Optimal design of cluster randomised trials with continuous recruitment and prospective baseline period

BACKGROUND: Cluster randomised trials, like individually randomised trials, may benefit from a baseline period of data collection. We consider trials in which clusters prospectively recruit or identify participants as a continuous process over a given calendar period, and ask whether and for how long investigators should collect baseline data as part of the trial, in order to maximise precision. METHODS: We show how to calculate and plot the variance of the treatment effect estimator for different lengths of baseline period in a range of scenarios, and offer general advice. RESULTS: In some circumstances it is optimal not to include a baseline, while in others there is an optimal duration for the baseline. All other things being equal, the circumstances where it is preferable not to include a baseline period are those with a smaller recruitment rate, smaller intracluster correlation, greater decay in the intracluster correlation over time, or wider transition period between recruitment under control and intervention conditions. CONCLUSION: The variance of the treatment effect estimator can be calculated numerically, and plotted against the duration of baseline to inform design. It would be of interest to extend these investigations to cluster randomised trial designs with more than two randomised sequences of control and intervention condition, including stepped wedge designs

Optimal design of cluster randomized trials allowing unequal allocation of clusters and unequal cluster size between arms

There are sometimes cost, scientific, or logistical reasons to allocate individuals unequally in an individually randomized trial. In cluster randomized trials we can allocate clusters unequally and/or allow different cluster size between trial arms. We consider parallel group designs with a continuous outcome, and optimal designs that require the smallest number of individuals to be measured given the number of clusters. Previous authors have derived the optimal allocation ratio for clusters under different variance and/or intracluster correlations (ICCs) between arms, allowing different but prespecified cluster sizes by arm. We derive closed-form expressions to identify the optimal proportions of clusters and of individuals measured for each arm, thereby defining optimal cluster sizes, when cluster size can be chosen freely. When ICCs differ between arms but the variance is equal, the optimal design allocates more than half the clusters to the arm with the higher ICC, but (typically only slightly) less than half the individuals and hence a smaller cluster size. We also describe optimal design under constraints on the number of clusters or cluster size in one or both arms. This methodology allows trialists to consider a range for the number of clusters in the trial and for each to identify the optimal design. Except if there is clear prior evidence for the ICC and variance by arm, a range of values will need to be considered. Researchers should choose a design with adequate power across the range, while also keeping enough clusters in each arm to permit the intended analysis method

Men who have sex with men: a comparison of a probability sample survey and a community based study

We compared characteristics of men who have sex with men (MSM) in a probability sample survey with a community based study in London. The majority of men in both surveys reported male sex partner(s) in the last year but MSM recruited through the population based survey had lower levels of HIV risk behaviour, reported fewer sexually transmitted infections and HIV testing than those recruited from gay venues. Community samples are likely to overestimate levels of risk behaviour among all MSM

Combined models for pre- and post-treatment longitudinal biomarker data: an application to CD4 counts in HIV-patients.

BACKGROUND: There has been some debate in the literature as to whether baseline values of a measurement of interest at treatment initiation should be treated as an outcome variable as part of a model for longitudinal change or instead used as a predictive variable with respect to the response to treatment. We develop a new approach that involves a combined statistical model for all pre- and post-treatment observations of the biomarker of interest, in which the characteristics of response to treatment are treated as a function of the 'true' value of the biomarker at treatment initiation. METHODS: The modelling strategy developed is applied to a dataset of CD4 counts from patients in the UK Register of HIV Seroconverters (UKR) cohort who initiated highly active antiretroviral therapy (HAART). The post-HAART recovery in CD4 counts for each individual is modelled as following an asymptotic curve in which the speed of response to treatment and long-term maximum are functions of the 'true' underlying CD4 count at initiation of HAART and the time elapsed since seroconversion. Following previous research in this field, the models developed incorporate non-stationary stochastic process components, and the possibility of between-patient differences in variability over time was also considered. RESULTS: A variety of novel models were successfully fitted to the UKR dataset. These provide reinforcing evidence for findings that have previously been reported in the literature, in particular that there is a strong positive relationship between CD4 count at initiation of HAART and the long-term maximum in each patient, but also reveal potentially important features of the data that would not have been easily identified by other methods of analysis. CONCLUSION: Our proposed methodology provides a unified framework for the analysis of pre- and post-treatment longitudinal biomarker data that will be useful for epidemiological investigations and simulations in this context. The approach developed allows use of all relevant data from observational cohorts in which many patients are missing pre-treatment measurements and in which the timing and number of observations vary widely between patients

