The importance of understanding the impact of preference in clinical trials of diabetes interventions 在糖尿病干预临床试验中了解患者偏好的影响的重要性 (2014)

How patients perceive their condition and associated treatment is an important consideration when making treatment decisions in self-management and progressive conditions such as diabetes. Achieving tight control of blood glucose requires that healthcare professionals (HCPs) and patients work together to address the clinical and psychosocial aspects of their illness.1, 2 Indeed the provision of care that is respectful of and responsive to individual patient preferences, needs, and values constitutes one of the core principles of evidence-based medicine.2 Patient involvement in clinical decision making increases empowerment and has been shown to improve outcomes and valuation of these outcomes.3, 4 However, clinical trials, whereby patients are randomly assigned to intervention groups, continue to provide the evidence base for marketing authorization of new interventions for diabetes. It is true that randomized clinical trials (RCTs) remain the most robust vehicle to collect the safety, efficacy and quality data required by regulatory authorities, but these designs assume clinical equipoise and remove the most important factor in how medical care is actually delivered: by choosing among alternatives. Although the process of randomization is designed to equally distribute potentially confounding prognostic variables between groups, thus maximizing the attribution of outcome to intervention, the act of randomization may unwittingly create biases in diabetes research that may threaten the validity of the findings, particularly in the modern era of comparative efficacy and relative efficacy assessment. One key bias that is often overlooked is that resulting from patient preference. Patients with strong intervention preferences may refuse randomization in clinical trials,5 particularly in diabetes, where there are effective treatments available to patients outside of a trial environment. The absence of these patients from the RCT may compromise external validity of the trial, beyond that which is commonly considered (e.g. purposefully narrow inclusion criteria).5-8 Other patients with strong treatment preferences may agree to be recruited and randomized; particularly where they place value on the potential outcomes, there is utility to be gained from participation, or the intervention is unavailable outside of the trial context.9 When these patients are not blinded to their intervention allocation (open-label RCTs), they may suffer resentful demoralization if they do not receive their preferred intervention, resulting in lower levels of adherence and persistence, whereas patients receiving their preferred intervention may be better motivated and engaged to adhere and persist better than average.6, 10 Two studies of continuous subcutaneous insulin infusion (CSII) and multiple exogenous daily insulin injections (MDI) are notable for their differences in consent and loss to follow-up.11 In a study by Knight et al.,12 patients were offered the opportunity to choose between CSII and MDI following a lecture on these treatment options. More than 90% of patients attending the lecture consented to participate in the study and less than 5% of patients dropped out over the 6-month follow-up. In a randomized cross-over design of MDI versus CSII, Staehr Johansen13 had a consent rate less than 40% and a drop out of more than 20% over a 12-month follow-up. In addition to potential adherence and persistence benefits of assignment to a preferred intervention cohort, reduced or heightened expectancies of therapeutic efficacy may further impact outcomes, mediated by behavioral change.14 Therefore, observed outcomes attributed to the intervention may, in fact, be a function of preference effects interacting