Skip to main content
Download PDF

Timing of adjuvant chemotherapy initiation and mortality among colon cancer patients at a safety-net health system

BMC Cancer volume 22, Article number: 593 (2022) Cite this article

Abstract

Background

Prior studies reported survival benefits from early initiation of adjuvant chemotherapy for stage III colon cancer, but this evidence was derived from studies that may be sensitive to time-related biases. Therefore, we aimed to estimate the effect of initiating adjuvant chemotherapy ≤8 or ≤ 12 weeks on overall and disease-free survival among stage III colon cancer patients using a study design that helps address time-related biases.

Methods

We used institutional registry data from JPS Oncology and Infusion Center, a Comprehensive Community Cancer Program. Eligible patients were adults aged < 80 years, diagnosed with first primary stage III colon cancer between 2011 and 2017, and received surgical resection with curative intent. We emulated a target trial with sequential eligibility. We subsequently pooled the trials and estimated risk ratios (RRs) along with 95% confidence limits (CL) for all-cause mortality and recurrence or death at 5-years between initiators and non-initiators of adjuvant chemotherapy ≤8 or ≤ 12 weeks using pseudo-observations and a marginal structural model with stabilized inverse probability of treatment weights.

Results

Our study population comprised 222 (for assessing initiation ≤8 weeks) and 310 (for assessing initiation ≤12 weeks) observations, of whom the majority were racial/ethnic minorities (64–65%), or uninsured with or without enrollment in our hospital-based medical assistance program (68–71%). Initiation of adjuvant chemotherapy ≤8 weeks of surgical resection did not improve overall survival (RR for all-cause mortality = 1.04, 95% CL: 0.57, 1.92) or disease-free survival (RR for recurrence or death = 1.07, 95% CL: 0.61, 1.88). The results were similar for initiation of adjuvant chemotherapy ≤12 weeks of surgical resection.

Conclusions

Our results suggest that the overall and disease-free survival benefits of initiating adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection may be overestimated in prior studies, which may be attributable to time-related biases. Nevertheless, our estimates were imprecise and differences in population characteristics are an alternate explanation. Additional studies that address time-related biases are needed to clarify our findings.

Peer Review reports

Introduction

Adjuvant chemotherapy following surgical resection of colon cancer is intended to eliminate micrometastatic disease and increase overall and disease-free survival [1]. Evidence from randomized controlled trials (RCTs) established that adjuvant chemotherapy increased survival among stage III colon cancer patients, [2,3,4] and thus adjuvant chemotherapy for stage III colon cancer is standard of care. In addition, evidence suggests that early initiation of adjuvant chemotherapy may improve survival among colon cancer patients, [5,6,7,8] and timing of adjuvant chemotherapy initiation is included in quality of care guidelines [9, 10]. Nevertheless, the evidence for timing of adjuvant chemotherapy was derived from observational studies, which may be sensitive to time-related biases [11, 12].

In contrast to RCTs, where the design ensures alignment of key elements such as treatment allocation, eligibility, and follow-up at a common starting point (i.e., time zero) to minimize time-related biases, this alignment requires additional methodologic considerations in observational studies [11, 12]. For example, prior studies [5,6,7,8] included comparisons of survival between patients who initiated adjuvant chemotherapy ≤8 weeks and patients who initiated adjuvant chemotherapy > 8 weeks following surgical resection. Such comparisons are problematic because patients must survive a specified duration before initiating treatment, and analyses that do not properly account for this duration will incur immortal time bias [11, 13,14,15,16,17]. Several studies have illustrated severe overestimation of treatment effects on survival in observational studies of cancer patients, which was attributable to immortal time bias [18,19,20]. Immortal time bias has not been addressed in the context of timing of adjuvant chemotherapy initiation for colon cancer survival and could have implications for current quality of care guidelines. Therefore, we aimed to estimate the effect of initiating adjuvant chemotherapy for stage III colon cancer within an interval of interest (8 or 12 weeks) following surgical resection on survival using a study design that helps align time zero and reduce immortal time bias.

