Sequential allocation in clinical trials comparing two exponential survival curves.

The problem of comparing two medical treatments with respect to survival is considered. Treatment outcome is assumed to follow an exponential distribution. The ratio of expected survivals associated with the two treatments is the clinical parameter of interest. A nuisance parameter is present, but it is removed by an invariance reduction and a sequential probability ratio test is applied to the invariant likelihood ratio. A class of data-dependent treatment assignment rules is identified over which the probability of correct treatment selection at the termination of the trial is approximately constant. A cost function, the weighted sum of total patients in the trial and the number assigned to the inferior treatment is introduced, and a treatment allocation rule conjectured to minimize the expected cost is constructed. Both analytic and simulation results show that it is an improvement over rules previously proposed. The methodology contained herein can be used to construct near-optimal rules in other testing contexts.     

Self-designing two-stage trials to minimize expected costs

In the design of clinical trials, the sample size for the trial is traditionally calculated from estimates of parameters of interest, such as the mean treatment effect, which can often be inaccurate. However, recalculation of the sample size based on an estimate of the parameter of interest that uses accumulating data from the trial can lead to inflation of the overall Type I error rate of the trial. The self-designing method of Fisher, also known as the variance-spending method, allows the use of all accumulating data in a sequential trial (including the estimated treatment effect) in determining the sample size for the next stage of the trial without inflating the Type I error rate. We propose a self-designing group sequential procedure to minimize the expected total cost of a trial. Cost is an important parameter to consider in the statistical design of clinical trials due to limited financial resources. Using Bayesian decision theory on the accumulating data, the design specifies sequentially the optimal sample size and proportion of the test statistic's variance needed for each stage of a trial to minimize the expected cost of the trial. The optimality is with respect to a prior distribution on the parameter of interest. Results are presented for a simple two-stage trial. This method can extend to nonmonetary costs, such as ethical costs or quality-adjusted life years.     

A mathematical model of the treatment and survival of patients with high-grade brain tumours

More years of life per patient are lost as the result of primary brain tumours than any other form of cancer. The most aggressive of these is known as glioblastoma (GBM). The median survival time of patients with GBM is under 10 months and the outlook has hardly improved over the past 20 years. Generally, these tumours are remarkably resistant to radiotherapy and yet about 2-3% of all GBMs appear to be cured.The objectives of this study were to formulate a mathematical and phenomenological model of tumour growth in a population of patients with GBM to predict survival, and to use the model to extract biological information from clinical data.The model describes the growth of the tumour and the resulting damage to the normal brain using simple concepts borrowed from chemical reaction engineering. Death is assumed to result when the amount of surviving normal brain falls to a critical level. Radiotherapy is assumed to destroy tumour but not healthy brain. Simple rules are included to represent approximately the clinician's decisions about what type of treatment to offer each patient. A population of patients is constructed by assuming that key parameters can be sampled from statistical distributions. Following Monte Carlo simulation, the model can be fitted to data from clinical trials.The model reproduces clinical data extremely accurately. This suggests that the long-term survivors are not a separate sub-population but are the ‘lucky tail’ of a unimodal distribution. The estimated values of radiation sensitivity (represented as SF2, the survival fraction after 2 Gy) suggest the presence of severe hypoxia, which renders cells less sensitive to radiation. The model can predict the probable age distribution of tumours at presentation. The model shows the complicated effects of waiting times for treatment on the survival outcomes, and is used to predict the effects of escalation of radiotherapy dose.The model may aid the design of clinical trials using radiotherapy for patients with GBM, especially in helping to estimate the size of trial required. It is also designed in a generic form, and might be applicable to other tumour types.     

Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials

Some clinical trials follow a design where patients are randomized to a primary therapy at entry followed by another randomization to maintenance therapy contingent upon disease remission. Ideally, analysis would allow different treatment policies, i.e., combinations of primary and maintenance therapy if specified up-front, to be compared. Standard practice is to conduct separate analyses for the primary and follow-up treatments, which does not address this issue directly. We propose consistent estimators for the survival distribution and mean restricted survival time for each treatment policy in such two-stage studies and derive large-sample properties. The methods are demonstrated on a leukemia clinical trial data set and through simulation.     