with intervention efficacy. In a systematic review of the empirical data concerning preference effects in RCTs, King et al.15 found evidence that preferences were related to outcomes in a proportion of studies, although the magnitude and direction of preference effects were inconsistent. Blinding is favored in RCTs to reduce effects associated with patient preference. Indeed, if participant blinding were perfectly maintained, preference effects would not manifest. Unfortunately, participant blinding is difficult to maintain,7 particularly where there are notable efficacy or side effect profiles of the drugs being compared, and therefore preferences could bias the outcomes observed in blinded trials just as they may for open-label trials.16 The bias arises via a two-way interaction in open-label trials (intervention preference × intervention received) and a three-way interaction in blinded trials (intervention preference × intervention guessed × intervention received).16 Newer interventions for the treatment of diabetes have potential benefits that may be appreciated by patients, including weight neutrality or loss, reduced hypoglycemia risk, and less frequent administration. Documenting and controlling for the presence or absence of preference effects in RCTs of these interventions is therefore key to generate more specific estimates of efficacy, and to ensure that clinical benefits are not overstated. Further, measuring outcomes among subgroups of patients with different preferences and motivations may help clinicians apply the results of RCTs to their practices. Hypothetical patient preference and utility (desirability) studies are frequently conducted alongside RCTs in order to ascertain the commercial potential for an intervention attribute and judge the trade-off between favorable and unfavorable effects. However, these do not allow for the assessment of preference effects on objective and subjective safety, efficacy, and quality outcome data. This can be achieved either through: (i) the conduct of a traditional RCT in which participants' preferences are assessed prior to randomization (using a well-developed patient-reported outcome [PRO] scale validated in different developmental populations) and primary analyses are stratified by stated preferences, to quantify these effects;16 or (ii) hybrid randomized and non-randomized designs, such as the partially randomized preference trial (PRPT; also known as the comprehensive cohort design)17, 18 or the double randomized preference trial (DRPT).18-20 In the PRPT, participants are asked about their informed preference for Intervention A or Intervention B. Those with strong preferences are provided with their intervention of choice, whereas those with relative ambivalence between Interventions A and B are randomized. In the DRPT, participants are randomized either to a traditional RCT or to a preference-based trial. These hybrid designs allow for estimation of preference effects and causal effects in subclasses defined by preference, and seek to answer the question “which treatment suits the patient best given the particular circumstances?”, rather than “which treatment is best?”.21 The impact of unmeasured confounding in such hybrid designs remains a concern.22 There are numerous articles providing statistical advice for the assessment of patient preference in RCTs and for the meaningful evaluation of hybrid designs.15, 16, 18, 21 The objective of this short Commentary is not to restate these methods. Rather, it aims to highlight the potential role of patient preferences in trials of interventions in diabetes, a condition that is characterized by self-management, patient values guiding clinical decision making, and a wide array of treatment options. Designs are needed that take account of doctors and patients as individuals with differing beliefs, needs, and