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Can observational data be used to determine drug effectiveness?

观察数据可以用来确定药物有效性吗？

基本信息

批准号：
1923714
负责人：
金额：
--
依托单位：
London School of Hygiene & Tropical Medicine
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1923714
关键词：
observational data used determine drug

项目摘要

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.

作为患者护理的标准部分收集的数据提供了在真实的世界（RW）环境中提供药物有效性证据的机会。随机对照试验（RCT）通常选择一个患者人群，最大限度地观察到统计学显著性治疗效果的机会，并且通常纳入的患者是该患者人群中最健康的子集。因此，对于那些不符合RCT纳入和排除标准的患者，如：病情较重或较轻的患者、合并症患者、合并用药患者、儿童、老年和妊娠患者，治疗有效性的证据可能不足，患者和医生必须将RCT结果外推到他们的病例中，这可能不合适。理想情况下，医生和患者应该有证据证明药物对具有相似特征的患者的有效性。如果这个项目成功，那么这将为提供此类证据铺平道路。RW数据的另一个优点是与疾病相关的长期结果的可用性，这些结果通常不能包括在RCTs.Methodology中。将选择具有3期RCT结果的合适药物，并在临床实践研究数据链（CPRD）RW数据中记录药物处方和结局指标。可以采用数据链接，将医院事件或ON死亡率数据与CPRD数据链接，以增加捕获所有可用结局数据的可能性。通过比较RCT人群与最终处方药物人群的基线特征以及当前的临床知识来确定感兴趣的亚群。RW数据中的患者队列将被选择为与RCT患者匹配。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，选择具有与试验队列匹配的总体基线人口统计学和病史分布的患者队列，并在患者水平上匹配个体患者特征。虽然广泛使用，但倾向评分匹配（PSM）可能会增加不平衡和偏倚（PSM悖论，King和Nielsen 2016）。作为该项目的一部分，将探索不同的匹配技术，以选择RCT患者与RW队列匹配以及治疗组之间匹配的最佳方法。一旦选择了合适的RW队列，将分析治疗效果。将考虑所有潜在偏倚来源，并制定适当的策略对其进行解释。混杂的来源将被识别和处理的方法，如边际结构模型。在敏感性分析中，可以使用多重插补来处理缺失数据。如果匹配的RW队列分析提供的治疗估计值与RCT结果一致，则可以认为这验证了使用真实的世界数据来评估特定药物/疾病领域的药物有效性。将进行敏感性分析以检验结果的稳健性;如果它们支持主要结果，则可以继续观察其他RW患者队列（未纳入RCT的患者）。如果RW结果显示的药物有效性与RCT预测的结果不相似，则将探讨原因-数据质量，患者选择方法，使用的分析模型，或最终由于药物有效性在真实的生活中与试验中证明的有效性不完全相同。将获得药物流行病学和医学大数据分析方面的专业知识。