A nitrous oxide/oxygen fixed mixture to reduce pain induced by the hypodermic injection: study protocol for a randomized, controlled trial.

BACKGROUND: Patients with hematological malignancies received multiple hypodermic injections of recombinant human granulocyte colony-stimulating factor. Procedural pain is one of the most common iatrogenic causes of pain in patients with hematological malignancies. It is also identified as the most commonly occurring problem in clinical care in the Department of Hematology and Oncology at Shenzhen University General Hospital. However, providing immediate relief from pain induced by hypodermic injection of recombinant human granulocyte colony-stimulating factor remains a major challenge. This trial aims to evaluate the safety and analgesic efficacy of a fixed nitrous oxide/oxygen mixture for patients with hematological malignancies and experiencing procedural pain caused by hypodermic injection of recombinant human granulocyte colony-stimulating factor in the department. METHODS: The nitrous oxide/oxygen study is a single-center, randomized, double-blind, placebo-controlled trial involving patients with hematological malignancies who require hypodermic injections of recombinant human granulocyte colony-stimulating factor for treatment. This trial was conducted in the Hematology and Oncology Department of Shenzhen University General Hospital. A total of 54 eligible patients were randomly allocated to either the fixed nitrous oxide/oxygen mixture group (n = 36) or the oxygen group (n = 18). Neither the investigators nor the patients known about the randomization list and the nature of the gas mixture in each cylinder. Outcomes were monitored at the baseline (T0), immediately after hypodermic injection of recombinant human granulocyte colony-stimulating factor (T1), and 5 min after hypodermic injection of recombinant human granulocyte colony-stimulating factor (T2) for each group. The primary outcome measure was the score in the numerical rating scale corresponding to the highest level of pain experienced during hypodermic injection of recombinant human granulocyte colony-stimulating factor. Secondary outcomes included the fear of pain, anxiety score, four physiological parameters, adverse effects, total time of gas administration, satisfaction from both patients and nurses, and the acceptance of the patients. DISCUSSION: This study focused on the safety and analgesic efficacy during hypodermic injection of recombinant human granulocyte colony-stimulating factor procedure. Data on the feasibility and safety of nitrous oxide/oxygen therapy was provided if proven beneficial to patients with hematological malignancies during hypodermic injection of recombinant human granulocyte colony-stimulating factor and widely administered to patients with procedural pain in the department. TRIAL REGISTRATION: Chinese Clinical Trial Register, ChiCTR2200061507. Registered on June 27, 2022. http://www.chictr.org.cn/edit.aspx?pid=170573&htm=4.

Valid statistical inference methods for a case-control study with missing data.

The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.

A new framework of statistical inferences based on the valid joint sampling distribution of the observed counts in an incomplete contingency table.

Some existing confidence interval methods and hypothesis testing methods in the analysis of a contingency table with incomplete observations in both margins entirely depend on an underlying assumption that the sampling distribution of the observed counts is a product of independent multinomial/binomial distributions for complete and incomplete counts. However, it can be shown that this independency assumption is incorrect and can result in unreliable conclusions because of the under-estimation of the uncertainty. Therefore, the first objective of this paper is to derive the valid joint sampling distribution of the observed counts in a contingency table with incomplete observations in both margins. The second objective is to provide a new framework for analyzing incomplete contingency tables based on the derived joint sampling distribution of the observed counts by developing a Fisher scoring algorithm to calculate maximum likelihood estimates of parameters of interest, the bootstrap confidence interval methods, and the bootstrap testing hypothesis methods. We compare the differences between the valid sampling distribution and the sampling distribution under the independency assumption. Simulation studies showed that average/expected confidence-interval widths of parameters based on the sampling distribution under the independency assumption are shorter than those based on the new sampling distribution, yielding unrealistic results. A real data set is analyzed to illustrate the application of the new sampling distribution for incomplete contingency tables and the analysis results again confirm the conclusions obtained from the simulation studies.

Design and sample size for evaluating combinations of drugs of linear and loglinear dose-response curves.

The study of drug combinations has become important in drug development due to its potential for efficacy at lower, less toxic doses and the need to move new therapies rapidly into clinical trials. The goal is to identify which combinations are additive, synergistic, or antagonistic. Although there exists statistical framework for finding doses and sample sizes needed to detect departure from additivity, e.g., the power maximized F-test, different classes of drugs of different does-response shapes require different derivation for calculating sample size and finding doses. Motivated by two anticancer combination studies that we are involved with, this article proposes dose-finding and sample size method for detecting departures from additivity of two drugs with linear and log-linear single dose-response curves. The first study involves combination of two drugs, where one single drug dose-response curve is linear and the other is log-linear. The second study involves combinations of drugs whose single drug dose-response curves are linear. The experiment had been planned with the common fixed ratio design before we were consulted, but the resulting data missed the synergistic combinations. However, the experiment based on the proposed design was able to identify the synergistic combinations as anticipated. Thus we shall summarize the analysis of the data collected according to the proposed design and discuss why the commonly used fixed ratio method failed and the implications of the proposed method for other combination studies.

