The Problem of Auto-Correlation in Parasitology
Explaining the contribution of host and pathogen factors in driving infection dynamics is a major ambition in parasitology. There is increasing recognition that analyses based on single summary measures of an infection (e.g., peak parasitaemia) do not adequately capture infection dynamics and so, the appropriate use of statistical techniques to analyse dynamics is necessary to understand infections and, ultimately, control parasites. However, the complexities of within-host environments mean that tracking and analysing pathogen dynamics within infections and among hosts poses considerable statistical challenges. Simple statistical models make assumptions that will rarely be satisfied in data collected on host and parasite parameters. In particular, model residuals (unexplained variance in the data) should not be correlated in time or space. Here we demonstrate how failure to account for such correlations can result in incorrect biological inference from statistical analysis. We then show how mixed effects models can be used as a powerful tool to analyse such repeated measures data in the hope that this will encourage better statistical practices in parasitology.
Vyšlo v časopise:
The Problem of Auto-Correlation in Parasitology. PLoS Pathog 8(4): e32767. doi:10.1371/journal.ppat.1002590
Kategorie:
Opinion
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.ppat.1002590
Souhrn
Explaining the contribution of host and pathogen factors in driving infection dynamics is a major ambition in parasitology. There is increasing recognition that analyses based on single summary measures of an infection (e.g., peak parasitaemia) do not adequately capture infection dynamics and so, the appropriate use of statistical techniques to analyse dynamics is necessary to understand infections and, ultimately, control parasites. However, the complexities of within-host environments mean that tracking and analysing pathogen dynamics within infections and among hosts poses considerable statistical challenges. Simple statistical models make assumptions that will rarely be satisfied in data collected on host and parasite parameters. In particular, model residuals (unexplained variance in the data) should not be correlated in time or space. Here we demonstrate how failure to account for such correlations can result in incorrect biological inference from statistical analysis. We then show how mixed effects models can be used as a powerful tool to analyse such repeated measures data in the hope that this will encourage better statistical practices in parasitology.
Zdroje
1. HarrisonF 2007 Microbial ecology of the cystic fibrosis lung. Microbiol 153 917 923
2. FärnertA 2008 Plasmodium falciparum population dynamics: only snapshots in time? Trends Parasitol 24 340 344
3. GrantAJRestifOMcKinleyTJSheppardMMaskellDJ 2008 Modelling within-host spatiotemporal dynamics of invasive bacterial disease. PloS Biol 6 757 770 doi:10.1371/journal.pbio.0060074
4. FrankSABarbourAG 2006 Within-host dynamics of antigenic variation. Infect Genet Evol 6 141 146
5. AllenJEMaizelsRM 2011 Diversity and dialogue in immunity to helminths. Nat Rev Immunol 11 375 388
6. MideoNAlizonSDayT 2008 Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends Ecol Evol 23 511 517
7. PatersonSLelloJ 2003 Mixed models: getting the best use of parasitological data. Trends Parasitol 19 370 375
8. PinheiroJCBatesDM 2000 Mixed-effects models in S and S-Plus New York Springer Verlag
9. ZuurAFIenoENWalkerNJSavelievAASmithGM 2009 Mixed effects models and extensions in ecology with R New York Springer Science and Business Media
10. ZuurAFIenoENSmithGM 2007 Introduction to mixed modelling. Analysing ecological data New York Springer Science and Business Media
11. BolkerBMBrooksMEClarkCJGeangeSWPoulsenJR 2009 Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 24 127 135
12. ElstonDAMossRBoulinierTArrowsmithCLambinX 2001 Analysis of aggregation, a worked example: numbers of ticks on red grouse chicks. Parasitol 122 563 569
13. SchabenbergerOPierceFJ 2002 Contemporary statistical models for the plant and soil sciences Boca Raton, FL CRC Press
14. SarterMFritschyJ-M 2008 Reporting statistical methods and statistical results in EJN. Eur J Neurosci 28 2363 2364
15. QuinnGPKeoughMJ 2002 Experimental design and data analysis for biologists Cambridge, UK Cambridge University Press
16. ReeceSEDrewDRGardnerA 2008 Sex ratio adjustment and kin discrimination in malaria parasites. Nature 453 609 615
17. PollittLCMideoNDrewDRSchneiderPColegraveNReeceSE 2011 Competition and the evolution of reproductive restraint in malaria parasites. Am Nat 177 358 367
18. MideoNSavillNJChadwickWSchneiderPReadAF 2011 Causes of variation in malaria infection dynamics: insights from theory and data. Am Nat 178 174 188
19. CrawleyMJ 2007 The R book Chichester Wiley-Blackwell
20. LittellRCMillikenGAStroupWWWolfingerRDSchabenberger 2006 SAS for mixed models. Second edition Cary, NC SAS Institute Inc
Štítky
Hygiena a epidemiológia Infekčné lekárstvo LaboratóriumČlánok vyšiel v časopise
PLOS Pathogens
2012 Číslo 4
- Parazitičtí červi v terapii Crohnovy choroby a dalších zánětlivých autoimunitních onemocnění
- Očkování proti virové hemoragické horečce Ebola experimentální vakcínou rVSVDG-ZEBOV-GP
- Koronavirus hýbe světem: Víte jak se chránit a jak postupovat v případě podezření?
Najčítanejšie v tomto čísle
- The Accessory Genome as a Cradle for Adaptive Evolution in Pathogens
- Systematic Review of Mucosal Immunity Induced by Oral and Inactivated Poliovirus Vaccines against Virus Shedding following Oral Poliovirus Challenge
- The Arbuscular Mycorrhizal Symbiosis: Origin and Evolution of a Beneficial Plant Infection
- Modelling the Evolutionary Dynamics of Viruses within Their Hosts: A Case Study Using High-Throughput Sequencing