Combination MCP-Mod for two-drug combination dose-ranging studies.

Combination therapies with multiple mechanisms of action can offer improved efficacy and/or safety profiles when compared to a single therapy with one mechanism of action. Consequently, the number of combination therapy studies have increased multi-fold, both in oncology and non-oncology indications. However, identifying the optimal doses of each drug in a combination therapy can require a large sample size and prolong study timelines, especially when full factorial designs are used. In this paper, we extend the MCP-Mod design of Bretz, Pinheiro, and Branson to a three-dimensional space to model the dose-response surface of a two-drug combination under the framework of Combination (Comb) MCP-Mod. The resulting model yields a set of dosages for each drug in the combination that elicits the target response so that an optimal dose for the combination can be selected for pivotal studies. We construct three-dimensional dose-response models for the combination and formulate the contrast test statistic to select the best model, which can then be used to select the optimal dose. Guidance to calculate power and sample size calculations are provided to assist study design. Simulation studies show that Comb MCP-Mod performs as well as the conventional multiple comparisons approach in controlling the family-wise error rate at the desired alpha level. However, Comb MCP-Mod is more powerful than the classical multiple comparisons approach in detecting dose-response relationships when treatment is non-null. The probability of correctly identifying the underlying dose-response relationship is generally higher when using Comb MCP-Mod than when using the multiple comparisons approach.

Efficacy and safety of oral ritlecitinib for the treatment of active nonsegmental vitiligo: A randomized phase 2b clinical trial.

BACKGROUND: Vitiligo is a chronic autoimmune disorder characterized by depigmented patches of the skin. OBJECTIVE: To evaluate the efficacy and safety of ritlecitinib, an oral JAK3 (Janus kinase)/TEC (tyrosine kinase expressed in hepatocelluar carcinoma) inhibitor, in patients with active nonsegmental vitiligo in a phase 2b trial (NCT03715829). METHODS: Patients were randomized to once-daily oral ritlecitinib ± 4-week loading dose (200/50 mg, 100/50 mg, 30 mg, or 10 mg) or placebo for 24 weeks (dose-ranging period). Patients subsequently received ritlecitinib 200/50 mg daily in a 24-week extension period. The primary efficacy endpoint was percent change from baseline in Facial-Vitiligo Area Scoring Index at week 24. RESULTS: A total of 364 patients were treated in the dose-ranging period. Significant differences from placebo in percent change from baseline in Facial-Vitiligo Area Scoring Index were observed for the ritlecitinib 50 mg groups with (-21.2 vs 2.1; P < .001) or without (-18.5 vs 2.1; P < .001) a loading dose and ritlecitinib 30 mg group (-14.6 vs 2.1; P = .01). Accelerated improvement was observed after treatment with ritlecitinib 200/50 mg in the extension period (n = 187). No dose-dependent trends in treatment-emergent or serious adverse events were observed across the 48-week treatment. LIMITATIONS: Patients with stable vitiligo only were excluded. CONCLUSIONS: Oral ritlecitinib was effective and well tolerated over 48 weeks in patients with active nonsegmental vitiligo.

A repeated measures approach to pooled and calibrated biomarker data.

Participant-level meta-analysis across multiple studies increases the sample size for pooled analyses, thereby improving precision in effect estimates and enabling subgroup analyses. For analyses involving biomarker measurements as an exposure of interest, investigators must first calibrate the data to address measurement variability arising from usage of different laboratories and/or assays. In practice, the calibration process involves reassaying a random subset of biospecimens from each study at a central laboratory and fitting models that relate the study-specific "local" and central laboratory measurements. Previous work in this area treats the calibration process from the perspective of measurement error techniques and imputes the estimated central laboratory value among individuals with only a local laboratory measurement. In this work, we propose a repeated measures method to calibrate biomarker measurements pooled from multiple studies with study-specific calibration subsets. We account for correlation between measurements made on the same person and between measurements made at the same laboratory. We demonstrate that the repeated measures approach provides valid inference, and compare it to existing calibration approaches grounded in measurement error techniques in an example describing the association between circulating vitamin D and stroke.

Statistical methods for analysis of combined biomarker data from multiple nested case-control studies.

By combining data across multiple studies, researchers increase sample size, statistical power, and precision for pooled analyses of biomarker-disease associations. However, researchers must adjust for between-study variability in biomarker measurements. Previous research often treats the biomarker measurements from a reference laboratory as a gold standard, even though those measurements are certainly not equal to their true values. This paper addresses measurement error and bias arising from both the reference and study-specific laboratories. We develop two calibration methods, the exact calibration method and approximate calibration method, for pooling biomarker data drawn from nested or matched case-control studies, where the calibration subset is obtained by randomly selecting controls from each contributing study. Simulation studies are conducted to evaluate the empirical performance of the proposed methods. We apply the proposed methods to a pooling project of nested case-control studies to evaluate the association between circulating 25-hydroxyvitamin D (25(OH)D) and colorectal cancer risk.

Statistical methods for biomarker data pooled from multiple nested case-control studies.

Pooling biomarker data across multiple studies allows for examination of a wider exposure range than generally possible in individual studies, evaluation of population subgroups and disease subtypes with more statistical power, and more precise estimation of biomarker-disease associations. However, circulating biomarker measurements often require calibration to a single reference assay prior to pooling due to assay and laboratory variability across studies. We propose several methods for calibrating and combining biomarker data from nested case-control studies when reference assay data are obtained from a subset of controls in each contributing study. Specifically, we describe a two-stage calibration method and two aggregated calibration methods, named the internalized and full calibration methods, to evaluate the main effect of the biomarker exposure on disease risk and whether that association is modified by a potential covariate. The internalized method uses the reference laboratory measurement in the analysis when available and otherwise uses the estimated value derived from calibration models. The full calibration method uses calibrated biomarker measurements for all subjects, including those with reference laboratory measurements. Under the two-stage method, investigators complete study-specific analyses in the first stage followed by meta-analysis in the second stage. Our results demonstrate that the full calibration method is the preferred aggregated approach to minimize bias in point estimates. We also observe that the two-stage and full calibration methods provide similar effect and variance estimates but that their variance estimates are slightly larger than those from the internalized approach. As an illustrative example, we apply the three methods in a pooling project of nested case-control studies to evaluate (i) the association between circulating vitamin D levels and risk of stroke and (ii) how body mass index modifies the association between circulating vitamin D levels and risk of cardiovascular disease.

Design and analysis considerations for combining data from multiple biomarker studies.

Pooling data from multiple studies improves estimation of exposure-disease associations through increased sample size. However, biomarker exposure measurements can vary substantially across laboratories and often require calibration to a reference assay prior to pooling. We develop two statistical methods for aggregating biomarker data from multiple studies: the full calibration method and the internalized method. The full calibration method calibrates all biomarker measurements regardless of the availability of reference laboratory measurements while the internalized method calibrates only non-reference laboratory measurements. We compare the performance of these two aggregation methods to two-stage methods. Furthermore, we compare the aggregated and two-stage methods when estimating the calibration curve from controls only or from a random sample of individuals from the study cohort. Our findings include the following: (1) Under random sampling for calibration, exposure effect estimates from the internalized method have a smaller mean squared error than those from the full calibration method. (2) Under the controls-only calibration design, the full calibration method yields effect estimates with the least bias. (3) The two-stage approaches produce average effect estimates that are similar to the full calibration method under a controls only calibration design and the internalized method under a random sample calibration design. We illustrate the methods in an application evaluating the relationship between circulating vitamin D levels and stroke risk in a pooling project of cohort studies.