Nigel Stallard

Effectiveness of a Web-Based Cognitive-Behavioral Tool to Improve Mental Well-Being in the General Population: Randomized Controlled Trial

Background Interventions to promote mental well-being can bring benefits to the individual and to society. The Internet can facilitate the large-scale and low-cost delivery of individually targeted health promoting interventions. Objective To evaluate the effectiveness of a self-directed Internet-delivered cognitive-behavioral skills training tool in improving mental well-being in a population sample. Methods This was a randomized trial with a waiting-list control. Using advertisements on a national health portal and through its mailing list, we recruited 3070 participants aged 18 or over, resident in England, and willing to give their email address and access a fully automated Web-based intervention. The intervention (MoodGYM) consisted of 5 interactive modules that teach cognitive-behavioral principles. Participants in the intervention arm received weekly email reminders to access the intervention. The control group received access to the intervention after the trial was completed and received no specific intervention or email reminders. Outcomes were assessed by using self-completion questionnaires. The primary outcome was mental well-being measured with the Warwick-Edinburgh Mental Well-being Scale (WEMWBS). Secondary outcomes were Center for Epidemiologic Studies Depression scale (CES-D) depression scores, Generalized Anxiety Disorder 7-item scale (GAD-7) anxiety scores, EuroQol Group 5-Dimension Self-Report Questionnaire (EQ-5D) quality of life scores, physical activity, and health service use. All outcomes were measured at baseline, and at 6- and 12-week follow-ups. Results A total of 1529 (49.80%) participants completed final follow-up at 12 weeks. Retention was 73.11% (1123/1536) in the control arm and 26.47% (406/1534) in the intervention arm. No relationship between baseline measures and withdrawal could be established. The analysis of WEMWBS mental well-being scores using a linear mixed model for repeated measures showed no difference between intervention and control group at baseline (difference –0.124 points, 95% CI –0.814 to 0.566), and significant improvements for the intervention group at 6 weeks (2.542 points, 95% CI 1.693-3.390) and at 12 weeks (2.876 points, 95% CI 1.933-3.819). The model showed a highly significant (P<.001) intervention by time interaction effect. There were also significant improvements in self-rated scores of depression and anxiety. Given the high level of attrition, a sensitivity analysis with imputed missing values was undertaken that also showed a significant positive effect of the intervention. Conclusions Participants allocated to the intervention arm had an average increase of approximately 3 points on the WEMWBS scale compared to no increase for participants in the control group. Three points on this scale is approximately one-third of a standard deviation. In a low-cost automated intervention designed to shift the population distribution of mental well-being, a small difference per individual could yield a major benefit in population terms. In common with other Web-based interventions, there were high rates of attrition. Further work is needed to improve acceptability, to evaluate against placebo effect, and to disaggregate the effect on mental well-being from the effect on depression and anxiety. Trial Registration International Standard Randomised Controlled Trial Number Register ISRCTN 48134476; http://www.controlled-trials.com/ISRCTN48134476 (Archived by WebCite® at http://www.webcitation.org/6DFgW2p3Q)

Reduction in the rate of methicillin‐resistant Staphylococcus aureus acquisition in surgical wards by rapid screening for colonization: a prospective, cross‐over study

Identification of patients colonized with methicillin-resistant Staphylococcus aureus (MRSA) and subsequent isolation and decolonization is pivotal to the control of cross infection in hospitals. The aim of this study was to establish if early identification of colonized patients using rapid methods alone reduces transmission. A prospective, cluster, two-period cross-over design was used. Seven surgical wards at a large hospital were allocated to two groups, and for the first 8 months four wards used rapid MRSA screening and three wards used a standard culture method. The groups were reversed for the second 8 months. Regardless of the method of detection, all patients were screened for nasal carriage on admission and then every 4 days. MRSA control measures remained constant. Results were analysed using a log linear Poisson regression model. A total of 12 682/13 952 patient ward episodes (PWE) were included in the study. Admission screening identified 453 (3.6%) MRSA-positive patient ward episodes, with a further 268 (2.2%) acquiring MRSA. After adjusting for other variables, rapid screening was shown to statistically reduce MRSA acquisition, with patients being 1.49 times (p 0.007) more likely to acquire MRSA in wards where they were screened using the culture method. Screening of surgical patients using rapid testing resulted in a statistically significant reduction in MRSA acquisition. This result was achieved in a routine surgical service with high bed occupancy and low availability of isolation rooms, making it applicable to the majority of health-care systems worldwide.

