Dataset Information

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

ABSTRACT:

Background

Power 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?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.

Results

Seventy-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.

Conclusion

The 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.

SUBMITTER: Ofuya M

PROVIDER: S-EPMC4063432 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Ofuya Mercy M Sauzet Odile O Peacock Janet L JL

Systematic reviews 20140612

<h4>Background</h4>Power 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 stud ...[more]

PMID: 24920271

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Project description:BackgroundRandomised controlled trials (RCTs) provide valuable information for developing harm profiles but current analysis practices to detect between-group differences are suboptimal. Drug trials routinely screen continuous clinical and biological data to monitor participant harm. These outcomes are regularly dichotomised into abnormal/normal values for analysis. Despite the simplicity gained for clinical interpretation, it is well established that dichotomising outcomes results in a considerable reduction in information and thus statistical power. We propose an automated procedure for the routine implementation of the distributional method for the dichotomisation of continuous outcomes proposed by Peacock and Sauzet, which retains the precision of the comparison of means.MethodsWe explored the use of a distributional approach to compare differences in proportions based on the comparison of means which retains the power of the latter. We applied this approach to the screening of clinical and biological data as a means to detect 'signals' for potential adverse drug reactions (ADRs). Signals can then be followed-up in further confirmatory studies. Three distributional methods suitable for different types of distributions are described. We propose the use of an automated approach using the observed data to select the most appropriate distribution as an analysis strategy in a RCT setting for multiple continuous outcomes. We illustrate this approach using data from three RCTs assessing the efficacy of mepolizumab in asthma or COPD. Published reference ranges were used to define the proportions of participants with abnormal values for a subset of 10 blood tests. The between-group distributional and empirical differences in proportions were estimated for each blood test and compared.ResultsWithin trials, the distributions varied across the 10 outcomes demonstrating value in a practical approach to selecting the distributional method in the context of multiple adverse event outcomes. Across trials, there were three outcomes where the method chosen by the automated procedure varied for the same outcome. The distributional approach identified three signals (eosinophils, haematocrit, and haemoglobin) compared to only one when using the Fisher's exact test (eosinophils) and two identified by use of the 95% confidence interval for the difference in proportions (eosinophils and potassium).ConclusionWhen dichotomisation of continuous adverse event outcomes aids clinical interpretation, we advocate use of a distributional approach to retain statistical power. Methods are now easy to implement. Retaining information is especially valuable in the context of the analysis of adverse events in RCTs. The routine implementation of this automated approach requires further evaluation.

Project description:BackgroundFour studies previously indicated that the effect of malaria infection during pregnancy on the risk of low birthweight (LBW; <2,500 g) may depend upon maternal nutritional status. We investigated this dependence further using a large, diverse study population.Methods and findingsWe evaluated the interaction between maternal malaria infection and maternal anthropometric status on the risk of LBW using pooled data from 14,633 pregnancies from 13 studies (6 cohort studies and 7 randomized controlled trials) conducted in Africa and the Western Pacific from 1996-2015. Studies were identified by the Maternal Malaria and Malnutrition (M3) initiative using a convenience sampling approach and were eligible for pooling given adequate ethical approval and availability of essential variables. Study-specific adjusted effect estimates were calculated using inverse probability of treatment-weighted linear and log-binomial regression models and pooled using a random-effects model. The adjusted risk of delivering a baby with LBW was 8.8% among women with malaria infection at antenatal enrollment compared to 7.7% among uninfected women (adjusted risk ratio [aRR] 1.14 [95% confidence interval (CI): 0.91, 1.42]; N = 13,613), 10.5% among women with malaria infection at delivery compared to 7.9% among uninfected women (aRR 1.32 [95% CI: 1.08, 1.62]; N = 11,826), and 15.3% among women with low mid-upper arm circumference (MUAC <23 cm) at enrollment compared to 9.5% among women with MUAC ? 23 cm (aRR 1.60 [95% CI: 1.36, 1.87]; N = 9,008). The risk of delivering a baby with LBW was 17.8% among women with both malaria infection and low MUAC at enrollment compared to 8.4% among uninfected women with MUAC ? 23 cm (joint aRR 2.13 [95% CI: 1.21, 3.73]; N = 8,152). There was no evidence of synergism (i.e., excess risk due to interaction) between malaria infection and MUAC on the multiplicative (p = 0.5) or additive scale (p = 0.9). Results were similar using body mass index (BMI) as an anthropometric indicator of nutritional status. Meta-regression results indicated that there may be multiplicative interaction between malaria infection at enrollment and low MUAC within studies conducted in Africa; however, this finding was not consistent on the additive scale, when accounting for multiple comparisons, or when using other definitions of malaria and malnutrition. The major limitations of the study included availability of only 2 cross-sectional measurements of malaria and the limited availability of ultrasound-based pregnancy dating to assess impacts on preterm birth and fetal growth in all studies.ConclusionsPregnant women with malnutrition and malaria infection are at increased risk of LBW compared to women with only 1 risk factor or none, but malaria and malnutrition do not act synergistically.

Dataset Information

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

Background

Results

Conclusion

Publications

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

Similar Datasets

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