Causal Effects of Time-Dependent Treatments in Older Patients with Non-Small Cell Lung Cancer

<div><p>Background</p><p>Treatment selection for elderly patients with lung cancer must balance the benefits of curative/life-prolonging therapy and the risks of increased mortality due to comorbidities. Lung cancer trials generally exclude patients with comorbidities and current treatment guidelines do not specifically consider comorbidities, so treatment decisions are usually made on subjective individual-case basis.</p><p>Methods</p><p>Impacts of surgery, radiation, and chemotherapy mono-treatment as well as combined chemo/radiation on one-year overall survival (compared to no-treatment) are studied for stage-specific lung cancer in 65+ y.o. patients. Methods of causal inference such as propensity score with inverse probability weighting (IPW) for time-independent and marginal structural model (MSM) for time-dependent treatments are applied to SEER-Medicare data considering the presence of comorbid diseases.</p><p>Results</p><p>122,822 patients with stage I (26.8%), II (4.5%), IIIa (11.5%), IIIb (19.9%), and IV (37.4%) lung cancer were selected. Younger age, smaller tumor size, and fewer baseline comorbidities predict better survival. Impacts of radio- and chemotherapy increased and impact of surgery decreased with more advanced cancer stages. The effects of all therapies became weaker after adjustment for selection bias, however, the changes in the effects were minor likely due to the weak selection bias or incompleteness of the list of predictors that impacted treatment choice. MSM provides more realistic estimates of treatment effects than the IPW approach for time-independent treatment.</p><p>Conclusions</p><p>Causal inference methods provide substantive results on treatment choice and survival of older lung cancer patients with realistic expectations of potential benefits of specific treatments. Applications of these models to specific subsets of patients can aid in the development of practical guidelines that help optimize lung cancer treatment based on individual patient characteristics.</p></div

Survival curves for treatment-specific and stage-specific patient groups.

<p>Rows correspond to stages I, II, IIIA, IIIB, and IV.</p

The causal effect of the lung cancer treatment modes (represented by HRs) evaluated in the Cox model for original and pseudorandomized population for one-year follow-up for treatments involving surgery (Sur), radio- (Rad) and chemotherapy (Che) vs. no treatment.

<p>*—estimate is not significant (0.05</p><p>**—estimate is not significant (0.3≤p)</p><p>The causal effect of the lung cancer treatment modes (represented by HRs) evaluated in the Cox model for original and pseudorandomized population for one-year follow-up for treatments involving surgery (Sur), radio- (Rad) and chemotherapy (Che) vs. no treatment.</p

The p-values of the <i>χ</i>²-tests for treatment-group comparison of stage-specific cohorts of lung cancer patients calculated for original and pseudorandomized (i.e., weighted) populations.

<p>The p-values of the <i>χ</i>²-tests for treatment-group comparison of stage-specific cohorts of lung cancer patients calculated for original and pseudorandomized (i.e., weighted) populations.</p

MSM estimates for two treatment groups (involving and not involving surgery) of lung cancer patients represented by ORs and respective IPW estimates for time-independent treatments represented by HRs.

<p>*—estimate is not significant (0.05</p><p>**—estimate is not significant (0.3≤p)</p><p>MSM estimates for two treatment groups (involving and not involving surgery) of lung cancer patients represented by ORs and respective IPW estimates for time-independent treatments represented by HRs.</p

Demographic, tumor, socio-economic characteristic and treatment modes for lung cancer patients with stages I, II, IIIA, IIIB, and IV.

<p>*—quintiles of distribution of entire patient groups, i.e., for all stages (0—lowest, 4—highest)</p><p>**—chemotherapy (Che), radiation therapy (Rad), and surgery (Sur)</p><p>^§-there are some minor inconsistencies in the recorded T and N statuses and respective AJCC stage (e.g., T3N0 should be stage II; T1-3N0 would be either stage I or stage II, not IIIA).</p><p>Values in brackets are percents of all patients (for Stage, i.e., first line of the Table) or stage-specific cohorts of patients.</p><p>Demographic, tumor, socio-economic characteristic and treatment modes for lung cancer patients with stages I, II, IIIA, IIIB, and IV.</p

The results of model fitting (presented as fitted parameters ±SE) for ACs and SCCs, SEER registry data, 1973–2003. (Parameters are summarized for both sexes and both races–see description of symbols used in headline in text).

<p>Notes: <i>c</i> - the generalized scale parameter of age dimension which characterizes the age of maximal incidence; <i>m</i> (<i>m</i>-stages) - the number of stages occurring during individual’s life and leading to the cancer diagnosis; <i>n</i> - the parameter running over different types of frailty distributions (e.g., <i>n</i> = 1 and <i>n</i> = 2 correspond to gamma-distribution and inverse Gaussian distribution); <i>σ</i> - characterizes the standard deviation of the frailty distribution (the distribution of cancer predisposition in population); <i>R_sex</i> and <i>R_race</i> describe the relative risks of cancer incidence in females and in African-American population, respectively; and characterizes the percent change in cancer incidence rates for a 10-year period.</p

The results of model fitting for ACs/SCCs for each cancer site.

<p>(Rates for different cancers are rescaled to use the same scale on all plots for comparison. The original rate can be calculated by dividing the values obtained from the plot to the rescaled factor).</p

The frequencies of stages at cancer diagnosis in the SEER Registry, 1973–2003, in percent. (Initial, final, and years of significant changes – if needed - in stages distribution are presented).

<p>Notes: * Only for prostate cancer (1983–2003 all localized and regional cases coded as “Localized/regional Prostate cases”.¹– Data on stages prevalence are available since 1983.</p

The estimated numbers of <i>m-</i>stages with respective standard errors (SE) for ACs (black dots) and SCCs (circles).

<p>Note: breast 850 – breast duct carcinoma, breast 852 – breast lobular carcinoma.</p