Can observational data be used to determine drug effectiveness?

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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.