AI 輔助網絡統合分析教學

Network Meta-analysis with R

醫學統計教學課程

2025-12-10

課程簡介

學習目標

  • 理解網絡統合分析的基本概念
  • 學會使用 R 進行網絡統合分析
  • 掌握結果解讀與學術寫作
  • 培養批判性思維

課程架構

Code 
flowchart LR
    A[概念理解] --> B[網絡幾何]
    B --> C[NMA 執行]
    C --> D[一致性評估]
    D --> E[排名分析]
    E --> F[學術報告]

flowchart LR
    A[概念理解] --> B[網絡幾何]
    B --> C[NMA 執行]
    C --> D[一致性評估]
    D --> E[排名分析]
    E --> F[學術報告]

模擬情境

研究問題：比較四種降血壓藥物（A、B、C、D）對收縮壓的相對效果

  • 12 篇 RCT 研究
  • 涉及多組直接與間接比較
  • 網絡統合分析

模擬資料

建立資料

Code 
set.seed(2024)

nma_data <- data.frame(
  study = c("Wang 2018", "Chen 2019", "Liu 2020", "Zhang 2020",
            "Kim 2021", "Tanaka 2021", "Smith 2022", "Johnson 2023",
            "Lee 2019", "Park 2020", "Brown 2021", "Davis 2022"),
  treat1 = c("A", "A", "A", "B", "B", "C", "A", "B", "A", "C", "B", "C"),
  treat2 = c("B", "C", "D", "C", "D", "D", "B", "C", "C", "D", "D", "D"),
  n1 = c(45, 68, 52, 120, 38, 85, 63, 95, 55, 72, 48, 90),
  n2 = c(43, 65, 50, 118, 40, 82, 60, 92, 53, 70, 50, 88),
  mean1 = c(-12.3, -10.5, -14.2, -8.5, -9.2, -7.8, -11.8, -8.2, -10.8, -7.5, -9.0, -7.2),
  sd1 = c(8.2, 7.5, 9.1, 7.8, 7.2, 8.5, 7.9, 8.3, 7.6, 8.1, 7.4, 8.0),
  mean2 = c(-3.2, -4.5, -5.1, -4.8, -5.5, -4.2, -3.8, -4.5, -4.2, -3.8, -5.2, -3.5),
  sd2 = c(7.8, 7.2, 8.5, 7.5, 6.9, 8.1, 7.5, 7.9, 7.3, 7.8, 7.0, 7.6)
)

資料預覽

Code 
knitr::kable(nma_data[, 1:5], caption = "研究基本資料")
研究基本資料
study treat1 treat2 n1 n2
Wang 2018 A B 45 43
Chen 2019 A C 68 65
Liu 2020 A D 52 50
Zhang 2020 B C 120 118
Kim 2021 B D 38 40
Tanaka 2021 C D 85 82
Smith 2022 A B 63 60
Johnson 2023 B C 95 92
Lee 2019 A C 55 53
Park 2020 C D 72 70
Brown 2021 B D 48 50
Davis 2022 C D 90 88

資料結構

欄位 說明
study 研究名稱
treat1, treat2 比較的兩種治療
n1, n2 各組樣本數
mean1, mean2 各組平均值
sd1, sd2 各組標準差

任務一：基本概念

什麼是網絡統合分析？

比喻

傳統統合分析像是「一對一比賽」，網絡統合分析像是「循環賽」

  • 合併多種治療的直接與間接比較
  • 同時比較三個以上的治療選項
  • 產生完整的治療排名

為什麼需要網絡統合分析？

傳統統合分析的限制

  • 只能比較兩種治療
  • 無法整合間接證據
  • 難以產生完整排名

網絡統合分析的優勢

  • 同時比較多種治療
  • 整合直接與間接證據
  • 產生治療效果排名

直接與間接證據

Code 
flowchart TD
    A[藥物 A] <-->|直接比較| B[藥物 B]
    A <-->|直接比較| C[藥物 C]
    B <-.->|間接比較| C

flowchart TD
    A[藥物 A] <-->|直接比較| B[藥物 B]
    A <-->|直接比較| C[藥物 C]
    B <-.->|間接比較| C

一致性假設

核心假設

網絡統合分析假設直接與間接證據一致

  • 若 A 優於 B，A 也優於 C
  • 則間接推論 B 與 C 的關係應與直接比較一致

任務二：網絡幾何

載入套件

Code 
library(netmeta)
library(meta)
library(dplyr)

