Methods: Quantitative variables (11)

The items from STROBE state that you should report:
- Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen, and why


Some key items to consider adding:
- If applicable, describe how effects of treatment were dealt with

Explanation

Investigators make choices regarding how to collect and analyse quantitative data about exposures, effect modifiers and confounders. For example, they may group a continuous exposure variable to create a new categorical variable (see box 4). Grouping choices may have important consequences for later analyses (Altman, 1994; Royston et al., 2006). We advise that authors explain why and how they grouped quantitative data, including the number of categories, the cut-points, and category mean or median values. Whenever data are reported in tabular form, the counts of cases, controls, persons at risk, person-time at risk, etc. should be given for each category. Tables should not consist solely of effect-measure estimates or results of model fitting.

Investigators might model an exposure as continuous in order to retain all the information. In making this choice, one needs to consider the nature of the relationship of the exposure to the outcome. As it may be wrong to assume a linear relation automatically, possible departures from linearity should be investigated. Authors could mention alternative models they explored during analyses (eg, using log transformation, quadratic terms or spline functions). Several methods exist for fitting a nonlinear relation between the exposure and outcome (Greenland, 1995; Royston et al., 1999, 2006). Also, it may be informative to present both continuous and grouped analyses for a quantitative exposure of prime interest.

In a recent survey, two thirds of epidemiological publications studied quantitative exposure variables (Pocock et al., 2004). In 42 of 50 articles (84%) exposures were grouped into several ordered categories, but often without any stated rationale for the choices made. Fifteen articles used linear associations to model continuous exposure but only 2 reported checking for linearity. In another survey, of the psychological literature, dichotomization was justified in only 22 of 110 articles (20%) (R. MacCallum et al., 2002).

Example

“Patients with a Glasgow Coma Scale less than 8 are considered to be seriously injured. A GCS of 9 or more indicates less serious brain injury. We examined the association of GCS in these two categories with the occurrence of death within 12 months from injury” (Linn et al., 2007).


Field-specific guidance

Anti-microbial stewardship programs (Tacconelli et al., 2016)
- Provide subgroup analyses for immunocompromised, surgical/medical patients and patients in intensive care units, if applicable

Nutritional data (Lachat et al., 2016)
- Explain the categorization of dietary/nutritional data (e.g., use of N-tiles and handling of nonconsumers) and the choice of reference category, if applicable

Seroepidemiologic studies for influenza (Horby et al., 2017)
- Describe the serological assay’s limit of detection and how this limit is defined or calculated. Describe how samples with a result below or on the borderline of the limit were handled in the analysis
- Describe and justify the titer or other result used to define “seropositivity,” or the antibody titer change or change in other assay result used to define “seroconversion.” Avoid the term “seroconversion” unless referring to change from undetectable to detectable antibody level. Otherwise report the fold-rise in titer. Avoid the term “infection” but report “seroprevalence at a titer of ….”
- If statements or inferences are made about protection from infection, describe what is known about the correlation between the assay results and protection from infection and illness

Resources

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References

Altman, D. (1994). Dangers of using “optimal” cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst, 86, 829–835.

Greenland, S. (1995). Avoiding Power Loss Associated with Categorization and Ordinal Scores in Dose-Response and Trend Analysis. Epidemiology, 6(4), 450–454. https://www.jstor.org/stable/3702100

Horby, P. W., Laurie, K. L., Cowling, B. J., Engelhardt, O. G., Sturm-Ramirez, K., Sanchez, J. L., Katz, J. M., Uyeki, T. M., Wood, J., Van Kerkhove, M. D., & the CONSISE Steering Committee. (2017). CONSISE statement on the reporting of Seroepidemiologic Studies for influenza (ROSES-I statement): An extension of the STROBE statement. Influenza and Other Respiratory Viruses, 11(1), 2–14. https://doi.org/10.1111/irv.12411

Lachat, C., Hawwash, D., Ocké, M. C., Berg, C., Forsum, E., Hörnell, A., Larsson, C., Sonestedt, E., Wirfält, E., Åkesson, A., Kolsteren, P., Byrnes, G., De Keyzer, W., Van Camp, J., Cade, J. E., Slimani, N., Cevallos, M., Egger, M., & Huybrechts, I. (2016). Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement. PLOS Medicine, 13(6), e1002036. https://doi.org/10.1371/journal.pmed.1002036

Linn, S., Levi, L., Grunau, P. D., Zaidise, I., & Zarka, S. (2007). Effect Measure Modification and Confounding of Severe Head Injury Mortality by Age and Multiple Organ Injury Severity. Annals of Epidemiology, 17(2), 142–147. https://doi.org/10.1016/j.annepidem.2006.08.004

MacCallum, R., zhang, S., preacher, K., & rucker, D. (2002). On the practice of dichotomization of quantitative variables. Psychol Methods, 19–40.

Pocock, S. J., Collier, T. J., Dandreo, K. J., Stavola, B. L. de, Goldman, M. B., Kalish, L. A., Kasten, L. E., & McCormack, V. A. (2004). Issues in the reporting of epidemiological studies: A survey of recent practice. The BMJ, 329(7471), 883. https://doi.org/10.1136/bmj.38250.571088.55

Royston, P., Altman, D. G., & Sauerbrei, W. (2006). Dichotomizing continuous predictors in multiple regression: A bad idea. Statistics in Medicine, 25(1), 127–141. https://doi.org/10.1002/sim.2331

Royston, P., Ambler, G., & Sauerbrei, W. (1999). The use of fractional polynomials to model continuous risk variables in epidemiology. International Journal of Epidemiology, 28(5), 964–974. https://doi.org/10.1093/ije/28.5.964

Tacconelli, E., Cataldo, M. A., Paul, M., Leibovici, L., Kluytmans, J., Schröder, W., Foschi, F., Angelis, G. D., Waure, C. D., Cadeddu, C., Mutters, N. T., Gastmeier, P., & Cookson, B. (2016). STROBE-AMS: Recommendations to optimise reporting of epidemiological studies on antimicrobial resistance and informing improvement in antimicrobial stewardship. BMJ Open, 6(2), e010134. https://doi.org/10.1136/bmjopen-2015-010134