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COCHRANE ROB 2 · 偏誤風險評讀

BFI 疲倦分數

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總體判斷 OVERALL+Low(低風險)
Domain 與 overall 判斷由程式依 Cochrane RoB 2 官方演算法計算;AI 只負責回答下方各訊號問題。符號:+ 低風險、? 有些疑慮、- 高風險(不只靠顏色)。
風險偏誤紅綠燈
中文摘要(給一般讀者)
這篇論文的偏誤風險與 limitation
本試驗為 SDT 治療癌因性疲憊的隨機、雙盲、安慰劑對照初步試驗,以 BFI 為主要結果、採 ITT 加 LOCF 分析。隨機化採電腦亂數表之排列區塊、以不透明信封與藥局獨立執行確保分配隱匿,基線兩組均衡,隨機化偏誤風險低。雙盲設計、安慰劑外觀相符,受試者與人員皆盲化,並以適當 ITT 分析估計分派效果,偏離介入之偏誤風險低。主要結果以 LOCF 將全部 50 位隨機受試者納入 ITT,缺失資料偏誤風險低。BFI 為癌症族群驗證之自評量表、兩組測量方式一致且評估者(受試者)盲化,測量偏誤風險低。試驗事前登錄(KCT0003442)、主要結果與分析方法預先指定且計畫未變更,選擇性報告偏誤風險低。整體各 domain 均為低風險。
詳細評讀(給專業讀者)

Domain 1:隨機化過程 — 判斷:Low

偏誤來源:隨機分組的過程——分配序列是否隨機產生、是否隱蔽到分組為止、基線(第一期開始時)是否平衡。
1.1 Was the allocation sequence random?
分配序列是否隨機產生?
1.2 Was the allocation sequence concealed until participants were enrolled and assigned to interventions?
分配是否隱蔽到分組為止?
1.3 Did baseline differences between intervention groups suggest a problem with the randomization process?
(第一期開始時)基線差異是否暗示隨機化有問題?

Domain 2:偏離預定介入 — 判斷:Low

偏誤來源:實際執行時偏離預定的介入——盲性如何、是否依「隨機分到的組別」分析(ITT)。
2.1 Were participants aware of their assigned intervention during the trial?
參與者是否(在每一期)知道自己的分組?
2.2 Were carers and people delivering the interventions aware of participants' assigned intervention during the trial?
照護/執行者是否(在每一期)知道分組?
2.3 Were there deviations from the intended intervention that arose because of the trial context?
是否因試驗情境造成偏離?
2.4 Were these deviations likely to have affected the outcome?
這些偏離是否可能影響結局?
2.5 Were these deviations from intended intervention balanced between groups?
這些偏離在兩組間是否平衡?
2.6 Was an appropriate analysis used to estimate the effect of assignment to intervention?
是否用了適當分析(ITT)?
2.7 Was there potential for a substantial impact (on the result) of the failure to analyse participants in the group to which they were randomized?
未依隨機分組分析,是否可能對結果造成重大影響?

Domain 3:缺失結局資料 — 判斷:Low

偏誤來源:結局資料缺失——是否幾乎所有人都有資料、缺失是否可能與病情(結局真實值)相關。
3.1 Were data for this outcome available for all, or nearly all, participants randomized?
是否幾乎所有受試者都有此結局的資料?
3.2 Is there evidence that the result was not biased by missing outcome data?
是否有證據顯示結果未受缺失資料偏誤?
3.3 Could missingness in the outcome depend on its true value?
缺失是否可能取決於結局真實值?
3.4 Is it likely that missingness in the outcome depended on its true value?
缺失是否很可能取決於結局真實值?

Domain 4:結局測量 — 判斷:Low

偏誤來源:結局的測量——測量工具是否恰當、評估者是否盲化、評估是否可能受分組知情影響。
4.1 Was the method of measuring the outcome inappropriate?
結局測量方法是否不適當?
4.2 Could measurement or ascertainment of the outcome have differed between intervention groups?
(每一序列內)結局測量是否可能因介入而異?
4.3 Were outcome assessors aware of the intervention received by study participants?
結局評估者是否知道受試者接受的介入?
4.4 Could assessment of the outcome have been influenced by knowledge of intervention received?
結局評估是否可能受介入知情影響?
4.5 Is it likely that assessment of the outcome was influenced by knowledge of intervention received?
結局評估是否很可能受介入知情影響?

Domain 5:選擇性報告 — 判斷:Low

偏誤來源:選擇性報告結果——是否依事前訂定的分析計畫分析、有無從多個結果中挑選有利者報告(交叉另含:是否因偵測到殘留而只報第一期)。
5.1 Were the data that produced this result analysed in accordance with a pre-specified analysis plan that was finalized before unblinded outcome data were available for analysis?
是否依事前訂定(取得未盲化資料前定版)的分析計畫分析?
5.2 Is the numerical result being assessed likely to have been selected, on the basis of the results, from multiple eligible outcome measurements (e.g. scales, definitions, time points) within the outcome domain?
此數值結果是否可能依結果,從多個量表/定義/時間點中選出?
5.3 Is the numerical result being assessed likely to have been selected, on the basis of the results, from multiple eligible analyses of the data?
此數值結果是否可能依結果,從多種分析中選出?
本評讀依 Cochrane Risk of Bias 2 (RoB 2) 工具,針對「單一結果 × 單一效應」評估。限制:僅依論文全文判讀(無完整研究計畫書/統計分析計畫),故 Domain 5(選擇性報告)常為「資訊不足」。AI 僅回答訊號問題,domain 與 overall 由官方決策表程式計算。