Models of growth with density regulation in more than one life stage

Discrete-time models of growth of populations with nonoverlapping generations and density regulation in two life stages are studied. It is assumed that there is no delay in the effects of density. Assigning exponential, linear, or hyperbolic functions to describe the dependence of preadult survival and fecundity on density, nine models are obtained. The dynamics of the model resulting from using the exponential function to describe the density dependence of both preadult survival and fecundity is analyzed: for large values of the intrinsic rate of increase there may exist up to three equilibrium population sizes, two stable. This indicates that a life history with two episodes of density regulation can give origin to alternative stable states. The models are fitted to recruitment data from growth experiments of Drosophila laboratory populations obtained with the Serial Transfer System Type 2 (Ayala et al., 1973. Theor. Pop. Biol. 4, 331-356) and collected by other authors. The results of the fittings suggest that this recruitment data can be adequately described with the models.     

Bayes Estimator of Survival Probability from Randomly Censored Observations with Weibull Distribution of Time to Death

This paper deals with Bayes estimation of survival probability when the data are randomly censored. Such a situation arises in case of a clinical trial which extends for a limited period T. A fixed number of patients (n) are observed whose times to death have identical Weibull distribution with parameters β and θ. The maximum times of observation for different patients are also independent uniform variables as the patients arrive randomly throughout the trial. For the joint prior distribution of (β, θ) as suggested by Sinha and Kale (1980, page 137) Bayes estimator of survival probability at time t (0<t<T) has been obtained. Considering squared error loss function it is the mean of the survival probability with respect to the posterior distribution of (β, θ). This estimator is then compared with the maximum likelihood estimator, by simulation, for various values of β, θ and censoring percentage. The proposed estimator is found to be better under certain conditions.     

Joint models for multivariate longitudinal and multivariate survival data

Joint modeling of longitudinal and survival data is becoming increasingly essential in most cancer and AIDS clinical trials. We propose a likelihood approach to extend both longitudinal and survival components to be multidimensional. A multivariate mixed effects model is presented to explicitly capture two different sources of dependence among longitudinal measures over time as well as dependence between different variables. For the survival component of the joint model, we introduce a shared frailty, which is assumed to have a positive stable distribution, to induce correlation between failure times. The proposed marginal univariate survival model, which accommodates both zero and nonzero cure fractions for the time to event, is then applied to each marginal survival function. The proposed multivariate survival model has a proportional hazards structure for the population hazard, conditionally as well as marginally, when the baseline covariates are specified through a specific mechanism. In addition, the model is capable of dealing with survival functions with different cure rate structures. The methodology is specifically applied to the International Breast Cancer Study Group (IBCSG) trial to investigate the relationship between quality of life, disease-free survival, and overall survival.     

Adaptive enrichment designs with a continuous biomarker

A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.     

A pilot randomised controlled trial of negative pressure wound therapy to treat grade III/IV pressure ulcers [ISRCTN69032034

ABSTRACT: BACKGROUND: Negative pressure wound therapy (NPWT) is widely promoted as a treatment for full thickness wounds, however there is a lack of high-quality research evidence regarding its clinical and cost effectiveness. A trial of NPWT for the treatment grade III/IV pressure ulcers would be worthwhile but premature without assessing whether such a trial is feasible. The aim of this pilot randomised controlled trial was to assess the feasibility of conducting a future full trial of NPWT for the treatment of grade III and IV pressure ulcers and to pilot all aspects of the trial. METHODS: This was a two centre (acute and community), pilot randomised controlled trial. Eligible participants were randomised to receive either NPWT or Standard Care (SC) (spun hydrocolloid, alginate or foam dressings). The primary outcome measure was time to healing of the reference pressure ulcer. Secondary outcome measures included recruitment rates, frequency of treatment visits, resources used and duration of follow-up. RESULTS: 312 patients were screened for eligibility into this trial over a 12-month recruitment period and 12/312 participants (3.8%) were randomised; six to NPWT and six to SC. Only one reference pressure ulcer healed (NPWT group) during follow up (time to healing 79 days). The mean number of treatment visits per week was 3.1 (NPWT) and 5.7 (SC). 6/6 TNP and 1/6 SC participants withdrew from their allocated trial treatment. The mean duration of follow-up was 3.8 (NPWT) and 5.0 (SC) months. CONCLUSIONS: This pilot trial yielded vital information for the planning of any future full study including a projected recruitment rate, required duration of follow up and extent of research nurse support required. Data were also used to inform cost-effectiveness and value of information analyses which were conducted alongside the pilot trial.     

