A pilot design for observational studies: Using abundant data thoughtfully.
Stat Med
; 39(30): 4821-4840, 2020 12 30.
Article
en En
| MEDLINE
| ID: mdl-33015867
ABSTRACT
Observational studies often benefit from an abundance of observational units. This can lead to studies that-while challenged by issues of internal validity-have inferences derived from sample sizes substantially larger than randomized controlled trials. But is the information provided by an observational unit best used in the analysis phase? We propose the use of a "pilot design," in which observations are expended in the design phase of the study, and the posttreatment information from these observations is used to improve study design. In modern observational studies, which are data rich but control poor, pilot designs can be used to gain information about the structure of posttreatment variation. This information can then be used to improve instrumental variable designs, propensity score matching, doubly robust estimation, and other observational study designs. We illustrate one version of a pilot design, which aims to reduce within-set heterogeneity and improve performance in sensitivity analyses. This version of a pilot design expends observational units during the design phase to fit a prognostic model, avoiding concerns of overfitting. In addition, it enables the construction of "assignment-control plots," which visualize the relationship between propensity and prognostic scores. We first show some examples of these plots, then we demonstrate in a simulation setting how this alternative use of the observations can lead to gains in terms of both treatment effect estimation and sensitivity analyses of unobserved confounding.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
Tipo de estudio:
Clinical_trials
/
Observational_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos