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Treatment non-compliance and missing data are common problems in clinical trials. Non-compliance is a broad term including any kind of deviation from the assigned treatment protocol, such as dose modification, treatment discontinuation or switch, often resulting in missing values. Missing values and treatment non-compliance may bias study results. Follow-up of all patients until the planned end of treatment period irrespective of their protocol adherence may provide useful information on the effectiveness of a study drug, taking the actual compliance into account. In this paper, we consider non-compliance as discontinuation of treatment and assume that the endpoint of interest is recorded for some non-complying patients after treatment discontinuation. As a result, the patient's longitudinal profile is dividable into on- and off-treatment observations. Within the framework of depression trials, which usually show a considerably high amount of dropouts, we compare different analysis strategies including both on- and off-treatment observations to gain insight into how the additional use of off-treatment data may affect the trial's outcome. We compare naïve strategies, which simply ignore off-treatment data or treat on- and off-treatment data in the same way, with more complex strategies based on piecewise linear mixed models, which assume different treatment effects for on- and off-treatment data. We show that naïve strategies may considerably overestimate treatment effects. Therefore, it is worthwhile to follow up as many patients as possible until the end of their planned treatment period irrespective of compliance, including all available data in an analysis that accounts for the different treatment conditions., (Copyright © 2013 John Wiley & Sons, Ltd.)

Roflumilast is a new phosphodiesterase 4 (PDE4) inhibitor developed by Byk Gulden Pharmaceuticals for the treatment of chronic obstructive pulmonary disease and asthma. A placebo-controlled, randomized, double-blind, two-period crossover study was performed to investigate the safety and efficacy of roflumilast in 16 patients with exercise-induced asthma. The patients received placebo or roflumilast (500 microg/day) for 28 days, each according to the randomly determined treatment sequences roflumilast/placebo and placebo/roflumilast. In both study periods, exercise challenge was performed 1 hour after dosing on days 1, 14, and 28. FEV1 was measured before exercise challenge, immediately after the end of exercise challenge, and then at 1, 3, 5, 7, 9, and 12 minutes after the end of challenge. Blood samples for the determination of lipopolysaccharide (LPS)-stimulated tumor necrosis factor alpha (TNF-alpha) in whole blood ex vivo as a surrogate marker for the inhibition of inflammatory cell activation were taken predose on days 1 and 28. Serial safety measurements were performed during both study periods. Analysis of variance for the crossover design showed a significant superiority of roflumilast over placebo on day 28. The mean percentage fall of FEV1 after exercise was reduced by 41% as compared to placebo (p = 0.021). An improvement of lung function during roflumilast treatment was also observed on days 1 and 14. The median TNF-alpha level decreased by 21% (p = 0.009) during roflumilast treatment but remained essentially constant under placebo. It is concluded that roflumilast is effective in the treatment of exercise-induced asthma. This result was accompanied by a significant reduction of TNF-alpha levels ex vivo. Treatment with roflumilast was safe and well tolerated.

We propose a logistic regression analysis of unmatched or frequency matched case-control studies with conditional maximum likelihood estimation through post hoc stratification. In this model fewer parameters have to be estimated. With a simulation study we show that parameter estimates have smaller variance and are less biased. Also, the residual confounding effect was quantified. A more refined post hoc stratification reduces computing time, but to the cost of a larger bias and a loss in efficiency. The model was also applied to data of unmatched case-control studies on laryngeal cancer, oesophageal cancer and lung cancer.