's modified OLS method, the Additive Binomial Regression Model fitted with the blm algorithm, the binary regression model fitted with the glm2 algorithm, and the Poisson model with identity link and robust standard errors fitted with the glm algorithm) for obtaining RD estimates when a binomial model fails to converge. Methods: We computed estimates of efficiency and bias with treatment arm efficacies of (a) 60% vs. 85%, (b) 95% vs. 90%, (iii) 95% vs. 98% using simulation studies. A total of 5,000 datasets were simulated under each of these three scenarios. Results: The modified OLS method and the binary regression model fitted using the glm2 algorithm in R provided unbiased, efficient estimates of RD across all assessed scenarios. In contrast, the Copy method yielded biased estimates of RD even when 100% convergence was achieved. The Poisson and Additive Binomial Regression models had 100% and almost 100% convergence rates respectively, but both produced very slightly biased RD estimates. Conclusion: The Copy method is not suitable for estimating RD when binomial regression model fitting fails to converge. Cheung's modified OLS or the binary regression model fitted using the glm2 algorithm in R should be the method of choice to overcome non-convergence with binomial models for calculating adjusted RD estimates. |
| first_indexed | 2024-03-07T04:23:06Z |
| format | Journal article |
| id | oxford-uuid:cbb09f1f-f114-48bc-8444-33ffaca0d0e3 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T04:23:06Z |
| publishDate | 2016 |
| publisher | Herbert Publications Limited |
| record_format | dspace |
| spelling | oxford-uuid:cbb09f1f-f114-48bc-8444-33ffaca0d0e32022-03-27T07:16:45ZModel choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:cbb09f1f-f114-48bc-8444-33ffaca0d0e3Symplectic Elements at OxfordHerbert Publications Limited2016Mukaka, MWhite, SAMwapasa, VKalilani-Phiri, LTerlouw, DFaragher, EBackground: Risk Difference (RD) is becoming the measure of choice for estimating effect size in antimalarial drug efficacy trials. Calculating RD using binomial regression is prone to model nonconvergence. Cheung's modified ordinary least squares (OLS) method is a proven technique for handling non-convergence when estimating RD. Other promising methods include the Poison, Additive Binomial Regression and binary regression models fitted using the statistical package R. (Deddens') Copy method that was primarily developed to overcome non-convergence of log-binomial regression models when estimating risk ratios is another potential method. Simulations were conducted to compare the performance of the Copy method against four alternatives (Cheung's modified OLS method, the Additive Binomial Regression Model fitted with the blm algorithm, the binary regression model fitted with the glm2 algorithm, and the Poisson model with identity link and robust standard errors fitted with the glm algorithm) for obtaining RD estimates when a binomial model fails to converge. Methods: We computed estimates of efficiency and bias with treatment arm efficacies of (a) 60% vs. 85%, (b) 95% vs. 90%, (iii) 95% vs. 98% using simulation studies. A total of 5,000 datasets were simulated under each of these three scenarios. Results: The modified OLS method and the binary regression model fitted using the glm2 algorithm in R provided unbiased, efficient estimates of RD across all assessed scenarios. In contrast, the Copy method yielded biased estimates of RD even when 100% convergence was achieved. The Poisson and Additive Binomial Regression models had 100% and almost 100% convergence rates respectively, but both produced very slightly biased RD estimates. Conclusion: The Copy method is not suitable for estimating RD when binomial regression model fitting fails to converge. Cheung's modified OLS or the binary regression model fitted using the glm2 algorithm in R should be the method of choice to overcome non-convergence with binomial models for calculating adjusted RD estimates. |
| spellingShingle | Mukaka, M White, SA Mwapasa, V Kalilani-Phiri, L Terlouw, D Faragher, E Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation |
| title | Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation |
| title_full | Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation |
| title_fullStr | Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation |
| title_full_unstemmed | Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation |
| title_short | Model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems: an assessment of methods of adjusted risk difference estimation |
| title_sort | model choices to obtain adjusted risk difference estimates from a binomial regression model with convergence problems an assessment of methods of adjusted risk difference estimation |
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