計算效果量

Code 
nma_data <- nma_data %>%
  mutate(
    sd_pooled = sqrt(((n1-1)*sd1^2 + (n2-1)*sd2^2) / (n1 + n2 - 2)),
    smd = (mean1 - mean2) / sd_pooled,
    se = sqrt((n1 + n2)/(n1 * n2) + smd^2/(2*(n1 + n2)))
  )

建立網絡物件

Code 
nma_result <- netmeta(
  TE = smd,
  seTE = se,
  treat1 = treat1,
  treat2 = treat2,
  studlab = study,
  data = nma_data,
  sm = "SMD",
  reference.group = "A",
  common = FALSE,
  random = TRUE
)

網絡圖

Code 
netgraph(nma_result,
         plastic = TRUE,
         thickness = "number.of.studies",
         points = TRUE,
         cex.points = 3,
         col = "steelblue",
         multiarm = TRUE,
         number.of.studies = TRUE)

網絡幾何解讀

元素 意義
節點 治療選項
節點大小 該治療的總樣本數
連線 存在直接比較
連線粗細 研究數量

任務三：網絡統合分析

執行網絡統合分析

Code 
summary(nma_result)
Original data:

             treat1 treat2      TE   seTE
Wang 2018         A      B -1.1365 0.2298
Chen 2019         A      C -0.8158 0.1805
Liu 2020          A      D -1.0328 0.2109
Zhang 2020        B      C -0.4835 0.1315
Kim 2021          B      D -0.5250 0.2304
Tanaka 2021       C      D -0.4334 0.1566
Smith 2022        A      B -1.0379 0.1921
Johnson 2023      B      C -0.4565 0.1482
Lee 2019          A      C -0.8854 0.2017
Park 2020         C      D -0.4652 0.1701
Brown 2021        B      D -0.5279 0.2056
Davis 2022        C      D -0.4741 0.1520

Number of treatment arms per study:
             narms
Wang 2018        2
Chen 2019        2
Liu 2020         2
Zhang 2020       2
Kim 2021         2
Tanaka 2021      2
Smith 2022       2
Johnson 2023     2
Lee 2019         2
Park 2020        2
Brown 2021       2
Davis 2022       2

Results (random effects model):

             treat1 treat2     SMD             95%-CI
Wang 2018         A      B -0.7609 [-1.0133; -0.5085]
Chen 2019         A      C -0.9934 [-1.2354; -0.7514]
Liu 2020          A      D -1.3482 [-1.6185; -1.0779]
Zhang 2020        B      C -0.2325 [-0.4460; -0.0190]
Kim 2021          B      D -0.5873 [-0.8264; -0.3481]
Tanaka 2021       C      D -0.3548 [-0.5594; -0.1502]
Smith 2022        A      B -0.7609 [-1.0133; -0.5085]
Johnson 2023      B      C -0.2325 [-0.4460; -0.0190]
Lee 2019          A      C -0.9934 [-1.2354; -0.7514]
Park 2020         C      D -0.3548 [-0.5594; -0.1502]
Brown 2021        B      D -0.5873 [-0.8264; -0.3481]
Davis 2022        C      D -0.3548 [-0.5594; -0.1502]

Number of studies: k = 12
Number of pairwise comparisons: m = 12
Number of treatments: n = 4
Number of designs: d = 6

Random effects model

Treatment estimate (other treatments vs 'A'):
     SMD           95%-CI    z  p-value
A      .                .    .        .
B 0.7609 [0.5085; 1.0133] 5.91 < 0.0001
C 0.9934 [0.7514; 1.2354] 8.05 < 0.0001
D 1.3482 [1.0779; 1.6185] 9.78 < 0.0001

Quantifying heterogeneity / inconsistency:
tau^2 = 0.0221; tau = 0.1485; I^2 = 41.2% [0.0%; 71.9%]

Tests of heterogeneity (within designs) and inconsistency (between designs):
                    Q d.f. p-value
Total           15.31    9  0.0829
Within designs   0.23    6  0.9998
Between designs 15.07    3  0.0018

Details of network meta-analysis methods:
- Frequentist graph-theoretical approach
- DerSimonian-Laird estimator for tau^2
- Calculation of I^2 based on Q

League Table

Code 
netleague <- netleague(nma_result,
                       bracket = "(",
                       digits = 2)
print(netleague)
League table (random effects model):
                                                               
                    A -1.08 (-1.44; -0.73) -0.85 (-1.18; -0.51)
 -0.76 (-1.01; -0.51)                    B -0.47 (-0.75; -0.19)
 -0.99 (-1.24; -0.75) -0.23 (-0.45; -0.02)                    C
 -1.35 (-1.62; -1.08) -0.59 (-0.83; -0.35) -0.35 (-0.56; -0.15)
                     
