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https://doi.org/10.1177/0962280219837656
Title: | Handling ties in continuous outcomes for confounder adjustment with rank-ordered logit and its application to ordinal outcomes | Authors: | Ning, Yilin Tan, Chuen Seng Maraki, Angeliki Ho, Peh Joo Hodgins, Sheilagh Comasco, Erika Nilsson, Kent W Wagner, Philippe Khoo, Eric YH Tai, E-Shyong Kao, Shih Ling Hartman, Mikael Reilly, Marie Stoer, Nathalie C |
Keywords: | Science & Technology Life Sciences & Biomedicine Physical Sciences Health Care Sciences & Services Mathematical & Computational Biology Medical Informatics Statistics & Probability Mathematics Rank-ordered logit model stratification tied observations continuous outcome ordinal outcome QUALITY-OF-LIFE REGRESSION-MODELS EFFICIENCY SMOKING TIMES |
Issue Date: | 1-Feb-2020 | Publisher: | SAGE PUBLICATIONS LTD | Citation: | Ning, Yilin, Tan, Chuen Seng, Maraki, Angeliki, Ho, Peh Joo, Hodgins, Sheilagh, Comasco, Erika, Nilsson, Kent W, Wagner, Philippe, Khoo, Eric YH, Tai, E-Shyong, Kao, Shih Ling, Hartman, Mikael, Reilly, Marie, Stoer, Nathalie C (2020-02-01). Handling ties in continuous outcomes for confounder adjustment with rank-ordered logit and its application to ordinal outcomes. STATISTICAL METHODS IN MEDICAL RESEARCH 29 (2) : 437-454. ScholarBank@NUS Repository. https://doi.org/10.1177/0962280219837656 | Abstract: | The rank-ordered logit (rologit) model was recently introduced as a robust approach for analysing continuous outcomes, with the linear exposure effect estimated by scaling the rank-based log-odds estimate. Here we extend the application of the rologit model to continuous outcomes with ties and ordinal outcomes treated as imperfectly-observed continuous outcomes. By identifying the functional relationship between survival times and continuous outcomes, we explicitly establish the equivalence between the rologit and Cox models to justify the use of the Breslow, Efron and perturbation methods in the analysis of continuous outcomes with ties. Using simulation, we found all three methods perform well with few ties. Although an increasing extent of ties increased the bias of the log-odds and linear effect estimates and resulted in reduced power, which was somewhat worse when the model was mis-specified, the perturbation method maintained a type I error around 5%, while the Efron method became conservative with heavy ties but outperformed Breslow. In general, the perturbation method had the highest power, followed by the Efron and then the Breslow method. We applied our approach to three real-life datasets, demonstrating a seamless analytical workflow that uses stratification for confounder adjustment in studies of continuous and ordinal outcomes. | Source Title: | STATISTICAL METHODS IN MEDICAL RESEARCH | URI: | https://scholarbank.nus.edu.sg/handle/10635/209055 | ISSN: | 09622802 14770334 |
DOI: | 10.1177/0962280219837656 |
Appears in Collections: | Staff Publications Elements |
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