A group‐sequential design for clinical trials with treatment selection

A group-sequential design for clinical trials that involve treatment selection was proposed by Stallard and Todd (Statist. Med. 2003; 22:689-703). In this design, the best among a number of experimental treatments is selected on the basis of data observed at the first of a series of interim analyses. This experimental treatment then continues together with the control treatment to be assessed in one or more further analyses. The method was extended by Kelly et al. (J. Biopharm. Statist. 2005; 15:641-658) to allow more than one experimental treatment to continue beyond the first interim analysis. This design controls the familywise type I error rate under the global null hypothesis, that is in the weak sense, but may not strongly control the error rate, particularly if the treatments selected are not the best-performing ones. In some cases, for example when additional safety data are available, the restriction that the best-performing treatments continue may be unreasonable. This paper describes an extension of the approach of Stallard and Todd that enables construction of a group-sequential design for comparison of several experimental treatments with a control treatment. The new method controls the type I error rate in the strong sense if the number of treatments included at each stage is specified in advance, and is indicated by simulation studies to be conservative when the number of treatments is chosen based on the observed data in a practically relevant way.

SAMPLE SIZE DETERMINATION FOR PHASE II CLINICAL TRIALS BASED ON BAYESIAN DECISION THEORY

This paper describes an application of Bayesian decision theory to the determination of sample size for phase II clinical studies. The approach uses the method of backward induction to obtain group sequential designs that are optimal with respect to some specified gain function. A gain function is proposed focussing on the financial costs of, and potential profits from, the drug development programme. On the basis of this gain function, the optimal procedure is also compared with an alternative Bayesian procedure proposed by Thall and Simon. The latter method, which tightly controls type I error rate, is shown to lead to an expected gain considerably smaller than that from the optimal test. Gain functions with respect to which Thall and Simons boundary is optimal are sought and it is shown that these can only be of the form considered, that is, with constant cost for phase III study and cost of the phase II study proportional to the sample size, if potential profit increases over time.

A newly devised scoring system for prediction of mortality in patients with colorectal cancer: a prospective study

BACKGROUND Postoperative morbidity and mortality from colorectal cancer varies widely across hospitals in the UK. We aimed to assess whether a newly developed score from the Association of Coloproctology of Great Britain and Ireland (ACPGBI) could predict mortality from colorectal cancer surgery as accurately as the Physiology and Operative Severity Score for enUmeration of Mortality and morbidity (POSSUM), Portsmouth POSSUM (P-POSSUM), or the ColoRectal POSSUM (CR-POSSUM). METHODS We analysed prospectively 618 patients with histologically confirmed colorectal cancer who had surgery to remove primary tumours done by colorectal surgeons or non-colorectal surgeons in a 3-year period. We compared observed mortality with those predicted by the ACPGBI, POSSUM, P-POSSUM, and CR-POSSUM scoring systems using the Hosmer-Lemeshow test and Receiver Operating Characteristic (ROC) curve analysis. FINDINGS Between April 1, 2002, and May 31, 2005, 618 consecutive patients with colorectal cancer had surgery to remove primary tumours. Overall observed 30-day mortality over the 3 years was 10.2% (95% CI 8.0-12.9). Overall predicted mortality (mean score) by use of POSSUM was 12.7% (11.7-13.7), by use of P-POSSUM was 4.4% (3.4-5.4), by use of CR-POSSUM was 9.6% (8.6-10.6), and by use of ACPGBI score was 8.1% (7.3-8.8). INTERPRETATION POSSUM overpredicted mortality, whereas P-POSSUM underpredicted mortality from colorectal-cancer surgery. CR-POSSUM was a more-accurate predictor of mortality in most analyses than was POSSUM and P-POSSUM. Although CR-POSSUM gave the closest prediction of overall mortality, analyses of subgroups of patients showed that ACPGBI score predicted overall mortality most accurately.

