Multistage designs for vaccine safety studies

In a placebo-controlled vaccine safety trial, the primary interest is to demonstrate that the vaccine is sufficiently safe, rejecting the null hypothesis that the relative risk of an adverse event attributable to the vaccine is above a prespecified value, greater than one. We develop sequential as well as multistage designs for such trials where the interim analyses are conducted not after a given number of subjects but rather after a given number of events observed. We show that these designs achieve significant improvement over single-stage conditional test in terms of both the required number of events to be observed and the required number of subjects to be enrolled.     

临床医生的个案研究设计

个案研究设计适合于提出研究假说，这种假说以后可以通过其他方法得到临床确认，这也适用于先导性临床试验(pilot study)，即在设计正式的临床试验之前，对要研究的药物进行一个初步的评估。其设计类型包括简单的A-B时序设计、经典的A-B-A反转设计、替代治疗设计、多基线设计。该设计应用的前提是治疗在较短时间能表现出最大效应，并且效应是短暂的，一旦治疗停止，则病情可逆，如果不是这种情况，则必须考虑成组设计。个案研究的方法一般依赖于“A”阶段的稳定基线期。该研究方法不适宜对急性或易变的疾病状况进行研究，而适用于对具有稳定病症表现的慢性或复发性疾病的研究。     

Covariate-adjusted response-adaptive designs for binary response

An adaptive allocation design for phase III clinical trials that incorporates covariates is described. The allocation scheme maps the covariate-adjusted odds ratio from a logistic regression model onto [0, 1]. Simulations assume that both staggered entry and time to response are random and follow a known probability distribution that can depend on the treatment assigned, the patient's response, a covariate, or a time trend. Confidence intervals on the covariate-adjusted odds ratio is slightly anticonservative for the adaptive design under the null hypothesis, but power is similar to equal allocation under various alternatives for n = 200. For similar power, the net savings in terms of expected number of treatment failures is modest, but enough to make this design attractive for certain studies where known covariates are expected to be important and stratification is not desired, and treatment failures have a high ethical cost.     

Mediation designs for tobacco prevention research

This paper describes research designs and statistical analyses to investigate how tobacco prevention programs achieve their effects on tobacco use. A theoretical approach to program development and evaluation useful for any prevention program guides the analysis. The theoretical approach focuses on action theory for how the program affects mediating variables and on conceptual theory for how mediating variables are related to tobacco use. Information on the mediating mechanisms by which tobacco prevention programs achieve effects is useful for the development of efficient programs and provides a test of the theoretical basis of prevention efforts. Examples of these potential mediating mechanisms are described including mediated effects through attitudes, social norms, beliefs about positive consequences, and accessibility to tobacco. Prior research provides evidence that changes in social norms are a critical mediating mechanism for successful tobacco prevention. Analysis of mediating variables in single group designs with multiple mediators are described as well as multiple group randomized designs which are the most likely to accurately uncover important mediating mechanisms. More complicated dismantling and constructive designs are described and illustrated based on current findings from tobacco research. Mediation analysis for categorical outcomes and more complicated statistical methods are outlined.     

New trial designs to assess antitumor and antiproliferative agents in prostate cancer

The discovery of multiple putative therapeutic targets and multipleputative agents for these targets in prostate cancer in the coming yearsposes significant challenges for clinical trial design. This is especiallytrue for cytostatic agents that are not expected to lead to frank tumorshrinkage or declines in the PSA. The most promising agents will need tobe identified early in their development. Since surrogate biologic markersare likely to play a critical role, the identification and validation ofthese markers is discussed. A number of non-traditional phase I and phaseII clinical trial designs, including pre-operative dosing for assessingdrug effect on a marker and the randomized discontinuation phase IIdesign, are also discussed in detail. Use of such designs as well assurrogate marker validation will likely be required to efficiently chooseappropriate agents for definitive study in the phase III setting.     

