A systematic review of methodological considerations in time to diagnosis and treatment in colorectal cancer research

Research focusing on timely diagnosis and treatment of colorectal cancer is necessary to improve outcomes for people with cancer. Previous attempts to consolidate research on time to diagnosis and treatment have noted varied methodological approaches and quality, limiting the comparability of findings. This systematic review was conducted to comprehensively assess the scope of methodological issues in this field and provide recommendations for future research. Eligible articles had to assess the role of any interval up to treatment, on any outcome in colorectal cancer, in English, with no limits on publication time. Four databases were searched (Ovid Medline, EMBASE, EMCARE and PsycInfo). Papers were screened by two independent reviewers using a twostage process of title and abstract followed by full text review. In total, 130 papers were included and had data extracted on specific methodological and statistical features. Several methodological problems were identified across the evidence base. Common issues included arbitrary categorisation of intervals (n = 107, 83%), no adjustment for potential confounders (n = 65, 50%), and lack of justification for included covariates where there was adjustment (n = 40 of 65 papers that performed an adjusted analysis, 62%). Many articles introduced epidemiological biases such as immortal time bias (n = 37 of 80 papers that used survival as an outcome, 46%) and confounding by indication (n = 73, 56%), as well as other biases arising from inclusion of factors outside of their temporal sequence. However, determination of the full extent of these problems was hampered by insufficient reporting. Recommendations include avoiding artificial categorisation of intervals, ensuring bias has not been introduced due to out-of-sequence use of key events and increased use of theoretical frameworks to detect and reduce bias. The development of reporting guidelines and domain-specific risk of bias tools may aid in ensuring future research can reliably contribute to recommendations regarding optimal timing and strengthen the evidence base.