tetano
Editor, Senior Moderator
Stat Med. 2019 Jul 17. doi: 10.1002/sim.8299. [Epub ahead of print]
[h=1]Reconciling randomized trial evidence on proximal versus distal outcomes, with application to trials of influenza vaccination for healthcare workers.[/h] Gustafson R[SUP]1[/SUP], Gustafson P[SUP]2[/SUP], Daly P[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] When synthesizing the body of evidence concerning a clinical intervention, impacts on both proximal and distal outcome variables may be relevant. Assessments will be more defensible if results concerning a proximal outcome align with those concerning a corresponding distal outcome. We present a method to assess the coherence of empirical clinical trial results with biologic and mathematical first principles in situations where the intervention can only plausibly impact the distal outcome indirectly via the proximal outcome. The method comprises a probabilistic sensitivity analysis, where plausible ranges for key parameters are specified, resulting in a constellation of plausible pairs of estimated intervention effects, for the proximal and distal outcomes, respectively. Both outcome misclassification and sampling variability are reflected in the method. We apply our methodology in the context of cluster randomized trials to evaluate the impacts of vaccinating healthcare workers on the health of elderly patients, where the proximal outcome is suspected influenza and the distal outcome is death. However, there is scope to apply the method for other interventions in other disease areas.
? 2019 John Wiley & Sons, Ltd.
[h=4]KEYWORDS:[/h] cluster-randomized trials; influenza; misclassification; probabilistic sensitivity analysis
PMID: 31317576 DOI: 10.1002/sim.8299
[h=1]Reconciling randomized trial evidence on proximal versus distal outcomes, with application to trials of influenza vaccination for healthcare workers.[/h] Gustafson R[SUP]1[/SUP], Gustafson P[SUP]2[/SUP], Daly P[SUP]1[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] When synthesizing the body of evidence concerning a clinical intervention, impacts on both proximal and distal outcome variables may be relevant. Assessments will be more defensible if results concerning a proximal outcome align with those concerning a corresponding distal outcome. We present a method to assess the coherence of empirical clinical trial results with biologic and mathematical first principles in situations where the intervention can only plausibly impact the distal outcome indirectly via the proximal outcome. The method comprises a probabilistic sensitivity analysis, where plausible ranges for key parameters are specified, resulting in a constellation of plausible pairs of estimated intervention effects, for the proximal and distal outcomes, respectively. Both outcome misclassification and sampling variability are reflected in the method. We apply our methodology in the context of cluster randomized trials to evaluate the impacts of vaccinating healthcare workers on the health of elderly patients, where the proximal outcome is suspected influenza and the distal outcome is death. However, there is scope to apply the method for other interventions in other disease areas.
? 2019 John Wiley & Sons, Ltd.
[h=4]KEYWORDS:[/h] cluster-randomized trials; influenza; misclassification; probabilistic sensitivity analysis
PMID: 31317576 DOI: 10.1002/sim.8299