Methods

Parts of this chapter have been reproduced with permission from Wing et al.1 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/. The text below includes minor additions and formatting changes to the original text.

Study design

A historical cohort study, with validation against RCT results.

Ethics approval and research governance

Scientific approval was provided by the London School of Hygiene & Tropical Medicine Research Ethics Committee (reference 11997) and the Independent Scientific Advisory Committee of the Medicines and Healthcare products Regulatory Agency (protocol number 17_114R). CPRD data are already approved via a National Research Ethics Committee for purely non-interventional research of this type. Approval for use of the TORCH trial3 data was obtained from the Wellcome Trust (London, UK), the relevant sponsor (GlaxoSmithKline plc, Brentford, UK) and an independent review panel.

Setting/data sources

Patient data used in this study were obtained from two different sources: the TORCH trial3 and the UK CPRD (linked to HES data).

The TORCH trial3

The TORCH trial3 was a placebo-controlled randomised trial of combined inhaler FP-SAL for the treatment of COPD, published in 2007. Patients were randomised to receive FP-SAL, fluticasone propionate (FP) alone, salmeterol (SAL) alone or placebo, and the primary comparison of interest was between FP-SAL and placebo.3 Key outcomes were expected benefits (with a primary outcome of decreased mortality and additional outcome of a decrease in the rate of COPD exacerbations) and an expected harm due to the immunosuppressive action of the corticosteroid FP (pneumonia). Although findings for the primary end point of mortality were null, this was thought to be because of poor statistical power as a result of a lower than anticipated mortality rate. Nonetheless, a lower rate of exacerbations was seen with FP-SAL and a higher rate of pneumonia was observed. As one of the largest trials in COPD, and with a 3-year follow-up, the TORCH trial3 was a landmark study and provided a validation point for our study. We obtained individual patient data from the TORCH trial3 via www.clinicalstudydatarequest.com (accessed 28 May 2021) for use in objective 1 (see Selection of participants).

Clinical Practice Research Datalink

The CPRD is a very large database of prospectively collected, anonymised UK population-based EHRs. CPRD primary care records comprise ≈ 8–10% of the UK population and contain comprehensive information on clinical diagnoses, prescribing, referrals, tests and demographic/lifestyle factors.9 To contribute to the database, general practices and other health centres must meet prespecified standards for research-quality data (i.e. be ‘up to standard’). Data quality/validity are, therefore, high and the data are nationally representative.9,10 A patient starts contributing follow-up time to the database at the date they join an ‘up-to-standard’ practice (or the date that their practice starts contributing up-to-standard data) and stop contributing follow-up time on the date of their death, their transfer out date (i.e. the date that they leave the database for reasons other than death) or the last collection date for their practice. Linkage between the primary care records in CPRD and HES is well established for > 60% of practices in the CPRD, providing a data set augmented with detailed secondary care diagnostic and procedural records. Algorithms have been established to identify COPD, COPD exacerbations, pneumonia (both hospital and primary care managed) and asthma in CPRD/HES-linked data (including validated algorithms for COPD and exacerbations).11–13 A high-level overview of these algorithms is provided in Table 1, and all diagnostic and therapeutic codelist files used to search the CPRD and HES databases for exposure, outcome and covariate information described subsequently in this report are available for download at https://datacompass.lshtm.ac.uk/1655/ (accessed 28 May 2021). For body mass index (BMI) and smoking status, the algorithms we applied looked for the nearest status in the period – 1 year to + 1 month from the index date (preferred). If this was not available, then the nearest in the period + 1 month to + 1 year after the index date was taken (second preferred). If this was not available, then the nearest before – 1 year from the index date was taken (third preferred) and if this not available, then we took the nearest after + 1 year from the index date (least preferred).

Selection of participants

Objective 1: validation of methods for measuring chronic obstructive pulmonary disease medication effectiveness in electronic health record data by comparing with trial results

For objective 1, two analyses were performed: (1) FP-SAL compared with no FP-SAL (for comparing with the TORCH trial3 FP-SAL vs. placebo analysis) and (2) FP-SAL compared with SAL only (for comparing with the TORCH trial3 FP-SAL vs. SAL analysis). The selection procedures for each of these analyses are detailed separately below.

