GCRI · Labs
研究プロトコル
A research protocol fixes, before data are collected, what a study asks, how it will answer and what would count as an answer: the hypotheses, the design, the controls, the outcomes, the sample and the analysis. Many failures of credibility trace back to a protocol that was vague, unwritten or changed after the data were seen. This page sets out how protocols are built, registered, reviewed and reported, and what ethics committees, editors and funders rely on when they accept a study’s claims.
作業の進め方。
プロトコルと追試のボード
Research protocols · Labs · Quarter 3
プロトコル
| PR-378 sensor drift | フィールド実験 | はい | 承認済み | 実行中 |
| PR-705 AI triage | ランダム化 | はい | 条件 | セットアップ |
| PR-694 message recall | サーベイ実験 | はい | 承認済み | 完了 |
| PR-360 heat exposure | コホート | いいえ | 保留中 | 草案 |
Replication R-25
- 元の効果
- 0.4 SD
- 再現の効果
- 0.2 SD
- 判定
- 小さく、同じ符号
- 公開済み
- データとコード付き
テストベッドの空き状況
図Protocol and replication board: example names and illustrative values, not a record or a live system.
Research protocols: how the risk develops.
Hypotheses, controls and scientific acceptance. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Flexibility becomes bias
When outcomes, exclusions, covariates or stopping rules are chosen after the data are seen, many analyses become possible and the most striking one tends to be reported. Each choice looks reasonable alone; together they inflate false positives and effect sizes.
Weak controls confound results
A missing control, unblinded assessment or non-random allocation lets something other than the intervention explain the difference. Design flaws cannot be repaired by analysis; they travel into every result and review built on the study.
Underpowered designs mislead twice
Small samples miss real effects, and the effects they do detect tend to be overestimated. Underpowered studies that reach significance seed literatures that later, larger replications cannot match.
Deviations go unreported
Protocols change for good reasons, such as slow recruitment or a safety signal. When a change is not dated, explained and reported, readers cannot tell a justified amendment from outcome switching, and reviews pool what should not be pooled.
How research protocols is modelled, measured and tested.
モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。
モデル、方程式、仮定
The model structure used for research protocols, the assumptions it makes and how each is tested.
Ri = f(Hi, Ei, Vi, Ci)ハザード、エクスポージャー、脆弱性、対処能力EAL = ∑k pk · Lk年間確率 pkxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error| 仮定 | 作られる理由 | 検証の方法 |
|---|---|---|
| 一つの事象の中では、ハザードとエクスポージャーは独立している | 損失の積分を扱いやすく保つ | 同時記録に当てはめたコピュラと比較 |
| 結合は動作点の近くで線形である | ショックを A を通じて伝播させる | 線形範囲を超えて摂動を与えて比較する |
| 脆弱性曲線は場所を越えて適用できる | 地域固有の曲線はめったに得られない | 観測された損失がある場合はそれに照らして検証 |
- 入力
- 来歴付きのバージョン管理されたデータセット
- 成果物
- 分布であり、単一の数値ではない
- コード
- モデルカードと試験とともに公開
- 再現
- 同じデータ、コード、環境、シード
Protocols are registered before results in every domain.
Protocols feed the Labs Evidence series. Registered 報告書 and EQUATOR guidelines set practice; studies with people follow the Declaration of Helsinki (revised 2024).
- Protocols for review.
- Replication proposals.