Methods

Study population

We used institutional registry data from JPS Oncology and Infusion Center (JPS), an accredited Comprehensive Community Cancer Program. The center is part of an urban safety-net health system, which is the primary source of care for socioeconomically marginalized populations in Tarrant County, TX. Eligible patients were diagnosed with first primary stage III colon cancer between 2011 and 2017, aged 18–79 years at cancer diagnosis, received surgical resection with curative intent, and received at least part of the first course treatment at JPS. We excluded patients for whom adjuvant chemotherapy was contraindicated.

Variables

Our primary outcome of interest was 5-year overall survival (i.e., complement of all-cause mortality) and secondary outcome of interest was 5-year disease-free survival (i.e., complement of recurrence or mortality [21]). These outcomes were selected because adjuvant chemotherapy is intended to improve overall and disease-free survival [1]. In addition, the American Society of Clinical Oncology statement about clinically meaningful outcomes defines overall survival as the primary outcome of interest [22]. Our exposure (intervention) of interest was initiation of adjuvant chemotherapy within 8 or 12 weeks of surgical resection (i.e., initiators vs. non-initiators within 8 weeks in one analysis and initiators vs. non-initiators within 12 weeks in a separate analysis). We also extracted baseline information from the registry including age at diagnosis, sex, race/ethnicity, insurance coverage, marital status, comorbidities, body mass index, tumor grade, and surgical procedure.

Data analysis

We emulated a sequence of observational “trials,” [11, 23,24,25] where study eligibility criteria were applied and intervention status was defined within a sequence of trials based on 2-week intervals through 8 or 12 weeks from surgical resection. One exception was that the interval for the first trial was 4 weeks because no one initiated adjuvant chemotherapy within 2 weeks of surgical resection. Baseline (i.e., time zero) for the first trial was the date of surgical resection. For the first trial, patients were classified as initiators if adjuvant chemotherapy was initiated ≤4 weeks (i.e., 28 days) of surgical resection, and as non-initiators if adjuvant chemotherapy was not initiated ≤4 weeks. We applied the eligibility criteria and initiator definition for sequential 2-week intervals, where time zero for each trial was the beginning of each interval. Consequently, patients could have been eligible for up to three trials for evaluating initiation ≤8 weeks and five trials for evaluating initiation ≤12 weeks of surgical resection but were no longer eligible for subsequent trials if adjuvant chemotherapy was initiated, the patient died, had recurrence (for disease-free survival), or were lost to follow-up in a previous trial.

We subsequently pooled data from all three (for evaluating initiation ≤8 weeks of surgical resection) or five trials (for evaluating initiation ≤12 weeks of surgical resection), which allowed for reducing variance [11, 23,24,25]. We fit a logistic regression model to compute stabilized inverse probability of treatment weights (IPTW) [26] for adjuvant chemotherapy initiation. Stabilized IPTW were based on a minimal sufficient set of covariates to reduce confounding bias identified using the back-door criterion in a directed acyclic graph of dependency assumptions [27,28,29] between adjuvant chemotherapy initiation and mortality or recurrence. The minimal sufficient set of covariates included baseline measurements of age, sex (male or female), race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, or non-Hispanic other), insurance coverage (uninsured without JPS Connection [a hospital-based medical assistance program for eligible individuals without insurance], uninsured with JPS Connection, or commercial/public insurance), marital status (single/never married, married, or divorced/separated/widowed), comorbidities classified by the National Cancer Institute [30] (0 or > 0), body mass index (BMI; BMI < 25, 25 ≤ BMI < 30, or BMI ≥ 30), tumor grade (well/moderately differentiated or poorly differentiated/undifferentiated), and surgical procedure (partial colectomy/segmental resection or hemicolectomy/subtotal/total colectomy). The standardized mean differences for covariates between initiators and non-initiators of adjuvant chemotherapy did not suggest meaningful imbalance after weighting except for one category of marital status in the 8-week analysis (Supplementary Table S1 and S2) [31].

