Please identify a quantitative research article evaluating mediation in your field and provide the citation.
Li, R., Daniel, R., & Rachet, B. (2016). How much do tumor stage and treatment explain socioeconomic inequalities in breast cancer survival? Applying causal mediation analysis to population-based data. European journal of epidemiology, 31(6), 603-611.
What is the primary discipline of the authors?
Epidemiology and Biostatistics
What is the exposure of interest?
Deprivation
What is the outcome of interest?
Survival status (dead vs. alive)
What is the hypothesized mediator of interest and how is it measured?
1) Stage at diagnosis
2) Treatment
Stage at diagnosis was obtained from cancer registry. Each patient was assigned one of four categories based TNM (tumor size, lymph nodes affected, and distant metastasis) cancer staging.
Surgical treatment was retrieved from national hospital dataset. Treatment codes were categorized and then dichotomized into major treatment (axillary dissection or axillary nodal procedures, breast conserving surgery, mastectomy, and plastic surgery) and minor or no surgery (other surgical procedures and none).
Describe the modeling approach and briefly report the estimated total, direct, and indirect effects (if these are reported).
Author employed g-computation formula using Monte Carlo simulation to allow for interactions and other nonlinearities. They conducted three analyses to investigate the mediating role of stage and treatment. They first estimate the proportion of the effects of deprivation on survival that was mediated by difference in stage at diagnosis. Then they estimated the proportion of the effect of deprivation on death that was mediated by differences in treatment (here stage at diagnosis was considered to be a confounder). In the third analysis they estimated the proportion of the effect of deprivation on treatment that is mediated by differential treatment. They stratified the outcome (dead vs. alive) according to time since diagnosis: 6 months, 1 year (conditioning on 6-month survival), 3 years (conditioning on 1-year survival), and 5 years (conditioning on 3-year survival). Analyses were performed separately on each of these four binary survival outcomes. They used multinomial regression to model stage at diagnosis and logistic regression was used for treatment and survival status. Age at diagnosis was modeled as cubic splines. To handle missing data, the authors used single stochastic imputation within the f-computation. Confounders adjusted for include effect of region, year of diagnosis.
Total causal effect: conditioning on 6-months was 2.77(2.17, 3.53) but conditioning on 3-years 1.67(1.39, 2.00)
Indirect effect of stage: conditioning on 6-months 1.43(1.27, 1.67) and conditioning on 5-years 1.08 (1.00, 1.61). Stage accounted for 35% (23, 24%) of the total effect of deprivation at 6-monthd and 30% (5, 54%) at 1 year.
Indirect effect of treatment: The authors did not find evidence for effect mediated by treatment
If the direct effect is reported, would you describe this as a natural direct effect, a controlled direct effect, or something else?
Direct effect was not reported
Do you think there is potential measurement error in the mediator and how would that affect the results?
Authors discuss effects of misclassification of both stage at diagnosis and treatment. The authors hypothesize that deprived cancer patients may be more likely to be misdiagnosis because they are likely to be treat by non-specialized enters and by less experienced surgeons. To test this hypothesis, they tested they tested what would happen if 10%, 30%, and 50% of the most deprived patients were under-staged. They concluded that up to 30% of the most deprived patients would have to be systematically under-staged compared to 0% in the most affluent group and that this is highly improbable and unsupported by the literature.
Setting aside the authors argument, if more deprived patients were in fact misdiagnosed so that they were assigned a stage lower cancer sage, the direct effect between deprivation and survival would be attenuated.
The authors used information on surgery alone since 1) the information on radiation and chemo therapy was too poor to be used 2) surgery is generally standard care in addition to the other two treatment options. The authors hypothesized that if there was under-estimation of treatment (i.e. surgery proportion) it is likely to affect the more affluent patients. They conducted sensitivity analysis to investigate how this misclassification may affect the effect estimates. They found no evidence of differential treatment on cancer survival the top 4 deprivation groups. They did find that treatment mediated about 30-40% of the differential mortality between the most deprived and least deprived patients. They concluded that surgical information is likely to be missed completely at random and therefore was unlikely to bias their results. Also, when the authors re-categorized treatment into 4 categories (originally it was a dichotomized as major or minor) the effect estimates remained unchanged.
Do you think there are unmeasured confounders of the mediator-outcome association and how would that affect the results of the mediation analysis?
The authors acknowledge that co-morbidity may be an important confounder not controlled for. This would lead to over-estimation of the beneficial effects of major surgery on mortality.
The authors further argue that since they found no mediating effect of treatment, co-morbidity would change the overall estimate only if stage and treatment were misclassified.
Do you have any critiques of the paper?
I think this paper was relatively well written. The authors tested various plausible models and addressed whether they believed there were systematic errors and how that would impact the effect estimates.