Deductive semiparametric estimation in Double-Sampling Designs with application to PEPFAR

Non-ignorable dropout is common in studies with long follow-up time, and it can bias study results unless handled carefully. A double-sampling design allocates additional resources to pursue a subsample of the dropouts and find out their outcomes, which can address potential biases due to non-ignorable dropout. It is desirable to construct semiparametric estimators for the double-sampling design because of their robustness properties. However, obtaining such semiparametric estimators remains a challenge due to the requirement of the analytic form of the efficient influence function (EIF), the derivation of which can be ad hoc and difficult for the double-sampling design. Recent work has shown how the derivation of EIF can be made deductive and computerizable using the functional derivative representation of the EIF in nonparametric models. This approach, however, requires deriving the mixture of a continuous distribution and a point mass, which can itself be challenging for complicated problems such as the double-sampling design. We propose semiparametric estimators for the survival probability in double-sampling designs by generalizing the deductive and computerizable estimation approach. In particular, we propose to build the semiparametric estimators based on a discretized support structure, which approximates the possibly continuous observed data distribution and circumvents the derivation of the mixture distribution. Our approach is deductive in the sense that it is expected to produce semiparametric locally efficient estimators within finite steps without knowledge of the EIF. We apply the proposed estimators to estimating the mortality rate in a double-sampling design component of the President's Emergency Plan for AIDS Relief (PEPFAR) program. We evaluate the impact of double-sampling selection criteria on the mortality rate estimates

Polydesigns and Causal Inference

In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full cohort design and data can identify the causal effects of interest, but can be sensitive to extreme regions of that design\u27s data, where model specification can have more impact; and (2) models on a reduced design (i.e., a subset of the full data), e.g., conditional likelihood on matched subsets of data, can avoid such sensitivity, but do not generally identify the causal effects. We propose a framework to assess how inference is sensitive to designs by exploring combinations of both the full and reduced designs. We show that using such a polydesign framework generates a rich class of methods that can identify causal effects and that can also be more robust to model specification than methods using only the full design. We discuss implementation of polydesign methods, and provide an illustration in the evaluation of a Needle Exchange Program

A Potential Outcomes Approach to Developmental Toxicity Analyses

Estimating the effects of a toxin on fetal development in animal models such as mice can be problematic, because the number of pups that develop and survive until birth may simultaneously affect developmental outcomes such as birth weight and be affected by the introduction of a toxin into the fetal environment. Also, comparing pups that survived until birth at a high dose of the toxin with pups that survived at low doses may underestimate the effect of the toxin, because the lower dose means include the less healthy pups that would not survive if exposed to a higher level of toxin. We consider this problem in a potential outcomes framework that defines the effect of the dose on the outcome as the difference between what the outcome would have been for a pup had the dam in which the pup develops been exposed to dose level Z = z * rather than dose level Z = z . To disentangle the direct effect of dose from the effect of litter size, we focus on effects defined within principal strata that are a function of the survival status of the pups at each of the possible dose levels. A unique contribution to the potential outcomes literature is that we allow the outcome for a subject to be dependent on the principal stratum to which other subjects within a cluster belong.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65819/1/j.1541-0420.2005.00506.x.pd

The demand for electricity in Greece, 1961-1975: an empirical investigation

This study attempts to provide quantitative estimates of the influence of changes in economic variables (relative prices and activity indicators) on electricity demand in Greece over the period 1961-1975. The fact that e1ectricity is not a product yielding direct satisfaction but it is demanded as a fuel input into activities that do provide utility, and which use a capital stock of some durability, creates the need for a distinction between demand in the short-run and demand in the long-run. Moreover, total electricity demand is disaggregated into demand by the household, commercial and industrial sectors. This disaggregation rests upon the assumption that the response of different sectors to changes in the economic environment is unlikely to be the same. The quantitative estimates suggest that in general short-run demand appears to be price and income inelastic for the household and commercial sectors, whereas for the industrial sector the evidence suggests a price elasticity very close to unity and an activity indicator (Index of Industrial Production) elasticity greater than unity. Long-run demand appears to be inelastic with respect to price changes only in the household sector. The activity indicators elasticities for all sectors are well above unity. The long-run formulations permit, the calculation of the speed with which actual demand adjusts to desired demand. The estimates suggest that this speed is relatively fast in the case of residential demand and relatively slow in the case of industrial demand

Designs in Partially Controlled Studies: Messages from a Review

The ability to evaluate effects of factors on outcomes is increasingly important for a class of studies that control some but not all of the factors. Although important advances have been made in methods of analysis for such partially controlled studies,work on designs for such studies has been relatively limited. To help understand why, we review main designs that have been used for such partially controlled studies. Based on the review, we give two complementary reasons that explain the limited work on such designs, and suggest a new direction in this area

Nested Markov Compliance Class Model in the Presence of Time-Varying Noncompliance

We consider a Markov structure for partially unobserved time-varying compliance classes in the Imbens–Rubin (1997, The Annals of Statistics 25, 305–327) compliance model framework. The context is a longitudinal randomized intervention study where subjects are randomized once at baseline, outcomes and patient adherence are measured at multiple follow-ups, and patient adherence to their randomized treatment could vary over time. We propose a nested latent compliance class model where we use time-invariant subject-specific compliance principal strata to summarize longitudinal trends of subject-specific time-varying compliance patterns. The principal strata are formed using Markov models that relate current compliance behavior to compliance history. Treatment effects are estimated as intent-to-treat effects within the compliance principal strata.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65981/1/j.1541-0420.2008.01113.x.pd

3D ultrastructural organization of whole Chlamydomonas reinhardtii cells studied by nanoscale soft x-ray tomography

The complex architecture of their structural elements and compartments is a hallmark of eukaryotic cells. The creation of high resolution models of whole cells has been limited by the relatively low resolution of conventional light microscopes and the requirement for ultrathin sections in transmission electron microscopy. We used soft x-ray tomography to study the 3D ultrastructural organization of whole cells of the unicellular green alga Chlamydomonas reinhardtii at unprecedented spatial resolution. Intact frozen hydrated cells were imaged using the natural x-ray absorption contrast of the sample without any staining. We applied different fiducial-based and fiducial-less alignment procedures for the 3D reconstructions. The reconstructed 3D volumes of the cells show features down to 30 nm in size. The whole cell tomograms reveal ultrastructural details such as nuclear envelope membranes, thylakoids, basal apparatus, and flagellar microtubule doublets. In addition, the x-ray tomograms provide quantitative data from the cell architecture. Therefore, nanoscale soft x-ray tomography is a new valuable tool for numerous qualitative and quantitative applications in plant cell biology

THE ROLE OF AN EXPLICIT CAUSAL FRAMEWORK IN AFFECTED SIB PAIR DESIGNS WITH COVARIATES

The affected sib/relative pair (ASP/ARP) design is often used with covariates to find genes that can cause a disease in pathways other than through those covariates. However, such covariates can themselves have genetic determinants, and the validity of existing methods has so far only been argued under implicit assumptions. We propose an explicit causal formulation of the problem using potential outcomes and principal stratification. The general role of this formulation is to identify and separate the meaning of the different assumptions that can provide valid causal inference in linkage analysis. This separation helps to (a) develop better methods under explicit assumptions, and (b) show the different ways in which these assumptions can fail, which is necessary for developing further specific designs to test these assumptions and confirm or improve the inference. Using this formulation in the specific problem above, we show that, when the covariate (e.g., addiction to smoking) also has genetic determinants, then existing methods, including those previously thought as valid, can declare linkage between the disease and marker loci even when no such linkage exists. We also introduce design strategies to address the problem