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Sensitivity analysis is an important statistical technique that assesses whether the results of phase III trials are robust and likely to be generalizable. Until recently, sensitivity analyses were rarely included in phase III trials, and they remain poorly understood by many oncologists. Sensitivity analyses are critical to understanding the strength of conclusions made in the primary analysis of a late-stage clinical trial. They examine the influence of protocol design errors, unintended biases, deviations from assumptions underlying statistical models, and any unanticipated treatment delivery or practice patterns on trial results. In trials with complex or subjective end points, they also allow an understanding of the extent to which a positive outcome is driven by a single, possibly subjective, and therefore biased, element of an end point. The purposes of this article are to explain how sensitivity analyses are performed, to discuss areas of a clinical trial where sensitivity analyses should focus, and to illuminate the importance of this technique in the rigorous evaluation of late-stage clinical trial data, using specific examples. This article focuses on late-stage trials that use progression-free survival or time to progression as their primary end point, because sensitivity analyses are particularly important in these cases for which the end point is potentially subject to bias. Three sources of potential bias are explored: assessment time, symptomatic (ie, nonradiologic) disease progression, and missing data. For each source of potential bias, case studies are presented to highlight the role that sensitivity analyses play in determining whether the trial's conclusions are robust.

Proinflammatory stimuli, after amyloid β (Aβ) deposition, have been hypothesized to create a self‐reinforcing positive feedback loop that increases amyloidogenic processing of the Aβ precursor protein (APP), promoting further Aβ accumulation and neuroinflammation in Alzheimer's disease (AD). Interleukin‐6 (IL‐6), a proinflammatory cytokine, has been shown to be increased in AD patients implying a pathological interaction. To assess the effects of IL‐6 on Aβ deposition and APP processing in vivo ,we overexpressed murine IL‐6 (mIL‐6) in the brains of APP transgenic TgCRND8 and TG2576 mice. mIL‐6 expression resulted in extensive gliosis and concurrently attenuated Aβ deposition in TgCRND8 mouse brains. This was accompanied by up‐regulation of glial phagocytic markers in vivo and resulted in enhanced microglia‐mediated phagocytosis of Aβ aggregates in vitro . Further, mIL‐6‐induced neuroinflammation had no effect on APP processing in TgCRND8 and had no effect on APP processing or steady‐state levels of Aβ in young Tg2576 mice. These results indicate that mIL‐6‐mediated reactive gliosis may be beneficial early in the disease process by potentially enhancing Aβ plaque clearance rather than mediating a neurotoxic feedback loop that exacerbates amyloid pathology. This is the first study that methodically dissects the contribution of mIL‐6 with regard to its potential role in modulating Aβ deposition in vivo .—Chakrabarty, P., Jansen‐West, K., Beccard, A., Ceballos‐Diaz, C., Levites, Y., Verbeeck, C., Zubair, A. C., Dickson, D., Golde, T. E., Das, P. Massive gliosis induced by interleukin‐6 suppresses Aβ deposition in vivo: evidence against inflammation as a driving force for amyloid deposition. FASEB J . 24, 548–559 (2010). www.fasebj.org

Abstract Introduction: Both smoking and alcohol consumption have been associated with modestly increased risks of colorectal cancer (CRC). Reports have suggested that these associations may differ by tumor molecular subtype, with stronger associations for microsatellite unstable (MSI-H) tumors. Methods: We used a population-based case-unaffected sibling design including 2,248 sibships (2,253 cases; 4,486 siblings) recruited to the Colon Cancer Family Registry to evaluate the association between smoking, alcohol consumption, and CRC. Associations were assessed using conditional logistic regression, treating sibship as the matching factor. Results: Although there were no statistically significant associations between any smoking variable and CRC overall, smoking did confer an increased risk of certain types of CRC. We observed an association between pack-years of smoking and rectal cancer [odds ratio (OR), 1.85; 95% confidence interval (CI), 1.23-2.79 for >40 pack-years versus nonsmokers; Ptrend = 0.03], and there was an increased risk of MSI-H CRC with increasing duration of smoking (OR, 1.94; 95% CI, 1.09-3.46 for >30 years of smoking versus nonsmokers). Alcohol intake was associated with a modest increase in risk for CRC overall (OR, 1.21; 95% CI, 1.03-1.44 for 12+ drinks per week versus nondrinkers), with more marked increases in risk for MSI-L CRC (OR, 1.85; 95% CI, 1.06-3.24) and rectal cancer (OR, 1.48; 95% CI, 1.08-2.02). Conclusions: We found associations between cigarette smoking and increased risks of rectal cancer and MSI-H CRC. Alcohol intake was associated with increased risks of rectal cancer and MSI-L CRC. These results highlight the importance of considering tumor phenotype in studies of risk factors for CRC. (Cancer Epidemiol Biomarkers Prev 2009;18(10):2745–50)