FP-SAL exposed compared with unexposed analysis

Step 1: selection of all potentially eligible patients

An initial cohort was selected from all HES-linked patients actively registered in the CPRD between 1 January 2004 and 1 January 2017, who fulfilled the TORCH trial3 inclusion criteria (Box 1).3 The date that an individual met all inclusion criteria with at least 12 months prior registration in the CPRD was the ‘eligible for the TORCH trial’3 inclusion date.

Step 2: selection of pool of unexposed patients

Patients who had time periods in which they were unexposed to FP-SAL on or after the ‘eligible for the TORCH trial’3 inclusion date and who did not meet any of the TORCH trial3 drug exposure exclusion criteria (see Box 1) were selected (Figure 2).3 The start of follow-up date (i.e. the index date) for the unexposed time period was selected as a random date between the start and end of the unexposed period (see Figure 2). Individuals in CPRD were able to contribute more than one such unexposed time period to the total pool of unexposed time periods (see Figure 2) to avoid placing a restriction on a study entry that would not have existed if the potential participants were going to be recruited to a trial (i.e. they could have been recruited to a trial during any one of the eligible periods and we did not want to restrict to only one of these periods at this stage just because we were performing a study using data that had already been collected). Unexposed time periods were then removed from the cohort if the patient met any of the remaining TORCH trial3 exclusion criteria prior to the index date.3

FIGURE 2 Management of FP-SAL-exposed and FP-SAL-unexposed time periods in selection of people from the CPRD. Step 1: selection of all potentially eligible patients. Six example patients in CPRD. Green arrow = date at which individual met TORCH trial3 inclusion criteria, grey time periods = FP-SAL exposed, white time periods = FP-SAL unexposed. Step 2: selection of pool of unexposed patients. Unexposed time periods selected and exclusion criteria relating to drug exposures applied. An unexposed record index date is then assigned as a random date within each unexposed period (indicated by diamond symbols) and further TORCH trial3 exclusion criteria applied based on this date. In this example, one unexposed record from each of persons 1, 4 and 6 were excluded prior to step 3. Step 3: selection of unexposed-to-FP-SAL time periods by 1 : 1 matching to TORCH trial3 participants. Dotted red lines indicate matching. Matching characteristics assessed on index date of specific unexposed time period and only one time period per person could be matched to the TORCH trial.3 Step 4: selection of exposed-to-FP-SAL time periods and application of TORCH trial3 exclusion criteria. In this example, one exposed time period from persons 3 and 6 was excluded based on TORCH trial3 exclusion criteria. Step 5: selection of comparable FP-SAL-exposed participants. Pre matching there was one record per person for the FP-SA-unexposed cohort and one or more per person for the FP-SAL-exposed cohort. After matching there were an equal number of each. Exposed and unexposed records then followed up and analysed from index date onwards following an ‘intention-to-treat’ approach. Reproduced with permission from Wing et al.15 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by/4.0/. The figure includes minor additions and formatting changes to the original. (Figure not reproduced here; see the original article.)

Step 3: selection of unexposed-to-FP-SAL people by 1 : 1 matching FP-SAL time periods to TORCH trial3 participants

Each individual participant from the TORCH trial3 [obtained via www.clinicalstudydatarequest.com (accessed 28 May 2021), as described Setting/data sources] was matched 1 : 1 with the closest available unexposed-to-FP-SAL time period on the following TORCH trial3 baseline characteristics: age, sex, BMI, 1-year history of exacerbations requiring hospitalisation, history of cardiovascular disease and lung function [forced expiratory volume in 1 second (FEV₁)]. An individual could contribute only one unexposed period to the final TORCH trial-matched3 unexposed cohort (see Figure 2) and, therefore, the output of this step was a cohort of unexposed-to-FP-SAL people. This trial-matching step was performed to obtain an unexposed cohort that was as similar as possible to that in the TORCH trial.3

Step 4: selection of exposed-to-FP-SAL time periods and application of TORCH trial3 exclusion criteria

We identified all prescriptions for FP-SAL that started (1) on or after the initial ‘eligible for the TORCH trial’3 inclusion date (specified in step 1) and (2) at least 4 weeks after the end of a prescription for any of the TORCH trial3 drugs. FP-SAL-exposed time periods were created with the index date assigned as the start of a FP-SAL prescription. The same exclusion criteria as applied to the unexposed FP-SAL time periods (step 3) were applied. If an individual contributed time periods to both the unexposed (step 2) and exposed (step 4) cohorts, they were contributing different periods of their person-time to each cohort (pre-FP-SAL treatment for step 2 vs. post-FP-SAL treatment for step 4) (see Figure 2).