Exact percentage points of the likelihood-ratio test for a change-point hazard-rate model

We wish to test that the hazard rate of survival or failure-time data is constant against the alternative of a change in hazard after an unspecified time. The likelihood ratio is unbounded but the exact null distribution of a restricted likelihood-ratio test statistic is found. This distribution is not affected by Type II censoring but it does depend very strongly on the interval in which the unknown change-point is assumed to lie. Some exact percentage points are given which are much larger than simulated points that have been reported in the literature.     

Bayesian subset analysis.

As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models. Exchange-ability among the interactions is a key assumption; thus, the results are of interest primarily in the context of examining a collection of subsets with no definite a priori distinction relative to treatment effect. Exchangeability leads to a shrinking of the posterior distributions of the interaction terms toward the natural origin of 0, offsetting the tendency of the estimated effects to disperse. The method is applied to parameter estimates from a proportional hazards regression analysis of survival data from a clinical trial, invoking the approximate multivariate normal distribution of the estimates. No subjective prior distributions are required. Vague priors are used for all of the regression coefficients except the treatment-by-covariate interactions, which are assumed to follow a normal distribution.     

Contrasting characteristics in increasing and decreasing phases of the <Emphasis Type="Italic">Nerita japonica</Emphasis> (Mollusca: Gastropoda) population on a boulder shore

The present study demonstrated the seasonal and annual fluctuations in population characteristics of Nerita japonica on a boulder shore at Amakusa, Japan, and examined key characteristics in the increasing and decreasing phases for population monitoring. Monthly quantitative quadrat sampling and subsequent mark-recapture experiments were carried out during two periods: from April 1989 to July 1990 (1st period), and from August 1991 to November 1992 (2nd period). The density of the population was in a decreasing phase during the 1st period and in an increasing phase during the 2nd period. The density increase coincided with the extension of the vertical distribution range to a lower tide level, increase in the number of recruitment events, increase in the proportion of juveniles within the population, and increase in the survival of juveniles and adults. The growth rate was slightly increased but the average shell length of adult cohorts did not show any change. Additional observations showed a continued density increase and range extension until 1994. On the other hand, the density decrease coincided with recruitment failure and a low rate of survival with an unchanged range of the vertical distribution. Thus, for monitoring the population of N. japonica, recruitment success and changes in density and distribution range are key characteristics, but the shell size is not. These findings show the potential usefulness of N. japonica as an indicator of the shore environment.     

Efficacy and safety of Chinese herbal medicine versus Lopinavir-Ritonavir in adult patients with coronavirus disease 2019: A non-randomized controlled trial

BackgroundTreatments for coronavirus disease 2019 (COVID-19) are limited by suboptimal efficacy.MethodsFrom January 30, 2020 to March 23, 2020, we conducted a non-randomised controlled trial, in which all adult patients with laboratory-confirmed COVID-19 were assigned to three groups non-randomly and given supportive treatments: Group A, Lopinavir-Ritonavir; Group B, Huashi Baidu Formula (a Chinese medicineformula made by the China Academy of Chinese Medical Sciences to treat COVID-19, which is now in the clinical trial period) and Lopinavir-Ritonavir; and Group C, Huashi Baidu Formula. The use of antibiotics, antiviruses, and corticosteroids was permitted in Group A and B. Traditional Chinese medicine injections were permitted in Group C. The primary outcomes were clinical remission time (interval from admission to the first time the patient tested negatively for novel coronavirus or an obvious improvement was observed from chest CT) and clinical remission rate (number of patients whose clinical time was within 16 days/total number of patients).ResultsA total of 60 adult patients with COVID-19 were enrolled at sites in Wuhan, China, and the sample size of each group was 20. In Groups A, B and C, the clinical remission rates were 95.0%%(19/20), 100.0%%(20/20) and 100.0%%(20/20), respectively. Compared with Groups A and B, the clinical remission time of Group C was significantly shorter (5.9 days vs. 10.8 days, p < 0.05; 5.9 days vs. 9.7 days, p < 0.05). There was no significant difference among Groups A, B, and C in terms of the time taken to be released from quarantine. The clinical biochemical indicators and safety indexes showed no significant differences among the three groups.ConclusionsOur findings suggest that Lopinavir-Ritonavir has some efficacy in the treatment of COVID-19, and the Huashi Baidu Formula might enhance this effect to an extent. In addition, superiority was displayed in the treatment of COVID-19 through a combination of the Huashi Baidu Formula and traditional Chinese medicine injection. In future, well-designed prospective double-blinded randomised control trials are required to confirm our findings.     