Fractional Brownian motion and multivariate-t models for longitudinal biomedical data, with application to CD4 counts in HIV-patients

Longitudinal data are widely analysed using linear mixed models, with 'random slopes' models particularly common. However, when modelling, for example, longitudinal pre-treatment CD4 cell counts in HIV-positive patients, the incorporation of non-stationary stochastic processes such as Brownian motion has been shown to lead to a more biologically plausible model and a substantial improvement in model fit. In this article, we propose two further extensions. Firstly, we propose the addition of a fractional Brownian motion component, and secondly, we generalise the model to follow a multivariate-t distribution. These extensions are biologically plausible, and each demonstrated substantially improved fit on application to example data from the Concerted Action on SeroConversion to AIDS and Death in Europe study. We also propose novel procedures for residual diagnostic plots that allow such models to be assessed. Cohorts of patients were simulated from the previously reported and newly developed models in order to evaluate differences in predictions made for the timing of treatment initiation under different clinical management strategies. A further simulation study was performed to demonstrate the substantial biases in parameter estimates of the mean slope of CD4 decline with time that can occur when random slopes models are applied in the presence of censoring because of treatment initiation, with the degree of bias found to depend strongly on the treatment initiation rule applied. Our findings indicate that researchers should consider more complex and flexible models for the analysis of longitudinal biomarker data, particularly when there are substantial missing data, and that the parameter estimates from random slopes models must be interpreted with caution. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

Patterns of sexualised recreational drug use and its association with risk behaviours and sexual health outcomes in men who have sex with men in London, UK: a comparison of cross-sectional studies conducted in 2013 and 2016

Objective: London has one of the highest identified prevalence of chemsex (sexualised recreational drug use) among men who have sex with men (MSM) in Europe. We examine MSM’s patterns of chemsex and its association with HIV/STI risk behaviours, STI diagnoses, and sexual healthcare-seeking behaviours, including if HIV testing behaviour met UK national guidelines (3-monthly if engaging in chemsex). Methods: Cross-sectional survey data from 2013 (n=905) and 2016 (n=739) were collected using anonymous, self-administered questionnaires from MSM recruited in commercial gay venues in London, UK. Descriptive and multivariable analyses, stratified by self-reported HIV status, were conducted. Adjusted prevalence ratios (aPR) with 95% confidence intervals (CI) were calculated. Results: Comparing the 2013 and 2016 surveys; chemsex prevalence in the past year remained stable, in both HIV-negative/unknown-status MSM (20.9% in 2013 vs 18.7% in 2016, p=0.301) and HIV-positive MSM (41.6% in 2013 vs 41.7% in 2016, p=0.992). Combined 2013-2016 data showed that compared to other MSM, those reporting chemsex were more likely to report HIV/STI risk behaviours, including condomless anal intercourse with serodifferent HIV-status partners (HIV-negative/unknown-status men: aPR 2.36, 95% CI 1.68-3.30; HIV-positive men: aPR 4.19, 95% CI 1.85-9.50), and STI diagnoses in the past year (HIV-negative/unknown-status men: aPR 2.10, 95% CI 1.64-2.69; HIV-positive men: aPR 2.56, 95% CI 1.57-4.20). 69.0% of HIV-negative/unknown-status men reporting chemsex attended sexual health clinics and 47.6% had tested for HIV more than once in the past year. Conclusions: Chemsex in London MSM remained stable but high, particularly among HIV-positive men. Irrespective of HIV status, chemsex was associated with engagement in HIV/STI risk behaviours. Frequency of HIV testing in the past year among HIV-negative/unknown-status men was below national recommendations. Promoting combination prevention strategies, including 3-monthly HIV/STI testing, access to PrEP/ART, and behavioural interventions among MSM reporting chemsex, remain vital to address sexual health inequalities in MSM

Virological efficacy of PI monotherapy for HIV-1 in clinical practice.