Step 5: selection of comparable FP-SAL-exposed participants by matching FP-SAL-exposed time periods to FP-SAL-unexposed people

Using the index date baseline characteristics, propensity scores for receiving FP-SAL were calculated for the (TORCH trial-matched3) FP-SAL-unexposed people selected in step 3 and the FP-SAL-exposed time periods selected in step 4. Each FP-SAL-unexposed (TORCH trial-matched3) person selected in step 3 was matched 1 : 1 with the FP-SAL-exposed time period from step 4 with the closest propensity score. We applied a matching without replacement approach, which meant that an individual could appear only once as an exposed participant in the final propensity score-matched cohort, meaning that this step selected FP-SAL-exposed participants from the initial pool of FP-SAL-exposed time periods. It was possible for the same person to be included in the FP-SAL-unexposed and FP-SAL-exposed cohorts, with different start of follow-up dates in each cohort. The matching of the (trial-matched) FP-SAL-unexposed cohort to the FP-SAL-exposed cohort was performed to obtain a FP-SAL-exposed cohort that was as comparable as possible to the (trial-matched) FP-SAL-unexposed cohort. Importantly, we did not apply matching to the TORCH trial3 to select our FP-SAL-exposed group because we wanted to develop propensity score methodology for obtaining balanced groups that could then be applied to the study of groups of patients who were not included in the trial (i.e. groups that we would never be able to find to match to in a trial because they were excluded from the trial) (see Objective 2: measurement of chronic obstructive pulmonary disease treatment effects in patients excluded from trials).

Selection of participants: FP-SAL-exposed participants compared with salmeterol-exposed participants

The participant selection approach was analogous to the FP-SAL-exposed compared with the FP-SAL-unexposed participant selection, except that the comparator group selected was those exposed to SAL (rather than those unexposed to FP-SAL). The resulting differences in participant selection were as follows. For step 1, the study period was from 1 January 2000 to 1 January 2017 (increased to ensure sufficient numbers of eligible SAL-exposed individuals). For step 2, instead of selecting unexposed-to-FP-SAL time periods occurring on or after the ‘eligible for the TORCH trial’3 inclusion date, we selected periods of SAL exposure. Individuals in the CPRD who had more than one SAL-exposed eligibility period within their record were able to contribute more than once to the pool of SAL-exposed participants (with the covariates and person-time contributed unique to the specific SAL-exposed eligibility period). The index date for each SAL-exposed record was the first date of the eligible SAL exposure period (i.e. the first day of the SAL prescription). All other aspects of step 2 and steps 3–6 were then as described for the FP-SAL-exposed compared with FP-SAL-unexposed participant selection (with SAL-exposed records in place of FP-SAL-unexposed records wherever mentioned).

Objective 2: measurement of chronic obstructive pulmonary disease treatment effects in patients excluded from trials

We selected separate cohorts of patients with a valid COPD diagnosis in the CPRD who would not have been eligible for inclusion in the TORCH trial3 (and, therefore, also not eligible for our objective 1) because of the following characteristics:

• aged > 80 years
• history of lung surgery
• history of long-term oxygen therapy
• evidence of drug/alcohol abuse
• an asthma diagnosis within the 5 years prior to study entry
• substantial comorbidity.

Separate cohorts were created and analysed for each of the specific characteristics listed, but in all other respects the people selected for each cohort met the TORCH trial3 criteria (see Box 1).

In relation to substantial comorbidity, the TORCH trial3 required people to be excluded from the trial if they had a serious uncontrolled disease with a likelihood of causing death within 3 years, and application of criteria based up TORCH trial3 criteria in objective 1 allowed us to select these people (although we recognise this criterion is subjective). Participants for each of the objective 2 cohorts were selected in a similar fashion to the objective 1 cohort, with the amended eligibility criteria specified above applied (i.e. step 1 was modified for selection of each of the objective 2 cohorts).

