On sample size determination of Dunnett's procedure for comparing several treatments with a control

Cathepsin S Alters the Expression of Pro-Inflammatory Cytokines and MMP-9, Partially through Protease-Activated Receptor-2, in Human Corneal Epithelial Cells

Cathepsin S (CTSS) activity is increased in tears of Sjögren's syndrome (SS) patients. This elevated CTSS may contribute to ocular surface inflammation. Human corneal epithelial cells (HCE-T cells) were treated with recombinant human CTSS at activity comparable to that in SS patient tears for 2, 4, 8, and 24 h. Acute CTSS significantly increased HCE-T cell gene and protein expression of interleukin 6 (IL-6), interleukin 8 (IL-8), tumor necrosis factor-α (TNF-α), and interleukin-1β (IL-1β) from 2 to 4 h, while matrix metalloproteinase 9 (MMP-9), CTSS, and protease-activated receptor-2 (PAR-2) were increased by chronic CTSS (24 h). To investigate whether the increased pro-inflammatory cytokines and proteases were induced by CTSS activation of PAR-2, HCE-T cells were transfected with PAR-2 siRNA, reducing cellular PAR-2 by 45%. Cells with reduced PAR-2 expression showed significantly reduced release of IL-6, TNF-α, IL-1β, and MMP-9 into culture medium in response to acute CTSS, while IL-6, TNF-α, and MMP-9 were reduced in culture medium, and IL-6 and MMP-9 in cell lysates, after chronic CTSS. Moreover, cells with reduced PAR-2 expression showed reduced ability of chronic CTSS to induce gene expression of pro-inflammatory cytokines and proteases. CTSS activation of PAR-2 may represent a potential therapeutic target for amelioration of ocular surface inflammation in SS patients.

Sequential Immunization with gp140 Boosts Immune Responses Primed by Modified Vaccinia Ankara or DNA in HIV-Uninfected South African Participants

BACKGROUND

The safety and immunogenicity of SAAVI DNA-C2 (4 mg IM), SAAVI MVA-C (2.9 x 109 pfu IM) and Novartis V2-deleted subtype C gp140 (100 mcg) with MF59 adjuvant in various vaccination regimens was evaluated in HIV-uninfected adults in South Africa.

METHODS

Participants at three South African sites were randomized (1:1:1:1) to one of four vaccine regimens: MVA prime, sequential gp140 protein boost (M/M/P/P); concurrent MVA/gp140 (MP/MP); DNA prime, sequential MVA boost (D/D/M/M); DNA prime, concurrent MVA/gp140 boost (D/D/MP/MP) or placebo. Peak HIV specific humoral and cellular responses were measured.

RESULTS

184 participants were enrolled: 52% were female, all were Black/African, median age was 23 years (range, 18-42 years) and 79% completed all vaccinations. 159 participants reported at least one adverse event, 92.5% were mild or moderate. Five, unrelated, serious adverse events were reported. The M/M/P/P and D/D/MP/MP regimens induced the strongest peak neutralizing and binding antibody responses and the greatest CD4+ T-cell responses to Env. All peak neutralizing and binding antibody responses decayed with time. The MVA, but not DNA, prime contributed to the humoral and cellular immune responses. The D/D/M/M regimen was poorly immunogenic overall but did induce modest CD4+ T-cell responses to Gag and Pol. CD8+ T-cell responses to any antigen were low for all regimens.

CONCLUSIONS

The SAAVI DNA-C2, SAAVI MVA-C and Novartis gp140 with MF59 adjuvant in various combinations were safe and induced neutralizing and binding antibodies and cellular immune responses. Sequential immunization with gp140 boosted immune responses primed by MVA or DNA. The best overall immune responses were seen with the M/M/P/P regimen.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01418235.

Sample size determination with familywise control of both type I and type II errors in clinical trials

The concept of controlling familywise type I and type II errors at the same time is essentially an integrated process to deal with multiplicity issues in clinical trials. The process will select a multiple testing procedure (MTP) which controls the familywise type I error and calculate the per hypothesis sample size such that the "studywise power" is maintained at desired level. The power of a study can be defined in several ways and it depends on the objective. In this article, we provide general guidance on how to make the selection of MTPs and calculate sample size simultaneously. We introduce the concept of strong and weak control of the familywise type II error and generalized familywise type II error. We also proposed the novel Bonferroni+ and optimal Bonferroni+ procedures to allocate per hypothesis type II error. We demonstrated the value of the proposed work as it cannot be replaced by simple simulations. A real clinical trial is discussed throughout the article as an example.

Step-up testing procedure for multiple comparisons with a control for a latent variable model with ordered categorical responses

In clinical studies, multiple comparisons of several treatments to a control with ordered categorical responses are often encountered. A popular statistical approach to analyzing the data is to use the logistic regression model with the proportional odds assumption. As discussed in several recent research papers, if the proportional odds assumption fails to hold, the undesirable consequence of an inflated familywise type I error rate may affect the validity of the clinical findings. To remedy the problem, a more flexible approach that uses the latent normal model with single-step and stepwise testing procedures has been recently proposed. In this paper, we introduce a step-up procedure that uses the correlation structure of test statistics under the latent normal model. A simulation study demonstrates the superiority of the proposed procedure to all existing testing procedures. Based on the proposed step-up procedure, we derive an algorithm that enables the determination of the total sample size and the sample size allocation scheme with a pre-determined level of test power before the onset of a clinical trial. A clinical example is presented to illustrate our proposed method.

Sample size determination in two-sided distribution-free treatment versus control multiple comparisons

The problem of power and sample size determination for distribution-free multiple comparison tests of K treatments versus a control group is addressed. We define the power as the probability of correctly rejecting one specified or all K hypotheses, corresponding to the per-pair and all-pairs power, respectively. The power formulas are derived for both joint ranking and pairwise ranking mechanism for general multiple comparison problems, followed by explicit form of these formulas when the single-step, step-down, or step-up adjustments are applied. The proposed power and sample size calculation methods apply to scenarios both when the underlying distributions are known and when they are unknown but a pilot study is available. Numerical methods via quasi-Monte Carlo integration and Monte Carlo integration are assessed. Our simulation studies show the accuracy of the power and sample size calculation formulas. We recommend the Monte Carlo integration as the calculation algorithm. An example from a mouse peritoneal cavity study is used to demonstrate the application of the methods.

Sample size determination for testing whether an identified treatment is best

Laska and Meisner (1989, Biometrics 45, 1139-1151) dealt with the problem of testing whether an identified treatment belonging to a set of k + 1 treatments is better than each of the other k treatments. They calculated sample size tables for k = 2 when using multiple t-tests or Wilcoxon-Mann-Whitney tests, both under normality assumptions. In this paper, we provide sample size formulas as well as tables for sample size determination for k > or = 2 when t-tests under normality or Wilcoxon-Mann-Whitney tests under general distribution assumptions are used.