A critical issue of using the variance of the total in the linearization method – In the context of unequal probability sampling

Yu, Jihnhee; Vexler, Albert; Jalal, Kabir

doi:10.1002/sim.8053

Cited by 4 publications

(1 citation statement)

References 8 publications

(13 reference statements)

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“…Suppose that

T

is a quantity of interest and

{IF}_{i} ((), T) ((),,, i = 1, \dots, n)

is the corresponding IF for each observation with the sample size

n

. Once the IFs are obtained, variance estimation is possible in the form of

italicVar (\overset{IF (T)}{true})

, where

\overset{IF ((), T)}{true}

indicates the sample mean of IFs where parameters in

{IF}_{i} ((), T)

are replaced by their sample estimates 21 . When the statistic of interest is a joint function of other quantities (say

f ((), T)

), the IF is obtained using the functional delta method 22 as

IF ((), f ((), T)) = \partial f ((), T) / \partial T \cdot IF ((), T),

where

f

is a differentiable function defined on the space of values for

T

.…”

Section: Methodsmentioning

confidence: 99%

Influence function methods to assess the effectiveness of influenza vaccine with survey data

Tian

Lillvis

et al. 2021

Health Services Research

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Objective: To examine a robust relative risk (RR) estimation for survey data analysis with ideal inferential properties under various model assumptions. Data sources: We employed secondary data from the Household Component of the 2000-2016 US Medical Expenditure Panel Survey (MEPS). Study design:We investigate a broad range of data-balancing techniques by implementing influence function (IF) methods, which allows us to easily estimate the variability for the RR estimates in the complex survey setting. We conduct a simulation study of seasonal influenza vaccine effectiveness to evaluate these approaches and discuss techniques that show robust inferential performance across model assumptions.Data collection/Extraction methods: Demographic information, vaccine status, and self-administered questionnaire surveys were obtained from the longitudinal data files. We linked this information with medical condition files and medical event to extract the disease type and associated expenditures for each medical visit. We excluded individuals who were 18 years or younger at the beginning of each panel.Principal findings: Under various model assumptions, the IF methods show robust inferential performance when the data-balancing procedures are incorporated. Once IF methods and data-balancing techniques are implemented, contingency table-based RR estimation yields a comparable result to the generalized linear model approach.We demonstrate the applicability of the proposed methods for complex survey data using 2000-2016 MEPS data. When employing these methods, we find a significant, negative association between vaccine effectiveness (VE) estimates and influenzaincurred expenditures. Conclusions:We describe and demonstrate a robust method for RR estimation and relevant inferences for influenza vaccine effectiveness using MEPS data. The proposed method is flexible and can be extended to weighted data for survey data analysis. Hence, these methods have great potential for health services research, especially when data are nonexperimental and imbalanced.

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“…Suppose that