We adjusted Kaplan-Meier estimators using stabilized IPTW [32] to generate marginal overall and disease-free survival for initiation and no initiation of adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection. For 5-year overall survival, patients were followed from date of surgical resection until death, loss to follow-up, or end of study, whichever occurred first. For 5-year disease-free survival, patients were followed from date of surgical resection until recurrence, death, loss to follow-up, or end of study, whichever occurred first. We also applied these weights in generalized linear models with pseudo-observations [33, 34] to construct marginal structural models [35] for estimating risk ratios (RR) for all-cause mortality and recurrence or death at 5 years comparing initiators and non-initiators of adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection. We estimated 95% confidence limits (CL) for RRs using clustered standard errors to account for repeated eligibility. Unlike Cox proportional hazard regression, the pseudo-observation approach does not require the proportional hazards assumption and allows estimating effect measures other than the hazard ratio, which is widely misinterpreted, has built-in selection bias, and no causal interpretation [36,37,38,39,40]. We estimated RR to provide a direct comparison of risk, which is easier to interpret than hazard [36, 38, 40].

Results

Our analyses were based on 222 evaluable observations after pooling 3 sequential trials for evaluating initiation ≤8 weeks and 310 evaluable observations after pooling 5 sequential trials for evaluating initiation ≤12 weeks of surgical resection. Table 1 summarizes the distribution of the baseline characteristics of these observations. The median age of the study population was 56 years (interquartile range [IQR]: 50–61), and the majority were female (56–57%), racial/ethnic minorities (64–65%), or uninsured with or without enrollment in our hospital-based medical assistance program (68–71%). Median time from diagnosis to surgical resection was 4 days (IQR: 1–28). Median time from surgical resection to adjuvant chemotherapy was 56 days (IQR: 46–78) for observations that initiated adjuvant chemotherapy in the 8-week analysis and 69 days (IQR: 51–86) for observations that initiated adjuvant chemotherapy in the 12-week analysis. For the 8-week analysis, 28% did not initiate adjuvant chemotherapy at any time, and for the 12-week analysis 34% did not initiate adjuvant chemotherapy at any time. The most common reason for not initiating adjuvant chemotherapy was patient refusal (71%). FOLFOX (including modified FOLFOX) was the most common adjuvant chemotherapy regimen (71–73%).

Table 1 Characteristics of study populations with stage III colon cancer eligible for adjuvant chemotherapy following surgical resection
Full size table

We observed 62 deaths and 57 recurrences within 5 years of surgical resection among observations in the 8-week analysis, and 84 deaths and 79 recurrences among observations in the 12-week analysis. Figures 1 and 2 illustrate the marginal overall and disease-free survival curves for initiators and non-initiators of adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection. The crude risk of all-cause mortality was 17% lower (RR = 0.83; 95% CL: 0.46, 1.51) and the crude risk of recurrence or mortality was 7% lower at 5 years (RR = 0.93; 95% CL: 0.55, 1.57) for patients who initiated adjuvant chemotherapy ≤8 weeks compared with patients who did not initiate adjuvant chemotherapy ≤8 weeks of surgical resection. The crude risk of all-cause mortality was 14% higher (RR = 1.14; 95% CL: 0.77, 1.68) and the crude risk of recurrence or mortality was 22% higher at 5 years (RR = 1.22; 95% CL: 0.85, 1.76) for patients who initiated adjuvant chemotherapy ≤12 weeks compared with patients who did not initiate adjuvant chemotherapy ≤12 weeks of surgical resection.

Fig. 1
figure 1

Marginal survival curvesa for initiation or no initiation of adjuvant chemotherapy ≤8 weeks of surgical resection for stage III colon cancer patients. A Overall survival. B Disease-free survival. (Adjusted for age, sex, race/ethnicity, insurance status, marital status, National Cancer Institue comorbidity index, body mass index, tumor grade, and surgical procedures)

Full size image
Fig. 2
figure 2

Marginal survival curvesa for initiation or no initiation of adjuvant chemotherapy ≤12 weeks of surgical resection for stage III colon cancer patients. A Overall survival. B Disease-free survival. (Adjusted for age, sex, race/ethnicity, insurance status, marital status, National Cancer Institue comorbidity index, body mass index, tumor grade, and surgical procedures)