A multivariate random-effects model with restricted parameters: application to assessing radiation therapy for brain tumours.

In clinical studies, multiple endpoints are often measured for each patient longitudinally. The multivariate random-effects or random coefficient model has been a useful method for analysis. However, medical research problems may impose restrictions on the model parameters of interests. For example, in a paediatric brain tumour study on radiation therapy, there is a natural ordering in the white matter relaxation time of brain tissues among different regions surrounding the primary tumour, i.e. the closer a specific region of brain tissues is to the centre of primary tumour, the shorter is the relaxation time. Such parameter constraints should be accounted for in the analysis. This article proposes a class of multivariate random coefficient models with restricted parameters and derives its maximum likelihood estimates (MLE). We propose a modified EM algorithm for the quadratic optimalization with linear inequality constraints necessary in deriving the MLE. The method is applied to analysing the paediatric brain tumour study.

Repeated-measures models with constrained parameters for incomplete data in tumour xenograft experiments.

In cancer drug development, xenograft experiments (models) where mice are grafted with human cancer cells are used to elucidate the mechanism of action and/or to assess efficacy of a promising compound. Demonstrated activity in this model is an important step to bring a promising compound to humans. A key outcome variable in these experiments is tumour volumes measured over a period of time, while mice are treated with an anticancer agent following certain schedules. However, a mouse may die during the experiment or may be sacrificed when its tumour volume quadruples and then incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumour shrinkage (<0.01 cm3) or random truncation. In addition, if no treatment were given to the tumour-bearing mice, the tumours would keep growing until the mice die or are sacrificed. This intrinsic growth of tumour in the absence of treatment constrains the parameters in the regression and causes further difficulties in statistical analysis. We develop a maximum likelihood method based on the expectation/conditional maximization (ECM) algorithm to estimate the dose-response relationship while accounting for the informative censoring and the constraints of model parameters. A real xenograft study on a new anti-tumour agent temozolomide combined with irinotecan is analysed using the proposed method.

Hierarchical models for tumor xenograft experiments in drug development.

In cancer drug development, demonstrated anticancer activity in animal models is an important step to bring a promising compound to clinic. Proper design and analysis of experiments using laboratory animals have received increasing attention recently. These experiments involve informatively censored longitudinal data with small samples. The problem is further complicated because of order constraints due to the intrinsic growth of control tumors without treatment. This article proposes a Bayesian hierarchical model to analyze informatively censored longitudinal data while accounting for the parameter constraints and providing valid small sample inference. We adopt a noniterative sampling approach, the inverse Bayes formulae (IBF) sampler, to generate independent posterior samples, which avoids convergence problems associated with Markov chain Monte-Carlo methods. To effectively deal with the restricted parameter problem, we use a linear transformation to simplify the constraints and exploit the IBF method to generate random samples from truncated multivariate normal distributions. Because diffuse priors are used, the posterior modes approximate the maximum likelihood estimates well, and the hierarchical model can be considered as an extended mixed-effects model. A real xenograft experiment on a new treatment is analyzed by using the proposed method.

Experimental design and sample size determination for testing synergism in drug combination studies based on uniform measures.

In anticancer drug development, the combined use of two drugs is an important strategy to achieve greater therapeutic success. Often combination studies are performed in animal (mostly mice) models before clinical trials are conducted. These experiments on mice are costly, especially with combination studies. However, experimental designs and sample size derivations for the joint action of drugs are not currently available except for a few cases where strong model assumptions are made. For example, Abdelbasit and Plackett proposed an optimal design assuming that the dose-response relationship follows some specified linear models. Tallarida et al. derived a design by fixing the mixture ratio and used a t-test to detect the simple similar action. The issue is that in reality we usually do not have enough information on the joint action of the two compounds before experiment and to understand their joint action is exactly our study goal. In this paper, we first propose a novel non-parametric model that does not impose such strong assumptions on the joint action. We then propose an experimental design for the joint action using uniform measure in this non-parametric model. This design is optimal in the sense that it reduces the variability in modelling synergy while allocating the doses to minimize the number of experimental units and to extract maximum information on the joint action of the compounds. Based on this design, we propose a robust F-test to detect departures from the simple similar action of two compounds and a method to determine sample sizes that are economically feasible. We illustrate the method with a study of the joint action of two new anticancer agents: temozolomide and irinotecan.

Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models.

In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.