Michael Elliott  Using Synergies Between Survey Statistics and Causal Inference to Improve Transportability of Clinical Trials  JPSM MPSDS Seminar
From Elisabeth Schneider
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Michael Elliott is a Professor of Biostatistics and a Research Professor at the Institute for Social Research. He received his Ph.D. in biostatistics in 1999 from the University of Michigan. Prior to joining the University of Michigan in 2005, he held an appointment as an Assistant Professor at the Department of Biostatistics and Epidemiology at the University of Pennsylvania School of Medicine. Dr. Elliott's research interests include the design and analysis of sample surveys, causal inference, and missing and latent variable data structures.
Using Synergies Between Survey Statistics and Causal Inference to Improve Transportability of Clinical Trials
Presented During: CS008  Contributed: Experimental and nonexperimental tools to inform healthcare policymaking
Medical researchers have understood
for many years that treatment effect estimates obtained from a randomized
clinical trial (RCT)  termed efficacy''  can differ from those
obtained in a general population  termed effectiveness''. Only in the
past decade has extensive work begun in the statistical literature to bridge
this gap using formal quantitative methods. As noted by Rod Little in a letter
to the editor in the New Yorker ...randomization in randomized clinical
trials concerns the allocation of the treatment, not the selection of individuals
for the study. The latter can have an important impact on the average size of a
treatment effect,'' with RCT samples often designed, sometimes explicitly, to
be more likely to include individuals for whom the treatment may be more
effective.
This issue has been various termed generalizability''
or transportability." Why do we care about transportability? In RCTs
we are in the happy situation were treatment assignment is randomized, so
confounding due to either observed or unobserved (pretreatment) covariates is
not an issue. But while randomization of treatment eliminates the effect of
unobserved confounders, at least net of noncompliance, it does not eliminate
the effect of unobserved effect modifiers, which can impact the causal effect
of treatment in a population that differs from the RCT sample population. The
impact of these interactions on the marginal effect of treatment thus can
differ between the RCT population and the final population of interest.
Concurrent with research into transportability has been research into making
population inference from nonprobability samples. There is a close overlap
between these two approaches, particularly with respect to the nonprobability
inference methods that rely on information from a relevant probability sample
of the target population to reduce selection bias effects. When there are
relevant censuses or probability samples of the target patient population of
interest, these methods can be adapted to transport information from the RCT to
the patient population. Because the RCT setting focuses on causal inference,
this adaptation involves extensions to estimate counterfactuals. Thus
approaches that treat population inference as a missing data problem are a
natural fit to connect these two strands of methodological innovation.
In particular, we propose to extend a pseudoweighting'' methodology from
other nonprobability settings to a doubly robust'' estimator that treats
sampling probabilities or weights as regression covariates to achieve consistent
estimation of population quantities. We explore our proposed approach and
compare with some standard existing methods in a simulation study to assess the
effectiveness of the approach under differing degrees of selection bias and
model misspecification, and compare it with results obtained using the RT data
only and with existing methods that use inverse probability weights. We apply
it to a study of pulmonary artery catheterization in critically ill patients
where we believe differences between the trial sample and the larger population
might impact overall estimates of treatment effects.
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