In medical research it is common to observe multiple endpoints that compete with each other. For example, the risk of competition for death from heart disease and cerebrovascular disease in patients with non-small-cell lung cancer increases with age. Traditional survival analysis will treat the competing risks by censoring, and the incidence of the true outcome will be overestimated, leading to competing-risks bias. A study of competing-risks bias found that up to 46% of studies reported on in the literature were affected by competing-risks bias, including in advanced medical journals. Meningioma accounts for 34% of all primary intracranial tumors, and approximately 7,000 new cases are diagnosed each year in the United States. Meningioma arises from arachnoid cells of the leptomeninges and may occur throughout the coverings of the central nervous system. Since most meningiomas follow a benign course, the State Central Cancer Registry did not collect nonmalignant cases with a diagnosis before 2004. However, studies have shown that up to 10% of meningiomas can show more-aggressive behavior and a higher tendency to relapse. The Benign Tumor Registration Amendment provides for the collection of data related to benign and borderline malignant brain tumors from 2004, which represents useful information about this common but underresearched tumor.
To the best of our knowledge, the present study is the first to use the SEER database to conduct a competing-risks analysis (including Fine-Gray model and CS model) of meningioma patients with the goal of identifying more-accurate prognostic factors. Approximately one-third (n = 398) of the meningioma patients analyzed in this study died of competing events. Using a competing-risks model, we found that age at diagnosis, sex, tumor grade, and SEER stage were risk factors for meningioma patients.
Meningioma can occur at any age, but the incidence of meningioma in people younger than 18 years is only 0.06/100,000. The incidence of meningioma increases with age, and is most common among elderly people older than 65 years. A previous study of atypical and anaplastic meningiomas found that for every additional year of age, the risk increased by 1.03, which is consistent with our results. In our study the Cox regression, Fine-Gray, and CS models all showed that being aged 35–64 and > 64 years were risk factors compared with an age of 18–34 years (p < 0.05). The prognosis is poor and the risk is high especially in the elderly (> 64 vs 18–34 years: Cox, HR = 5.483; Fine-Gray, HR = 4.486; CS, HR = 5.982). This might be because morbidity and mortality rates are higher, there are more surgical complications, and the functional prognosis is worse in elderly patients with craniotomy or subtotal resection than in younger patients.
Cox regression revealed that race other than white or black was a risk factor compared with being white (HR = 1.283, p = 0.042). However, we did not observe this result in the two competing-risks models, which indicates that the results of Cox regression are not accurate because it does not consider competing risks. Garzon-Muvdi et al. also demonstrated that race other than white or black and unknown race are not risk factors for atypical and anaplastic meningiomas compared with whites in an analysis using the Fine-Gray model (HR = 0.37, p = 0.320). All three models in the present study showed that being male is a risk factor for meningioma, which is consistent with previous findings.[25–27]
Compared with married patients, DSW was a risk factor in both the Cox model (HR = 1.398, p < 0.001) and the CS model (HR = 1.277, p < 0.05), but not in the Fine-Gray model (HR = 1.215, p > 0.05). We also found this difference for chemotherapy and the median household income. Although both the Fine-Gray and CS models are competing-risks models, they produced different results, which is due to the effects being stronger in the CS model than in the Fine-Gray model. Although the directions of the correlations were essentially the same in the two models, and their HRs were similar, they can still produce different results. This has also happened in previous studies, and it explains why two competing-risks models need to be employed. The CS model is more suitable for answering etiology studies, while the Fine-Gray model is increasingly being used for clinical predictive models and risk determination.[11, 19]
All three models showed that grade I is a protective factor compared to unknown grade, and that grade II/III is a risk factor. The WHO staging system classifies meningiomas into grades I, II, and III. A meningioma of grade I has a low recurrence and low invasive growth, while meningiomas of grades II and III exhibit high recurrence, high invasive growth, poor prognosis, and high mortality. We also found that all three models showed distant SEER stage to be a risk factor compared to an unknown SEER stage. However, the Cox regression model appeared to underestimate this risk (Cox, HR = 1.345; Fine-Gray, HR = 1.711; CS, HR = 1.660).
Regarding the treatment, the Fine-Gray model indicated that none of treatments—surgery, radiotherapy, or chemotherapy—exerted statistically significant effects (p > 0.05). Moreover, the CS model indicated that not receiving chemotherapy was a protective factor (HR = 0.640, p < 0.05). Surgery currently remains the cornerstone in the clinical diagnosis and treatment of malignant meningioma. However, there is still a lack of clear guidelines for chemotherapy. Traditional chemotherapeutic agents are not very effective against meningioma, but hormone therapy is being investigated for patients with inoperable tumors, and radiation therapy is increasingly recommended as a standard adjuvant therapy for patients with malignant meningioma.
The Cox regression performed in the present study revealed that receiving the first indication of a malignant primary tumor, having at least a bachelor’s degree, and the median household income affected the survival of meningioma patients, whereas these results were not found in the competing-risks model. This may also be due to the presence of competing-risks bias.
The large sample is one of the main strengths of this study. However, our research was also subject to limitations. First, it had inherent limitations due to its retrospective design. Second, important information is missing from the SEER database, such as the Simpson rating. Third, the records in the SEER database are not complete, and patients may be misclassified. Finally, because this study is the first to use two competing-risks models for the risk assessment of meningioma patients, further research is needed to verify the present results.