Heart failure with preserved ejection fraction: clinical characteristics of 4133 patients enrolled in the I-PRESERVE trial.

BACKGROUND: We describe the baseline characteristics of subjects randomised in the largest placebo-controlled, morbidity-mortality trial to date in patients with heart failure and preserved ejection fraction - the irbesartan in heart failure with preserved systolic function trial (I-PRESERVE). METHODS AND RESULTS: 4133 patients with a mean age of 72 years (a third were 75 years or older) were randomised and 60% were women. The mean (SD) LVEF was 59 (9)% and almost 80% of patients were in NYHA Class III or IV. Approximately 80% of patients were also overweight or obese. Heart failure was reported by investigators to have a hypertensive aetiology in 64% of patients. Prior myocardial infarction was relatively uncommon (24%), as was coronary revascularisation (13%). Atrial fibrillation and diabetes each occurred in between a quarter and a third of patients. The following treatments were used at baseline: diuretic 83%, beta-blocker 59%, calcium channel blocker 40%, ACE inhibitor 25%, spironolactone 15% and digoxin 14%. CONCLUSIONS: Patients in I-PRESERVE are broadly representative of those seen in epidemiological studies and, because of this, the results of this trial should be generally applicable to "real world" patients with heart failure and preserved ejection fraction.

Predicting event times in clinical trials when randomization is masked and blocked.

BACKGROUND: Timing for interim or final analysis of data in an event-based trial is often determined by the accrual of events during the study. Existing Bayesian methods may be used to predict the date of the landmark event using observed enrollment, event, and loss times when treatment arm information is masked. PURPOSE: For event-based trials with a blocked randomization, knowledge of blocks in which patients are enrolled can provide additional information to improve predictions versus models that only assume a known treatment allocation proportion. We therefore propose to incorporate blocking information into existing methods for prediction. METHODS: We derive a latent variable (LV) extension of a mixture model used in Donovan JM, Elliott MR, Heitjan DF. Predicting Event Times in Clinical Trials When Treatment Arm is Masked. J Biopharmaceut Stat 2006; 16:343-56 to incorporate block randomization (constrained LV) for predicting the landmark event and compare this model with (a) methods where blocking information is ignored (unconstrained LV), and (b) methods assuming a single population (SP). RESULTS: Comparison of the constrained and unconstrained LV models in our application shows that the constrained LV model has narrower prediction intervals. Simulation studies show that the constrained LV model can have better coverage probabilities for the prediction intervals than SP models if a treatment effect is present, and prediction intervals from the constrained LV model are narrower than those for the unconstrained LV model. These differences varied by block size and prediction time. LIMITATIONS: We have limited focus to the exponential model for events. Coverage for the LV models may be somewhat reduced if no treatment effect is present. CONCLUSIONS: Extra information provided by knowledge of blocking can be used to decrease prediction interval width versus the unconstrained LV model, while providing better coverage properties than the SP model if a treatment effect is present.

Predicting event times in clinical trials when treatment arm is masked.

Because power is primarily determined by the number of events in event-based clinical trials, the timing for interim or final analysis of data is often determined based on the accrual of events during the course of the study. Thus, it is of interest to predict early and accurately the time of a landmark interim or terminating event. Existing Bayesian methods may be used to predict the date of the landmark event, based on current enrollment, event, and loss to follow-up, if treatment arms are known. This work extends these methods to the case where the treatment arms are masked by using a parametric mixture model with a known mixture proportion. Posterior simulation using the mixture model is compared with methods assuming a single population. Comparison of the mixture model with the single-population approach shows that with few events, these approaches produce substantially different results and that these results converge as the prediction time is closer to the landmark event. Simulations show that the mixture model with diffuse priors can have better coverage probabilities for the prediction interval than the nonmixture models if a treatment effect is present.

Occupational goals of mothers of children with disabilities: influence of temporal, social, and emotional contexts.

OBJECTIVE: The purpose of this study was to describe the occupational concerns and goals of mothers who care for children with disabilities. METHOD: Retrospective data collected from 38 mothers of children with disabilities using the Canadian Occupational Performance Measure (COPM) were analyzed qualitatively. RESULTS: Six themes emerged: (I) doing and being alone: taking care of my own health and well-being; (II) doing and being with others: expanding my social life; (III) improving my child's quality of life; (IV) household management: organizing time and resources; (V) balancing work, home, and community responsibilities; and (VI) sharing the workload. CONCLUSION: Overarching patterns in the data suggested that the occupational performance of mothers of children with disabilities is constrained by time, overlaid by difficult emotions, and involves a desire for increased social contact. Qualitative analysis of data from the COPM may provide insight into contextual factors that affect occupational performance as well as signaling point of entry for therapists to facilitate client-centered occupational goals.