米国における乳がん死亡率の分析-1975年から2019年
Jennifer L Caswell-Jin1, Liyang P Sun2, Diego Munoz2
1Department of Medicine, Stanford University School of Medicine, Stanford, California.
JAMA
|January 16, 2024
まとめ
アメリカでは検診と治療の進歩により 乳がんによる死亡率が著しく低下しています マンモグラフィーのスクリーニングと併せて初期段階および転移性乳がんの治療により,1975年から2019年にかけての死亡率は全体的に58%減少しました.
科学分野:
- 腫瘍学
- 公衆衛生
- バイオ統計学
背景:
- 米国では1975年から2019年の間に乳がんによる死亡率が減少した.
- この減少に対する転移性乳がん治療の具体的な影響は不明である.
研究 の 目的:
- 乳がんのスクリーニング,初期段階 (I-III) の治療,および転移性治療による乳がんの死亡率の減少との関連をシミュレートする.
- 死亡率の減少に対するこれらの介入の相対的な貢献を定量化する.
主な方法:
- 4つのがん介入と監視モデリングネットワーク (CISNET) のモデルを使用した.
- 1975年から2019年までの米国乳がん死亡率のシミュレーションは,集約された観察および臨床試験データを用いて行われました.
- 年齢調整による死亡率と転移後の生存期間の中央値の評価
主要な成果:
- 検診と治療により,乳がんによる死亡率は1975年と比較して2019年に58%減少した.
- ステージI-IIIの乳がんの治療は,この減少の47%を占めた.
- 転移性乳がんの治療は29%, マンモグラフィーのスクリーニングは25%でした.
結論:
- 乳がんのスクリーニングと治療は,1975年から2019年の間に米国の死亡率を大幅に減少させた.
- 早期段階と転移した疾患の両方の治療は,スクリーニングとともに,生存結果を改善するために不可欠です.
- 特にER陽性/ ERBB2陽性乳がんでは,転移性再発後の生存期間の中央値が大幅に改善されました.
関連する概念動画
01:21Cancer Survival Analysis
353
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
353
01:20Comparing the Survival Analysis of Two or More Groups
195
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
195
01:24Kaplan-Meier Approach
146
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
146
01:18Introduction To Survival Analysis
239
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
The primary goal of survival analysis is to estimate survival time—the time...
239
01:20Actuarial Approach
79
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
79
01:25Statistical Methods for Analyzing Epidemiological Data
367
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
367