Calibrated propensity score method for survey nonresponse in cluster sampling

Model-based standardization using an outcome model with random effects

Model-based standardization uses a statistical outcome model or exposure model to estimate a population-average association that is unconfounded by selected covariates. With it, one can compare groups using a distribution of confounders identical in each group to that of a standard population. We develop an approach based on an outcome model, in which the mean of the outcome is modeled conditional on the exposure and the confounders. In our approach, there is a confounder that clusters the observations into a very large number of categories. We treat the parameters for the clusters as random effects. We use a between-within model to account for the association of the random effects not only with the exposure but also with the cluster population sizes. We review alternative approaches presented in the literature, and we compare the outcome-modeling approach to recently proposed exposure-modeling approaches incorporating random effects. To illustrate, we use 2014 to compare proportions of acute respiratory tract infection diagnoses with an antibiotic prescription for emergency department versus outpatient visits, adjusting for confounding by unmeasured patient level variables and measured diagnosis-level variables. We also present results of a simulation study.

Combining Multiple Observational Data Sources to Estimate Causal Effects

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with supplementary information on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators. Supplementary materials for this article are available online.

Improved calibration estimators for the total cost of health programs and application to immunization in Brazil

Multi-stage/level sampling designs have been widely used by survey statisticians as a means of obtaining reliable and efficient estimates at a reasonable implementation cost. This method has been particularly useful in National country-wide surveys to assess the costs of delivering public health programs, which are generally originated in different levels of service management and delivery. Unbiased and efficient estimates of costs are essential to adequately allocate resources and inform policy and planning. In recent years, the global health community has become increasingly interested in estimating the costs of immunization programs. In such programs, part of the cost correspond to vaccines and it is in most countries procured at the central level, while the rest of the costs are incurred in states, municipalities and health facilities, respectively. As such, total program cost is a result of adding these costs, and its variance should account for the relation between the totals at the different levels. An additional challenge is the missing information at the various levels. A variety of methods have been developed to compensate for this missing data. Weighting adjustments are often used to make the estimates consistent with readily-available information. For estimation of total program costs this implies adjusting the estimates at each level to comply with the characteristics of the country. In 2014, A National study to estimate the costs of the Brazilian National Immunization Program was initiated, requested by the Ministry of Health and with the support of international partners. We formulate a quick and useful way to compute the variance and deal with missing values at the various levels. Our approach involves calibrating the weights at each level using additional readily-available information such as the total number of doses administered. Taking the Brazilian immunization costing study as an example, this approach results in substantial gains in both efficiency and precision of the cost estimate.