Full size image

The adjusted risk (i.e., after weighting) of 5-year all-cause mortality was 4% higher for patients who initiated adjuvant chemotherapy ≤8 weeks compared with patients who did not initiate adjuvant chemotherapy ≤8 weeks of surgical resection, but our data were compatible with 43% lower risk or 92% higher risk of mortality (RR = 1.04, 95% CL: 0.57, 1.92). The adjusted risk of recurrence or mortality at 5 years was 7% higher for patients who initiated adjuvant chemotherapy ≤8 weeks compared with patients who did not initiate adjuvant chemotherapy ≤8 weeks of surgical resection, but our data were compatible with 39% lower risk or 88% higher risk of recurrence or mortality (RR = 1.07, 95% CL: 0.61, 1.88). The results were similar for initiation of adjuvant chemotherapy ≤12 weeks of surgical resection (Table 2).

Table 2 Risk ratios (RRs) for all-cause mortality and recurrence or mortality at 5-years between initiators and non-initiators of adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection for stage III colon cancer patients
Full size table

Discussion

Our analysis aimed to align time zero and reduce immortal time bias, which were sources of error in prior studies about timing of adjuvant chemotherapy initiation for stage III colon cancer patients < 80 years. Our results suggest that the overall and disease-free survival benefits of initiating adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection may be overestimated in prior studies. Nevertheless, our data were compatible with either meaningful benefit or harm of initiation within specified intervals. Imprecision and potential sources of error require further consideration when interpreting our results.

Imprecision in survival analysis is a function of number of events (i.e., death or recurrence in our analysis) and person-time. A larger sample size or longer follow-up (assuming more events) could provide more precise estimates, but our sample size was limited to available data. Precision is certainly important but only addresses random error. Quantification of effects with reduced systematic error (i.e., mitigated biases) is also critical. Our analysis prioritized mitigating key biases in prior studies that could mislead interpretation. Consequently, our estimates may be useful despite imprecision, [41, 42] particularly if estimates from multiple studies with similar approaches are summarized in a meta-analysis to improve precision [41].

As with any observational study, our estimates may be sensitive to violations of exchangeability [43, 44] (i.e., unmeasured confounding or selection bias). For example, data were unavailable to allow adjustment for frailty at diagnosis. Nevertheless, unmeasured confounding by frailty would create bias away from the null because we would expect an inverse relation between frailty and initiation of adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection (i.e., initiation may require additional time for frail patients) and frailty would increase mortality risk. Adjustment would thus move the estimate further toward the null [45]. In addition, the crude and adjusted 95% confidence limits for risk ratios were largely overlapping despite adjustment for multiple covariates, particularly for the more stable 12-week estimates. The lack of notable differences in crude and adjusted estimates suggests that confounding may not be as prominent of a concern once time zero is aligned, which is a phenomenon observed in prior studies that explored the effects of misaligned time zero [23, 46]. Lastly, survival could be affected by adherence to adjuvant chemotherapy [47, 48], but addressing adherence would change the question of interest and require a different study design [49]. Our study was designed to address the effect of initiating adjuvant chemotherapy, which is the question of interest relevant to quality of care guidelines and the basis of prior studies.

Our findings differ from prior studies [50,51,52,53], in which point estimates suggested 25–55% lower mortality hazards for initiation of adjuvant chemotherapy ≤8 weeks after surgical resection compared with later initiation or no initiation among colon cancer patients. Time-related biases [11, 12, 14, 15, 54] from misaligned start of eligibility, time of treatment assignment, and start of follow-up are a key consideration for effect heterogeneity between our study and prior studies. For example, immortal time bias is a concern in prior studies [50,51,52,53, 55] because follow-up time was measured from surgical resection, but adjuvant chemotherapy was initiated after follow-up time began. The consequence is misclassified person-time, where the time between start of follow-up and treatment initiation is considered “immortal.” Several studies have reported substantial bias away from the null because of immortal time, [19, 20, 25, 56] and this bias can be more severe than unmeasured confounding [23, 46]. We used analytic methods to mitigate immortal time bias, [11, 57] which may partly explain why our point estimates are closer to the null than prior studies. In addition, prior studies [50,51,52] excluded patients who were eligible for but did not initiate adjuvant chemotherapy, which incurs selection bias [58]. Lastly, effect heterogeneity across studies may be related to clinical setting and population characteristics. For example, our study was conducted in a cancer center that provides care for socioeconomically marginalized populations. Our estimates may be closer to the null if adverse effects of social determinants of health override benefits of earlier treatment initiation [59, 60]. Consequently, our results may generalize to other safety-net settings but not necessarily academic cancer centers.