Implementation of group sequential logrank tests in a maximum duration trial

To control the Type I error probability in a group sequential procedure using the logrank test, it is important to know the information times (fractions) at the times of interim analyses conducted for purposes of data monitoring. For the logrank test, the information time at an interim analysis is the fraction of the total number of events to be accrued in the entire trial. In a maximum information trial design, the trial is concluded when a prespecified total number of events has been accrued. For such a design, therefore, the information time at each interim analysis is known. However, many trials are designed to accrue data over a fixed duration of follow-up on a specified number of patients. This is termed a maximum duration trial design. Under such a design, the total number of events to be accrued is unknown at the time of an interim analysis. For a maximum duration trial design, therefore, these information times need to be estimated. A common practice is to assume that a fixed fraction of information will be accrued between any two consecutive interim analyses, and then employ a Pocock or O'Brien-Fleming boundary. In this article, we describe an estimate of the information time based on the fraction of total patient exposure, which tends to be slightly negatively biased (i.e., conservative) if survival is exponentially distributed. We then present a numerical exploration of the robustness of this estimate when nonexponential survival applies. We also show that the Lan-DeMets (1983, Biometrika 70, 659-663) procedure for constructing group sequential boundaries with the desired level of Type I error control can be computed using the estimated information fraction, even though it may be biased. Finally, we discuss the implications of employing a biased estimate of study information for a group sequential procedure.     

Evaluation of sample size and power for analyses of survival with allowance for nonuniform patient entry, losses to follow-up, noncompliance, and stratification

When designing a clinical trial to test the equality of survival distributions for two treatment groups, the usual assumptions are exponential survival, uniform patient entry, full compliance, and censoring only administratively at the end of the trial. Various authors have presented methods for estimation of sample size or power under these assumptions, some of which allow for an R-year accrual period with T total years of study, T greater than R. The method of Lachin (1981, Controlled Clinical Trials 2, 93-113) is extended to allow for cases where patients enter the trial in a nonuniform manner over time, patients may exit from the trial due to loss to follow-up (other than administrative), other patients may continue follow-up although failing to comply with the treatment regimen, and a stratified analysis may be planned according to one or more prognostic covariates.     

A semiparametric likelihood approach to joint modeling of longitudinal and time-to-event data

Joint models for a time-to-event (e.g., survival) and a longitudinal response have generated considerable recent interest. The longitudinal data are assumed to follow a mixed effects model, and a proportional hazards model depending on the longitudinal random effects and other covariates is assumed for the survival endpoint. Interest may focus on inference on the longitudinal data process, which is informatively censored, or on the hazard relationship. Several methods for fitting such models have been proposed, most requiring a parametric distributional assumption (normality) on the random effects. A natural concern is sensitivity to violation of this assumption; moreover, a restrictive distributional assumption may obscure key features in the data. We investigate these issues through our proposal of a likelihood-based approach that requires only the assumption that the random effects have a smooth density. Implementation via the EM algorithm is described, and performance and the benefits for uncovering noteworthy features are illustrated by application to data from an HIV clinical trial and by simulation.     

