Uniformly minimum variance conditionally unbiased estimation in multi-arm multi-stage clinical trials

Multi-arm multi-stage clinical trials compare several experimental treatments with a control treatment, with poorly performing treatments dropped at interim analyses. This leads to inferential challenges, including the construction of unbiased treatment effect estimators. A number of estimators unbiased conditional on treatment selection have been proposed, but are specific to certain selection rules, may ignore the comparison to the control and are not all minimum variance. We obtain estimators for treatment effects compared to the control that are uniformly minimum variance unbiased conditional on selection with any specified rule or stopping for futility

Estimation after subpopulation selection in adaptive seamless trials

During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Validation of the mothers object relations scales in 2–4 year old children and comparison with the child–parent relationship scale

Background: The quality of the parent–child relationship has an important effect on a wide range of child outcomes. The evaluation of interventions to promote healthy parenting and family relationships is dependent on outcome measures which can quantify the quality of parent–child relationships. Between the Mothers’ Object Relations – Short Form (MORS-SF) scale for babies and the Child–parent Relationship Scale (C-PRS) there is an age gap where no validated scales are available. We report the development and testing of an adaptation of the MORS-SF; the MORS (Child) scale and its use in children from the age of 2 years to 4 years. This scale aims to capture the nature of the parent–child relationship in a form which is short enough to be used in population surveys and intervention evaluations. Methods: Construct and criterion validity, item salience and internal consistency were assessed in a sample of 166 parents of children aged 2–4 years old and compared with that of the C-PRS. The performance of the MORS (Child) as part of a composite measure with the HOME inventory was compared with that of the C-PRS using data collected in a randomised controlled trial and the national evaluation of Sure Start. Results: MORS (Child) performed well in children aged 2–4 with high construct and criterion validity, item salience and internal consistency. One item in the C-PRS failed to load on either subscale and parents found this scale slightly more difficult to complete than the MORS (Child). The two measures performed very similarly in a factor analysis with the HOME inventory producing almost identical loadings. Conclusions: Adapting the MORS-SF for children aged 2–4 years old produces a scale to assess parent–child relationships that is easy to use and outperforms the more commonly used C-PRS in several respects

Dose selection in seamless phase II/III clinical trials based on efficacy and toxicity

Seamless phase II/III clinical trials are attractive in development of new drugs because they accelerate the drug development process. Seamless phase II/III trials are carried out in two stages. After stage 1 (phase II stage), an interim analysis is performed and a decision is made on whether to proceed to stage 2 (phase III stage). If the decision is to continue with further testing, some dose selection procedure is used to determine the set of doses to be tested in stage 2. Methodology exists for the analysis of such trials that allows complete flexibility of the choice of doses that continue to the second stage. There is very little work, however, on optimizing the selection of the doses. This is a challenging problem as it requires incorporation of the dose-response relationship, of the observed safety profile and of the planned analysis method. In this thesis we propose a dose-selection procedure for binary outcomes in adaptive seamless phase II/III clinical trials that incorporates the dose response relationship, and explicitly incorporates both efficacy and toxicity. The choice of the doses to continue to stage 2 is made by comparing the predictive power of the potential sets of doses which might continue to stage 2

Multimodal analysis of the effects of dexamethasone on high-altitude cerebral oedema : protocol for a pilot study

Background Acute mountain sickness (AMS) is a cluster of symptoms that commonly occur in those ascending to high altitudes. Symptoms can include headaches, nausea, insomnia and fatigue. Exposure to high altitude can also lead to high-altitude cerebral oedema (HACE), which is a potential cause of death whilst mountaineering. Generally, AMS precedes the development of HACE. Historical studies have demonstrated the effectiveness of regular dexamethasone administration in reducing the symptoms of AMS. However, the mechanism by which dexamethasone works to reduce symptoms AMS remains poorly understood. Further studies, simulating altitude using hypoxic tents, have characterised the effect of prolonged exposure to normobaric hypoxia on cerebral oedema and blood flow using MRI. This randomised trial assesses the effect of dexamethasone on hypoxia-induced cerebral oedema in healthy adult volunteers. Methods/design D4H is a double-blind placebo-controlled randomised trial assessing the effect of dexamethasone on hypoxia-induced cerebral oedema. In total, 20 volunteers were randomised in pairs to receive either 8.25 mg dexamethasone or normal saline placebo intravenously after 8 h of hypoxia with an FiO2 of 12%. Serial MRI images of the brain and spinal cord were obtained at hours 0, 7, 11, 22 and 26 of the study along with serum and urinary markers to correlate with the severity of cerebral oedema and the effect of the intervention. Discussion MRI has been used to identify changes in cerebral vasculature in the development of AMS and HACE. Dexamethasone is effective at reducing the symptoms of AMS; however, the mechanism of this effect is unknown. If this study demonstrates a clear objective benefit of dexamethasone in this setting, future studies may be able to demonstrate that dexamethasone is an effective therapy for oedema associated with brain and spinal cord ischaemia beyond AMS

