"Spatial heterogeneity of environmental risk in randomized prevention trials: consequences and modeling".
Background: In the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus fa...
| Publicado en: | BMC Medical Research Methodology Vol. 19; no. 1 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
7/15/2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137489914&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137489914 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712288 1CI1 jtl: BMC Medical Research Methodology issn: 14712288 maglogo: N pubinfo: dt: 7/15/2019 vid: 19 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 137489914 137489914 NLM31307393 137489914 10.1186/s12874-019-0759-z NLM31307393 137489914 ppct: 1 formats: tig: atl: "Spatial heterogeneity of environmental risk in randomized prevention trials: consequences and modeling". aug: au: Guindo, Abdoulaye Sagara, Issaka Ouedraogo, Boukary Sallah, Kankoe Assadou, Mahamadoun Hamady Healy, Sara Duffy, Patrick Doumbo, Ogobara K. Dicko, Alassane Giorgi, Roch Gaudart, Jean affil: Aix Marseille Univ, INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France sug: subj: Environmental Exposure Infection Control Clinical Trials Models, Statistical Communicable Diseases Transmission Study Design Sex Factors Malaria Transmission Risk Factors Human Malaria Prevention and Control Validation Studies Comparative Studies Evaluation Research Multicenter Studies Impact of Events Scale ab: Background: In the context of environmentally influenced communicable diseases, proximity to environmental sources results in spatial heterogeneity of risk, which is sometimes difficult to measure in the field. Most prevention trials use randomization to achieve comparability between groups, thus failing to account for heterogeneity. This study aimed to determine under what conditions spatial heterogeneity biases the results of randomized prevention trials, and to compare different approaches to modeling this heterogeneity.Methods: Using the example of a malaria prevention trial, simulations were performed to quantify the impact of spatial heterogeneity and to compare different models. Simulated scenarios combined variation in baseline risk, a continuous protective factor (age), a non-related factor (sex), and a binary protective factor (preventive treatment). Simulated spatial heterogeneity scenarios combined variation in breeding site density and effect, location, and population density. The performances of the following five statistical models were assessed: a non-spatial Cox Proportional Hazard (Cox-PH) model and four models accounting for spatial heterogeneity-i.e., a Data-Generating Model, a Generalized Additive Model (GAM), and two Stochastic Partial Differential Equation (SPDE) models, one modeling survival time and the other the number of events. Using a Bayesian approach, we estimated the SPDE models with an Integrated Nested Laplace Approximation algorithm. For each factor (age, sex, treatment), model performances were assessed by quantifying parameter estimation biases, mean square errors, confidence interval coverage rates (CRs), and significance rates. The four models were applied to data from a malaria transmission blocking vaccine candidate.Results: The level of baseline risk did not affect our estimates. However, with a high breeding site density and a strong breeding site effect, the Cox-PH and GAM models underestimated the age and treatment effects (but not the sex effect) with a low CR. When population density was low, the Cox-SPDE model slightly overestimated the effect of related factors (age, treatment). The two SPDE models corrected the impact of spatial heterogeneity, thus providing the best estimates.Conclusion: Our results show that when spatial heterogeneity is important but not measured, randomization alone cannot achieve comparability between groups. In such cases, prevention trials should model spatial heterogeneity with an adapted method.Trial Registration: The dataset used for the application example was extracted from Vaccine Trial #NCT02334462 ( ClinicalTrials.gov registry). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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