Multiply Robust Causal Inference with Double Negative Control Adjustment for Categorical Unmeasured Confounding

Unmeasured confounding is a threat to causal inference in observational studies. In recent years, use of negative controls to mitigate unmeasured confounding has gained increasing recognition and popularity. Negative controls have a longstanding tradition in laboratory sciences and epidemiology to rule out non-causal explanations, although they have been used primarily for bias detection. Recently, Miao et al. (2018) have described sufficient conditions under which a pair of negative control exposure and outcome variables can be used to nonparametrically identify the average treatment effect (ATE) from observational data subject to uncontrolled confounding. In this paper, we establish nonparametric identification of the ATE under weaker conditions in the case of categorical unmeasured confounding and negative control variables. We also provide a general semiparametric framework for obtaining inferences about the ATE while leveraging information about a possibly large number of measured covariates. In particular, we derive the semiparametric efficiency bound in the nonparametric model, and we propose multiply robust and locally efficient estimators when nonparametric estimation may not be feasible. We assess the finite sample performance of our methods in extensive simulation studies. Finally, we illustrate our methods with an application to the postlicensure surveillance of vaccine safety among children

Water management reforms in the Yellow River Basin: implications for water savings, farm incomes and poverty

River basins / Water management / Governance / Water use / Crop production / Models / Farm income / Poverty / Water users’ associations / China / Yellow River Basin

Blasts-more than meets the eye: Evaluation of post-induction day 21 bone marrow in CBFB rearranged acute leukemia

Induction chemotherapy is often the first therapeutic intervention for acute myeloid leukemia (AML). Evaluation of post induction bone marrow provides critical information for clinical management; in general increased blast countsor increased marrow cellularity is an ominous sign, suggestive of ineffective therapy, and may warrant additional rounds of chemotherapy. However, increased blasts alone are not necessarily predictive of recurrent/persistent disease. Here we report a very unusual observation in a case of AML with a core binding factor beta (CBFB) rearrangement. In this case the day 21 post-induction marrow biopsy showed a high blast count (approximately 20%), however,subsequent fluorescence in-situ hybridization studies were negative for CBFB rearrangement. We compared this finding to post-induction marrows from a series of 6 AML cases with CBFB rearrangements, none of which showed an increased blast count. This case illustrates that increased blast counts, even those comprising 20% of cells, are not de facto evidence of induction failure, and that correlation with ancillary studies such as fluorescence in-situhybridization should be used to distinguish a persistent neoplastic clone, from a brisk marrow recovery

Micro-resonator soliton generated directly with a diode laser

An external-cavity diode laser is reported with ultralow noise, high power coupled to a fiber, and fast tunability. These characteristics enable the generation of an optical frequency comb in a silica micro-resonator with a single-soliton state. Neither an optical modulator nor an amplifier was used in the experiment. This demonstration greatly simplifies the soliton generation setup and represents a significant step forward to a fully integrated soliton comb system.Comment: 7 pages, 5 figure

Characterization of Two Novel Toti-Like Viruses Co-infecting the Atlantic Blue Crab, Callinectes sapidus, in Its Northern Range of the United States

The advancement of high throughput sequencing has greatly facilitated the exploration of viruses that infect marine hosts. For example, a number of putative virus genomes belonging to the Totiviridae family have been described in crustacean hosts. However, there has been no characterization of the most newly discovered putative viruses beyond description of their genomes. In this study, two novel double-stranded RNA (dsRNA) virus genomes were discovered in the Atlantic blue crab (Callinectes sapidus) and further investigated. Sequencing of both virus genomes revealed that they each encode RNA dependent RNA polymerase proteins (RdRps) with similarities to toti-like viruses. The viruses were tentatively named Callinectes sapidus toti-like virus 1 (CsTLV1) and Callinectes sapidus toti-like virus 2 (CsTLV2). Both genomes have typical elements required for −1 ribosomal frameshifting, which may induce the expression of an encoded ORF1–ORF2 (gag-pol) fusion protein. Phylogenetic analyses of CsTLV1 and CsTLV2 RdRp amino acid sequences suggested that they are members of two new genera in the family Totiviridae. The CsTLV1 and CsTLV2 genomes were detected in muscle, gill, and hepatopancreas of blue crabs by real-time reverse transcription quantitative PCR (RT-qPCR). The presence of ~40 nm totivirus-like viral particles in all three tissues was verified by transmission electron microscopy, and pathology associated with CsTLV1 and CsTLV2 infections were observed by histology. PCR assays showed the prevalence and geographic range of these viruses, to be restricted to the northeast United States sites sampled. The two virus genomes co-occurred in almost all cases, with the CsTLV2 genome being found on its own in 8.5% cases, and the CsTLV1 genome not yet found on its own. To our knowledge, this is the first report of toti-like viruses in C. sapidus. The information reported here provides the knowledge and tools to investigate transmission and potential pathogenicity of these viruses

Proximal Causal Inference for Complex Longitudinal Studies

A standard assumption for causal inference about the joint effects of time-varying treatment is that one has measured sufficient covariates to ensure that within covariate strata, subjects are exchangeable across observed treatment values, also known as "sequential randomization assumption (SRA)". SRA is often criticized as it requires one to accurately measure all confounders. Realistically, measured covariates can rarely capture all confounders with certainty. Often covariate measurements are at best proxies of confounders, thus invalidating inferences under SRA. In this paper, we extend the proximal causal inference (PCI) framework of Miao et al. (2018) to the longitudinal setting under a semiparametric marginal structural mean model (MSMM). PCI offers an opportunity to learn about joint causal effects in settings where SRA based on measured time-varying covariates fails, by formally accounting for the covariate measurements as imperfect proxies of underlying confounding mechanisms. We establish nonparametric identification with a pair of time-varying proxies and provide a corresponding characterization of regular and asymptotically linear estimators of the parameter indexing the MSMM, including a rich class of doubly robust estimators, and establish the corresponding semiparametric efficiency bound for the MSMM. Extensive simulation studies and a data application illustrate the finite sample behavior of proposed methods