As for objective 1, each participant was allowed to have multiple FP-SAL-exposed and FP-SAL-unexposed eligibility periods in their record (as described in Figure 2). In contrast to objective 1, there was no matching of unexposed patients to TORCH trial3 patients, as we did not require a TORCH trial-analogous3 cohort for this analysis (i.e. no step 3). Instead, we were specifically putting together cohorts of people who were not included in the TORCH trial3 (and, therefore, would not be available or matching). All other selection steps were as applied for objective 1, including the use of propensity score matching to obtain comparable unexposed and exposed groups for analysis. This meant that our overall approach for objective 2 (and objective 3) was to apply the TORCH trial3 inclusion and exclusion criteria to both the unexposed and exposed groups, but modify the criteria according to the specific trial exclusion criteria that we were interested in including (e.g. for those aged > 80 years we would include only those aged > 80 years, but would still apply the other criteria detailed in Box 1). We would then skip the TORCH trial-matching3 step (as there were no people over the age of 80 years in the TORCH trial3), but would apply our propensity score matching approach to obtain comparable exposed and unexposed groups.

Objective 3: determination of treatment effects in an understudied disease stage

We selected separate cohorts of patients who had a valid COPD diagnosis in the CPRD and who would not have been eligible for inclusion in the TORCH trial3 (or our objective 1) because of having milder COPD than those recruited, as determined by spirometry. This cohort, therefore, included periods of time from people who had a COPD diagnosis and whose spirometry measurements were a > 60% predicted FEV₁ (vs. the TORCH trial3 requirement of a < 60% FEV₁) and/or a FEV₁/forced vital capacity (FVC) ratio of > 70% (vs. the TORCH trial3 requirement of a FEV₁/FVC ratio of < 70%). We are aware that study protocols often require the presence of obstructive spirometry (i.e. a FEV₁/FVC ratio of < 0.7) for identification of patients with COPD; however, based on previous validation work11 in the CPRD of the diagnosis of COPD and National Institute for Health and Care Excellence (NICE) guidance that recommends that clinicians should ‘think about a diagnosis of COPD in younger people who have symptoms of COPD, even when their FEV₁/FVC ratio is above 0.7’,5 (© NICE 2010 Chronic obstructive pulmonary disease in over 16s: diagnosis and management. Available from www.nice.org.uk/guidance/cg101. All rights reserved. Subject to Notice of rights. NICE guidance is prepared for the National Health Service in England. All NICE guidance is subject to regular review and may be updated or withdrawn. NICE accepts no responsibility for the use of its content in this product/publication.) our criteria for mild COPD will allow individuals to be included who have a diagnostic code for COPD in the CPRD and a FEV₁/FVC ratio of > 0.7.5,11

Exposures, outcomes and covariates

Exposures

For all objectives, exposures were determined using CPRD prescribing records and codelists for COPD treatments [codelists are available from https://datacompass.lshtm.ac.uk/1655/ (accessed 28 May 2021)].

For all objectives, being prescribed FP-SAL was the primary exposure of interest and the comparison exposure groups were (1) people not being prescribed FP-SAL and (2) people being prescribed SAL only. In addition to FP-SAL and SAL, periods of exposure to oral corticosteroids (OCSs), ICS, FP, any LAMA or any LABA were identified to facilitate application of the inclusion and exclusion criteria described in Selection of participants.

For all drug exposures, duration of an exposure period was derived by multiplying the CPRD quantity variable by any relevant dose information stored in the packtype variable and then dividing by the value in the numeric daily dose CPRD variable. For example, for a prescription record with quantity = 1, packtype = ‘60 dose inhaler’ and numeric daily dose = 2, the duration of the exposure period was (1 × 60)/2 = 30 days. For prescription records where it was not possible to calculate this exposure period (e.g. because of a missing quantity variable), the median value for that specific drug substance and packtype combination was imputed as the exposure duration. To attempt to account for any uncertainty in the end date of an exposure period (e.g. because of people not taking the medicine as directed or relying on additional medication previously prescribed and kept at home), a grace period of half the median duration for the specific drug substance/pack type combination was added to the calculated exposure duration to estimate the end date of the exposure period.