Harnessing D-amino acids for peptide motif designs

In examining the use of d -amino acids in designing specific peptide folding motifs, the tetrapeptide Boc-d -Glu-Ala-Gly-Lys-NHMe 1 and its analog 2 featuring l -Glu were synthesized for a comparison of their solution conformations by NMR spectroscopy. The temperature coefficients of amide proton resonances, NOE data, side-chain CH₂ anisotropies and salt titration results suggest a weak type _II reverse-turn conformation for peptide 2 , and a tandem type II ’ turn-3₁₀-helix conformation of appreciable conformational stability for peptide 1 in apolar solvents. The latter is of potential interest as the N-terminal helix cap that could support the design of longer 3₁₀ helices. Possible origins of appreciable difference in the conformational stabilities of the diastereomers are discussed.     

Balanced designs for multiple crossover studies.

Experimental situations are considered, in which t different treatments are administered to n cases (volunteers, patients, animals etc.) during p periods (p less than or equal to t) in a way that each of these cases receives p different treatments in sequence (crossover designs). In order to minimize the influence of carry-over effects the designs of the trials are constructed with regard to three conditions of balance: (a) Each case receives the same number of treatments, (b) each treatment is applied the same number of times in each period, and (c) each possible transition between two consecutive periods appears the same number of times during the experiment. The minimal numbers of cases are given for complete (p = t) and incomplete (p less than t) designs balanced according to all the above mentioned conditions. Furthermore, the designs themselves (plans of the experiments) are also listed for the minimal number of cases for 2 less than or equal to p less than or equal to t less than or equal to 10.     

Adaptive designs for comparative effectiveness research trials

Abstract

Medical and health policy decision-makers require improved design and analysis methods for comparative effectiveness research (CER) trials. In CER trials, there may be limited information to guide initial design choices. In general settings, adaptive designs (ADs) have effectively overcome limits on initial information. However, CER trials have fundamental differences from standard clinical trials including population heterogeneity and a vaguer concept of a “minimum clinically meaningful difference”. The objective of this article is to explore the use of a particular form of ADs for comparing treatments within the CER trial context. To achieve this, the authors review the current state of clinical CER. They also identify areas of CER as particularly strong candidates for application of novel AD and illustrate the potential usefulness of the designs and methods for two group comparisons. The authors found that ADs can stabilize power. Furthermore, the designs ensure adequate power for true effects are at least at clinically significant pre-planned effect size, or when variability is larger than expected. The designs allow for sample size savings when the true effect is larger or when variability is smaller than planned. The authors conclude that ADs in CER have great potential to allow trials to successfully and efficiently make important comparisons.     

Biomarker threshold adaptive designs for survival endpoints

Due to the importance of precision medicine, it is essential to identify the right patients for the right treatment. Biomarkers, which have been commonly used in clinical research as well as in clinical practice, can facilitate selection of patients with a good response to the treatment. In this paper, we describe a biomarker threshold adaptive design with survival endpoints. In the first stage, we determine subgroups for one or more biomarkers such that patients in these subgroups benefit the most from the new treatment. The analysis in this stage can be based on historical or pilot studies. In the second stage, we sample subjects from the subgroups determined in the first stage and randomly allocate them to the treatment or control group. Extensive simulation studies are conducted to examine the performance of the proposed design. Application to a real data example is provided for implementation of the first-stage algorithms.     

Tailoring new gene delivery designs for specific targets

Advancing biotechnology spurs the development of new pharmaceutically engineered gene delivery vehicles with the capability to target specific cell types. The low density lipoprotein (LDL) element of the terplex gene carrier is shown to be efficient in delivery to smooth muscle cells as well as inducing minimal toxicity to A7R5 cells in culture. The terplex system condensed plasmid DNA into polyplex sizes ranging from 100 to 400 nm. Terplex demonstrated higher transfection efficacy than Lipofectin. Lactose was conjugated through a poly(ethyl glycol) (PEG) spacer to poly(L-lysine) (PLL) to tailor a gene carrier capable of condensing plasmid DNA. This cationic polymer targeted Hep G2 cells preferentially in culture. The inclusion of a lactose targeting moiety greatly increased the efficacy of the gene carrier over PEG-PLL and Lipofectin. A biodegradable nanoparticle gene carrier, polysaccharide-graft-PLL and poly(D,L-lactic acid), was synthesized and characterized. The nanoparticle size was controllable by changing the copolymer concentration, where the particles could be as small as 60 nm. When polysaccharide was used in the copolymer, the nanoparticle became more DNA adsorbent. The JLI mAb was linked to PLL. This gene carrier targets the antigen confined to stage II immature cortical thymocytes for leukemia therapy. A 20,000 mw PLL backbone was sufficient for compaction of DNA. Folic acid linked to PLL through a PEG spacer is currently being studied for DNA condensation and delivery to membrane associated folate binding protein positive endothelial cancer cells.     

