Optimal compliance prediction models for estimating causal effects

Paper presented at the 2nd Strathmore International Mathematics Conference (SIMC 2013), 12 - 16 August 2013, Strathmore University, Nairobi, Kenya.Understanding the causal relationship between intervention and outcome is at the heart of most research in the health sciences, and a variety of statistical methods have been developed to address causality. However, noncompliance with treatment assignment is a key source of complication for causal inference. Estimation of causal effects is likely to be compounded by the presence of noncompliance in both treatment arms of clinical trials where the intention-to-treat (ITT) analysis produces a biased estimator for the true causal estimate even under homogeneous treatment effects assumption. Principal stratification method has been developed to address such posttreatment complications by stratifying the population into partially latent classes (principal strata) based on potential values observed after randomization (e.g. noncompliance) under each of the levels of randomized intervention. The present work combines the two strategies of model selection and principal stratification with a novel application to a real data from a trial conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. The causal model links the resulting two marginal prediction models with a user-defined sensitivity parameter which is a function of the correlation between the two compliance behaviours. The method's key assumption of conditional prediction is verified for our data via sensitivity analysis comparing results of causal estimates using different sets of predictors of compliance. We adjust for noncompliance in both treatment arms under a Bayesian framework to produce causal risk ratio estimates for each principal stratum. The results suggested better efficacy for HRT among those who would comply with it compared to those who would comply with either HRT or placebo: compliance with HRT treatment only and with either treatment allocation would reduce the risk for death (reinfarction) by 47%(25%) and 13%(60%) respectively.Understanding the causal relationship between intervention and outcome is at the heart of most research in the health sciences, and a variety of statistical methods have been developed to address causality. However, noncompliance with treatment assignment is a key source of complication for causal inference. Estimation of causal effects is likely to be compounded by the presence of noncompliance in both treatment arms of clinical trials where the intention-to-treat (ITT) analysis produces a biased estimator for the true causal estimate even under homogeneous treatment effects assumption. Principal stratification method has been developed to address such posttreatment complications by stratifying the population into partially latent classes (principal strata) based on potential values observed after randomization (e.g. noncompliance) under each of the levels of randomized intervention. The present work combines the two strategies of model selection and principal stratification with a novel application to a real data from a trial conducted to ascertain whether or not unopposed oestrogen (hormone replacement therapy - HRT) reduced the risk of further cardiac events in postmenopausal women who survive a first myocardial infarction. The causal model links the resulting two marginal prediction models with a user-defined sensitivity parameter which is a function of the correlation between the two compliance behaviours. The method's key assumption of conditional prediction is verified for our data via sensitivity analysis comparing results of causal estimates using different sets of predictors of compliance. We adjust for noncompliance in both treatment arms under a Bayesian framework to produce causal risk ratio estimates for each principal stratum. The results suggested better efficacy for HRT among those who would comply with it compared to those who would comply with either HRT or placebo: compliance with HRT treatment only and with either treatment allocation would reduce the risk for death (reinfarction) by 47%(25%) and 13%(60%) respectively

Facilitating Activity and Self-management for people with Arthritic knee, hip or lower back pain (FASA) : a cluster randomised controlled trial

Chronic musculoskeletal pain including osteoarthritis (OA) can significantly limit the functional independence of individuals. The spine and hip and knee are predominantly affected; management guidelines for each recommend exercise and education to support self-management.Objectives: This study investigated the effectiveness of a generic exercise and self-management intervention for people over-50 with hip/knee OA and/or lower back pain compared to continued GP management.Design: Single blind, cluster randomised controlled trial.Method: Participants who had previously consulted with hip/knee OA and/or chronic lower back pain were recruited from 45 GP practices in SW England. Practices were randomly allocated to receive continued GP care (control) or continued GP care and a 6-week group exercise and self-management intervention facilitated by a physiotherapist and located in a community-based physiotherapy department. The primary outcome measure was the Dysfunction Index of the Short Musculoskeletal Functional Assessment (DI-SMFA) measured at six month post-rehabilitation.Results: 349 participants were recruited and allocated to the intervention (n = 170) or control (n = 179) arms; the attrition rate was 13% at the 6 month primary end-point. One minor adverse event in the intervention group that required no medical input was reported. Intervention arm participants reported better function at 6 months compared with continued GP management alone (−3.01 difference in DI-SMFA [95%CI -5.25, −0.76], p = 0.01).Conclusions: A generic exercise and self-management intervention resulted in statistically significant changes in function after six-months compared with GP management alone, but clinical significance of these findings is less clear. This may be an effective way of managing group interventions for lower limb OA and chronic lower back pain

Clinical effectiveness and cost-effectiveness of cognitive behavioural therapy as an adjunct to pharmacotherapy for treatment-resistant depression in primary care: the CoBalT randomised controlled trial