priorities that can influence their attitudes and behavior when choosing and making use of interventions. In order to increase robustness of study designs, aid interpretation of outcomes, assist regulatory authorities in considering whether a new drug merits approval in the saturated marketplace of diabetes therapy, and create more generalizable clinical development studies, this Commentary aims to reiterate and expand on the earlier recommendations from Bradley,8 namely that motivations and preferences (of the patient, HCP, and relevant others) for the interventions to be studied should always be measured when undertaking trials. The effects of preferences on outcomes may then be investigated within a conventional RCT or else used to inform an alternative design. However, it should still be remembered that many patients with strong preferences may have declined to participate in such a trial. None declared. 在进展性疾病例如糖尿病的自我管理中，患者对其疾病状态和治疗的认知是决定治疗策略时要考虑的一个重要事项。要获得严格的血糖控制需要医疗保健专业人员（healthcare professionals，HCPs）与患者紧密协作以解决他们所患疾病的临床与心理方面的各种问题1, 2。在提供护理时尊重与适应每个患者的偏好、需要以及价值观，这确实这是循证医学的一个核心原则的组成部分2。患者参与临床决策可以增加自主授权，目前已经证实可以改善患者的预后以及对这些预后的评估3, 4。 然而，临床试验，通过将患者随机分配到各个干预组中去，还在继续为新型糖尿病干预药物的上市准入提供基础证据。事实上随机临床试验（RCTs）仍然是收集监管部门所要求的药物安全性、有效性以及质量相关的数据的最有力途径，但是这些设计都假设了临床均势，并且通过：选择备选治疗方案，将医疗保健实际工作时遇到的最重要影响因素排除在外了。 虽然设计了随机化的方法以使潜在的混杂预后变量能够平等地分配到各个治疗组中去，从而使结果的变化能够最大程度地归因于干预的影响，但是随机化行为却可能无意中又产生了可能会影响研究结果准确性的偏倚，特别是现代在对比疗效与评估相对疗效的时候。有一个关键的经常被忽略的偏倚就是由患者偏好引起的。 具有强烈干预偏好的患者可能在临床试验时拒绝被随机化5，特别是在糖尿病试验中，因为对于患者来说在试验环境之外还可以获得其他有效的治疗方法。除却一些通常被考虑到的其他影响因素（例如有目的地缩小入选标准）外，RCT缺少这些患者的参与可能会影响试验的外在效度5-8。其他具有强烈治疗偏好的患者可能愿意被招募入选研究并且被随机化；特别是当他们比较重视潜在治疗效果的时候，他们通过参与可以获得益处，或者在试验范围之外不能够获得这种干预9。当干预治疗分配对患者是非盲的时候（开放标签的RCTs），如果没有被分配到他们偏爱的干预组中去，那么他们就有可能对此愤愤不平，导致他们的依从性与持久性水平更低，反之若患者接受了他们所偏爱的干预治疗，那么他们参与试验的依从性与持久性就会比一般人更好6, 10。在两项有关持续皮下输注胰岛素（continuous subcutaneous insulin infusion，CSII）与每日多次注射外源性胰岛素（multiple exogenous daily insulin injections，MDI）的研究中，知情同意与失访率都具有显著的差异11。在Knight等的一项研究中12，患者在听完有关这些治疗方法的讲座后有机会自己选择CSII或者MDI进行治疗。在参加讲座的患者中90%以上的人同意参与这项研究，并且在超过6个月的随访中脱落的患者低于5%。在Staehr Johansen13的一项随机交叉设计对比MDI与CSII的研究中，知情同意率低于40%，并且在超过12个月的随访中脱落率大于20%。 患者被分配到他们偏爱的干预组中去除了在依从性与持久性方面具有潜在的获益以外，降低或者提高治疗效果的预期值还可能会进一步地影响试验结果，这是通过行为改变导致的14。因此，观察到的原本要归因于不同干预方法的结果，实际上却可能是偏爱效应与干预的疗效相互作用的结果。在一个对RCTs中偏爱效应的经验性数据的系统综述中，King等15发现在一定比例的研究中患者的偏爱与研究结果之间具有相关性，虽然偏爱效应的程度与方向并不一致。 在RCTs中，盲法有助于减少患者偏爱效应的影响。确实，如果对参与者保持很好的遮盲，那么偏爱效应就不明显了。不幸的是，很难对参与者保持遮盲7，特别是被比较的药物具有显著疗效或者副作用的时候，因此就与开放标签试验一样，在遮盲试验中患者的偏爱效应也可能使观察到的结果产生偏倚16。在开放标签试验中通过两种相互作用产生了偏倚（对干预的偏爱×接受的干预），而在遮盲试验中通过三种相互作用产生了偏倚（对干预的偏爱×对干预的猜测×接受的干预）16。 治疗糖尿病时患者可能比较喜欢使用具有潜在获益的新型干预方法，包括对体重的影响是中性的或者可以降低体重、减少低血糖风险以及给药次数更少的干预方法。因此，为了能够更明确地评估疗效以及确保临床获益没有被夸大，对在RCTs中这些干预方法是否存在偏爱效应还需要进行记录与控制，这一点很关键。此外，测定偏爱与动机不同的亚组患者的结果可能有助于临床医生将RCTs的结果应用到他们的实践中去。为了能够明确一种干预方法的商业潜力以及在有利影响与不利影响之间作出判断，经常与RCTs在一起同时进行的还有假设的患者偏爱以及效用（需求）研究。然而，这些都不能用来评估偏爱效应对客观与主观上的安全性、有效性以及品质结果数据造成的影响。 这种评估只能通过下列两种途径中的一种来完成：（i）为了定量化地评估这些影响，进行一项传统的RCT时，在随机化之前就要评估参与者的偏爱（使用事先就制定好的在不同的人群进行过验证的患者报告结果[patient-reported outcome，PRO]量表），并且根据患者表述的偏爱进行初步的分层分析，以对其影响进行量化16；（ii）将随机化与非随机化设计混合在一起，例如部分随机化患者偏爱试验（partially randomized preference trial，PRPT；也被称为全面队列设计）17, 18或者双随机化患者偏爱试验（double randomized preference trial，DRPT）18-20。在PRPT中，要求患者事先告知偏爱干预A还是干预B。向那些具有强烈偏爱的患者提供干预选择，反之将那些对于选择干预A还是干预B相对犹豫不定的患者进行随机化。在DRPT中，参与者被随机分配到传统的RCT组或者基于患者偏爱的试验组。在根据患者偏爱分组的亚组分析中，这些混合设计的试验可以用来评估患者的偏爱效应以及干预方法的因果效应，并且可以试图回答“在给定的特殊环境下这个患者最适合使用何种治疗方法？”这个问题，而不是“何种治疗方法最佳？”的问题21。在这些混合设计中仍然还有一些令人担心的无法测定的混杂影响22。 有很多论文对RCTs中患者偏爱效应的评估以及对混合设计的有意义的评估都提出了统计学建议15, 16, 18, 21。这篇简短评论的目的并不是要重新叙述这些方法。而是要强调患者偏爱在糖尿病干预试验中的潜在影响，因为糖尿病是一种需要自我管理、患者价值观指导临床决策并且具有广泛治疗选择的疾病。设计的时候需要考虑到医生与患者的个体化需求，因为他们的信仰、需求以及优先选择各不相同，这些都有可能在他们选择与使用干预方法时影响他们的态度与行为。为了使研究设计更加完善，有助于结果的解释，协助监管部门判断一种新药在已经饱和的糖尿病治疗药物市场中是否有优点并且值得批准上市，以及进行更有推广性的临床研究，这篇评论旨在重申与详述Bradley早先就已经提出的建议8，亦即在进行试验时都应该测定（患者、HCP以及相关的其他人）对这些研究干预方法的动机与偏爱情况。那么在常规的RCT中也应该调查患者的偏爱对结果的影响，否则需要使用另外一种设计。然而，我们还应该记得许多具有强烈偏爱的患者可能拒绝参加这种试验。 无需申明。