Outcomes

Outcomes were COPD exacerbation, all-cause mortality, pneumonia and time to treatment discontinuation, and these were defined as follows:

• COPD exacerbation – defined using a CPRD-HES algorithm that was developed previously by one of the co-authors of this study.11
• All-cause mortality – recorded in Office for National Statistics mortality statistics (i.e. data that are linked to CPRD data).
• Pneumonia – defined using a CPRD-HES algorithm that was published previously by authors of this study.12
• Time to COPD treatment discontinuation – treatment discontinuation classified as a period of ≥ 90 days with no further prescription for the specific drug.

Covariates

Covariates available for inclusion in the propensity score models included lung function, age, sex, alcohol consumption, vascular disease, prescriptions for aspirin or statins, prior treatment with other COPD medication, type 2 diabetes, history of cancer, renal disease and health-care utilisation (i.e. rate of consultations, hospitalisations, hospital procedures and drug prescriptions).

Handling missing data

Complete records analysis was applied, given the small numbers of missing data (only socioeconomic status, alcohol consumption or BMI had any missing data, and all were < 5% missing).

Sample size considerations

Objective 1

Assuming a baseline conservative exacerbation rate of 0.5 per patient per year,11 we required a sample of 408 patients per treatment group to detect a reduction in annual exacerbation rate of 0.4 per year, with 80% power and 5% significance. Our estimated sample size based on feasibility work assessing the number of people meeting TORCH trial3 inclusion criteria was ≈ 12,000, providing ample power for the main outcomes of interest but also allowing stratification by patient characteristics to determine stratified results. For example, to detect a reduction from 0.5 to 0.4 exacerbations per year with 80% power and 1% significance we would have needed ≈ 600 people in each treatment group.

Objectives 2 and 3

We were also confident that we would have sufficient numbers to allow well-powered analyses for objectives 2 and 3. For example, a feasibility count looking at the number of people aged > 80 years eligible for inclusion in objective 2 estimated that there would be > 2000 people in each exposure group.

We were aware that further application of TORCH trial3 exclusion criteria would lessen sample sizes further, but it was not possible to estimate the extent that this would happen from the data that were available to us prior to undertaking the study.

Blinding

Ascertainment of all outcomes was performed using pre-existing automated algorithms for detecting these outcomes in the CPRD (as detailed in Outcomes). Although the data management was performed separately for each drug exposure and, therefore, the person performing the analysis knew which exposure they were obtaining outcomes for at this stage, the code used was identical for each exposure and no edits to the code were permitted based on knowledge of exposure status.

Statistical analysis

Propensity score for addressing confounding

The propensity score for objective 1 was constructed using the principle that predictors of the exposure (i.e. FP-SAL) and outcome (i.e. exacerbations, mortality and pneumonia) or outcome only should be included. We considered a wide range of variables as the pool of initial variables for inclusion (as listed in Covariates) based on a priori knowledge of potential association with exposure or outcome, such as age, sex, BMI, alcohol consumption and a wide range of comorbidities (e.g. type 2 diabetes, coronary heart disease, cerebrovascular disease, peripheral vascular disease, heart failure, hypertension, renal disease and cancer). We also considered adjusting for health-care utilisation intensity (e.g. number of prior visits, hospitalisations, number of distinct medications used, number of procedures), as these are generic correlates of disease state and the likelihood of recording completeness. Our group has substantial prior experience of building propensity models.16–19

For us to then select variables for inclusion in the propensity score, we removed those variables from the pool of initial variables not associated with outcome in crude analysis before applying multivariable logistic regression (on drug exposure status) to generate propensity scores.1 Variables were selected for inclusion in the final propensity score multivariable logistic regression model using log-likelihood ratio tests (LRTs) for goodness of fit. Starting from an initial fully adjusted model that included all initial variables found to be associated with outcome, goodness of fit was tested after removing variables sequentially from the logistic regression model (starting with the variable most weakly associated with exposure in the fully adjusted model). Variables with a LRT p-value > 0.1 were removed from the model until all variables remaining in the model had a LRT p-value < 0.1. These remaining variables were the final variables that we used to calculate the propensity score. Separate propensity scores were developed in this way for each outcome. Standardised differences were used to assess any residual imbalances after matching (with a standardised difference > 0.1 indicating substantial/important imbalance).18

