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A power analysis framework to aid the design of robust semi-field vector control experiments
by
Kiware, Samson S.
, Lwetoijera, Dickson W.
, Ng’habi, Kija R.
, Viana, Mafalda
, Johnson, Paul C. D.
, Kipingu, Andrea M.
in
Animals
/ Anopheles
/ Aquatic insects
/ Biomedical and Life Sciences
/ Biomedicine
/ Computer simulation
/ Computer-generated environments
/ Control
/ Culicidae
/ Datasets
/ Design
/ Design of experiments
/ Entomology
/ Experimental design
/ Field tests
/ Generalized linear models
/ Infectious Diseases
/ Insecticides
/ Intervention
/ Malaria
/ Methods
/ Microbiology
/ Mixed-effects models
/ Monte Carlo simulation
/ Mosquito Control - methods
/ Mosquito Vectors
/ Mosquitoes
/ Parasites
/ Parasitology
/ Public Health
/ Pyriproxyfen
/ Research Design
/ Sampling
/ Simulation-based
/ Statistical power
/ Testing
/ Tropical Medicine
/ Type I error
/ Vector control
/ Vectors (Biology)
2025
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A power analysis framework to aid the design of robust semi-field vector control experiments
by
Kiware, Samson S.
, Lwetoijera, Dickson W.
, Ng’habi, Kija R.
, Viana, Mafalda
, Johnson, Paul C. D.
, Kipingu, Andrea M.
in
Animals
/ Anopheles
/ Aquatic insects
/ Biomedical and Life Sciences
/ Biomedicine
/ Computer simulation
/ Computer-generated environments
/ Control
/ Culicidae
/ Datasets
/ Design
/ Design of experiments
/ Entomology
/ Experimental design
/ Field tests
/ Generalized linear models
/ Infectious Diseases
/ Insecticides
/ Intervention
/ Malaria
/ Methods
/ Microbiology
/ Mixed-effects models
/ Monte Carlo simulation
/ Mosquito Control - methods
/ Mosquito Vectors
/ Mosquitoes
/ Parasites
/ Parasitology
/ Public Health
/ Pyriproxyfen
/ Research Design
/ Sampling
/ Simulation-based
/ Statistical power
/ Testing
/ Tropical Medicine
/ Type I error
/ Vector control
/ Vectors (Biology)
2025
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
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A power analysis framework to aid the design of robust semi-field vector control experiments
by
Kiware, Samson S.
, Lwetoijera, Dickson W.
, Ng’habi, Kija R.
, Viana, Mafalda
, Johnson, Paul C. D.
, Kipingu, Andrea M.
in
Animals
/ Anopheles
/ Aquatic insects
/ Biomedical and Life Sciences
/ Biomedicine
/ Computer simulation
/ Computer-generated environments
/ Control
/ Culicidae
/ Datasets
/ Design
/ Design of experiments
/ Entomology
/ Experimental design
/ Field tests
/ Generalized linear models
/ Infectious Diseases
/ Insecticides
/ Intervention
/ Malaria
/ Methods
/ Microbiology
/ Mixed-effects models
/ Monte Carlo simulation
/ Mosquito Control - methods
/ Mosquito Vectors
/ Mosquitoes
/ Parasites
/ Parasitology
/ Public Health
/ Pyriproxyfen
/ Research Design
/ Sampling
/ Simulation-based
/ Statistical power
/ Testing
/ Tropical Medicine
/ Type I error
/ Vector control
/ Vectors (Biology)
2025
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A power analysis framework to aid the design of robust semi-field vector control experiments
Journal Article
A power analysis framework to aid the design of robust semi-field vector control experiments
2025
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Overview
Background
Semi-field experiments are an efficient way of assessing the impacts of potential new vector control tools (VCTs) before field trials. However, their design is critically important to ensure their results are unbiased and informative. An essential element of the design of semi-field experiments is power analysis, which empowers researchers to ensure that only designs with adequate statistical power are adopted. In this study, a methodology was developed, and its use was demonstrated in a tutorial, to determine the required number of semi-field chambers, sampling frequency and the number of mosquitoes required to achieve sufficient power for evaluating the impact of a single VCT or two in combination.
Methods
By analysing data simulated from a generalized linear mixed-effects model, power was estimated for various experimental designs, including short- (24 h) vs. long-term (3 months) experiments and single vs. combined application of interventions (e.g., insecticide-treated nets combined with pyriproxyfen autodissemination).
Results
Although power increased with increasing number of chambers, sampling frequency and the number of mosquitoes, the number of chambers and variance between chambers were the dominant factors determining power relative to all other design choices. High variance between chambers decreased power, highlighting the importance of making conditions similar among chambers, by reducing variation if possible and by rotating variables if not
.
As compared to a single intervention, an additional intervention required an increase in the number of chambers, while short and long experiments were similar in terms of key aspects such as the number of chambers per treatment.
Conclusion
Determining the most efficient experimental design for a semi-field experiment will depend on a balance of design choices and resource constraints. The power analysis framework and tutorial provided here can aid in the robust design of these widely used experiments and ultimately facilitate the development of new vector control tools (VCTs).
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
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