Systemic Safety in Ranibizumab-Treated Patients with Neovascular Age-Related Macular Degeneration: A Patient-Level Pooled Analysis

Topic This study evaluated the cardiovascular/cerebrovascular safety profile of ranibizumab 0.5 mg versus sham ± verteporfin in patients with neovascular age-related macular degeneration (nAMD). In addition, comparisons of ranibizumab 0.3 mg with sham and ranibizumab 0.5 mg to 0.3 mg were performed. Clinical Relevance Intravitreal anti–vascular endothelial growth factor (VEGF) agents carry potential increased systemic risks, including cardiovascular or cerebrovascular events. Pooled safety analyses allow better interpretation of safety outcomes seen in individual clinical trials, especially for less common events. To our knowledge, this is the largest patient-level pooled analysis of patients with nAMD treated with ranibizumab. Methods Patient-level pooled analysis of data from 7 Genentech- and Novartis-sponsored phase II, III, and IV studies in nAMD that were completed by December 31, 2013. Pairwise comparisons (primary comparison: ranibizumab 0.5 mg [globally approved dose for nAMD] vs. sham or verteporfin) were performed using Cox proportional hazard regression (hazard ratios [HRs], 95% confidence intervals [CIs]) and rates per 100 patient-years. Standardized Medical Dictionary for Regulatory Activities queries (SMQs) and extended searches were used to identify relevant safety endpoints, including arterial thromboembolic events (ATEs), myocardial infarction (MI), stroke or transient ischemic attack (TIA), stroke (excluding TIA), vascular deaths, and major vascular events as defined by the Antiplatelet Trialists' Collaboration (APTC). Results The HRs (95% CIs) for the primary comparison of ranibizumab 0.5 mg (n=480) versus sham or verteporfin (n=462) were 1.16 (0.72–1.88) for ATE, 1.33 (0.59–2.97) for MI, 1.43 (0.54–3.77) for stroke excluding TIA, 1.25 (0.61–2.55) for stroke or TIA, 0.57 (0.18–1.78) for vascular death, and 1.12 (0.64–1.98) for APTC events. Hazard ratio 95% CIs included 1, indicating no significant treatment differences, for all endpoints for comparison of ranibizumab 0.5 mg versus sham or verteporfin. Conclusions The rates of cardiovascular and cerebrovascular events were low in these patients with nAMD and not clinically meaningfully different for patients treated with ranibizumab 0.5 mg versus sham or verteporfin, which supports the favorable benefit–risk profile of ranibizumab in the patient population with nAMD. Pooling these studies allows an analysis with higher power and precision compared with individual study analyses

Analysis Of Change In Longitudinal Ordinal Data With Multiple Treatments (categorial Data, Repeated Measures, Stochastic Ordering, Mantel-haenszel).

Many experiments are designed to collect both baseline and follow-up observations using the same ordinal scale. Unfortunately, there are few statistical methods available in the literature to analyze such data. In clinical trials, this problem can be further complicated by truncated response profiles and/or the presence of missing observations. In an attempt to address this deficiency, this dissertation proposes analytic strategies to compare treatments based on ordinal data. All the proposed strategies emphasize comparing change between baseline and follow-up across treatments. Two approaches are considered. The first one is an extension of the Cochran/Mantel-Haenszel procedure. Besides its simplicity, this approach is especially appropriate when the frequencies within the observed tables are sparse due to small sample sizes. Univariate and multivariate test statistics are developed which contrast treatment subgroups at progressive cutpoints along the cumulative response profiles defined in terms of change from baseline. The second approach involves fitting structural log-linear models to the data. These models contain parameters representing the baseline classifications, the direction of change and the rates of change. The rates of change may depend not only upon the direction of change, but also upon the baseline classification. Methods to select a parsimonious model via model reduction are proposed. Also considered are methods for testing hypotheses concerning treatment differences. Using this approach, comparisons among treatments are facilitated by applying the concepts of stochastic ordering within baseline classifications to the corresponding cumulative response distributions. Both approaches are modified to accommodate truncated response profiles and missing data. These two approaches are furthermore generalized for the situation where there are two or more follow-up observations. This latter generalization is carried out under the assumption of a first order markov chain. Examples from clinical drug trials are used to illustrate the proposed approaches. Finally, topics for further research are suggested.Ph.D.Biological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/127960/2/8702731.pd