何を、どの程度うまく測定しているか。
各指標には、その単位、更新頻度、背後にある出典の種類、値が持つことになるエビデンスのラベルが付いています。値は表示しません：プログラムが自らの権限のもとで値を提供します。
| 指標 | 単位 | 周期 | 出典の種類 | ラベル |
|---|---|---|---|---|
| Registration before data collection | percent of studies with a time-stamped registration | 年次 | Trial and study registries | 公表統計 |
| Primary outcome consistency | percent of reports matching the registered primary outcome | 年次 | Registry and publication comparison | 観測 |
| Sample size justification | percent of protocols with a power or precision rationale | 年次 | Protocol audit | 観測 |
| Reporting guideline adherence | percent of applicable checklist items reported | 研究ごと | Assessment against CONSORT, PRISMA, ARRIVE or STROBE | 推定 |
| Protocol amendments | count per study, each with a stated reason | 研究ごと | Registry history and ethics committee records | 公表統計 |
| Registered report availability | number of journals offering the format | 年次 | Center for Open Science journal list | 報告値 |
| Results posting timeliness | months from primary completion to posted results | 研究ごと | Trial registries | 公表統計 |
| Randomisation and blinding reported | percent of eligible studies | 年次 | Risk-of-bias assessments | 推定 |
エビデンスの出所。
名前の明示された公開データセットと機関の記録。それぞれにライセンス区分があります。合意に基づく出典は、その条件のもとでのみ使用し、再公開することはありません。
| 出典 | 提供するもの | ライセンス区分 |
|---|---|---|
| OSF Registries (Center for Open Science) | Time-stamped preregistrations and registered report protocols across disciplines | Open, licence per registration |
| ClinicalTrials.gov (US National Library of Medicine) | Registered trials, protocol change history and posted results | パブリックドメイン |
| WHO International Clinical Trials Registry Platform | Search across primary national trial registries | Free, WHO terms of use |
| PROSPERO (NIHR) | Registered protocols for systematic reviews | Free access, terms of use |
| protocols.io | Versioned, citable step-by-step laboratory and field methods | Open, CC BY for public protocols |
| EQUATOR Network library | Reporting guidelines by study design, with extensions | Free access, licence per guideline |
| Crossref metadata | Links between registrations, articles, corrections and funders | Open metadata, no restrictions |
誰が決め、エビデンスが何に役立つか。
ラボ prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| 誰が決定するか | 質問 | このページが提供するエビデンス |
|---|---|---|
| Research ethics committees and institutional review boards | Whether a protocol’s risks, consent and design justify approval, and whether an amendment needs fresh review | Complete protocol, sample size rationale, amendment history with reasons |
| Journal editors | Whether to accept a registered report in principle, or whether a submitted analysis matches its plan | Time-stamped registration compared line by line with the manuscript |
| 研究資金提供者 | Whether a proposal’s design can answer its question before money is committed | Power or precision rationale, control conditions, data management plan |
| Medicines and device regulators | Whether trial evidence was generated under the protocol submitted to them | Registry history, deviation logs, results posting record |
このトピックが生み出す成果物
- Protocol templates stating hypotheses, controls, outcomes and analysis plans before data collection
- Registration records with time stamps and version history
- Deviation logs that date and explain every amendment
- Reporting checklists completed against the relevant EQUATOR guideline
What experts ask of any protocol.
- What exactly is the hypothesis, and what result would count against it?
- What is the comparison: a control group, a baseline period, a counterfactual model or a randomised arm?
- Which outcomes are primary, which are secondary, and how will each be measured?
- What sample, duration or number of runs is needed to detect an effect of the size that matters?
- What would make the team stop early, for safety or for futility, and who decides?
- What will be published if the result is null or the experiment fails?
Why experiments drift without a protocol.
Every experiment involves choices: which observations to exclude, which covariates to adjust for, which time window to use. Each choice may be reasonable alone, but when they are made after seeing the data, the number of possible analyses grows quickly and one of them will usually show something. This is the problem methodologists call analytic flexibility, and it is a central reason why published effects often shrink or vanish when repeated.
Interdisciplinary work adds its own drivers. Disciplines differ in what they treat as a control, how they handle missing data and what level of evidence they expect. A hydrologist, an economist and an epidemiologist may each consider a design rigorous by their own standard while disagreeing about what it shows. A protocol forces those differences into the open before work starts.
Incentives matter too. Positive, novel findings are easier to publish and fund, which rewards flexibility and penalises null results. Pre-registration and registered reports are the main structural responses.
Tools protocols draw on, and where they stop.
- 01
Pre-registration
A time-stamped record of hypotheses and analysis plans. It limits post hoc changes but cannot stop a poorly chosen question.
- 02
Power and design analysis
Estimates how much data is needed to detect a meaningful effect, using 確率と統計. It depends on assumed effect sizes that are often uncertain.
- 03
Causal designs
Randomisation, difference-in-differences, regression discontinuity and instrumental variables from 因果推論. Each rests on assumptions that must be stated and, where possible, tested.
- 04
Acceptance criteria
Pre-agreed thresholds for when a method, model or prototype is considered to work. They make success testable but can be gamed if set too loosely.
A protocol cannot make a bad measurement good or fix a biased sample. Deviations from protocol are permitted when justified, but are reported with their reasons.
Where this page’s references lead.
このページの読解の基準となる機関と文書。状態と版は変わります。各リンクは発行機関へ移動します。
質問と回答。
What is the difference between a preregistration and a registered report?
Does preregistration forbid exploratory analysis?
Which reporting guideline applies to my study?
Can a protocol be changed after registration?
Is pre-registration required for every Labs study?
Can a protocol change during a study?
Who writes the protocol?
このページからエビデンスを提供する。
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
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