In summary, assuming no substantial effect of biases, our results suggest that initiating adjuvant chemotherapy ≤8 or ≤ 12 weeks of surgical resection for stage III colon cancer patients < 80 years may not be as beneficial as reported in prior studies. Nevertheless, our results were imprecise and require confirmation. Future studies that also address time-related biases and have larger samples or longer follow-up may provide greater precision. Alternatively, estimates from multiple similar studies may be combined in a meta-analysis to improve precision. Such evidence could be valuable considering that cancer care delivery organizations dedicate considerable resources to meet guidelines for timely care, but some guidelines may not be optimized for meaningful outcomes or for certain settings.

Availability of data and materials

The data analyzed for the current study are available on reasonable request to the corresponding author and review by the JPS Health Network External Data Governance Committee (research@jpshealth.org).

Abbreviations

RR:

Risk ratio

CL:

Confidence limits

RCT:

Randomized controlled trials

BMI:

Body mass index

IPTW:

Inverse probability of treatment weights

IQR:

Interquartile range

References

  1. Grávalos C, García-Escobar I, García-Alfonso P, Cassinello J, Malón D, Carrato A. Adjuvant chemotherapy for stages II, III and IV of colon cancer. Clin Transl Oncol. 2009;11(8):526–33.

    Article  PubMed  CAS  Google Scholar 

  2. Laurie JA, Moertel CG, Fleming TR, Wieand HS, Leigh JE, Rubin J, et al. Surgical adjuvant therapy of large-bowel carcinoma: an evaluation of levamisole and the combination of levamisole and fluorouracil. The north central Cancer treatment group and the Mayo Clinic. J Clin Oncol. 1989;7(10):1447–56.

    Article  CAS  PubMed  Google Scholar 

  3. Moertel CG, Fleming TR, Macdonald JS, Haller DG, Laurie JA, Goodman PJ, et al. Levamisole and fluorouracil for adjuvant therapy of resected colon carcinoma. N Engl J Med. 1990;322(6):352–8.

    Article  CAS  PubMed  Google Scholar 

  4. Moertel CG, Fleming TR, Macdonald JS, Haller DG, Laurie JA, Tangen CM, et al. Fluorouracil plus levamisole as effective adjuvant therapy after resection of stage III colon carcinoma: a final report. Ann Intern Med. 1995;122(5):321–6.

    Article  CAS  PubMed  Google Scholar 

  5. Hanna TP, King WD, Thibodeau S, Jalink M, Paulin GA, Harvey-Jones E, et al. Mortality due to cancer treatment delay: systematic review and meta-analysis. BMJ. 2020;371:m4087.

    Article  PubMed  Google Scholar 

  6. Petrelli F, Zaniboni A, Ghidini A, Ghidini M, Turati L, Pizzo C, et al. Timing of adjuvant chemotherapy and survival in colorectal, gastric, and pancreatic cancer a systematic review and meta-analysis. Cancers (Basel). 2019;11(4):550.

    Article  CAS  Google Scholar 

  7. Biagi JJ, Raphael MJ, Mackillop WJ, Kong W, King WD, Booth CM. Association between time to initiation of adjuvant chemotherapy and survival in colorectal cancer: a systematic review and meta-analysis. JAMA. 2011;305(22):2335–42.

    Article  CAS  PubMed  Google Scholar 

  8. Des Guetz G, Nicolas P, Perret GY, Morere JF, Uzzan B. Does delaying adjuvant chemotherapy after curative surgery for colorectal cancer impair survival? A meta-analysis. Eur J Cancer. 2010;46(6):1049–55.

