Optimal designs for matching adjusted indirect comparison (MAIC)
| dc.contributor.author | Zheng, Xiang | |
| dc.contributor.examiningcommittee | Yang, Po (Statistics) | en_US |
| dc.contributor.examiningcommittee | Martsynyuk, Yuliya (Statistics) | en_US |
| dc.contributor.supervisor | Mandal, Saumen | |
| dc.date.accessioned | 2023-03-14T20:17:39Z | |
| dc.date.available | 2023-03-14T20:17:39Z | |
| dc.date.copyright | 2023-02-25 | |
| dc.date.issued | 2023-02-25 | |
| dc.date.submitted | 2023-02-26T00:54:26Z | en_US |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | The thesis aimed to develop a method for optimizing design subject to matching the pre-defined baseline characteristics in clinical trials. As part of a clinical trial, a new treatment must be compared with a competitor treatment in order to determine its effect on the patient before the new treatment is launched. Ideally, we can directly compare the new treatment with competitor treatment in randomized controlled trials (RCTs). However, direct comparison is difficult to achieve due to various factors, such as time, price, regulation, and patents. A matching-adjusted indirect comparison (MAIC) method leverages all available data by adjusting average patient characteristics in trials with Individual patient data (IPD) to match those reported in the aggregate trials data (AgD). MAIC is a reweighting method in which the weights are calculated by deriving the propensity scores in the Individual patient’s data. This can reduce the bias. As IPD matches to the pre-defined baseline characteristics, we make use of optimal design theory and convert this into a constrained optimization problem. The Lagrangian method is used to determine the optimal design subject to satisfying the constraints of baseline characteristics. We formulate the Lagrangian and then transform the constrained problem to one where we simultaneously maximize several functions of the design weights. These functions have a common maximum of zero. In order to find the optimal design, we used the software R and a class of multiplicative algorithms. We then perform a sensitivity analysis and compare the Lagrangian method and the MAIC method by calculating the effective sample sizes (ESS). The higher the value of ESS the less information is lost due to reweighting. The Lagrangian method performs better than the MAIC method. Owing to the broad applicability of optimal design, we have tried to make use of this theory in order to obtain a better methodology in MAIC. The proposed methodology is quite flexible and can be applied to different types of constraints. The methodology can be applied to situation where there is a lack of direct comparison. It will also reduce the time and cost of running experiments. | en_US |
| dc.description.note | May 2023 | en_US |
| dc.description.sponsorship | Prof. Mandal, Saumendranath ’s NSERC grant. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/37206 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | optimal design | en_US |
| dc.subject | matching-adjusted indirect comparisons (MAIC) method | en_US |
| dc.subject | clinical trial | en_US |
| dc.subject | constrained optimization | en_US |
| dc.subject | maic | en_US |
| dc.title | Optimal designs for matching adjusted indirect comparison (MAIC) | en_US |
| dc.type | master thesis | en_US |
| local.subject.manitoba | no | en_US |
| project.funder.identifier | U of M: https://doi.org/10.13039/100010318 | en_US |
| project.funder.name | University of Manitoba | en_US |