Sources of selection bias in evaluating social programs: An interpretation of conventional measures and evidence on the effectiveness of matching as a program evaluation method

This paper decomposes the conventional measure of selection bias in observational studies into three components. The first two components are due to differences in the distributions of characteristics between participant and nonparticipant (comparison) group members: the first arises from differences in the supports, and the second from differences in densities over the region of common support. The third component arises from selection bias precisely defined. Using data from a recent social experiment, we find that the component due to selection bias, precisely defined, is smaller than the first two components. However, selection bias still represents a substantial fraction of the experimental impact estimate. The empirical performance of matching methods of program evaluation is also examined. We find that matching based on the propensity score eliminates some but not all of the measured selection bias, with the remaining bias still a substantial fraction of the estimated impact. We find that the support of the distribution of propensity scores for the comparison group is typically only a small portion of the support for the participant group. For values outside the common support, it is impossible to reliably estimate the effect of program participation using matching methods. If the impact of participation depends on the propensity score, as we find in our data, the failure of the common support condition severely limits matching compared with random assignment as an evaluation estimator.

Strategy for monitoring T cell responses to NY-ESO-1 in patients with any HLA class I allele

NY-ESO-1 elicits frequent antibody responses in cancer patients, accompanied by strong CD8+ T cell responses against HLA-A2-restricted epitopes. To broaden the range of cancer patients who can be assessed for immunity to NY-ESO-1, a general method was devised to detect T cell reactivity independent of prior characterization of epitopes. A recombinant adenoviral vector encoding the full cDNA sequence of NY-ESO-1 was used to transduce CD8-depleted peripheral blood lymphocytes as antigen-presenting cells. These modified antigen-presenting cells were then used to restimulate memory effector cells against NY-ESO-1 from the peripheral blood of cancer patients. Specific CD8+ T cells thus sensitized were assayed on autologous B cell targets infected with a recombinant vaccinia virus encoding NY-ESO-1. Strong polyclonal responses were observed against NY-ESO-1 in antibody-positive patients, regardless of their HLA profile. Because the vectors do not cross-react immunologically, only responses to NY-ESO-1 were detected. The approach described here allows monitoring of CD8+ T cell responses to NY-ESO-1 in the context of various HLA alleles and has led to the definition of NY-ESO-1 peptides presented by HLA-Cw3 and HLA-Cw6 molecules.

Parallel human genome analysis: microarray-based expression monitoring of 1000 genes.

Microarrays containing 1046 human cDNAs of unknown sequence were printed on glass with high-speed robotics. These 1.0-cm2 DNA "chips" were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. Array elements that displayed differential expression patterns under given experimental conditions were characterized by sequencing. The identification of known and novel heat shock and phorbol ester-regulated genes in human T cells demonstrates the sensitivity of the assay. Parallel gene analysis with microarrays provides a rapid and efficient method for large-scale human gene discovery.