via AIDS, by O’Hagan, Justin J.; Hernán, Miguel A.; Walensky, Rochelle P.; Lipsitch, Marca
Recent HIV prevention trials have given hope that a suite of interventions that effectively reduce individuals’ risk of HIV infection will soon be widely available. In two studies, the RV144 and CAPRISA 004 trials, the relative risk of infection increased toward the null value of one over time. The RV144 and CAPRISA investigators interpreted these trends as evidence that the interventions’ effects declined over the study period and suggested that their respective findings may be explained by waning vaccine efficacy and decreasing adherence. Here, we discuss these trends in the trials’ results and note that, in addition to the possible mechanisms cited by the investigators, their apparent waning efficacy may be explained in part by selection bias due to heterogeneity in infection risk, an explanation that has not been considered previously. This bias arises when study participants vary in their susceptibility to infection, for example, because of differences in immune systems or exposure to infection. This can lead to increasing differences in the composition of the study population in each trial arm over time as those at highest risk become infected, and can occur despite comparability between arms at baseline. This issue is termed ‘frailty’ in statistics and demography, in which a large body of literature addresses the matter. We also discuss several methods that can improve understanding of the effects of infectious disease interventions and risk factors by assessing the impact of frailty on results.
Variation in frailty among study participants likely creates trends in the incidence of infection over the course of a study. To understand this phenomenon, consider a theoretical placebo-controlled randomized trial, in which the risk of infection varies among participants. The highest risk individuals in such a trial are expected to become infected earlier, leaving a pool of lower risk individuals at later time points. If the intervention being tested is effective, the decline in the incidence of infection over time will be larger in the placebo arm because these individuals experience no direct protection from the intervention, and so those at high-risk will be quickly depleted, thereby lowering the infection rate over follow-up. However, high-risk individuals in the active arm may remain uninfected due to the protection conferred by the intervention, so the active arm's infection rate will be less affected. Consequently, the time-specific rate ratio for treatment vs. placebo will increase over time from a value of less than one initially to a value that may exceed one later. This phenomenon has also been termed ‘survivor bias’, ‘survivor cohort effect’, ‘crossing of hazards’ and ‘depletion of susceptibles’, and is observed in both chronic and infectious disease research.
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[Content that is linked from other sources is for informational purposes and should not construe a Mapping Pathways position.]
Recent HIV prevention trials have given hope that a suite of interventions that effectively reduce individuals’ risk of HIV infection will soon be widely available. In two studies, the RV144 and CAPRISA 004 trials, the relative risk of infection increased toward the null value of one over time. The RV144 and CAPRISA investigators interpreted these trends as evidence that the interventions’ effects declined over the study period and suggested that their respective findings may be explained by waning vaccine efficacy and decreasing adherence. Here, we discuss these trends in the trials’ results and note that, in addition to the possible mechanisms cited by the investigators, their apparent waning efficacy may be explained in part by selection bias due to heterogeneity in infection risk, an explanation that has not been considered previously. This bias arises when study participants vary in their susceptibility to infection, for example, because of differences in immune systems or exposure to infection. This can lead to increasing differences in the composition of the study population in each trial arm over time as those at highest risk become infected, and can occur despite comparability between arms at baseline. This issue is termed ‘frailty’ in statistics and demography, in which a large body of literature addresses the matter. We also discuss several methods that can improve understanding of the effects of infectious disease interventions and risk factors by assessing the impact of frailty on results.Variation in frailty among study participants likely creates trends in the incidence of infection over the course of a study. To understand this phenomenon, consider a theoretical placebo-controlled randomized trial, in which the risk of infection varies among participants. The highest risk individuals in such a trial are expected to become infected earlier, leaving a pool of lower risk individuals at later time points. If the intervention being tested is effective, the decline in the incidence of infection over time will be larger in the placebo arm because these individuals experience no direct protection from the intervention, and so those at high-risk will be quickly depleted, thereby lowering the infection rate over follow-up. However, high-risk individuals in the active arm may remain uninfected due to the protection conferred by the intervention, so the active arm's infection rate will be less affected. Consequently, the time-specific rate ratio for treatment vs. placebo will increase over time from a value of less than one initially to a value that may exceed one later. This phenomenon has also been termed ‘survivor bias’, ‘survivor cohort effect’, ‘crossing of hazards’ and ‘depletion of susceptibles’, and is observed in both chronic and infectious disease research.
Read the rest.
[Content that is linked from other sources is for informational purposes and should not construe a Mapping Pathways position.]
