Difference-in-differences (DID) is commonly used for causal inference in time-series cross-sectional data. It requires the assumption that the average outcomes of treated and control units would have followed parallel paths in the absence of treatment. In this paper, we propose a method that not only relaxes this often-violated assumption, but also unifies the synthetic control method (Abadie, Diamond, and Hainmueller 2010) with linear fixed effects models under a simple framework, of which DID is a special case. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method has several advantages. First, it allows the treatment to be correlated with unobserved unit and time heterogeneities under reasonable modeling assumptions. Second, it generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. Third, with a built-in cross-validation procedure, it avoids specification searches and thus is easy to implement. An empirical example of Election Day Registration and voter turnout in the United States is provided.

Multiplicative interaction models are widely used in social science to examine whether the relationship between an outcome and an independent variable changes with a moderating variable. Current empirical practice tends to overlook two important problems. First, these models assume a linear interaction effect that changes at a constant rate with the moderator. Second, estimates of the conditional effects of the independent variable can be misleading if there is a lack of common support of the moderator. Replicating 46 interaction effects from 22 recent publications in five top political science journals, we find that these core assumptions often fail in practice, suggesting that a large portion of findings across all political science subfields based on interaction models are fragile and model dependent. We propose a checklist of simple diagnostics to assess the validity of these assumptions and offer flexible estimation strategies that allow for nonlinear interaction effects and safeguard against excessive extrapolation. These statistical routines are available in both R and STATA.

Camptothecins are broad-spectrum anticancer drugs that specifically target DNA topoisomerase I (Topo I). The formation of a cleavable drug-Topo I-DNA complex results in lethal double-strand DNA breakage and cell death. However, de novo or acquired clinical resistance to camptothecins is common. Studies of the camptothecin analog irinotecan suggest the following general mechanisms of resistance: (i) variable levels of the enzymes involved in the conversion of irinotecan; (ii) reduced cellular accumulation from active drug efflux; (iii) reduced levels of Topo I expression; (iv) alterations in the structure of Topo I from different mutations; (v) alterations in the cellular response to camptothecin-Topo I-DNA complex formation, which involves proteasome degradation of Topo I and/or enhanced DNA repair; and (vi) activation of the transcription factor nuclear factor kappa B by DNA damage and subsequent suppression of apoptosis. Multiple approaches using pharmacological and biological modulation to circumvent the above mechanisms of resistance have been incorporated into ongoing clinical trials and are expected to enhance the antitumor activity of irinotecan and reduce its systemic toxicity.

The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2012) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

Purpose: This study evaluated the effects of black raspberries (BRBs) on biomarkers of tumor development in the human colon and rectum including methylation of relevant tumor suppressor genes, cell proliferation, apoptosis, angiogenesis, and expression of Wnt pathway genes.Experimental Design: Biopsies of adjacent normal tissues and colorectal adenocarcinomas were taken from 20 patients before and after oral consumption of BRB powder (60 g/d) for 1-9 weeks. Methylation status of promoter regions of five tumor suppressor genes was quantified. Protein expression of DNA methyltransferase 1 (DNMT1) and genes associated with cell proliferation, apoptosis, angiogenesis, and Wnt signaling were measured.Results: The methylation of three Wnt inhibitors, SFRP2, SFRP5, and WIF1, upstream genes in Wnt pathway, and PAX6a, a developmental regulator, was modulated in a protective direction by BRBs in normal tissues and in colorectal tumors only in patients who received BRB treatment for an average of 4 weeks, but not in all 20 patients with 1-9 weeks of BRB treatment. This was associated with decreased expression of DNMT1. BRBs modulated expression of genes associated with Wnt pathway, proliferation, apoptosis, and angiogenesis in a protective direction.Conclusions: These data provide evidence of the ability of BRBs to demethylate tumor suppressor genes and to modulate other biomarkers of tumor development in the human colon and rectum. While demethylation of genes did not occur in colorectal tissues from all treated patients, the positive results with the secondary endpoints suggest that additional studies of BRBs for the prevention of colorectal cancer in humans now appear warranted.