ArticleBMC medical research methodology2016
Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.
Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Forecasting the Regional Demand for Medical Workers in Kazakhstan: The Functional Principal Component Analysis Approach.International journal of environmental research and public health · 2025Article
- Predicting health outcomes with intensive longitudinal data collected by mobile health devices: a functional principal component regression approach.BMC medical research methodology · 2024Article
- Functional principal component analysis for identifying the child growth pattern using longitudinal birth cohort data.BMC medical research methodology · 2022Article
- Interpol review of controlled substances 2016-2019.Forensic science international. Synergy · 2020Review
- Erratum to: Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.BMC medical research methodology · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally.
methodsWe analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data.
resultsThe first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data.
conclusionFPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.
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