Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials
Abstract Background In randomized clinical trials (RCTs) with non-compliance, evaluating the causal effects of interventions would lead to a more precise estimation of treatment effect when the estimand of interest is the effect of treatment amongst compliers. While there is a large body of literatu...
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2025-02-01
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| Online Access: | https://doi.org/10.1186/s12874-025-02493-6 |
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| author | Junxian Zhu Jialiang Li A. Mark Richards Mark Y. Chan Bee-Choo Tai |
| author_facet | Junxian Zhu Jialiang Li A. Mark Richards Mark Y. Chan Bee-Choo Tai |
| author_sort | Junxian Zhu |
| collection | DOAJ |
| description | Abstract Background In randomized clinical trials (RCTs) with non-compliance, evaluating the causal effects of interventions would lead to a more precise estimation of treatment effect when the estimand of interest is the effect of treatment amongst compliers. While there is a large body of literature addressing the issue of non-compliance for continuous, binary, and time-to-event outcomes, this issue is seldom discussed for ordinal outcomes. Methods In this paper, we consider one-sided non-compliance. We introduce an extension of the inverse probability weighting (IPW) method for handling non-compliance involving an ordinal outcome by fully utilizing the information of non-compliance and defining it as a categorical variable to describe the extent of non-compliance. This is in contrast to the usual convention where compliance is regarded as a binary variable. We provide the identification and asymptotic distribution of the proposed method. We compare the proposed method (IPW_Dnew) with intention-to-treat (ITT), per protocol (PP), instrumental variable (IV), and IPW method via a simulation study and real-life data from the JOBS II intervention trial and the IMMACULATE trial. Results Simulation results demonstrate that the proposed method performs better than other methods in terms of bias, coverage, mean squared error, power and Type I error under various scenarios, particularly in situations with selection bias and partial compliance. In the empirical study, a substantial estimate of partial compliance by IPW_Dnew implies that there may be a partial compliance effect. Conclusion For ordinal outcome in the presence of non-compliance, we suggest using the proposed method to estimate the causal effect of treatment amongst compliers and partial compliers, especially when there exists selection bias. |
| format | Article |
| id | doaj-art-8325510a67d1478f9e97b4ddb29646b8 |
| institution | OA Journals |
| issn | 1471-2288 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
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| series | BMC Medical Research Methodology |
| spelling | doaj-art-8325510a67d1478f9e97b4ddb29646b82025-08-20T02:01:38ZengBMCBMC Medical Research Methodology1471-22882025-02-0125111310.1186/s12874-025-02493-6Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trialsJunxian Zhu0Jialiang Li1A. Mark Richards2Mark Y. Chan3Bee-Choo Tai4Saw Swee Hock School of Public Health, National University of SingaporeDepartment of Statistics and Data Science, National University of SingaporeCardiovascular Research Institute, Yong Loo-Lin School of Medicine, National University of SingaporeCardiovascular Research Institute, Yong Loo-Lin School of Medicine, National University of SingaporeSaw Swee Hock School of Public Health, National University of SingaporeAbstract Background In randomized clinical trials (RCTs) with non-compliance, evaluating the causal effects of interventions would lead to a more precise estimation of treatment effect when the estimand of interest is the effect of treatment amongst compliers. While there is a large body of literature addressing the issue of non-compliance for continuous, binary, and time-to-event outcomes, this issue is seldom discussed for ordinal outcomes. Methods In this paper, we consider one-sided non-compliance. We introduce an extension of the inverse probability weighting (IPW) method for handling non-compliance involving an ordinal outcome by fully utilizing the information of non-compliance and defining it as a categorical variable to describe the extent of non-compliance. This is in contrast to the usual convention where compliance is regarded as a binary variable. We provide the identification and asymptotic distribution of the proposed method. We compare the proposed method (IPW_Dnew) with intention-to-treat (ITT), per protocol (PP), instrumental variable (IV), and IPW method via a simulation study and real-life data from the JOBS II intervention trial and the IMMACULATE trial. Results Simulation results demonstrate that the proposed method performs better than other methods in terms of bias, coverage, mean squared error, power and Type I error under various scenarios, particularly in situations with selection bias and partial compliance. In the empirical study, a substantial estimate of partial compliance by IPW_Dnew implies that there may be a partial compliance effect. Conclusion For ordinal outcome in the presence of non-compliance, we suggest using the proposed method to estimate the causal effect of treatment amongst compliers and partial compliers, especially when there exists selection bias.https://doi.org/10.1186/s12874-025-02493-6Ordinal outcomeNon-complianceInverse probability weightingSelection biasRandomized clinical trial |
| spellingShingle | Junxian Zhu Jialiang Li A. Mark Richards Mark Y. Chan Bee-Choo Tai Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials BMC Medical Research Methodology Ordinal outcome Non-compliance Inverse probability weighting Selection bias Randomized clinical trial |
| title | Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials |
| title_full | Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials |
| title_fullStr | Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials |
| title_full_unstemmed | Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials |
| title_short | Accounting for extent of non-compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials |
| title_sort | accounting for extent of non compliance when estimating treatment effects on an ordinal outcome in randomized clinical trials |
| topic | Ordinal outcome Non-compliance Inverse probability weighting Selection bias Randomized clinical trial |
| url | https://doi.org/10.1186/s12874-025-02493-6 |
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