Abstract Multiple genome-wide association studies have identified several susceptibility variants for colon cancer at 8q24. However, the functional roles of these variants have yet to be elucidated. Here, we evaluated the potential role of these markers in tumor progression and examined association with commonly observed structural abnormalities in this region, c-MYC amplification and chromosome fragility at FRA8C and FRA8D. We first replicated the previously reported association by testing 1,178 cases and 1,009 clinic-based controls with eight markers localized to three specific regions at 8q24. We observed significant associations with colon cancer risk with markers rs13254738 (ordinal odds ratio, 0.82; 95% confidence interval, 0.072-0.94; Ptrend = 0.0037) and rs6983267 (ordinal odds ratio, 1.17; 95% confidence interval, 1.03-1.32, Ptrend = 0.013). Survival analysis was done using a separate set of 460 cases to evaluate the clinical significance of these markers. Overall, univariate analysis did not detect survival differences for any of the markers. We also tested a subset of the 460 cases (n = 380) for structural abnormalities at or near the c-MYC locus using fluorescence in situ hybridization analysis. Furthermore, we evaluated a small number of cases homozygous for the rs6983267 alleles to test for differences in fragile site induction. None of the 8q markers correlated with amplification at the c-MYC locus as detected by fluorescence in situ hybridization, and no clear pattern of breakage was observed at the FRA8C and FRA8D sites. In this study, we confirm the association for several single nucleotide polymorphisms at 8q24 in colon cancer but have not detected any structural role relating to c-MYC amplification or chromosomal fragility. Finally, these risk alleles do not seem to be associated with survival. (Cancer Epidemiol Biomarkers Prev 2009;18(9):2492–500)

Purpose Racial disparities in colorectal cancer (CRC) survival are documented, but there are few data on comparative response to chemotherapy. A subgroup analysis of a multisite National Cancer Institute–sponsored trial (N9741) was performed comparing outcomes of black and white patients with metastatic CRC receiving uniform treatment. Patients and Methods Adverse events (AEs), response rate (RR), time to progression (TTP), overall survival (OS), and dose-intensity were examined as a function of self-reported race in 1,412 patients treated with irinotecan/fluorouracil, fluorouracil/oxaliplatin, or irinotecan/oxaliplatin. Pharmacogenetic analysis was performed on 486 patients with blood available for germline DNA analysis. Results OS was 1.5 months shorter and TTP was 0.6 months shorter in black than white patients (OS: hazard ratio [HR] = 1.13; 95% CI, 0.90 to 1.42; TTP: HR = 0.91, 95% CI, 0.73 to 1.13); neither difference was statistically significant. RR was significantly higher in whites (41%) than blacks (28%; P = .008). Grade 3 or greater AEs were also higher in whites (48%) than blacks (34%; P = .004). These relationships were maintained in multivariate models adjusting for arm, age, sex, and performance status. There was no difference in dose-intensity of delivered therapy. Significant racial differences in prevalence of pharmacogenetic variants were observed, although small sample size precluded investigating the relationship between treatment, race, and genotype. Conclusion OS and TTP are similar in black and white patients treated per protocol with standardized therapy for metastatic CRC. However, RR and AEs vary considerably by race. The marked racial differences in relevant pharmacogenetics, a potential explanation for differing RR and AEs, are worthy of future study.