Dichotomisation of a continuous outcome and effect on meta-analyses: illustration of the distributional approach using the outcome birthweight.

Mercy Ofuya,Odile Sauzet,Janet L Peacock

doi:10.1186/2046-4053-3-63

Mercy Ofuya, Odile Sauzet + Show 1 more

Open Access

https://doi.org/10.1186/2046-4053-3-63

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Abstract

BackgroundPower and precision are greater in meta-analyses than individual study analyses. However, dichotomisation of continuous outcomes in certain studies poses a problem as estimates from primary studies can only be pooled if they have a common outcome. Meta-analyses may include pooled summaries of either or both the continuous and dichotomous forms, and potentially have a different combination of studies for each depending on whether the outcome was dichotomised in the primary studies or not. This dual-outcome issue can lead to loss of power and/or selection bias. In this study we aimed to illustrate how dichotomisation of a continuous outcome in primary studies may result in biased estimates of pooled risk and odds ratios in meta-analysis using secondary analyses of published meta-analyses with the outcome, birthweight, which is commonly analysed both as continuous, and dichotomous (low birthweight: birthweight < 2,500 g).MethodsMeta-analyses published in January 2010 - December 2011 were obtained using searches in PubMed, Embase, Web of Science, and Cochrane Database of Systematic Reviews with the outcome birthweight. We used a distributional method to estimate the pooled odds/risk ratio of low birthweight and its standard error as a function of the data reported in the primary studies of the included meta-analyses where accessible.ResultsSeventy-six meta-analyses were identified. Thirty-seven percent (28/76) of the meta-analyses reported only the dichotomous form of the outcome while 26% (20/76) reported only the continuous form. In one meta-analysis (1/76), birthweight was analysed as continuous for one intervention and as binary for another and 36% (27/76) presented both dichotomous and continuous birthweight summaries. In meta-analyses with a continuous outcome, primary studies data were accessible in 39/48 and secondary analyses using the distributional approach provided consistent inferences for both the continuous and distributional estimates in 38/39.ConclusionThe distributional method applied in primary studies allows both a continuous and dichotomous outcome to be estimated providing consistent inferences. The use of this method in primary studies may restrict selective outcome bias in meta-analyses.

Highlights

Power and precision are greater in meta-analyses than individual study analyses
Primary studies with a common outcome can be pooled in a meta-analysis and so dichotomisation of continuous outcomes presents a difficulty over and above the loss of power [4], underestimation of effect size [5], and the need for larger samples [6,7] associated with the practice
Primary studies included in the calculation of pooled estimates may differ for the continuous and dichotomous form according to data presented in the separate reports and, a meta-analysis may not include all the primary studies carried out on a research question, leading to an incomplete and potentially biased summary of the evidence

Summary

Introduction

Power and precision are greater in meta-analyses than individual study analyses. dichotomisation of continuous outcomes in certain studies poses a problem as estimates from primary studies can only be pooled if they have a common outcome. Meta-analyses may include pooled summaries of either or both the continuous and dichotomous forms, and potentially have a different combination of studies for each depending on whether the outcome was dichotomised in the primary studies or not. This dual-outcome issue can lead to loss of power and/or selection bias. Primary studies included in the calculation of pooled estimates may differ for the continuous and dichotomous form according to data presented in the separate reports and, a meta-analysis may not include all the primary studies carried out on a research question, leading to an incomplete and potentially biased summary of the evidence. Information from the same primary study may be used in both meta-analyses, making the results repetitive and not necessarily confirmatory [9]

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