Standardized methods needed to measure lag time between cancer diagnosis, treatment
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A review of established associations between cancer waiting times and treatment outcomes has identified discrepancies in the measurement and reporting of lag times between diagnosis and treatment.
Researchers emphasized the need for updated, more standardized methods of lag time measurement to account for different time points in the cancer care continuum and use of newer treatments.
“We discovered before the study that there really wasn’t much out there [on lag times between diagnosis and treatment] so we decided to systematically assess the literature; initially, we were hoping to do a meta-analysis to assess how lag time impacts oncologic outcomes,” Parker Tope, MSc, research assistant in the division of cancer epidemiology at McGill University in Montreal, Canada, told Healio. “However, the lack of standardization precluded our ability to assess these lag times in such a way. So instead, we decided to map what is known and clearly identify the clinical and statistical gaps in the literature.”
Tope spoke with Healio about the findings of the study, published in eLife, and the importance of understanding lag times in cancer care delivery in the wake of the COVID-19 pandemic.
Healio: What initially inspired you to study lag times between cancer diagnosis and treatment?
Tope: I had been part of the COVID-19 and Cancer Task Force at McGill University. As part of this team in the department of oncology, we discovered many clinician researchers were starting to conduct their own studies evaluating how pandemic-induced lag times were impacting oncologic outcomes. So, we felt it was necessary to really look at what had been done in this area previously.
After we reviewed what is known and assessed gaps in the literature, we concluded that more standardization is needed in reporting so that cancer epidemiologists can assess and pool these results to detect meaningful differences.
We found a lack of clarity in how certain endpoints were reported. For example, for meta-analyses including studies that used cancer registries for their study populations, the timing of whether or not a patient is starting treatment or is being coded as starting treatment did not necessarily align with when they truly started treatment. Further, it is not always clear whether a visit to a primary care provider is the diagnostic date or whether that diagnostic date is later.
Another inconsistency has more to do with how lag time as an exposure was treated statistically. In regression modeling, you can either choose to classify your exposure categorically or continuously. Time is continuous. However, for a lot of these studies, they used predefined cutoffs that had been chosen in the 1980s and ’90s, when some of these systemic therapies had been really burgeoning in the oncology community. They were choosing cutoffs of, for example, 7 weeks after diagnosis or 7 weeks before surgery to define whether a lag time was early or late. So, we felt that categorization needs to be questioned, because there can be statistical differences in how lag time impacts oncologic outcomes based on the time points that are chosen.
Lastly, the studies we looked at did not always take into account the disease stage or prognosis of individual patients and whether this had an impact on lag time between points of care.
Healio: How has the COVID-19 pandemic affected lag times in cancer care delivery, and why is it important to understand these lag times in relation to COVID?
Tope: One reviewer really stressed that point throughout the peer-review process. We provided this map and this resource to researchers during the pandemic, but the map only reflects the known impact of lag time on oncologic outcomes prior to the pandemic. During the pandemic, disruptions and changes in standard of care and the health system flow have had massive effects as far as which patients are prioritized and how the treatments they undergo impact outcomes.
Healio: What did your team conclude needs to be done to standardize reporting on cancer care lag times?
Tope: What has become more and more common are sets of guidelines and recommendations on reporting specifically. We believe changes should be made specifically to the Aarhus statement, which is a set of recommendations and checklists related to how to best conduct research on lag times in cancer diagnosis.
Obviously, there are guidelines for how we should be conducting a study, depending on what exposure or study population a research team evaluates. In regard to reporting these guidelines, research on lag times should be streamlined so that comparisons can be made within and across countries, as well as other population-level contexts. The Aarhus statement could be expanded to include lag times across the entire cancer care continuum, not just diagnosis. Personalized medicine is becoming increasingly common within oncology, and this opens up another area of study that should be implemented into these reporting guidelines.
For more information:
Parker Tope, MSc, can be reached at Division of Cancer Epidemiology, McGill University, 100 Maisonneuve Blvd. West, Suite 720, Montreal, Quebec H4A 3T2; email: parker.tope@mcgill.ca.
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