The variable list used for the propensity score model obtained in objective 1 was the basis for propensity score modelling in objectives 2 and 3, but additional variables from the pool of initial variables were also considered, given the different nature of the patient populations being studied in these objectives. We also assessed the impact of adjusting for the propensity score (rather than matching) for these analyses.

Methods of analysis

For all objectives, comparisons were made according to FP-SAL (or other drugs being analysed, as specified in Exposures) status for rate of COPD exacerbation, pneumonia and mortality over 3 years. All analyses were performed according to the ‘intention-to-treat’ principle (as was carried out in the TORCH trial3), meaning that if a participant entered the study as either an exposed or an unexposed participant then they would remain assigned to that exposure category for the entire duration of their follow-up (irrespective as to whether or not their true exposure status changes). For exacerbations, a negative binomial model was used, accounting for variability between patients in the number and frequency of exacerbations, with the number of exacerbations as the outcome and the log of treated time as an offset variable. Time to mortality and treatment change was analysed using Cox proportional hazards regression. Risk of pneumonia was analysed using Poisson regression. This mirrors the TORCH trial3 end points of major benefit and harm. We anticipated that the propensity matching process would allow us to assemble treated and untreated groups that were very similar with respect to baseline characteristics, except FP-SAL treatment status. However, this was tested by assessing standardised differences for each baseline variable. If substantial differences were noted for important variables, we made further adjustments to the statistical models. This could also include examining the effect of using a greedy matching approach (i.e. where once a match is made it is fixed) compared with an optimal matching approach (i.e. where the algorithm reconsiders all previously made matches before making a new match) to obtain the closest propensity score match and/or matching at a ratio other than 1 : 1.19

Validation of results against the TORCH trial3

Our findings were validated against the TORCH trial3 as part of objective 1 by determining whether or not results of the CPRD FP-SAL compared with no FP-SAL treatment analysis were compatible with the TORCH trial3 exacerbations rate ratio for FP-SAL compared with placebo [0.75, 95% confidence interval (CI) 0.69 to 0.81]. This outcome was selected because it is an outcome of key significance for people with COPD5 and the result in TORCH trial3 shows a clear benefit with 95% confidence limits of < 1. We set two criteria that needed to be met for us to conclude that results were consistent. First, the effect size needed to be clinically comparable with TORCH trial3 findings (i.e. the rate ratio for exacerbations in the CPRD had to be between 0.65 and 0.9). This range was deliberately not symmetrical around the TORCH trial3 estimate of 0.75, as we anticipated that the treatment effect in routine clinical care would be weaker than that seen in the optimised setting of a randomised trial. We recognised that this rule could be met with a poorly powered, inconclusive result, and so a second criterion was that the 95% CI for the rate ratio had to exclude 1. For the FP-SAL with SAL alone comparison (see Exposures), the 95% CI also needed to exclude 1 and the rate ratio had to be between 0.81 and 0.95 (compared with the TORCH trial3 FP-SAL vs. SAL result of 0.88, 95% CI 0.81 to 0.95).

