21d. Additional Analyses

What to write

Methods for any additional analyses (eg, subgroup and sensitivity analyses), distinguishing prespecified from post hoc

Examples

“We conducted prespecified sensitivity analyses to examine the effect of our assumption that participants who withdrew or were lost to follow-up returned to smoking: (1) a complete case analysis and (2) multiple imputation to impute missing smoking abstinence and reduction data. Multiple imputation was performed using the fully conditional specification approach with 5 imputed data sets and results combined using the Rubin rules (eMethods in Supplement 2). Other prespecified sensitivity analyses examined the effect of imbalances in baseline participant characteristics using multiple logistic regression models to estimate odds ratios and 95% CIs [confidence intervals] for point prevalence abstinence at 12 and 24 weeks, adjusting for characteristics for which the absolute value of the standardized difference was 0.1 or greater. We conducted additional post hoc analyses: (1) to examine potential clustering by site using generalized linear mixed models with a random effect for site to estimate odds ratios and 95% CIs for point prevalence abstinence at 12 and 24 weeks, and (2) to compare the baseline characteristics of participants with self-reported smoking data at 12 weeks (primary end point) with those of participants without self-reported smoking data. Statistical analyses were performed using SAS statistical software (version 9.4; SAS Institute).”1

“Several prespecified sensitivity analyses were done. First, assessment of the effect of missing data on the primary outcome was done using multiple imputation by chained equations method (MICE). This imputation model included all the variables in the primary ITT [intention to treat] analysis, secondary outcomes (from each timepoint), and baseline variables associated with the missingness of the primary outcome. 20 imputed datasets were generated and combined using Rubin’s rules, and the primary analysis model was then repeated using the imputed data. We specified a priori the following potential exploratory analyses to assess effect modification on the primary outcome: baseline hypertension, baseline MMSE [Mini-Mental State Examination], baseline age, time since Alzheimer’s disease diagnosis, baseline brain volume, and change in systolic blood pressure. A post-hoc analysis was also done to investigate for differences between aggregated and disaggregated MRI [magnetic resonance imaging] data (according to MRI scanner modality) for the primary outcomes.”2

“Four sensitivity analyses were done examining the primary outcome: restricted to women who had not received antibiotics in the 7 days before delivery, to examine whether any masking of a prophylactic effect was occurring by inclusion of pretreated women; excluding women prescribed antibiotics (other than the trial intervention) within the first 24 h after delivery, and who might therefore already have had an infection at the time of administration of the intervention; restricted to women whose primary outcome was obtained between weeks 6 and 10 after delivery to exclude any biases by over-reporting of outcomes from data returned at a later timepoint or under-reporting of outcomes in data returned at an earlier timepoint; and including centre as a random effect. No subgroup analyses were planned; however, we did a post-hoc subgroup analysis of the primary outcome according to mode of birth (forceps or vacuum extraction). More stringent 99% CIs [confidence intervals] are presented for the estimate of RR [risk ratio] for this post-hoc subgroup analysis.”3

“A prespecified subgroup analysis for the primary outcomes, testing for an interaction for baseline anxiety, depression, and opioid use, defined using their median values was completed. Prespecified sensitivity analyses for the primary outcome, excluding participants included in process evaluation interviews, adjusting for the imbalance of death, and split by baseline pain disorders were also completed. Because of the potential for type I error due to multiple comparisons, findings for analyses of secondary end points should be interpreted as exploratory. Statistical analyses were conducted using Stata version 16.1 (StataCorp).”4

Explanation

Sensitivity analyses can be important additional analyses to examine the robustness of the primary trial results under a range of assumptions about the data, methods, and models that differ from those of the primary analysis. When the findings from a sensitivity analysis are consistent with the primary trial findings, trialists can be confident that any assumptions in the primary analysis had little impact—strengthening the trial results. Morris and colleagues provide a principled approach to guide any sensitivity analyses by posing three questions to trialists: does the proposed sensitivity analysis address the same question as the primary analysis; is it possible for the proposed sensitivity analysis to return a different result to the primary analysis; and if the results do differ, is there any uncertainty as to which will be believed.5,6

Subgroup analyses are another set of additional analyses that are widely carried out and reported.710 Here, the focus is on those analyses that look for evidence of a difference in treatment effect in complementary subgroups (eg, older and younger participants), a comparison known as a test of interaction.11 Empirical analyses of subgroup difference claims for factors such as age, sex, race, ethnicity, and other factors show selective reporting, frequent lack of proper statistical support, and poor independent corroboration.1214

A common but misleading approach is to compare P values for separate analyses of the treatment effect in each group. Categorising continuous variables to create subgroups is often done for simplicity and because it is perceived as easier to understand and communicate. Major limitations of the approach include the splitting of a continuous variable into discrete subgroups by arbitrarily chosen cut-off points that lack clinical or biological plausibility, which loses information, and thus reduces statistical power.15 Choosing cut-off points based on achieving statistical significance should be avoided. It is incorrect to infer a subgroup effect (interaction) from one significant (in one subgroup) and one non-significant P value (in another subgroup). The rationale for any subgroups should be outlined (including how they are defined), along with whether the subgroups were specified a priori in the protocol or statistical analysis plan or were done post hoc. Because of the high risk for spurious findings, subgroup analyses are often discouraged. Post hoc subgroup comparisons (analyses done after looking at the data) are especially likely not to be confirmed by further studies. Most of these analyses do not have substantial credibility.

