17. Other analyses

What to write

Report other analyses done—e.g., analyses of subgroups and interactions, and sensitivity analyses.

Explanation

In addition to the main analysis other analyses are often done in observational studies. They may address specific subgroups, the potential interaction between risk factors, the calculation of attributable risks, or use alternative definitions of study variables in sensitivity analyses.

There is debate about the dangers associated with subgroup analyses, and multiplicity of analyses in general. In our opinion, there is too great a tendency to look for evidence of subgroup-specific associations, or effect-measure modification, when overall results appear to suggest little or no effect. On the other hand, there is value in exploring whether an overall association appears consistent across several, preferably pre-specified subgroups especially when a study is large enough to have sufficient data in each subgroup. A second area of debate is about interesting subgroups that arose during the data analysis. They might be important findings, but might also arise by chance. Some argue that it is neither possible nor necessary to inform the reader about all subgroup analyses done as future analyses of other data will tell to what extent the early exciting findings stand the test of time. We advise authors to report which analyses were planned, and which were not (see also items 4. Study design, 12b. Statistical methods – subgroups and interactions and 20. Interpretation). This will allow readers to judge the implications of multiplicity, taking into account the study’s position on the continuum from discovery to verification or refutation.

A third area of debate is how joint effects and interactions between risk factors should be evaluated: on additive or multiplicative scales, or should the scale be determined by the statistical model that fits best (see also 12b. Statistical methods – subgroups and interactions and 12b. Statistical methods – subgroups and interactions )? A sensible approach is to report the separate effect of each exposure as well as the joint effect—if possible in a table, as in the first example above, or in the study by Martinelli et al.. Such a table gives the reader sufficient information to evaluate additive as well as multiplicative interaction (how these calculations are done is shown in 12b. Statistical methods – subgroups and interactions ). Confidence intervals for separate and joint effects may help the reader to judge the strength of the data. In addition, confidence intervals around measures of interaction, such as the Relative Excess Risk from Interaction (RERI) relate to tests of interaction or homogeneity tests. One recurrent problem is that authors use comparisons of P-values across subgroups, which lead to erroneous claims about an effect modifier. For instance, a statistically significant association in one category (e.g., men), but not in the other (e.g., women) does not in itself provide evidence of effect modification. Similarly, the confidence intervals for each point estimate are sometimes inappropriately used to infer that there is no interaction when intervals overlap. A more valid inference is achieved by directly evaluating whether the magnitude of an association differs across subgroups.

Sensitivity analyses are helpful to investigate the influence of choices made in the statistical analysis, or to investigate the robustness of the findings to missing data or possible biases (see also 12b. Statistical methods – subgroups and interactions). Judgement is needed regarding the level of reporting of such analyses. If many sensitivity analyses were performed, it may be impractical to present detailed findings for them all. It may sometimes be sufficient to report that sensitivity analyses were carried out and that they were consistent with the main results presented. Detailed presentation is more appropriate if the issue investigated is of major concern, or if effect estimates vary considerably.

Pocock and colleagues found that 43 out of 73 articles reporting observational studies contained subgroup analyses. The majority claimed differences across groups but only eight articles reported a formal evaluation of interaction (see 12b. Statistical methods – subgroups and interactions).

Examples

“Analysis of Oral Contraceptive Use, Presence of Factor V Leiden Allele, and Risk for Venous Thromboembolism4,8”

“Sensitivity of the Rate Ratio for Cardiovascular Outcome to an Unmeasured Confounder9”

