Waiting time paradox in the effects of time to diagnosis or treatment on mortality
We read with great interest the systematic review and meta-analysis conducted by Hanna et al. quantifying the mortality risk due to cancer treatment delay based on 34 studies for seven common cancers . Their work indicates that even prior to the Covid-19 pandemic, a four-week delay in commencing cancer treatment could impact survival, raising concerns of how delays caused by the present Covid-19 pandemic may lead to worsening cancer outcomes [2-5].
Cancer treatment delays have been frequently studied and the authors are to be commended for considering the ‘waiting time paradox’ in the analysis . The waiting time paradox, the effect that patients with more severe symptoms and a poor prognosis are referred early, is an important source of bias in studies investigating the impact of diagnostic and treatment delays on cancer survival [6-8]. Such an effect may be more evident in research on cancers that lack early symptoms and/or signs (like pancreatic or lung cancer), as mixed associations (positive, no and negative associations) with survival have been reported [6,8-11].
The waiting time paradox particularly is present in studies investigating diagnostic intervals (like time from symptom onset to diagnosis), but is still relevant in studies addressing the time from diagnosis to the first cancer treatment as studied by Hanna et al. . For example, sicker patients may receive emergency treatment shortly after diagnosis and die more quickly after cancer treatment. There is potentially an additional effect relating to the optimal timing of subsequent treatment. Patients who receive the next treatment before complete recovery from surgery may have a higher mortality risk .
Hanna et al. attempted to address the issue of the waiting time paradox by including only studies which conducted specific sub-analyses and/or had inclusion/exclusion criteria for cancer patients with short intervals (e.g., “less than four weeks”) or poor outcomes (e.g., “death within four to eight weeks of diagnosis”) .
We suggest that such an approach is imperfect for primary studies examining the effects of time to diagnosis or treatment on mortality. Instead, more recent analytic approaches such as restricted cubic spline regression may be better as it accounts for non-linear associations between time and mortality [12,13]. In this manner, all patients with different intervals are analysed as a whole and the effect of the waiting time paradox can be identified when the mortality risk presents a U-shape association. The issue remains though of how best to account for the waiting time paradox in meta-analyses. The approach applied by Hanna et al. was pragmatic but probably does not account for it fully. Meta-regression approaches should be developed that address the waiting time paradox and which might better identify optimal times to diagnosis and treatment of cancer.
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Competing interests: No competing interests