 -1.03 (-1.54; -0.53)
 -0.53 (-0.89; -0.16)
 -0.46 (-0.70; -0.21)
                    D

Lower triangle: results from network meta-analysis (column vs row)
Upper triangle: results from direct comparisons (row vs column)

森林圖：相對於參照組

Code 
forest(nma_result,
       reference.group = "A",
       sortvar = TE,
       smlab = "SMD vs. Treatment A",
       drop.reference.group = TRUE,
       label.left = "Favors treatment",
       label.right = "Favors A")

學術寫作範例

Results

This network meta-analysis included 12 RCTs comparing 4 treatments. Using treatment A as the reference, all treatments significantly reduced blood pressure.

任務四：一致性評估

為什麼要評估一致性？

  • 確保直接與間接證據不矛盾
  • 驗證網絡統合分析的有效性
  • 識別潛在的異質性來源

全域一致性檢定

Code 
decomp <- decomp.design(nma_result)
print(decomp)
Q statistics to assess homogeneity / consistency

                    Q df p-value
Total           15.31  9  0.0829
Within designs   0.23  6  0.9998
Between designs 15.07  3  0.0018

Design-specific decomposition of within-designs Q statistic

 Design    Q df p-value
    A:B 0.11  1  0.7422
    A:C 0.07  1  0.7970
    B:C 0.02  1  0.8915
    C:D 0.04  2  0.9814
    B:D 0.00  1  0.9926

Between-designs Q statistic after detaching of single designs
(influential designs have p-value markedly different from 0.0018)

 Detached design     Q df p-value
             B:C  3.65  2  0.1609
             A:B  4.41  2  0.1101
             C:D 10.86  2  0.0044
             A:D 11.76  2  0.0028
             A:C 12.26  2  0.0022
             B:D 14.33  2  0.0008

Q statistic to assess consistency under the assumption of
a full design-by-treatment interaction random effects model

                    Q df p-value tau.within tau2.within
Between designs 15.07  3  0.0018          0           0

局部一致性檢定

Code 
netsplit_result <- netsplit(nma_result)
print(netsplit_result)
Separate indirect from direct evidence (SIDE) using back-calculation method

Random effects model: 

 comparison k prop     nma  direct  indir.    Diff     z p-value
        B:A 2 0.50  0.7609  1.0814  0.4371  0.6443  2.50  0.0124
        C:A 2 0.52  0.9934  0.8482  1.1519 -0.3037 -1.23  0.2191
        D:A 1 0.29  1.3482  1.0328  1.4745 -0.4417 -1.45  0.1478
        B:C 2 0.57 -0.2325 -0.4707  0.0847 -0.5554 -2.52  0.0116
        B:D 2 0.43 -0.5873 -0.5266 -0.6331  0.1065  0.43  0.6657
        C:D 3 0.69 -0.3548 -0.4575 -0.1274 -0.3301 -1.46  0.1431

Legend:
 comparison - Treatment comparison
 k          - Number of studies providing direct evidence
 prop       - Direct evidence proportion
 nma        - Estimated treatment effect (SMD) in network meta-analysis
 direct     - Estimated treatment effect (SMD) derived from direct evidence
 indir.     - Estimated treatment effect (SMD) derived from indirect evidence
 Diff       - Difference between direct and indirect treatment estimates
 z          - z-value of test for disagreement (direct versus indirect)
 p-value    - p-value of test for disagreement (direct versus indirect)

一致性森林圖

Code 
forest(netsplit_result,
       show = "both",
       fontsize = 8)

一致性解讀標準

指標 閾值 解讀
Q (設計間) p < 0.10 存在設計間不一致性
節點分割 p 值 p < 0.10 該比較存在不一致性

任務五：異質性評估

異質性指標

Code 
cat("Tau²:", round(nma_result$tau^2, 4), "\n")
Tau²: 0.0221
Code 
cat("I² (within-design):", round(nma_result$I2, 1), "%\n")
I² (within-design): 0.4 %

I² 解讀標準

I² 值 異質性程度
< 25% 低度
25-50% 中度
50-75% 中高度
> 75% 高度

討論重點

  • 網絡內異質性 vs. 設計間異質性
  • τ² 的臨床意義
  • 高異質性時如何解讀排名

任務六：排名分析

P-score 排名

Code 
ranking <- netrank(nma_result, small.values = "desirable")
print(ranking)
  P-score
A  1.0000
B  0.6612
C  0.3387
D  0.0001

排名圖

Code 
plot(ranking)