Bayesian graphical models for genomewide association studies

As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single-locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single-nucleotide polymorphisms. Analysis is quick (<5 min), and we are able to localize the causative site to a very short interval.

An Adaptive Group Sequential Design for Phase II/III Clinical Trials that Select a Single Treatment From Several

ABSTRACT There is increasing interest in combining Phases II and III of clinical development into a single trial in which one of a small number of competing experimental treatments is ultimately selected and where a valid comparison is made between this treatment and the control treatment. Such a trial usually proceeds in stages, with the least promising experimental treatments dropped as soon as possible. In this paper we present a highly flexible design that uses adaptive group sequential methodology to monitor an order statistic. By using this approach, it is possible to design a trial which can have any number of stages, begins with any number of experimental treatments, and permits any number of these to continue at any stage. The test statistic used is based upon efficient scores, so the method can be easily applied to binary, ordinal, failure time, or normally distributed outcomes. The method is illustrated with an example, and simulations are conducted to investigate its type I error rate and power under a range of scenarios.

A conditional error function approach for subgroup selection in adaptive clinical trials.

Growing interest in personalised medicine and targeted therapies is leading to an increase in the importance of subgroup analyses. If it is planned to view treatment comparisons in both a predefined subgroup and the full population as co-primary analyses, it is important that the statistical analysis controls the familywise type I error rate. Spiessens and Debois (Cont. Clin. Trials, 2010, 31, 647-656) recently proposed an approach specific for this setting, which incorporates an assumption about the correlation based on the known sizes of the different groups, and showed that this is more powerful than generic multiple comparisons procedures such as the Bonferroni correction. If recruitment is slow relative to the length of time taken to observe the outcome, it may be efficient to conduct an interim analysis. In this paper, we propose a new method for an adaptive clinical trial with co-primary analyses in a predefined subgroup and the full population based on the conditional error function principle. The methodology is generic in that we assume test statistics can be taken to be normally distributed rather than making any specific distributional assumptions about individual patient data. In a simulation study, we demonstrate that the new method is more powerful than previously suggested analysis strategies. Furthermore, we show how the method can be extended to situations when the selection is not based on the final but on an early outcome. We use a case study in a targeted therapy in oncology to illustrate the use of the proposed methodology with non-normal outcomes.

A confirmatory seamless phase II/III clinical trial design incorporating short-term endpoint information

Seamless phase II/III designs allow strong control of the familywise type I error rate when the most promising of a number of experimental treatments is selected at an interim analysis to continue along with the control treatment. If the primary endpoint is observed only after long-term follow-up it may be desirable to use correlated short-term endpoint data available at the interim analysis to inform the treatment selection. If short-term data are available for some patients for whom the primary endpoint is not available, basing treatment selection on these data may, however, lead to inflation of the type I error rate. This paper proposes a method for the adjustment of the usual group-sequential boundaries to maintain strong control of the familywise error rate even when short-term endpoint data are used for the treatment selection at the first interim analysis. This method allows the use of the short-term data, leading to an increase in power when these data are correlated with the primary endpoint data.

A New Clinical Trial Design Combining Phases 2 and 3: Sequential Designs with Treatment Selection and a Change of Endpoint

This paper describes a method for designing a clinical trial to combine aspects of Phases 2 and 3 of the clinical development program. Specifically, a group sequential design is presented, which incorporates treatment selection based upon a short-term “provisional” endpoint, as is often undertaken in Phase 2 trials, followed by a comparison of the selected treatment with control in terms of a longer-term primary endpoint. An example is given illustrating the methodology and we discuss how this approach may reduce the total number of patients required in the evaluation process without compromising its integrity, leading to more ethical and efficient clinical studies.