Background: Only one-third of patients with depression respond fully to treatment with antidepressant medication. However, there is little robust evidence to guide the management of those whose symptoms are 'treatment resistant'.<p></p> Objective: The CoBalT trial examined the clinical effectiveness and cost-effectiveness of cognitive behavioural therapy (CBT) as an adjunct to usual care (including pharmacotherapy) for primary care patients with treatment-resistant depression (TRD) compared with usual care alone.<p></p> Design: Pragmatic, multicentre individually randomised controlled trial with follow-up at 3, 6, 9 and 12 months. A subset took part in a qualitative study investigating views and experiences of CBT, reasons for completing/not completing therapy, and usual care for TRD.<p></p> Setting: General practices in Bristol, Exeter and Glasgow, and surrounding areas.<p></p> Participants: Patients aged 18-75 years who had TRD [on antidepressants for 6 weeks, had adhered to medication, Beck Depression Inventory, 2nd version (BDI-II) score of 14 and fulfilled the International Classification of Diseases and Related Health Problems, Tenth edition criteria for depression]. Individuals were excluded who (1) had bipolar disorder/psychosis or major alcohol/substance abuse problems; (2) were unable to complete the questionnaires; or (3) were pregnant, as were those currently receiving CBT/other psychotherapy/secondary care for depression, or who had received CBT in the past 3 years.<p></p> Interventions: Participants were randomised, using a computer-generated code, to usual care or CBT (12-18 sessions) in addition to usual care.<p></p> Main outcome measures: The primary outcome was 'response', defined as 50% reduction in depressive symptoms (BDI-II score) at 6 months compared with baseline. Secondary outcomes included BDI-II score as a continuous variable, remission of symptoms (BDI-II score of < 10), quality of life, anxiety and antidepressant use at 6 and 12 months. Data on health and social care use, personal costs, and time off work were collected at 6 and 12 months. Costs from these three perspectives were reported using a cost-consequence analysis. A cost-utility analysis compared health and social care costs with quality adjusted life-years.<p></p> Results: A total of 469 patients were randomised (intervention: n = 234; usual care: n = 235), with 422 participants (90%) and 396 (84%) followed up at 6 and 12 months. Ninety-five participants (46.1%) in the intervention group met criteria for 'response' at 6 months compared with 46 (21.6%) in the usual-care group {odds ratio [OR] 3.26 [95% confidence interval (CI) 2.10 to 5.06], p < 0.001}. In repeated measures analyses using data from 6 and 12 months, the OR for 'response' was 2.89 (95% CI 2.03 to 4.10, p < 0.001) and for a secondary 'remission' outcome (BDI-II score of < 10) 2.74 (95% CI 1.82 to 4.13, p < 0.001). The mean cost of CBT per participant was £910, the incremental health and social care cost £850, the incremental QALY gain 0.057 and incremental cost-effectiveness ratio £14,911. Forty participants were interviewed. Patients described CBT as challenging but helping them to manage their depression; listed social, emotional and practical reasons for not completing treatment; and described usual care as mainly taking medication.<p></p> Conclusions: Among patients who have not responded to antidepressants, augmenting usual care with CBT is effective in reducing depressive symptoms, and these effects, including outcomes reflecting remission, are maintained over 12 months. The intervention was cost-effective based on the National Institute for Health and Care Excellence threshold. Patients may experience CBT as difficult but effective. Further research should evaluate long-term effectiveness, as this would have major implications for the recommended treatment of depression.<p></p&gt

Adaptive designs in clinical trials: why use them, and how to run and report them.

Adaptive designs can make clinical trials more flexible by utilising results accumulating in the trial to modify the trial's course in accordance with pre-specified rules. Trials with an adaptive design are often more efficient, informative and ethical than trials with a traditional fixed design since they often make better use of resources such as time and money, and might require fewer participants. Adaptive designs can be applied across all phases of clinical research, from early-phase dose escalation to confirmatory trials. The pace of the uptake of adaptive designs in clinical research, however, has remained well behind that of the statistical literature introducing new methods and highlighting their potential advantages. We speculate that one factor contributing to this is that the full range of adaptations available to trial designs, as well as their goals, advantages and limitations, remains unfamiliar to many parts of the clinical community. Additionally, the term adaptive design has been misleadingly used as an all-encompassing label to refer to certain methods that could be deemed controversial or that have been inadequately implemented.We believe that even if the planning and analysis of a trial is undertaken by an expert statistician, it is essential that the investigators understand the implications of using an adaptive design, for example, what the practical challenges are, what can (and cannot) be inferred from the results of such a trial, and how to report and communicate the results. This tutorial paper provides guidance on key aspects of adaptive designs that are relevant to clinical triallists. We explain the basic rationale behind adaptive designs, clarify ambiguous terminology and summarise the utility and pitfalls of adaptive designs. We discuss practical aspects around funding, ethical approval, treatment supply and communication with stakeholders and trial participants. Our focus, however, is on the interpretation and reporting of results from adaptive design trials, which we consider vital for anyone involved in medical research. We emphasise the general principles of transparency and reproducibility and suggest how best to put them into practice