SUCRA 概念

Surface Under the Cumulative Ranking (SUCRA)

  • 範圍：0% 到 100%
  • 越高代表該治療排名越好的機率越高

排名的不確定性

謹慎解讀排名

  • 排名差異可能不具統計顯著性
  • 應同時考慮信賴區間
  • 最佳治療不一定顯著優於第二名

任務七：次群體分析

次群體網絡統合分析

Code 
nma_data$period <- ifelse(
  as.numeric(gsub(".*?(\\d{4}).*", "\\1", nma_data$study)) < 2021,
  "2018-2020",
  "2021-2023"
)
table(nma_data$period)

2018-2020 2021-2023 
        6         6

注意事項

網絡統合迴歸的限制

  • 需要更多研究數
  • 共變數需在網絡中有足夠變異
  • 生態謬誤風險

任務八：發表偏誤

比較校正漏斗圖

Code 
funnel(nma_result,
       order = c("A", "B", "C", "D"),
       legend = TRUE)

漏斗圖解讀

  • 每個比較用不同符號標記
  • 對稱性評估需考慮多重比較
  • 小型研究效應可能因比較而異

任務九：敏感度分析

模型選擇敏感度

Code 
nma_fixed <- netmeta(
  TE = smd,
  seTE = se,
  treat1 = treat1,
  treat2 = treat2,
  studlab = study,
  data = nma_data,
  sm = "SMD",
  reference.group = "A",
  common = TRUE,
  random = FALSE
)

comparison_df <- data.frame(
  Comparison = c("B vs A", "C vs A", "D vs A"),
  Random = round(c(nma_result$TE.random["B", "A"],
                   nma_result$TE.random["C", "A"],
                   nma_result$TE.random["D", "A"]), 3),
  Fixed = round(c(nma_fixed$TE.common["B", "A"],
                  nma_fixed$TE.common["C", "A"],
                  nma_fixed$TE.common["D", "A"]), 3)
)
knitr::kable(comparison_df, caption = "模型比較")
模型比較
Comparison Random Fixed
B vs A 0.761 0.730
C vs A 0.993 1.003
D vs A 1.348 1.361

穩健性判斷

  • 排除任一研究後，排名是否改變？
  • 固定效應與隨機效應結果是否一致？
  • 結論是否穩健？

任務十：學術報告

PRISMA-NMA 報告要素

必要項目

  1. 網絡幾何圖
  2. League table
  3. 一致性評估

進階項目

  1. 排名（SUCRA/P-score）
  2. 發表偏誤
  3. 敏感度分析

結果摘要

Code 
# 建立排名摘要
rank_summary <- data.frame(
  Treatment = names(ranking$Pscore.random),
  P_score = round(ranking$Pscore.random, 3)
)
rank_summary <- rank_summary[order(-rank_summary$P_score), ]

summary_df <- data.frame(
  指標 = c("納入研究數", "治療選項數", "總樣本數",
           "最佳治療", "P-score", "I²"),
  數值 = c(nma_result$k,
           nma_result$n,
           sum(nma_data$n1) + sum(nma_data$n2),
           rank_summary$Treatment[1],
           paste0(rank_summary$P_score[1] * 100, "%"),
           paste0(round(nma_result$I2, 1), "%"))
)
knitr::kable(summary_df, caption = "網絡統合分析結果摘要")
網絡統合分析結果摘要
指標 數值
納入研究數 12
治療選項數 4
總樣本數 1642
最佳治療 A
P-score 100%
I² 0.4%

檢核清單

準備階段

項目 完成
明確定義研究問題 (PICO) ☐
事先註冊 (PROSPERO) ☐
確認網絡可連通 ☐

分析階段

項目 完成
繪製網絡幾何圖 ☐
執行網絡統合分析 ☐
評估一致性 ☐
計算治療排名 ☐
敏感度分析 ☐

報告階段

項目 完成
遵循 PRISMA-NMA 指引 ☐
呈現 League table ☐
GRADE 評估證據品質 ☐

課程總結

學習重點回顧

  1. 概念理解：直接證據、間接證據、混合證據
  2. 網絡幾何：連通性、節點、邊
  3. 一致性：全域檢定、局部檢定
  4. 排名：P-score、SUCRA
  5. 報告：PRISMA-NMA 指引

NMA vs. 傳統統合分析

面向 傳統統合分析 網絡統合分析
比較數 2 種治療 ≥ 3 種治療
證據類型 僅直接 直接 + 間接
排名 無 有
一致性 不適用 需評估

延伸學習資源

問答時間

感謝參與！

有任何問題歡迎討論