Serially balanced designs for two sets of treatments

Serially balanced designs are useful in applications in which repeated measurements of multiple samples (treatments) are being taken according to an ordered sequence. The design permits the estimation of direct treatment and carryover effects. When the samples comprise two groups, where the sequence requires samples from one group to be separated by samples from another group (e.g., when a wash step has to be performed between treated samples), then the sequence has to be arranged in such a way as to accommodate this requirement. The solution to the construction of such a serially balanced sequence is given according to the construction method given by Altan et al. (2004) for the first group of treatments in combination with the use of an F-square to dictate placement of the second group of treatments.     

Bayesian optimal adaptive designs for delayed-response dose-finding studies

Bayesian adaptive design has been broadly recognized as a method of improving the efficiency of determining dose-response relationships in clinical trials, thus leading to reliable dose selection for phase III clinical trials. However, in some disease areas such as diabetes and obesity, patients may need to be studied for several weeks or months for a drug effect to emerge. These delayed-response studies provide challenges for using traditional adaptive design methods. Many current methods for analyzing the data at the time of the interim analysis only use the last observation from patients who have completed the study. Data for those patients who have not completed the study are often ignored or imputed via last observation carried forward (LOCF) or other imputation method. Therefore, data collected at intermediate timepoints are not fully used for decision making. These approaches are useful for studies where the final responses can be quickly observed. However, in delayed-response studies, where longitudinal data are normally collected for each patient, using all available information instead of just endpoint values is critical to improving efficiency. Fu and Manner (2010) proposed an integrated two-component prediction (ITP) model for delayed-response adaptive design. In this paper, we extend their ITP model to incorporate a dose-response model in it and propose an ITP Emax model. Furthermore, we derive a method to find the minimum effective dose (MED) for our newly proposed model by using an optimal design theorem. By using the proposed method, a better understanding of the dose-response relationship and the MED was achieved more efficiently. Potential sample size reduction is also discussed in this paper.     

Sequential designs for logistic phase I clinical trials

Both parametric and nonparametric sequential designs and estimation methods are implemented in phase I clinical trials. In this article, we take a systematic approach, consisting of a start-up design, a follow-on design, a sequential dose-finding design, and an estimation method, to find an efficient estimate of the maximum tolerated dose under the assumption that the dose-response curve has a true underlying logistic distribution. In particular, for the problem of the nonexistence of the maximum likelihood estimates of the logistic parameters, a constraint on the probability of an undetermined maximum likelihood estimator (MLE) is incorporated into the parametric sequential designs. In addition, this approach can also be extended to incorporate ethical considerations, which prohibit an administered dose from exceeding the maximum acceptable dose. Comparison based on simulation studies between the systematic designs and nonparametric designs are described both for continuous dose spaces and discrete dose spaces, respectively.     

Analysis strategies for adaptive designs with multiple endpoints

The problem of analysis strategies for adaptive designs with multiple endpoints is considered. We first provide a review of the commonly used methods for addressing the issue of multiplicity in classical designs, followed by the gatekeeper procedure and the fractal gatekeeper procedure. The latter will be applied to address the issue of multiplicity in adaptive trials with multiple endpoints based on the invariance property of the test statistic employed. Practical examples are presented to illustrate the proposed fractal gatekeeper method when analyzing data obtained from an adaptive trial with multiple endpoints.     

Optimal serial dilutions designs for drug discovery experiments

Dose-response studies are an essential part of the drug discovery process. They are typically carried out on a large number of chemical compounds using serial dilution experimental designs. This paper proposes a method of selecting the key parameters of these designs (maximum dose, dilution factor, number of concentrations and number of replicated observations for each concentration) depending on the stage of the drug discovery process where the study takes place. This is achieved by employing and extending results from optimal design theory. Population D- and D(S)-optimality are defined and used to evaluate the precision of estimating the potency of the tested compounds. The proposed methodology is easy to use and creates opportunities to reduce the cost of the experiments without compromising the quality of the data obtained in them.