TY - JOUR
T1 - Observational studies and the difficult quest for causality
T2 - lessons from vaccine effectiveness and impact studies
AU - Lipsitch, Marc
AU - Jha, Ayan
AU - Simonsen, Lone
N1 - © The Author 2016; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Although randomized placebo-controlled trials (RCT) are critical to establish efficacy of vaccines at the time of licensure, important remaining questions about vaccine effectiveness (VE)-used here to include individual-level measures and population-wide impact of vaccine programmes-can only be answered once the vaccine is in use, from observational studies. However, such studies are inherently at risk for bias. Using a causal framework and illustrating with examples, we review newer approaches to detecting and avoiding confounding and selection bias in three major classes of observational study design: cohort, case-control and ecological studies. Studies of influenza VE, especially in seniors, are an excellent demonstration of the challenges of detecting and reducing such bias, and so we use influenza VE as a running example. We take a fresh look at the time-trend studies often dismissed as 'ecological'. Such designs are the only observational study design that can measure the overall effect of a vaccination programme [indirect (herd) as well as direct effects], and are in fact already an important part of the evidence base for several vaccines currently in use. Despite the great strides towards more robust observational study designs, challenges lie ahead for evaluating best practices for achieving robust unbiased results from observational studies. This is critical for evaluation of national and global vaccine programme effectiveness.
AB - Although randomized placebo-controlled trials (RCT) are critical to establish efficacy of vaccines at the time of licensure, important remaining questions about vaccine effectiveness (VE)-used here to include individual-level measures and population-wide impact of vaccine programmes-can only be answered once the vaccine is in use, from observational studies. However, such studies are inherently at risk for bias. Using a causal framework and illustrating with examples, we review newer approaches to detecting and avoiding confounding and selection bias in three major classes of observational study design: cohort, case-control and ecological studies. Studies of influenza VE, especially in seniors, are an excellent demonstration of the challenges of detecting and reducing such bias, and so we use influenza VE as a running example. We take a fresh look at the time-trend studies often dismissed as 'ecological'. Such designs are the only observational study design that can measure the overall effect of a vaccination programme [indirect (herd) as well as direct effects], and are in fact already an important part of the evidence base for several vaccines currently in use. Despite the great strides towards more robust observational study designs, challenges lie ahead for evaluating best practices for achieving robust unbiased results from observational studies. This is critical for evaluation of national and global vaccine programme effectiveness.
U2 - 10.1093/ije/dyw124
DO - 10.1093/ije/dyw124
M3 - Journal article
C2 - 27453361
SN - 0300-5771
VL - 45
SP - 2060
EP - 2074
JO - International Journal of Epidemiology
JF - International Journal of Epidemiology
IS - 6
ER -