    Article  PubMed  Google Scholar 

  9. National Quality Forum Cancer Fall 2019 Cycle Measures. https://www.qualityforum.org/ProjectMeasures.aspx?projectID=86163&cycleNo=2&cycleYear=2019.

  10. American Society of Clinical Oncology. QOPI Certification Track 2021 Measures Summary. 2020. https://practice.asco.org/sites/default/files/drupalfiles/QOPI-2021-Round-1-Measure-Summary-QCP-Track.pdf. Accessed 28 Mar 2022.

  11. Hernan MA, Sauer BC, Hernandez-Diaz S, Platt R, Shrier I. Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses. J Clin Epidemiol. 2016;79:70–5.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Suissa S, Dell'Aniello S. Time-related biases in pharmacoepidemiology. Pharmacoepidemiol Drug Saf. 2020;29(9):1101–10.

    Article  PubMed  Google Scholar 

  13. Suissa S. Immortal time bias in pharmaco-epidemiology. Am J Epidemiol. 2008;167(4):492–9.

    Article  PubMed  Google Scholar 

  14. Lash TL, Cole SR. Immortal person-time in studies of cancer outcomes. J Clin Oncol. 2009;27(23):e55–6.

    Article  PubMed  Google Scholar 

  15. Hanley JA, Foster BJ. Avoiding blunders involving ‘immortal time’. Int J Epidemiol. 2014;43(3):949–61.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Platt R, Hutcheon J, Suissa S. Immortal time Bias in epidemiology. Curr Epidemiol Rep. 2019;6(1):23–7.

    Article  Google Scholar 

  17. Yadav K, Lewis RJ. Immortal time Bias in observational studies. JAMA. 2021;325(7):686–7.

    Article  PubMed  Google Scholar 

  18. Giobbie-Hurder A, Gelber RD, Regan MM. Challenges of guarantee-time bias. J Clin Oncol. 2013;31(23):2963–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Weberpals J, Jansen L, van Herk-Sukel MPP, Kuiper JG, Aarts MJ, Vissers PAJ, et al. Immortal time bias in pharmacoepidemiological studies on cancer patient survival: empirical illustration for beta-blocker use in four cancers with different prognosis. Eur J Epidemiol. 2017;32(11):1019–31.

    Article  PubMed  Google Scholar 

  20. Emilsson L, Garcia-Albeniz X, Logan RW, Caniglia EC, Kalager M, Hernan MA. Examining Bias in studies of statin treatment and survival in patients with Cancer. JAMA Oncol. 2018;4(1):63–70.

    Article  PubMed  Google Scholar 

  21. U.S. Food & drug administration: clinical trial endpoints for the approval of cancer drugs and biologics. 2018.

    Google Scholar 

  22. Ellis LM, Bernstein DS, Voest EE, Berlin JD, Sargent D, Cortazar P, et al. American society of clinical oncology perspective: raising the bar for clinical trials by defining clinically meaningful outcomes. J Clin Oncol. 2014;32(12):1277–80.

    Article  PubMed  Google Scholar 

  23. Hernan MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19(6):766–79.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Gran JM, Roysland K, Wolbers M, Didelez V, Sterne JA, Ledergerber B, et al. A sequential cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV cohort study. Stat Med. 2010;29(26):2757–68.

    Article  PubMed  Google Scholar 

  25. Karim ME, Gustafson P, Petkau J, Tremlett H, Long-Term B. Adverse effects of Beta-interferon for multiple sclerosis study G: comparison of statistical approaches for dealing with immortal time Bias in drug effectiveness studies. Am J Epidemiol. 2016;184(4):325–35.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168(6):656–64.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48.

    Article  CAS  PubMed  Google Scholar 

  28. Pearl J. Causality: models, reasoning, and inference. 2nd ed. Cambridge: Cambridge University Press; 2009.

    Book  Google Scholar 

  29. Digitale JC, Martin JN, Glymour MM. Tutorial on directed acyclic graphs. J Clin Epidemiol. 2022;142:264–7. https://doi.org/10.1016/j.jclinepi.2021.08.001. Epub 2021 Aug 8.