Sensitivity analyses

Handling measurement of adherence to medication

We considered that adherence to issued prescribing in general practice is likely to vary according to the treatment issued. For example, short-course antibiotic treatment is notoriously not well adhered to, whereas long-term life-saving treatment, such as antiretroviral medication, is more likely to be taken as prescribed. Although we were not aware of published figures for adherence for COPD medication in UK general practice, we reviewed the records for a random sample of 30 people with COPD starting treatment with FP-SAL to look at adherence patterns over the course of 1 year. Of 30 patients, 20 (67%) were still receiving seretide (FP-SAL) 1 year after starting treatment. Of the 20 patients who received seretide for a full year, 15 (75%) received sufficient prescriptions to suggest at least 50% adherence over the year and eight (40%) had sufficient prescriptions to suggest ≥ 80% adherence. As expected, we considered that this suggested that (1) adherence is likely to be poorer in routine clinical care than in the trial population (in the TORCH trial3 80% of participants were estimated to have adherence at ≥ 80%) and (2) there is a wide range of adherence in routine care. Although we acknowledge that prescribing can be only a proxy for used medication, we believed that it was not an unreasonable assumption that the amount of medication prescribed would be correlated with the amount consumed. We aimed to assess adherence for the cohort that we select for objective 1 beyond 1 year and report the findings. In the event that our analyses in objective 1 detected a null or poorer treatment effect than anticipated, we planned to conduct a sensitivity analysis restricted to people estimated to be covered by FP-SAL treatment for 80% of their follow-up.

Misclassification of (1) drug exposure periods and (2) outcome status

We considered that it would be possible that an individual may still be exposed to FP-SAL for some time after a prescription has finished (e.g. if they have medication at home that they have not used from a previous prescription). This would mean that people may become eligible for inclusion in the unexposed group while they are actually still exposed. We planned to conduct a sensitivity analysis if our results from objective 1 differed from the TORCH trial3 results. In this analysis, we would include an additional (grace) exposed period that was equivalent to the length of a single prescription at the end of each actual exposed period and only classify individuals as eligible for inclusion as unexposed at the end of this additional period.

Misclassification of outcome can also have an impact on our results, given the routine nature of the data. Our initial approach for the detection of COPD exacerbations was to use a validated case definition from previous work that maximises positive predictive value while maintaining a relatively high sensitivity.13 We therefore planned to perform a sensitivity analysis in which we assessed the impact of applying the alternative case definitions for COPD exacerbations from this publication if our objective 1 results differed from the TORCH trial.3

Safety reporting and disclosure

As this study was a non-interventional study that used stored electronic health data in the UK CPRD (with no recruitment of patients or intervention), there was no requirement for safety reporting or disclosure.

Deviations from original protocol

• In objective 3, when looking at people with milder COPD, as defined by spirometry, we originally specified that we would also look at people with no exacerbations at all in the year post COPD diagnosis. However, we decided not to analyse this outcome because we were already looking at a milder group, as defined by spirometry, and had limited time, as we effectively had to perform the main data management and analysis steps twice (i.e. once for the comparison of FP-SAL with no FP-SAL and then once for the comparison of FP-SAL with SAL because the FP-SAL and no FP-SAL comparison produced results that were different from the TORCH trial3).
• For objective 2, there were a number of subgroups we planned to analyse, but were unable to because of small numbers in these groups. These subgroups were people with a history of lung surgery, people with a history of long-term oxygen therapy, people with substantial comorbidity or people with evidence of drug/alcohol abuse. See Chapter 3, Note on results presented in results and discussion part 2 for an overview of the actual numbers available for analysis from these groups.
• For objectives 1–3, in the protocol there were secondary analyses specified where we would repeat each analysis comparing COPD treatments other than FP-SAL with no treatment (e.g. no treatment vs. LABA, LAMA, LABA + LAMA, LABA + ICS other than FP-SAL and LABA + LAMA + ICS). We did not perform these secondary analyses comparing these exposures with no treatment because of the amount of work we had to do in repeating our primary analyses (i.e. FP-SAL vs. no FP-SAL followed by FP-SAL vs. SAL), but have specified comparing these treatments with SAL as future research (see Chapter 4, Prioritised list of recommendations for future research). However, the specific comparisons would need updated based on updated NICE guidance20 published since the start of the project.

Patient and public involvement

We invited four patient and public representatives to be involved and advise on the project [via Breath Easy, www.blf.org.uk/support-for-you/breathe-easy (accessed 1 June 2021)], one of whom accepted our invitation and attended each of the four Steering Group meetings that took place during the project. During these meetings, the patient and public involvement representative provided feedback on whether or not the results we were presenting were clear and understandable, provided feedback on our plans on next steps based on results and provided insight on aspects of COPD treatment from a COPD patient’s perspective (e.g. in relation to how a patient might adhere to/not adhere to COPD medication, and how a patient might typically go about managing COPD medications, which had been prescribed in UK primary care, at home).