SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS

Coarse Structural Nested Mean Models (SNMMs, Robins (2000)) and G-estimation can be used to estimate the causal effect of a time-varying treatment from longitudinal observational studies. However, they rely on an untestable assumption of no unmeasured confounding. In the presence of unmeasured confounders, the unobserved potential outcomes are not missing at random, and standard G-estimation leads to biased effect estimates. To remedy this, we investigate the sensitivity of G-estimators of coarse SNMMs to unmeasured confounding, assuming a nonidentifiable bias function which quantifies the impact of unmeasured confounding on the average potential outcome. We present adjusted G-estimators of coarse SNMM parameters and prove their consistency, under the bias modeling for unmeasured confounding. We apply this to a sensitivity analysis for the effect of the ART initiation time on the mean CD4 count at year 2 after infection in HIV-positive patients, based on the prospective Acute and Early Disease Research Program.

Medienart:

E-Artikel

Erscheinungsjahr:

2018

Erschienen:

2018

Enthalten in:

Zur Gesamtaufnahme - volume:28

Enthalten in:

Statistica Sinica - 28(2018), 4 vom: 24. Okt., Seite 1703-1723

Sprache:

Englisch

Beteiligte Personen:

Yang, Shu [VerfasserIn]
Lok, Judith J [VerfasserIn]

Links:

Volltext

Themen:

Censoring
Confounding by indication
Estimating equations
HIV/AIDS research
Journal Article
Non-ignorable
Sequential randomization

Anmerkungen:

Date Revised 01.10.2020

published: Print

Citation Status PubMed-not-MEDLINE

doi:

10.5705/ss.202016.0133

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM29477470X