  30. National Cancer Institute. NCI Comorbidity Index Overview. 2021. https://healthcaredelivery.cancer.gov/seermedicare/considerations/comorbidity.html. Accessed 28 Mar 2022.

  31. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661–79.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Xie J, Liu C. Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data. Stat Med. 2005;24(20):3089–110.

    Article  PubMed  Google Scholar 

  33. Andersen PK, Perme MP. Pseudo-observations in survival analysis. Stat Methods Med Res. 2010;19(1):71–99.

    Article  PubMed  Google Scholar 

  34. Andersen PK, Syriopoulou E, Parner ET. Causal inference in survival analysis using pseudo-observations. Stat Med. 2017;36(17):2669–81.

    Article  PubMed  Google Scholar 

  35. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–60.

    Article  CAS  PubMed  Google Scholar 

  36. Hernan MA. The hazards of hazard ratios. Epidemiology. 2010;21(1):13–5.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Aalen OO, Cook RJ, Roysland K. Does cox analysis of a randomized survival study yield a causal treatment effect? Lifetime Data Anal. 2015;21(4):579–93.

    Article  PubMed  Google Scholar 

  38. Royston P, Parmar MK. Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Med Res Methodol. 2013;13:152.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Sutradhar R, Austin PC. Relative rates not relative risks: addressing a widespread misinterpretation of hazard ratios. Ann Epidemiol. 2018;28(1):54–7.

    Article  PubMed  Google Scholar 

  40. Trinquart L, Bill-Axelson A, Rider JR. Restricted mean survival times to improve communication of evidence from Cancer randomized trials and observational studies. Eur Urol. 2019;76(2):137–9.

    Article  PubMed  Google Scholar 

  41. Hernán MA. Causal analyses of existing databases: no power calculations required. J Clin Epidemiol. 2022;144:203–5.

  42. Smith AH, Bates MN. Confidence limit analyses should replace power calculations in the interpretation of epidemiologic studies. Epidemiology. 1992;3(5):449–52.

    Article  CAS  PubMed  Google Scholar 

  43. Greenland S, Robins JM. Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol. 1986;15(3):413–9.

    Article  CAS  PubMed  Google Scholar 

  44. Greenland S, Robins JM. Identifiability, exchangeability and confounding revisited. Epidemiol Perspect Innov. 2009;6:4.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Mehio-Sibai A, Feinleib M, Sibai TA, Armenian HK. A positive or a negative confounding variable? A simple teaching aid for clinicians and students. Ann Epidemiol. 2005;15(6):421–3.

    Article  PubMed  Google Scholar 

  46. Maringe C, Benitez Majano S, Exarchakou A, Smith M, Rachet B, Belot A, et al. Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data. Int J Epidemiol. 2020;49(5):1719–29.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Grothey A, Sobrero AF, Shields AF, Yoshino T, Paul J, Taieb J, et al. Duration of adjuvant chemotherapy for stage III Colon Cancer. N Engl J Med. 2018;378(13):1177–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lieu C, Kennedy EB, Bergsland E, Berlin J, George TJ, Gill S, et al. Duration of Oxaliplatin-containing adjuvant therapy for stage III Colon Cancer: ASCO clinical practice guideline. J Clin Oncol. 2019;37(16):1436–47.

    Article  CAS  PubMed  Google Scholar 

  49. Hernan MA. How to estimate the effect of treatment duration on survival outcomes using observational data. BMJ. 2018;360:k182.

    Article  PubMed  Google Scholar 

  50. Bayraktar UD, Chen E, Bayraktar S, Sands LR, Marchetti F, Montero AJ, et al. Does delay of adjuvant chemotherapy impact survival in patients with resected stage II and III colon adenocarcinoma? Cancer. 2011;117(11):2364–70.