BACKGROUND: Clinical trials of PI monotherapy indicate that most participants maintain viral suppression and emergent protease resistance is rare. However, outcomes among patients receiving PI monotherapy for clinical reasons, such as toxicity or adherence issues, are less well studied. METHODS: An observational study of patients attending an HIV treatment centre in London, UK, who had received PI monotherapy between 2004 and 2013, was conducted using prospectively collected clinical data and genotypic resistance reports. Survival analysis techniques were used to examine the times to virological failure and treatment discontinuation. RESULTS: Ninety-five patients had PI monotherapy treatment for a median duration of 126 weeks. Virological failure occurred during 64% of episodes and 8% of patients developed emergent protease mutations. We estimate failure occurs in half of episodes within 2 years following initiation. Where PI monotherapy was continued following virological failure, 68% of patients achieved viral re-suppression. Despite a high incidence of virological failure, many patients continued PI monotherapy and 79% of episodes were ongoing at the end of the study. The type of PI used, the presence of baseline protease mutations and the plasma HIV RNA at initiation did not have a significant impact on treatment outcomes. CONCLUSIONS: There was a higher incidence of virological failure and emerging resistance in our UK clinical setting than described in PI monotherapy clinical trials and other European observational studies. Despite this, many patients continued PI monotherapy and regained viral suppression, indicating this strategy remains a viable option in certain individuals following careful clinical evaluation

Superiority and non-inferiority: two sides of the same coin?

BACKGROUND: The classification of phase 3 trials as superiority or non-inferiority has become routine, and it is widely accepted that there are important differences between the two types of trial in their design, analysis and interpretation. MAIN TEXT: There is a clear rationale for the superiority/non-inferiority framework in the context of regulatory trials. The focus of our article is non-regulatory trials with a public health objective. First, using two examples from infectious disease research, we show that the classification of superiority or non-inferiority trials is not always straightforward. Second, we show that several arguments for different approaches to the design, analysis and interpretation of superiority and non-inferiority trials are unconvincing when examined in detail. We consider, in particular, the calculation of sample size (and the choice of delta or the non-inferiority margin), intention-to-treat versus per-protocol analyses, and one-sided versus two-sided confidence intervals. We argue that the superiority/non-inferiority framework is not just unnecessary but can have a detrimental effect, being a barrier to clear scientific thought and communication. In particular, it places undue emphasis on tests for significance or non-inferiority at the expense of estimation. We emphasise that these concerns apply to phase 3 non-regulatory trials in general, not just to those where the classification of the trial as superiority or non-inferiority is ambiguous. CONCLUSIONS: Guidelines and statistical practice should abandon the sharp division between superiority and non-inferiority phase 3 non-regulatory trials and be more closely aligned to the clinical and public health questions that motivate the trial

Sexual behaviour in Britain: partnerships, practices, and HIV risk behaviours.

BACKGROUND: Sexual behaviour is a major determinant of sexual and reproductive health. We did a National Survey of Sexual Attitudes and Lifestyles (Natsal 2000) in 1999-2001 to provide population estimates of behaviour patterns and to compare them with estimates from 1990-91 (Natsal 1990). METHODS: We did a probability sample survey of men and women aged 16-44 years who were resident in Britain, using computer-assisted interviews. Results were compared with data from respondents in Natsal 1990. FINDINGS: We interviewed 11161 respondents (4762 men, 6399 women). Patterns of heterosexual and homosexual partnership varied substantially by age, residence in Greater London, and marital status. In the past 5 years, mean numbers of heterosexual partners were 3.8 (SD 8.2) for men, and 2.4 (SD 4.6) for women; 2.6% (95% CI 2.2-3.1) of both men and women reported homosexual partnerships; and 4.3% (95% CI 3.7-5.0) of men reported paying for sex. In the past year, mean number of new partners varied from 2.04 (SD 8.4) for single men aged 25-34 years to 0.05 (SD 0.3) for married women aged 35-44 years. Prevalence of many reported behaviours had risen compared with data from Natsal 1990. Benefits of greater condom use were offset by increases in reported partners. Changes between surveys were generally greater for women than men and for respondents outside London. INTERPRETATION: Our study provides updated estimates of sexual behaviour patterns. The increased reporting of risky sexual behaviours is consistent with changing cohabitation patterns and rising incidence of sexually transmitted infections. Observed differences between Natsal 1990 and Natsal 2000 are likely to result from a combination of true change and greater willingness to report sensitive behaviours in Natsal 2000 due to improved survey methodology and more tolerant social attitudes