An alternative and stronger approach, which avoids the need to specify cut-off points to assess the interaction between a continuous variable (eg, age) and treatment effect would be to fit a regression model, which can be presented graphically to examine how the estimated treatment effects varies with the level of the variable.16 These analyses are more complex, requiring model assumptions to capture the relationship (linear or non-linear) between the variable and the treatment effect. Authors should clearly describe the statistical methods used to explore the treatment-covariate interaction.

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References

1.
Eisenberg MJ, Hébert-Losier A, Windle SB, et al. Effect of e-cigarettes plus counseling vs counseling alone on smoking cessation: A randomized clinical trial. JAMA. 2020;324(18):1844. doi:10.1001/jama.2020.18889
2.
Kehoe PG, Turner N, Howden B, et al. Safety and efficacy of losartan for the reduction of brain atrophy in clinically diagnosed alzheimer’s disease (the RADAR trial): A double-blind, randomised, placebo-controlled, phase 2 trial. The Lancet Neurology. 2021;20(11):895-906. doi:10.1016/s1474-4422(21)00263-5
3.
Knight M, Chiocchia V, Partlett C, et al. Prophylactic antibiotics in the prevention of infection after operative vaginal delivery (ANODE): A multicentre randomised controlled trial. The Lancet. 2019;393(10189):2395-2403. doi:10.1016/s0140-6736(19)30773-1
4.
Sandhu HK, Booth K, Furlan AD, et al. Reducing opioid use for chronic pain with a group-based intervention: A randomized clinical trial. JAMA. 2023;329(20):1745. doi:10.1001/jama.2023.6454
5.
Morris TP, Kahan BC, White IR. Choosing sensitivity analyses for randomised trials: principles. BMC Medical Research Methodology. 2014;14(1). doi:10.1186/1471-2288-14-11
6.
Parpia S, Morris TP, Phillips MR, et al. Sensitivity analysis in clinical trials: Three criteria for a valid sensitivity analysis. Eye. 2022;36(11):2073-2074. doi:10.1038/s41433-022-02108-0
7.
Williamson SF, Grayling MJ, Mander AP, et al. Subgroup analyses in randomized controlled trials frequently categorized continuous subgroup information. Journal of Clinical Epidemiology. 2022;150:72-79. doi:10.1016/j.jclinepi.2022.06.017
8.
Sun X, Briel M, Busse JW, et al. The influence of study characteristics on reporting of subgroup analyses in randomised controlled trials: Systematic review. BMJ. 2011;342(mar28 1):d1569-d1569. doi:10.1136/bmj.d1569
9.
Paratore C, Zichi C, Audisio M, et al. Subgroup analyses in randomized phase III trials of systemic treatments in patients with advanced solid tumours: A systematic review of trials published between 2017 and 2020. ESMO Open. 2022;7(6):100593. doi:10.1016/j.esmoop.2022.100593
10.
Brand KJ, Hapfelmeier A, Haller B. A systematic review of subgroup analyses in randomised clinical trials in cardiovascular disease. Clinical Trials. 2021;18(3):351-360. doi:10.1177/1740774520984866
11.
Brankovic M, Kardys I, Steyerberg EW, et al. Understanding of interaction (subgroup) analysis in clinical trials. European Journal of Clinical Investigation. 2019;49(8). doi:10.1111/eci.13145
12.
Wallach JD, Sullivan PG, Trepanowski JF, Steyerberg EW, Ioannidis JPA. Sex based subgroup differences in randomized controlled trials: Empirical evidence from cochrane meta-analyses. BMJ. Published online November 2016:i5826. doi:10.1136/bmj.i5826
13.
Liu P, Ioannidis JPA, Ross JS, et al. Age-treatment subgroup analyses in cochrane intervention reviews: A meta-epidemiological study. BMC Medicine. 2019;17(1). doi:10.1186/s12916-019-1420-8
14.
Liu P, Ross JS, Ioannidis JP, Dhruva SS, Vasiliou V, Wallach JD. Prevalence and significance of race and ethnicity subgroup analyses in cochrane intervention reviews. Clinical Trials. 2019;17(2):231-234. doi:10.1177/1740774519887148
15.
Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080.1. doi:10.1136/bmj.332.7549.1080
16.
Royston P, Sauerbrei W. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Statistics in Medicine. 2004;23(16):2509-2525. doi:10.1002/sim.1815

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Citation

For attribution, please cite this work as:
Hopewell S, Chan AW, Collins GS, et al. CONSORT 2025 statement: updated guideline for reporting randomised trials. BMJ. 2025;389:e081123. doi:10.1136/bmj-2024-081123

Reporting Guidelines are recommendations to help describe your work clearly

Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.

Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.

Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.

Easier writing

Following guidance makes writing easier and quicker.

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Maximum impact

From nobel prizes to null results, articles have more impact when everyone can use them.

Who reads research?

You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.

Cohort studies

A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).

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Case-control studies

A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.

An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.

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Cross-sectional studies

A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.

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Systematic reviews

A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.

The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a [meta-analysis]{.defined data-bs-toggle="offcanvas" href="#glossaryItemmeta_analyses" aria-controls="offcanvasExample" role="button"}.

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Systematic review protocols

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Meta analyses of Observational Studies

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Randomised Trials

A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.

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Randomised Trial Protocols

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Qualitative research

Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behaviour. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

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Case Reports

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Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify people with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).

Prediction Models

Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.

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Animal Research

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Quality Improvement in Healthcare

Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.

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Economic Evaluations in Healthcare

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Meta Analyses

A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.

How Meta-analyses and Systematic Reviews Work Together

Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.

Why Don't All Systematic Reviews Use a Meta-Analysis?

Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.

Protocol

A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.

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