The target trial that we plan to emulate in this study is a multicenter open-label two-parallel arm superiority randomized trial. The intervention of interest is stereotactic ablative radiotherapy (SABR) within 3 months of a stage IA NSCLC diagnosis. The control group is the standard surgical procedure of video-assisted thoracoscopic surgical lobectomy (VATS) within the same time-period after diagnosis. The primary outcome will be overall survival 1-year and 5-years after diagnosis. The research question is outlined using the PICO (Population, Intervention, Comparison and Outcome) structure (Table 1).
Table 1
Key components of the research question addressed in the protocol.
Component | Description |
Population | NSCLC patients 18 years old and older diagnosed at stage IA |
Intervention | SABR radiotherapy treatment within 3 months after diagnosis |
Comparator | Video-assisted Lobectomy within 3 months after diagnosis |
Outcomes | All-cause mortality (after 1 and 5 years) |
Data collection
Necessary data for our emulated target trial design will be determined through a non-parametric graphical model, also known as a DAG (directed acyclic graph). Using this graphical tool, we will be able to visualize the causal relationship between the exposure (SABR vs VATS) and the outcome (survival), by identifying potential confounders and mediators of the causal effect to be measured. The DAG was created through several discussions among our team of experienced radiation oncologists, surgeons, epidemiologists, and biostatisticians. The associations demonstrated in our graph were also based on published literature, current treatment guidelines, and previous clinical studies like the STARS and ROSEL trials.
According to our DAG, patient characteristics such as age, comorbidities, performance status, hospital volume, and the level of socioeconomic deprivation at the time of diagnosis were all factors that affected both the treatment to be received by the patient and their chances of survival. Therefore, these factors were regarded as our baseline confounders.
Hospital volume was considered a confounder due to the influence of VATS’s steep learning curve on the quality of treatment being delivered. Studies have reported that up to 60 operations are necessary for optimal performance of VATS resections (20, 21). VATS performed in low-volume hospitals were also found to be associated with significant postoperative morbidity and 90-day mortality (22, 23).
On the other hand, chances of receiving adjuvant treatment or experiencing post-operative toxicities are influenced by the treatment strategy the patient has received. Surgery in stage I–II NSCLC has a major advantage, over SABR, in its ability to invasively stage lymph nodes, thereby allowing adjuvant chemotherapy to be administered if nodal metastasis was discovered (24). In comparison, SABR treated patients often have to rely on additional diagnostic tools, such as Endobronchial ultrasound bronchoscopy (EBUS), to evaluate the level of nodal involvement after an initial clinical assessment. Lacking the accessibility of surgery, a false-negative EBUS might eventually lead a proportion of SABR treated patients to continue harboring the disease and therefore, experiencing more frequent regional recurrences. A PET/CT (positron emission tomography-computed tomography) scan performed prior to SABR, however, has been shown to mitigate that risk (25). Another point of concern is post-operative toxicities. A systematic review, published in 2008, found that 16% of patients receiving VATS reported complications (26). In 2012, Paul et al. reported an even higher proportion (40%) being affected by postoperative complications (27). In comparison, SABR was found to have a 0.7% cumulative procedure-related mortality (28). These two factors, toxicities and adjuvant therapy, were therefore considered mediators (intermediators between the exposure and outcome) given their effect on cancer survival.
After creating the DAG, we identified a minimal sufficient adjustment set to estimate the total effect of SABR on overall survival using online software DAGITTY (version 3.0) (29). The adjustment set included: age, comorbidities, performance status, hospital volume, and the socioeconomic deprivation level (Fig. 1).
Data source
In compliance with the Cancer Screening and Registry Act (Krebsfrüherkennungs- und -registergesetz (KFRG)), information on cancer patients will be collected from routinely collected hospital data supplemented with cancer registry data from the German states of North Rhine-Westphalia (18 million population) and Saxony-Anhalt (2.2 million population).Participating university hospitals will receive data of patients treated in the respective centers from the registries, as mandated by regional cancer registry law, provided that at least one case report for the patient has been submitted by the reporting clinic to the cancer registry and no objection has been made by the patient. If no case report has been submitted for a patient, even if they were treated at the university clinic, we will not be able to receive data for that case. We will receive anonymized data from the hospital, making it unlikely to extract individual information from the data received.
Information from the cancer registry data will include information on demographics such as sex, age at diagnosis, and administrative district of residence at time of diagnosis, as well as data about the tumor at the time of diagnosis, including date of diagnosis, anatomic location of tumor (topography) and morphology, and tumor grading and stage. Additionally, the data will include information on delivered treatments, death events, and cause of death for deceased cases. Demographic data linked to treatment and diagnostic information will only be accessible through the reporting clinic and limited to patients who have been treated there.