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

ABSTRACT: BACKGROUND: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed) controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 hours' duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45cmH2O and peak pressure of 60cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4-6 mL/kg of predicted body weight) and targeting plateau pressure [less than or equal to]30cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from day 1 to day 28; ICU, in-hospital and 6-month survival. ART is an event guided-trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum-recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.     

1975 current results of the first 100 cytologically typed acute lymphoid leukemia submitted to BCG active immunotherapy

Summary The first 100 acute lymphoid (and undifferentiated) leukemias, (of which the smears at the first presentation of the disease are still available for typing), treated successively with remission induction chemotherapy, complementary cell-reducing chemoradiotherapy and then active immunotherapy with irradiated pooled allogeneic leukemic cells and fresh Pasteur Institute BCG applied on scarifications, have been reviewed, especially in connection with BCG application.Tolerance of BCG has been good. Its application had to be stopped due to a side effect (choroiditis) in only one patient. This toxic cost is negligible compared to that of so-called maintenance chemotherapy.No subject of our first control trial started in 1963 has relapsed between 3 and 13 years.In the overall group of the 100 patients studied, no relapse has been observed after 48 months, which is quite different to the observations of frequent relapses after that time in patients submitted to maintenance chemotherapy.Moreover, second remissions are obtained in 94% of the patients who relapsed early under immunotherapy, and their life expectancy after a second remission is as high as it is after the first remission.The median of survival is longer than 5 years.The action of active immunotherapy on the immune machinery has been followed by several assays, of which the increase of null cells (which include K-cells) may be the most interesting.Several prognostic factors have been demonstrated among which are sex, the volume of the neoplasia, meningeal localizations, and the cytological types. Age has no prognostic value in immunotherapy patients, contrary to maintenance chemotherapy patients. Also the cytological types behave differently under immunotherapy and under maintenance chemotherapy. The disease-free survival of more than 85% of the microlymphoblastic patients submitted to immunotherapy is not observed in J. Bernard's patients submitted to maintenance chemotherapy, which suggests that this high cure rate is due to active immunotherapy. Hence, these prognostic factors are probably factors of sensitivity to active immunotherapy. A statistical computerized study has shown that there is a link between the cytological types and other prognostic factors and that they all depend on the cytological type.Hence, our present protocol is adapted to this immunotherapy sensitivity factor. It comprises a nonrisk preimmunotherapy chemotherapy for the microlymphoblastic type, and a longer and more intensive chemotherapy for less immunotherapy sensitive types.     

Exploiting antitumor immunity to overcome relapse and improve remission duration

Cancer survivors often relapse due to evolving drug-resistant clones and repopulating tumor stem cells. Our preclinical study demonstrated that terminal cancer patient's lymphocytes can be converted from tolerant bystanders in vivo into effective cytotoxic T-lymphocytes in vitro killing patient's own tumor cells containing drug-resistant clones and tumor stem cells. We designed a clinical trial combining peginterferon α-2b with imatinib for treatment of stage III/IV gastrointestinal stromal tumor (GIST) with the rational that peginterferon α-2b serves as danger signals to promote antitumor immunity while imatinib's effective tumor killing undermines tumor-induced tolerance and supply tumor-specific antigens in vivo without leukopenia, thus allowing for proper dendritic cell and cytotoxic T-lymphocyte differentiation toward Th1 response. Interim analysis of eight patients demonstrated significant induction of IFN-γ-producing-CD8(+), -CD4(+), -NK cell, and IFN-γ-producing-tumor-infiltrating-lymphocytes, signifying significant Th1 response and NK cell activation. After a median follow-up of 3.6 years, complete response (CR) + partial response (PR) = 100%, overall survival = 100%, one patient died of unrelated illness while in remission, six of seven evaluable patients are either in continuing PR/CR (5 patients) or have progression-free survival (PFS, 1 patient) exceeding the upper limit of the 95% confidence level of the genotype-specific-PFS of the phase III imatinib-monotherapy (CALGB150105/SWOGS0033), demonstrating highly promising clinical outcomes. The current trial is closed in preparation for a larger future trial. We conclude that combination of targeted therapy and immunotherapy is safe and induced significant Th1 response and NK cell activation and demonstrated highly promising clinical efficacy in GIST, thus warranting development in other tumor types.