Lack of adherence to study protocol and missing data are often unavoidable in clinical trials, and both increase the need to differentiate between the ideal treatment effect if the medication is taken as directed and the treatment effect in presence of the actual adherence pattern. In this regard, estimands have become the focus of attention. An estimand is simply that which is being estimated. In the context of treatment benefit, an estimand may address either efficacy or effectiveness aspects. Defining the estimand of interest is an essential step to take before deciding on trial design and primary analysis. The choice of estimand has consequences for various other factors to be considered during any clinical trial's planning phase. This study presents a process chart including all aspects to consider during planning. After deciding on the primary estimand, the trial design should be specified, followed by the primary analysis. Both should appropriately address the chosen estimand. Finally, sensitivity analyses should be taken into account. Provided are suggestions for all the planning steps involved, especially on choosing between efficacy and effectiveness, and relevant examples from clinical practice to illustrate them. It is recommended that one bear in mind the process chart during planning of any clinical trial and give reasonable justification for each decision in the study protocol.

Randomized controlled trials (RCTs) aim at providing reliable estimates of treatment benefit. Missing data and nonadherence to treatment are distinct problems that can substantially impede this task. In practice, the fact that the handling of missing data due to nonadherence affects the question that is addressed is often ignored. Estimands allow precisely predefining the question of interest. Estimands are definitions of that which is being estimated with regard to population, endpoint, and handling of postrandomization events (eg, nonadherence). Depending on the situation, different estimands are of relevance. Therefore, it is important that the intention-to-treat (ITT) principle, which is considered the gold standard for analyzing RCTs, does not restrict an RCT's primary objective to only one of several relevant estimands. Although much ambiguity is involved around what is considered to constitute the ITT principle, many associate ITT with completely following up all patients and including all data of all randomized patients as allocated into the analysis. This would restrict primary objectives to estimating the effect of treatment policy and is certainly not warranted in all situations. To maintain the advantage of having the clear recommendation to follow the ITT principle while allowing for various relevant estimands as primary objective, we argue that the appropriate way forward is to define ITT as including all randomized patients into the analysis set and each patient is to be allocated to his or her randomized treatment. This definition comprises the actual intent of ITT and can be fully implemented also in settings where complete follow-up is impossible.

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.

BackgroundElderly patients are particularly vulnerable to adverse drug reactions, especially if they are affected by additional risk factors such as multimorbidity, polypharmacy, impaired renal function and intake of drugs with high risk potential. Apart from these clinical parameters, drug safety and efficacy can be influenced by pharmacogenetic factors. Evidence-based recommendations concerning drug-gene-combinations have been issued by international consortia and in drug labels. However, clinical benefit of providing information on individual patient factors in a comprehensive risk assessment aiming to reduce the occurrence and severity of adverse drug reactions is not evident. Purpose of this randomized controlled trial is to compare the effect of a concise individual risk information leaflet with standard information on risk factors for side effects.Methods/DesignThe trial was designed as a prospective, two-arm, randomized, controlled, multicenter, pragmatic study. 960 elderly, multimorbid outpatients in general medicine are included if they take at least one high risk and one other long-term drug (polymedication). As high risk “index drugs” oral anticoagulants and antiplatelets were chosen because of their specific, objectively assessable side effects. Following randomization, test group patients receive an individualized risk assessment leaflet evaluating their personal data concerning bleeding- and thromboembolic-risk-scores, potential drug-drug-interactions, age, renal function and pharmacogenetic factors. Control group patients obtain a standardized leaflet only containing general information on these criteria. Follow-up period is 9 months for each patient. Primary endpoint is the occurrence of a thromboembolic/bleeding event or death. Secondary endpoints are other adverse drug reactions, hospital admissions, specialist referrals and medication changes due to adverse drug reactions, the patients’ adherence to medication regimen as well as health related quality of life, mortality and resulting costs.DiscussionDespite extensive evidence of risk factors for adverse drug reactions, there are few prospective trial data about an individualized risk assessment including pharmacogenetic information to increase patient safety. By conducting a health economic analysis, we will evaluate if the application of an individualized drug therapy in daily routine is cost-effective.Trial registrationGerman Clinical Trials Register: DRKS00006256, date of registration 09/01/15.

Background: Dose requirements of vitamin K antagonists are associated with CYP2C9 and VKORC1, but, compared to warfarin, less data is available about phenprocoumon. Furthermore, the effects on dose stability and anticoagulation quality are still unclear. Methods: Aim was to scrutinize phenprocoumon dose requirements, dose stability and anticoagulation quality in association to CYP2C9 and VKORC1 in a natural cohort of elderly primary care patients. As a subgroup within the IDrug study, phenprocoumon treated patients with at least two INR values within three months before enrollment (n = 209) were analyzed concerning average weekly dose, standard deviation of weekly dose (intra-subject variability), constant dose (yes/no), average INR and TTR grouped by CYP2C9 and VKORC1 (and combinations). Results: Average weekly dose per patient was 14.4 ± 5.3 mg, 11.9 ± 4.0 mg and 11.2 ± 4.3 mg in CYP2C9 wildtypes, *2 and *3 carriers (p < .0001) and 16.0 ± 4.2 mg, 13.3 ± 5.1 mg and 8.0 ± 2.7 mg per week in VKORC1 CC, CT and TT genotypes, respectively (p < .0001). Significant differences concerning intra-subject variability were detected among all groups (p < .0001) with the smallest variability in CYP2C9*3 carriers. TTR medians were 75.4%, 79.4% and 100% in wildtypes, *2 and *3 carriers, respectively (p = 0.0464). The proportion of patients with perfect control was highest among *3 carriers, but this result was not significant (p = 0.0713). Discussion: Our analyses support the results of previous investigations regarding genotype-associated dose requirements and raise the hypothesis that dose stability and anticoagulation quality may be increased in CYP2C9*3 carriers. However, our data should be treated cautiously due to the small sample size.