Training

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References

1.
Pocock SJ, Collier TJ, Dandreo KJ, et al. Issues in the reporting of epidemiological studies: A survey of recent practice. BMJ. 2004;329(7471):883. doi:10.1136/bmj.38250.571088.55
2.
Gøtzsche PC. Believability of relative risks and odds ratios in abstracts: Cross sectional study. BMJ. 2006;333(7561):231-234. doi:10.1136/bmj.38895.410451.79
3.
Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology. 1990;1(1):43-46. doi:10.1097/00001648-199001000-00010
4.
Botto LD. Commentary: Facing the challenge of gene-environment interaction: The two-by-four table and beyond. American Journal of Epidemiology. 2001;153(10):1016-1020. doi:10.1093/aje/153.10.1016
5.
Martinelli I, Taioli E, Battaglioli T, et al. Risk of venous thromboembolism after air travel: Interaction with thrombophilia and oral contraceptives. Archives of Internal Medicine. 2003;163(22):2771. doi:10.1001/archinte.163.22.2771
6.
Anderson HR, Atkinson RW, Peacock JL, Sweeting MJ, Marston L. Ambient particulate matter and health effects: Publication bias in studies of short-term associations. Epidemiology. 2005;16(2):155-163. doi:10.1097/01.ede.0000152528.22746.0f
7.
Kyzas PA, Loizou KT, Ioannidis JPA. Selective reporting biases in cancer prognostic factor studies. JNCI: Journal of the National Cancer Institute. 2005;97(14):1043-1055. doi:10.1093/jnci/dji184
8.
Vandenbroucke JP, Koster T, Rosendaal FR, Briët E, Reitsma PH, Bertina RM. Increased risk of venous thrombosis in oral-contraceptive users who are carriers of factor v leiden mutation. The Lancet. 1994;344(8935):1453-1457. doi:10.1016/s0140-6736(94)90286-0
9.
Wei L, MacDonald TM, Walker BR. Taking glucocorticoids by prescription is associated with subsequent cardiovascular disease. Annals of Internal Medicine. 2004;141(10):764-770. doi:10.7326/0003-4819-141-10-200411160-00007

Citation

For attribution, please cite this work as:
von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The STROBE reporting guideline for writing up observational studies in epidemiology. The EQUATOR Network guideline dissemination platform. doi:10.1234/equator/1010101

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.

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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).

Source.

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.

Source.

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.

Source

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.

Source

Systematic review protocols

TODO

Meta analyses of Observational Studies

TODO

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.

Source

Randomised Trial Protocols

TODO

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 behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

Source

Case Reports

TODO

Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify subjects 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.

Source

Animal Research

TODO

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.

Source

Economic Evaluations in Healthcare

TODO

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.

Source

Cohort_studies

In cohort studies, the investigators follow people over time. They obtain information about people and their exposures at baseline, let time pass, and then assess the occurrence of outcomes. Investigators commonly make contrasts between individuals who are exposed and not exposed or among groups of individuals with different categories of exposure. Investigators may assess several different outcomes, and examine exposure and outcome variables at multiple points during follow-up. Closed cohorts (for example birth cohorts) enrol a defined number of participants at study onset and follow them from that time forward, often at set intervals up to a fixed end date. In open cohorts the study population is dynamic - people enter and leave the population at different points in time (for example inhabitants of a town). Open cohorts change due to deaths, births, and migration, but the composition of the population with regard to variables such as age and gender may remain approximately constant, especially over a short period of time. In a closed cohort cumulative incidences (risks) and incidence rates can be estimated; when exposed and unexposed groups are compared, this leads to risk ratio or rate ratio estimates. Open cohorts estimate incidence rates and rate ratios.

Case_control_studies

In case-control studies, investigators compare exposures between people with a particular disease outcome (cases) and people without that outcome (controls). Investigators aim to collect cases and controls that are representative of an underlying cohort or a cross-section of a population. That population can be defined geographically, but also more loosely as the catchment area of health care facilities. The case sample may be 100% or a large fraction of available cases, while the control sample usually is only a small fraction of the people who do not have the pertinent outcome. Controls represent the cohort or population of people from which the cases arose. Investigators calculate the ratio of the odds of exposures to putative causes of the disease among cases and controls (see Item 16c). Depending on the sampling strategy for cases and controls and the nature of the population studied, the odds ratio obtained in a case-control study is interpreted as the risk ratio, rate ratio or (prevalence) odds ratio [@pmed-0040297-b016; @pmed-0040297-b017]. The majority of published case-control studies sample open cohorts and so allow direct estimations of rate ratios.

Cross-sectional_studies

In cross-sectional studies, investigators assess all individuals in a sample at the same point in time, often to examine the prevalence of exposures, risk factors or disease. Some cross-sectional studies are analytical and aim to quantify potential causal associations between exposures and disease. Such studies may be analysed like a cohort study by comparing disease prevalence between exposure groups. They may also be analysed like a case-control study by comparing the odds of exposure between groups with and without disease. A difficulty that can occur in any design but is particularly clear in cross-sectional studies is to establish that an exposure preceded the disease, although the time order of exposure and outcome may sometimes be clear. In a study in which the exposure variable is congenital or genetic, for example, we can be confident that the exposure preceded the disease, even if we are measuring both at the same time.