Can observational data be used to determine drug effectiveness?

作为患者护理的标准部分收集的数据提供了在现实世界（RW）环境中提供药物有效性证据的机会。随机对照试验（RCT）通常选择能够最大限度地观察到统计学上显著的治疗效果的患者群体，并且通常包括该患者群体中最健康的患者。因此，对于那些不符合theRCT纳入和排除标准的患者，如：病情较重或较轻的患者、有合并症的患者、有合并症的患者、儿童、老年人和孕妇患者，可能缺乏治疗效果的证据。患者和医生必须根据他们的病例推断RCT结果，这可能是不合适的。理想情况下，医生和病人将有证据证明药物对具有相似特征的病人的有效性。如果这个项目成功，那么这将为提供这样的证据铺平道路。RW数据的另一个优势是可获得与疾病相关的长期结果，而这些结果通常不能包括在随机对照试验中。将选择一种合适的药物，该药物具有可用的3期RCT结果，并且药物处方和结果测量记录在临床实践研究数据链（CPRD） RWdata中。可采用数据链接，将医院事件或onmortality数据与CPRD数据联系起来，以增加获取所有可用结果数据的可能性。感兴趣的亚群将通过比较RCT人群与最终开药人群的基线特征和当前临床知识来确定。将在RW数据中选择一组与RCT患者相匹配的患者。可以尝试各种方法来选择合适的RW数据队列：将RCT的纳入和排除标准应用于RW数据集，选择与试验队列相匹配的总体基线人口统计学和病史分布的患者队列，并在患者水平上进行个体患者特征的匹配。倾向得分匹配（PSM）虽然被广泛使用，但可能会增加不平衡和偏见（PSM悖论，King和Nielsen 2016）。因此，作为该项目的一部分，将探索不同的匹配技术，以选择从RCT患者到rw队列的匹配以及治疗组之间的匹配的最佳方法。一旦选择了合适的RW队列，将分析治疗效果。将考虑所有潜在的偏见来源，并实施适当的策略来解释它们。混淆的来源将被识别和处理的方法，如边际结构模型。在进行敏感性分析时，多重输入可用于处理缺失数据。如果对匹配RW队列的分析提供了与RCT结果一致的治疗估计，那么这可能被认为是对使用真实世界数据评估特定药物/疾病领域药物有效性的验证。将进行敏感性分析以检验结果的稳健性；如果他们支持主要结果，就可以继续观察其他RW患者队列（那些没有被纳入RCT的患者）。如果RW结果没有显示出与RCT预测相似的药物有效性，那么将探讨原因-数据质量，患者选择方法，使用的分析模型，或者最终由于药物有效性在现实生活中与试验中显示的有效性确实不相同。该学生将满足MRC技能优先级应用于电子健康记录的定量技能。将获得药物流行病学和医学大数据分析方面的专业知识。

Data collected as a standard part of patient care provides an opportunity to provideevidence on drug effectiveness in a real world (RW) setting. Randomised Controlled Trials(RCT) typically select a patient population that will maximize the chance of observing astatistically significant treatment effect and often the patients included are the healthiestsubset of that patient population. As a result evidence on treatment effectiveness can belacking for patients who would not have met the inclusion and exclusion criteria of theRCT such as: those with a more severe or mild form of the disease, patients withcomorbidities, patients with concomitant medications, and pediatric, geriatric, andpregnant patients.Patients and doctors must extrapolate RCT results to their patient case which may not beappropriate. Ideally the doctor and patient would have evidence on the effectiveness ofthe drug in patients with a similar profile. Should this project be successful then this wouldpave the way for providing such evidence. An additional advantage of RW data is theavailability of long-term outcomes related to the disease which often cannot be included inRCTs.Methodology A suitable drug with Phase 3 RCT results available and drug prescriptionand outcome measures recorded in the Clinical Practice Research Datalink (CPRD) RWdata will be selected. Data linkage may be employed, linking hospital episode or ONSmortality data to the CPRD data to increase the likelihood of capturing all availableoutcome data. Subpopulations of interest will be identified by comparing the baselinecharacteristics of the RCT population to the population who were eventually prescribedthe drug and from current clinical knowledge.A cohort of patients in the RW data will be selected matching the RCT patients. Variousmethods may be trialled to select a suitable RW data cohort: applying the inclusion andexclusion criteria of the RCT to the RW dataset, selecting a cohort of patients with an 2overall baseline demographic and medical history distribution matching the trial cohort,and matching at the patient level on the individual patient characteristics. Though widelyused, propensity score matching (PSM) may increase imbalance and bias (PSM paradox,King and Nielsen 2016). As part of this project different matching techniques will thereforebe explored to select the best method both in matching from the RCT patients to the RWcohort and in matching between treatment groups.Once a suitable RW cohort has been selected the treatment effectiveness will beanalysed. All potential sources of bias will be considered with suitable strategiesimplemented to account for them. Sources of confounding will be identified and addressedwith methods such as marginal structural models. Multiple imputation may be used to dealwith missing data in the conduct of sensitivity analyses.If the analysis of the matched RW cohort provides treatment estimates consistent with theresults of the RCT then this may be considered validation of the use of real world data forassessing drug effectiveness in that particular drug/disease area. Sensitivity analyses willbe performed to test the robustness of the results; if they support the primary result onecan proceed to looking at the other cohorts of RW patients (those who would not havebeen included in the RCT). If the RW results do not show drug effectiveness similar to thatpredicted by the RCT then the reasons will be explored - data quality, method of selectionof patients, analysis model used, or finally due to the drug effectiveness truly not being thesame in real life as to the efficacy demonstrated in the trial.This studentship will meet the MRC skill priority of quantitative skills applied to electronichealth records. Expertise will be gained in pharmacoepidemiology and in medical big dataanalysis.