Cochrane Evidence Synthesis and Methods最新文献

This tutorial provides guidance on creating clear and informative summary of findings tables for systematic reviews of interventions.

In the 2022 Cochrane Lecture [1], Jimmy Volmink, recognized as a pioneer of evidence-based medicine in Africa, challenged Cochrane to enhance its equity efforts and suggested five ways to do so. We, as members of the Campbell and Cochrane Health Equity Thematic group*, fully agree with his suggestions and have developed an actionable plan, described below. We invite the global community to join us in our efforts to meet the equity gaps in research and practice.

Population health and healthcare delivery should be equitable and the research that guides it equity sensitive. By this, we mean that we need to focus on the distribution of health outcomes in the population not just overall health. That is, people should have equal opportunities for health and are not subjected to systemic discrimination or structural barriers to health. It is an ambitious goal and one that many of us who work in healthcare delivery and health research are striving for.

This includes those of us who work in evidence synthesis. Synthesizers of other researchers' evidence may think that our handling of equity cannot be better than the handling of equity in the research we synthesize. We, as members of the Campbell and Cochrane Health Equity Thematic Group, disagree. To truly address inequity, evidence synthesis must take into account equity considerations in a systematic and explicit manner, regardless of how equity was addressed in the original research. We believe that evidence synthesis should lead the way in promoting equity, rather than simply reflecting the approaches taken in the primary research that is included in our reviews (Box 1).

We fully agree that Cochrane cannot succeed in better addressing health equity in systematic reviews without also addressing inequities in its own organization and governance. As members of the Campbell and Cochrane Health Equity Thematic group, we commit to the following actions.

Tamara Lotfi: Conceptualization, writing—original draft, reviewing and editing. Vivian Welch: Conceptualization, writing—original draft, review & editing. Jordi P. Pardo: Conceptualization, writing—original draft, review & editing. Jennifer Petkovic: Conceptualization, writing—original draft, review & editing. Shaun Treweek: Writing—review & editing. Andrea Darzi: Writing—review & editing. Rebecca Glover: Writing—review & editing. Declan Devane: Writing—review & editing. Meera Viswanathan: Writing—review & editing. Lawrence Mbuagbaw: Writing—review & editing. Kevin Pottie: Writing—review & editing. Elizabeth Kristjansson: Writing—review & editing. Shahab Sayfi: Writing—review & editing. Lara Maxwell: Writing—review & editing. Olivia Magwood: Writing—review & editing. Damian Francis: Writing—review & editing. Dru Riddle: Writing—review & ed

In Equation 4 (computation of the standard error [SE] of the mean difference [MD] at posttreatment time point), we wrote SD instead of SE, which is the correct one (see attached figure below).

We apologize for this error.

This tutorial focuses on “intention-to-treat” analyses and missing outcome data in systematic reviews. There is a lack of consensus on the definition of the ITT approach. We will explain the principles of an intention-to-treat analysis, and outline the key issues you need to consider when planning, conducting and writing up your systematic review.

The authors of studies included in systematic reviews may use the term “intention-to-treat” or “intent-to-treat” (ITT) to describe the approach taken when reporting and analyzing outcome data. The ITT approach has two principles.

Principle A: Outcome data are reported and/or analysed according to the participant's assigned intervention, regardless of the intervention they actually received or their adherence to their assigned intervention. For randomised controlled trials, this approach is sometimes referred to as an “as-randomised” analysis.

Study authors make decisions about which approach to take based on whether they are interested in determining the effect of allocation to an intervention (regardless of whether the intervention was received as intended), the effect of receiving an intervention, or the effect of adhering to an intervention (as specified in the trial protocol).

Principle B: Outcome data are measured for all randomised participants.

If some participants do not contribute data for the outcome of interest at the required follow-up time (i.e., there are missing outcome data), data may be imputed. Various imputation methods are available, from simply assuming that all participants with missing data had a particular outcome (e.g., study authors may assume that all participants with missing data experienced a poor outcome, such as treatment failure), to more complex methods such as multiple imputation.

This principle is not met if study authors report and/or analyze outcome data only for participants with nonmissing outcome data (this approach is sometimes referred to as a “complete-case analysis”).

When choosing whether to ignore or impute missing data, and when selecting an imputation method, study authors should consider whether missing data are likely to be “missing at random” or not. Data are “missing at random” if the fact that the data are missing is unrelated to the true data values. Complete-case analyses, and some imputation methods, may lead to biased results if the missing data is “missing not at random.” Table 1 provides examples of data that are “missing at random” and data that are “missing not at random.”

There is no consensus on the definition of the ITT approach [1, 2]. Some study authors use the term ITT when applying both principles; others use the term when applying just one principle. Study authors may use the term “modified ITT” approach, which also has no consistent definition. The estimated intervention effect in a study may be impacted by the study author's choice of ITT approach. If this