Variance Estimation. (XXXXXX, VARSTR) MEPS has a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family- level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the Xxxxxx-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. Replicate weights have not been developed for the MEPS data. Instead, the variables needed to calculate appropriate standard errors based on the Xxxxxx-series linearization method are included on this file as well as all other MEPS public use files. Software packages that permit the use of the Xxxxxx-series linearization method include SUDAAN, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of each package, analysts should refer to the corresponding software user documentation. Using the Xxxxxx-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The variance strata variable is named VARSTR, while the variance PSU variable is named VARPSU. Specifying a “with replacement” design in a computer software package, such as SUDAAN, provides standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), one can expect at least 100 degrees of freedom for the 2009 full year data associated with the corresponding estimates of variance and usually substantially more. Prior to 2002, MEPS variance strata and PSUs were developed independently from year to year, and the last two characters of the strata and PSU variable names denoted the year. However, beginning with the 2002 Point-in-Time PUF, the variance strata and PSUs were developed to be compatible with MEPS data associated with the NHIS sample design used through 2006. Such data can be pooled and the variance strata and PSU variabl...
Variance Estimation. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, the complex sample design of MEPS for both person and family-level analyses must be taken into account. Various approaches can be used to develop such estimates of variance including use of the Xxxxxx series or replication methodologies. Replicate weights have not been developed for the MEPS 2000 data. Using a Xxxxxx Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The corresponding variables on the 2000 MEPS full year utilization data base are VARSTR00 and VARPSU00, respectively. Specifying a “with replacement” design in a computer software package, such as SUDAAN, should provide standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), one can expect at least 60 degrees of freedom for the 2000 full year data associated with the corresponding estimates of variance
Variance Estimation. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, one needs to take into account the complex sample design of MEPS. Various approaches can be used to develop such estimates of variance including use of the Xxxxxx series or various replication methodologies. Replicate weights have not been developed for the MEPS 1998 data. Variables needed to implement a Xxxxxx series estimation approach are provided in the file and are described in the paragraph below. Using a Xxxxxx Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The corresponding variables on the MEPS full year utilization database are VARSTR98 and VARPSU98, respectively. Specifying a “with replacement” design in a computer software package such as SUDAAN (Xxxx, 1996) should provide standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), there are over 100 degrees of freedom associated with the corresponding estimates of variance. The following illustrates these concepts using two examples from Section 4.2. Examples 2 and 3 from Section 4.2 Using a Xxxxxx Series approach, specifying VARSTR98 and VARPSU98 as the variance estimation strata and PSUs (within these strata) respectively and specifying a with replacement design in a computer software package SUDAAN will yield standard error estimates of $5.57 and 0.0130 for the estimated mean of out-of-pocket payment and the estimated mean proportion of total expenditures paid by private insurance respectively.
Variance Estimation. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, one needs to take into account the complex sample design of MEPS for both person and family level analyses. Various approaches can be used to develop such estimates of variance including a Xxxxxx series method for variance estimation or various replication methodologies. Replicate weights have not been developed for the MEPS data. We will describe the variables needed to implement a Xxxxxx series estimation approach. Using a Xxxxxx Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The variables VARST13 and PSU13 on this MEPS data file (updated versions of corresponding variables provided in previously released MEPS public use files) serve to identify the sampling strata and primary sampling units required by the variance estimation programs. Specifying a “with replacement” design in a computer software package such as SUDAAN should provide estimated standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the number available. For variables of interest distributed throughout the country (and thus the MEPS sample PSUs), one can generally expect to have at least 100 degrees of freedom associated with the estimated standard errors for national estimates based on this MEPS database.
Variance Estimation. Variance provides a means of assessing and reporting the precision of a point estimate, playing a critical role in the interval estimation, hypothesis testing, and power calculation. In this section, asymptotic variance formulas for different IRA statistics in the review are summarized, which allows approximate variance estimations for those IRA measures based Xxxxxx-corrected IRA statistics with ICC interpretations Xxxxxxx and Xxxxxxxx’s 𝑟11 Equal when 𝑛10 = 𝑛01 Mak’s 𝜌˜ Xxxxx’x κ Equal when 𝑛10 = 𝑛01 Xxxxx’x π Equal when 𝑛11= 𝑛00 Equal when 𝑛11 = 𝑛00 and 𝑛10 = 𝑛01 Xxxx’s 𝑌 (Holher’s adjusted κ) Xxxxxxxxxxxx’s α Xxx Xxxx’x 𝐼2 r Gwet’s 𝐴��1 Xxxxxxx et al.’s 𝑆 (Xxxx et al.’s prevalence-adjusted bias-adjusted κ) Figure 1: Connections among the included interrater agreement (IRA) statistics. Solid lines with arrow represent method extension with additional considerations, solid lines without arrow indicate connection in mathematical form, and dashed lines with arrow suggest the directions of asymptotic convergence. on the observed data from Table 1. e|κ Xxxxxx, Xxxxx, and Xxxxxxx [47] proposed a variance estimator for Xxxxx’x κ under the nonnull case of IRA, and Gwet [12] rewrote the variance formula under the two-rater case for better comparability with the estimated variances of several other IRA statistics. The estimated variance of κ is given as Vˆar(κ) = npˆ (1 − pˆ ) − 4(1 − κ) pˆ ωˆ + pˆ (1 − ωˆ) − κpˆ N (1 − pˆe|κ )2 a a 11 00 + 4(1 − κ)2 pˆ ωˆ2 + 1 pˆ (2pˆ + pˆ )2 + 1 pˆ 01 a 01 10 a 00 (2pˆ + pˆ )2 + pˆ (1 − ωˆ)2 ,, where ωˆ = pˆ11 + pˆ10/2 + pˆ01/2. The performance of this large-sample variance of κ have been evaluated via Monte Carlo simulations in Xxxxxxxxx and Xxxxxx [48] and Fleiss and Xxxxxxxxx [49]. ˆ For Xxxxxxx et al.’s S and the several equivalent IRA measures mentioned in the review, since the asymptotic variance estimator of percent agreement could be obtained as Var(pˆa) = pˆa(1 − pˆa)/N based on the binomial properties, we can get the variance estimator for S = 2pˆa − 1 as ˆ Var(S) = N pˆa(1 − pˆa). For Xxxxx’x π, Gwet [12] proposed a nonparametric variance estimator with linearization techniques to remedy the formula proposed by Xxxxxx [50], which was under the hypothesis of no agreement and was not valid for building up confidence intervals. Under our scenario of interest, the variance formula of π can be written as 1 N (1 − pˆe|π )2 a 11 Vˆar(π) = npˆ (1 − pˆ ) − 4(1 − π) pˆ 00 e|π ωˆ + pˆ (1 − ωˆ) − pˆ pˆ 11 ˆ + ( 4...
Variance Estimation. The MEPS is based on a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family-level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the Xxxxxx-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. MEPS analysts most commonly use the Xxxxxx Series approach. However, an option is also provided to apply the BRR approach when needed to develop variances for more complex estimators.