The participating university clinics will provide information on comorbidities, diagnostic tools used for staging, and performance status. We will also request the clinics to provide the rationale behind the treatment decision. The full list of variables that will be collected is available in the Supplementary attachment.
Study population and eligibility criteria
The planned inclusion and exclusion criteria are as follows:
Inclusion criteria:
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Age 18 years or more
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Non-small cell lung cancer determined histologically
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Stage IA diagnosis defined by any combination of T1a,N0,M0, T1b,N0,M0, and T1c, N0, M0.
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PET/CT scan is required to confirm staging and nodal involvement.
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Performance score of Karnofsky > 60% or ECOG score < 2 before any treatment.
Exclusion Criteria:
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Direct evidence of regional or distant metastases
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Synchronous primary or prior malignancy in the past 3 years other than nonmelanomatous skin cancer or in situ cancer.
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Previous lung or mediastinal radiotherapy.
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Major surgery within the past 1 year.
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Serious medical comorbidities or other contraindications to SABR or VATS.
Briefly, the study population will include non-small cell lung cancer patients aged 18 years or more, diagnosed at stage IA from the linked dataset, between 2016 and 2021, with no record of previous malignancy in the past 3 years. In emulating a target trial, the eligibility criteria should adhere to the positivity assumption, ensuring that every patient has a non-zero probability of receiving either treatment option. This means that all included patients have to be eligible for both treatment strategies, SABR and VATS, before treatment assignment.
Diagnosis of LC will be based on the tenth edition of the International Classification of Diseases (ICD-10: C34) that are used by the cancer registry. Similarly, the non-small-cell variant of LC will be determined by the morphology codes corresponding to the following subtypes (Squamous cell carcinoma, adenocarcinoma, large cell carcinoma, unspecified, and other specified). Stage IA will be defined through the following combinations of clinical TNM (tumor-node-metastasis) staging: T1a, N0, M0, T1b, N0, M0, or T1c, N0, M0. Confirmation that a PET/CT scan was performed will be required to confirm stage and nodal involvement in all patients. This information will be provided by the participating clinics.
Eligibility to both treatments will be also based on information recorded by the clinics. Only patients with a performance score of Karnofsky > 60% (ECOG (Eastern Cooperative Oncology Group) < 2) will be included in our study. In addition, patients with recorded serious medical comorbidities or other contraindications to SABR or surgery will be excluded. Finally, patients with previous history of major surgeries or radiotherapy will not be included in our study.
Treatment strategies and assignment procedures
In our two-arm open label study design, the intervention arm will consist of SABR treated patients whereas patients receiving VATS will be in the control group. All individuals will enter the study at the time of diagnosis (time 0). As the date of treatment initiation does not usually coincide with the start of follow-up (time 0), we will allow a 3-month “grace period” for treatment to begin to include individuals in both arms. This grace period will help us account for potential immortal time bias arising from the possible unequal waiting periods experienced by patients receiving SABR versus surgery.
VATS treated patients will be identified through the German Operation and Procedure Key (OPS) codes (OPS code: 5-324.6 ff). SABR on the other hand, will be defined using the “single dose”, “total dose”, and “date of radiotherapy” variables. Patients receiving radiation doses of 5 or more Gys (Grays) in 10 or fewer fractions will be included in our intervention arm. The registry data should show that SABR (or VATS) was initiated during the three-month grace period with a “curative” intent. Patients that did not begin SABR or have not yet undergone VATS within the first three months after diagnosis will not be considered in our analysis.
Randomization will be emulated by cloning two exact copies of each patient with one clone allocated to each study arm, hence doubling the size of our dataset. By doing so, we will assume that all patients were equally likely to be offered SABR or VATS, independent of the actual treatment they will subsequently receive. Furthermore, cloning the patients ensures that the study arms are identical at baseline, with regard to demographics and clinical characteristics at the time of diagnosis. Therefore, a patient who, according to the registry data, has received surgery will be included in both arms, the intervention and control. The same procedure applies to patients who received SABR, they will be included in both arms as well. As a result, each patient will become his/her control and by doing so, we will be controlling for confounding bias at baseline.
Outcomes and Follow-up period
For a target trial emulation to be successful, it is crucial to accurately define the baseline, or time zero, when follow-up begins in the observational data. This is when the eligibility criteria are met, and from which point study outcomes are measured. In our target trial, follow-up naturally starts when a treatment strategy is decided upon, typically aligning with or just before treatment begins. Properly aligning eligibility verification, treatment assignment, and follow-up commencement is essential to avoid flawed conclusions. Our three-month grace period provides the necessary leeway to accommodate delays in treatment start, ensuring uniform initiation times across all study arms and preserving the validity of our comparisons.