Impact of sitagliptin on endometrial mesenchymal stem-like progenitor cells : a randomised, double-blind placebo-controlled feasibility trial

Background: Recurrent pregnancy loss (RPL) is associated with the loss of endometrial mesenchymal stem-like progenitor cells (eMSC). DPP4 inhibitors may increase homing and engraftment of bone marrow-derived cells to sites of tissue injury. Here, we evaluated the effect of the DPP4 inhibitor sitagliptin on eMSC in women with RPL, determined the impact on endometrial decidualization, and assessed the feasibility of a full-scale clinical trial. Methods: A double-blind, randomised, placebo-controlled feasibility trial on women aged 18 to 42 years with a history of 3 or more miscarriages, regular menstrual cycles, and no contraindications to sitagliptin. Thirty-eight subjects were randomised to either 100 mg sitagliptin daily for 3 consecutive cycles or identical placebo capsules. Computer generated, permuted block randomisation was used to allocate treatment packs. Colony forming unit (CFU) assays were used to quantify eMSC in midluteal endometrial biopsies. The primary outcome measure was CFU counts. Secondary outcome measures were endometrial thickness, study acceptability, and first pregnancy outcome within 12 months following the study. Tissue samples were subjected to explorative investigations. Findings: CFU counts following sitagliptin were higher compared to placebo only when adjusted for baseline CFU counts and age (RR: 1.52, 95% CI: 1.32–1.75, P<0.01). The change in CFU count was 1.68 in the sitagliptin group and 1.08 in the placebo group. Trial recruitment, acceptability, and drug compliance were high. There were no serious adverse events. Explorative investigations showed that sitagliptin inhibits the expression of DIO2, a marker gene of senescent decidual cells. Interpretation: Sitagliptin increases eMSCs and decreases decidual senescence. A large-scale clinical trial evaluating the impact of preconception sitagliptin treatment on pregnancy outcome in RPL is feasible and warranted. Funding: Tommy's Baby Charity. Clinical trial registration: EU Clinical Trials Register no. 2016-001120-54

Effectiveness and cost-effectiveness of a universal parenting skills programme in deprived communities : multicentre randomised controlled trial

Objective: To evaluate the effectiveness and cost utility of a universally provided early years parenting programme. Design: Multicentre randomised controlled trial with cost-effectiveness analysis. Setting: Early years centres in four deprived areas of South Wales. Participants: Families with children aged between 2 and 4 years. 286 families were recruited and randomly allocated to the intervention or waiting list control. Intervention: The Family Links Nurturing Programme (FLNP), a 10-week course with weekly 2 h facilitated group sessions. Main outcome measures: Negative and supportive parenting, child and parental well-being and costs assessed before the intervention, following the course (3 months) and at 9 months using standardised measures. Results: There were no significant differences in primary or secondary outcomes between trial arms at 3 or 9 months. With ‘+’ indicating improvement, difference in change in negative parenting score at 9 months was +0.90 (95%CI −1.90 to 3.69); in supportive parenting, +0.17 (95%CI −0.61 to 0.94); and 12 of the 17 secondary outcomes showed a non-significant positive effect in the FLNP arm. Based on changes in parental well-being (SF-12), the cost per quality-adjusted life year (QALY) gained was estimated to be £34 913 (range 21 485–46 578) over 5 years and £18 954 (range 11 664–25 287) over 10 years. Probability of cost per QALY gained below £30 000 was 47% at 5 years and 57% at 10 years. Attendance was low: 34% of intervention families attended no sessions (n=48); only 47% completed the course (n=68). Also, 19% of control families attended a parenting programme before 9-month follow-up. Conclusions: Our trial has not found evidence of clinical or cost utility for the FLNP in a universal setting. However, low levels of exposure and contamination mean that uncertainty remains. Trial registration: The trial is registered with Current Controlled Trials ISRCTN13919732

Extrapolating parametric survival models in health technology assessment : a simulation study

Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods’ suitability for selecting a parametric model through simulation of data replicating the follow-up of intervention arms for various time-to-event outcomes from 4 clinical trials. Follow-up was replicated through the consideration of recruitment duration and minimum and maximum follow-up times. Ten thousand simulations of each scenario were performed. We demonstrate that the different methods can result in disagreement over the best model and that it is inappropriate to base model selection solely on goodness-of-fit statistics without consideration of hazard behavior and plausibility of extrapolations. We show that typical trial follow-up can be unsuitable for extrapolation, resulting in unreliable estimation of multiple parameter models, and infer that selecting survival models based only on goodness-of-fit statistics is unsuitable due to the high level of uncertainty in a cost-effectiveness analysis. This article demonstrates the potential problems of overreliance on goodness-of-fit statistics when selecting a model for extrapolation. When follow-up is more mature, BIC appears superior to the other selection methods, selecting models with the most accurate and least biased estimates of RMST