Dataset Information

Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints.

ABSTRACT: In clinical trials, it is often of interest to compare and order several candidate regimens based on multiple endpoints. For example, in HIV vaccine development, immune response profiles induced by vaccination are key for selecting vaccine regimens to advance to efficacy evaluation. Motivated by the need to rank and choose a few vaccine regimens based on their immunogenicity in phase I trials, Huang et al. (Biostatistics 18(2):230-243, 2017) proposed a ranking/filtering/selection algorithm that down-selects vaccine regimens to satisfy the superiority and non-redundancy criteria, based on multiple immune response endpoints. In practice, many candidate immune response endpoints can be correlated with each other. An important question that remains to be addressed is how to choose a parsimonious set of the available immune response endpoints to effectively compare regimens. In this paper, we propose novel algorithms for selecting immune response endpoints to be used in regimen down-selection, based on importance weights assigned to individual endpoints and their correlation structure. We show through extensive simulation studies that pre-selection of endpoints can substantially improve performance of the subsequent regimen down-selection process. The application of the proposed method is demonstrated using a real example in HIV vaccine research, although the methods are also applicable in general to clinical research for dimension reduction when comparing regimens based on multiple candidate endpoints.

SUBMITTER: Huang Y

PROVIDER: S-EPMC7224061 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints.

Huang Ying Y Tarkhan Aliasghar A

Statistics in biosciences 20200415 3

In clinical trials, it is often of interest to compare and order several candidate regimens based on multiple endpoints. For example, in HIV vaccine development, immune response profiles induced by vaccination are key for selecting vaccine regimens to advance to efficacy evaluation. Motivated by the need to rank and choose a few vaccine regimens based on their immunogenicity in phase I trials, Huang et al. (Biostatistics 18(2):230-243, 2017) proposed a ranking/filtering/selection algorithm that ...[more]

PMID: 32421096

Similar Datasets

Project description:Background and objectiveEmergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes. Accurate forecasts of future service demand would enable intelligent resource allocation that could alleviate the problem. There has been continued academic interest in ED forecasting but the number of used explanatory variables has been low, limited mainly to calendar and weather variables. In this study we investigate whether predictive accuracy of next day arrivals could be enhanced using high number of potentially relevant explanatory variables and document two feature selection processes that aim to identify which subset of variables is associated with number of next day arrivals. Performance of such predictions over longer horizons is also shown.MethodsWe extracted numbers of total daily arrivals from Tampere University Hospital ED between the time period of June 1, 2015 and June 19, 2019. 158 potential explanatory variables were collected from multiple data sources consisting not only of weather and calendar variables but also an extensive list of local public events, numbers of website visits to two hospital domains, numbers of available hospital beds in 33 local hospitals or health centres and Google trends searches for the ED. We used two feature selection processes: Simulated Annealing (SA) and Floating Search (FS) with Recursive Least Squares (RLS) and Least Mean Squares (LMS). Performance of these approaches was compared against autoregressive integrated moving average (ARIMA), regression with ARIMA errors (ARIMAX) and Random Forest (RF). Mean Absolute Percentage Error (MAPE) was used as the main error metric.ResultsCalendar variables, load of secondary care facilities and local public events were dominant in the identified predictive features. RLS-SA and RLS-FA provided slightly better accuracy compared ARIMA. ARIMAX was the most accurate model but the difference between RLS-SA and RLS-FA was not statistically significant.ConclusionsOur study provides new insight into potential underlying factors associated with number of next day presentations. It also suggests that predictive accuracy of next day arrivals can be increased using high-dimensional feature selection approach when compared to both univariate and nonfiltered high-dimensional approach. Performance over multiple horizons was similar with a gradual decline for longer horizons. However, outperforming ARIMAX remains a challenge when working with daily data. Future work should focus on enhancing the feature selection mechanism, investigating its applicability to other domains and in identifying other potentially relevant explanatory variables.

Dataset Information

Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints.

Publications

Methods for Feature Selection in Down-Selection of Vaccine Regimens Based on Multivariate Immune Response Endpoints.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets