Generalizability of causal inference in observational studies under retrospective convenience sampling.

Abstract:

:Many observational studies adopt what we call retrospective convenience sampling (RCS). With the sample size in each arm prespecified, RCS randomly selects subjects from the treatment-inclined subpopulation into the treatment arm and those from the control-inclined into the control arm. Samples in each arm are representative of the respective subpopulation, but the proportion of the 2 subpopulations is usually not preserved in the sample data. We show in this work that, under RCS, existing causal effect estimators actually estimate the treatment effect over the sample population instead of the underlying study population. We investigate how to correct existing methods for consistent estimation of the treatment effect over the underlying population. Although RCS is adopted in medical studies for ethical and cost-effective purposes, it also has a big advantage for statistical inference: When the tendency to receive treatment is low in a study population, treatment effect estimators under RCS, with proper correction, are more efficient than their parallels under random sampling. These properties are investigated both theoretically and through numerical demonstration.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Hu Z,Qin J

doi

10.1002/sim.7808

subject

Has Abstract

pub_date

2018-05-20 00:00:00

eissn

0277-6715

issn

1097-0258

pub_type

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