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Field
Primary Outcomes (End Points)
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Before
1a. Analytical approach. Ascertainment bias—a systematic difference in the ability or effort to detect an outcome—plagues studies of vector surveillance. A naïve evaluation of an intervention leads to perverse interpretations because a better surveillance system might uncover and eliminate more cases, while a poorer one would leave cases undetected for a longer period, thereby allowing the creation, and eventual discovery, of more cases. To avoid this paradox, our approach is to estimate the rate of detection and elimination of infestations. Using an approach from Queuing theory we estimate the rate of detecting and removing infested households, through iterative filtering, that allows for inference on parameters for stochastic processes. These methods have been integrated into an R package (pomp) allowing for ease of use and replication. Using pomp we will derive point estimates and credible intervals for the removal rate for both arms of the trial. By comparing the credible intervals around these estimates, we will ascertain if the immune arm significantly increases the rate of detection and elimination of vector foci.
Alternative analytical approach. Our methods are new, and, as with any new approach, we may run into computational issues that prevent us from implementing them in the manner that we envision. We therefore have a more traditional approach to testing our hypothesis, which, although less powerful, is easily implemented. Under the alternative approach the primary study hypothesis is: the number of infested houses uncovered will be greater in the immune arm early in the study, and greater in the conventional arm later in the study. We will model the number of confirmed infestations using a negative binomial regression, and considering as covariates the study arm, the year of the study (1-4) and the interaction of the two (arm * year). Our expectation, under our alternative primary hypothesis, is a significantly positive coefficient for the study arm, and a significantly negative coefficient for the interaction of study arm and year.
Originally planned Year 3 Interim analysis (2023-Mar-01) We compare the rate of detection of infested households (defined as the number of households detected / the number of person days of effort in the arm) in an intervention versus control arm.
*We hypothesize that we will find more infested households in the intervention than in the control arm.
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After
1a. Analytical approach. Ascertainment bias—a systematic difference in the ability or effort to detect an outcome—plagues studies of vector surveillance. A naïve evaluation of an intervention leads to perverse interpretations because a better surveillance system might uncover and eliminate more cases, while a poorer one would leave cases undetected for a longer period, thereby allowing the creation, and eventual discovery, of more cases. To avoid this paradox, our approach is to estimate the rate of detection and elimination of infestations. Using an approach from Queuing theory we estimate the rate of detecting and removing infested households, through iterative filtering, that allows for inference on parameters for stochastic processes. These methods have been integrated into an R package (pomp) allowing for ease of use and replication. Using pomp we will derive point estimates and credible intervals for the removal rate for both arms of the trial. By comparing the credible intervals around these estimates, we will ascertain if the immune arm significantly increases the rate of detection and elimination of vector foci.
Alternative analytical approach. Our methods are new, and, as with any new approach, we may run into computational issues that prevent us from implementing them in the manner that we envision. We therefore have a more traditional approach to testing our hypothesis, which, although less powerful, is easily implemented. Under the alternative approach the primary study hypothesis is: the number of infested houses uncovered will be greater in the immune arm early in the study, and greater in the conventional arm later in the study. We will model the number of confirmed infestations using a negative binomial regression, and considering as covariates the study arm, the year of the study (1-4) and the interaction of the two (arm * year). Our expectation, under our alternative primary hypothesis, is a significantly positive coefficient for the study arm, and a significantly negative coefficient for the interaction of study arm and year.
Originally planned Year 3 Interim analysis (2023-Mar-01) We compare the rate of detection of infested households (defined as the number of households detected / the number of person days of effort in the arm) in an intervention versus control arm.
*We hypothesize that we will find more infested households in the intervention than in the control arm.
FOLLOWING THE INTERIM ANALYSIS: The interim analysis showed significantly more infested houses detected in the intervention arm than control, and prompted the decision to provide the intervention to all arms. Control arms began receiving the intervention on May 15th, 2023. Our hypothesis for the following year (May 15 2023 - May 15 2024) is: The number of infested households detected will be greater in the catchments originally assigned to the control arm (because the control arm, presumably, was less efficient detecting and eliminating the infestations in the original trial) than those originally assigned to the intervention.
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