Our primary outcome is overall survival after 1 and 5 years. Overall survival will be calculated until the date of death from any cause or until the end of follow-up, whichever comes first. Vital status is ascertained using death certificates and information from the registration offices. Patients lost to follow-up before death or still alive at the last vital status assessment will be right-censored at the date of the last vital status assessment or at the censor date, whichever came first. Secondary outcome measures will include cancer-specific survival and recurrence-free survival. Cancer-specific survival will be calculated up to the date of lung cancer-associated death, which in this case will be defined with the ICD-10 codes (C33-C34) under the “cause of death” variable recorded by the cancer registry data. Recurrence-free survival will be calculated up to the date of first recurrence (local, regional, or distant) or death, whichever occurred first.
Statistical analysis plan
Demographic and clinical characteristics according to study arm will be described using common descriptive statistics. A flow chart illustrating the number of individuals assigned to each treatment arm, those who follow the protocol, and those who are censored or excluded, and reasons for exclusion will be included in our final analysis.
The effect of SABR versus VATS within 3 months of diagnosis on survival will be measured through the differences between the study arms in: (i) 1-year and 5-year survival probabilities; and (ii) restricted mean survival times (RMSTs over a 1-year and 5-years window).
After cloning our sample at baseline, we will then proceed to censor a clone when the treatment actually received by the patient is no longer compatible with the treatment strategy of the arm they entered. For example, a patient who according to the registry data received surgery, will be censored at the time of surgery in the SABR treatment arm (where they are included as a clone). This will also apply for patients receiving SABR.
This artificial censoring induces informative censoring (i.e. selection bias over time), as described by Hernan, since treatment received typically depends on individual characteristics and is not random. To account for this introduced selection bias, we use the inverse-probability-of-censoring weights (IPCW) method where uncensored observations are up-weighted to represent censored observations with similar characteristics and thus to allow the unbiased estimation of the causal effect of interest. A standard approach to estimate the weights is to predict the individual probabilities of remaining uncensored at each time of event using a Cox regression model.
For the per-protocol analysis, we will focus exclusively on patients who completed the full course of SABR treatment, providing a more precise measure of its efficacy. This analysis will exclude patients who initiated but did not complete SABR or switched to surgery, thereby isolating the effect of complete adherence to the SABR protocol.
In parallel, we will conduct an analysis that approximates an intention-to-treat approach, including all individuals who initiated SABR within the 3-month grace period, regardless of subsequent treatment changes. This will offer an observational counterpart to the intention-to-treat principle used in randomized controlled trials.
Survival curves will be estimated in each arm using a weighted nonparametric Kaplan-Meier estimator. We will calculate the 95% confidence intervals for the difference in 1-year and 5-year survival and difference in RMSTs using non-parametric bootstrap with 1000 replicates.
All analyses will be performed using R (30).
Missing Data
To handle missing data in our proposed analysis, we will use a multiple imputation fully conditional approach for all confounders with missing values (31). This method involves specifying conditional regression models for each missing value in a variable, based on the values of the other variables in the imputation model. We will follow this approach to minimize any potential bias that may arise due to missing information.
Subgroup and sensitivity analysis
While VATs is considered as the gold standard treatment for stage IA, we will be also be interested in observing the proportion of patients receiving other surgical procedures for stage IA NSCLC treatment. Previous studies have shown that less invasive procedures such as segmentectomy and wedge resection are better tolerated than lobectomy and may improve survival (32). As a subgroup analysis, we could compare overall survival of patients receiving lobectomy, wedge resection, and segmentectomy to our intervention of interest, SABR.
Sensitivity analyses will be conducted to assess the effects of decisions made during data cleaning and variable transformation. A quantitative bias analysis will be conducted to evaluate the potential impact of unmeasured confounders, providing a more comprehensive understanding of bias than the E-value alone (33). Although E-values will be included to measure the possible extent of unmeasured confounding on our estimates and their confidence intervals, we recognize the limitations of relying solely on this metric and will ensure a broader analysis is undertaken (34). Additionally, the influence of varying the grace period length will be examined, considering a 6-month window to determine its effect on the study outcomes.
Given the potential for misclassification of the cause of death in patients with multiple metastases, especially in cases where the cancer primary site is unknown (e.g., ICD-10 code C80), it is essential to address this in our sensitivity analysis. The analysis will explore how different classifications of the cause of death, particularly the miscoding or shifting to C80 in the death certificate, might influence the results of cancer-specific survival for lung cancer.