    Article  PubMed  Google Scholar 

  51. Massarweh NN, Haynes AB, Chiang YJ, Chang GJ, You YN, Feig BW, et al. Adequacy of the National quality forum’s Colon cancer adjuvant chemotherapy quality metric: is 4 months soon enough? Ann Surg. 2015;262(2):312–20.

    Article  PubMed  Google Scholar 

  52. Becerra AZ, Aquina CT, Mohile SG, Tejani MA, Schymura MJ, Boscoe FP, et al. Variation in delayed time to adjuvant chemotherapy and disease-specific survival in stage III Colon Cancer patients. Ann Surg Oncol. 2017;24(6):1610–7.

    Article  PubMed  Google Scholar 

  53. Turner MC, Farrow NE, Rhodin KE, Sun Z, Adam MA, Mantyh CR, et al. Delay in adjuvant chemotherapy and survival advantage in stage III Colon Cancer. J Am Coll Surg. 2018;226(4):670–8.

    Article  PubMed  Google Scholar 

  54. Suissa S. Immortal time bias in observational studies of drug effects. Pharmacoepidemiol Drug Saf. 2007;16(3):241–9.

    Article  PubMed  Google Scholar 

  55. Zeig-Owens R, Gershman ST, Knowlton R, Jacobson JS. Survival and time interval from surgery to start of chemotherapy among colon cancer patients. J Registry Manag. 2009;36(2):30–41 quiz 61-32.

    PubMed  Google Scholar 

  56. Cui Y, Wen W, Zheng T, Li H, Gao YT, Cai H, et al. Use of antihypertensive medications and survival rates for breast, colorectal, lung, or stomach Cancer. Am J Epidemiol. 2019;188(8):1512–28.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hernan MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615–25.

    Article  PubMed  Google Scholar 

  59. Alcaraz KI, Wiedt TL, Daniels EC, Yabroff KR, Guerra CE, Wender RC. Understanding and addressing social determinants to advance cancer health equity in the United States: a blueprint for practice, research, and policy. CA Cancer J Clin. 2020;70(1):31–46.

    Article  PubMed  Google Scholar 

  60. Lu Y, Gehr AW, Narra K, Lingam A, Ghabach B, Meadows RJ, Ojha RP. Impact of prognostic factor distributions on mortality disparities for socioeconomically disadvantaged cancer patients. Ann Epidemiol. 2022;65:31–7.

Download references

Acknowledgements

Not applicable.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Center for Epidemiology & Healthcare Delivery Research, JPS Health Network, 1500 S. Main Street, Fort Worth, TX, 76104, USA

    Yan Lu, Aaron W. Gehr, Rachel J. Meadows & Rohit P. Ojha

  2. Department of Medical Education, TCU School of Medicine, 3430 Camp Bowie Blvd, Fort Worth, TX, 76107, USA

    Rachel J. Meadows & Rohit P. Ojha

  3. Oncology and Infusion Center, JPS Health Network, 1450 8th Ave, Fort Worth, TX, 76104, USA

    Bassam Ghabach, Latha Neerukonda & Kalyani Narra

Authors
  1. Yan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron W. Gehr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachel J. Meadows
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bassam Ghabach
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Latha Neerukonda
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kalyani Narra
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Rohit P. Ojha
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YL: investigation, formal analysis, data curation, visualization, writing – original draft, project administration. AWG: investigation, writing – review and editing. RJM: investigation, writing – review and editing. BG: investigation, writing – review and editing. LN: investigation, writing – review and editing. KN: investigation, writing – review and editing. RPO: conceptualization, investigation, methodology, formal analysis, supervision, writing – review and editing. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Rohit P. Ojha.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the North Texas Regional Institutional Review Board (IRB# 2017–19). Given the use of existing registry data, the need for informed consent was waived by the IRB.

Consent for publication

Not applicable.

Competing interests

The authors have no financial or non-financial competing interests to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: 

Supplementary Table S1, S2.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Y., Gehr, A.W., Meadows, R.J. et al. Timing of adjuvant chemotherapy initiation and mortality among colon cancer patients at a safety-net health system. BMC Cancer 22, 593 (2022). https://doi.org/10.1186/s12885-022-09688-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12885-022-09688-w

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us