Evidence map›Paper›PMID 29696162›Full record

ArticleContemporary clinical trials communications2018

Different ways to estimate treatment effects in randomised controlled trials.

Twisk J, Bosman L, Hoekstra T, Rijnhart J, Welten M, Heymans M

Erratum issuedAbstract read
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In one paragraph

Article in Contemporary clinical trials communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 276 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
276citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

276 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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216 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Twisk JDepartment of Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, the Netherlands.
Bosman LDepartment of Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, the Netherlands.
Hoekstra TDepartment of Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, the Netherlands.
Rijnhart JDepartment of Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, the Netherlands.
Welten MDepartment of Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, the Netherlands.
Heymans MDepartment of Epidemiology and Biostatistics, VU Medical Centre, Amsterdam, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRegarding the analysis of RCT data there is a debate going on whether an adjustment for the baseline value of the outcome variable should be made. When an adjustment is made, there is a lot of misunderstanding regarding the way this should be done. Therefore, the aims of this educational paper are: 1) to explain different methods used to estimate treatment effects in RCTs, 2) to illustrate the different methods with a real life example and 3) to give an advise on how to analyse RCT data.

methodsLongitudinal analysis of covariance, repeated measures analysis in which also the baseline value is used as outcome and the analysis of changes were theoretically explained and applied to an example dataset investigating a systolic blood pressure lowering treatment.

resultsIt was shown that differences at baseline should be taken into account and that regular repeated measures analysis and regular analysis of changes did not adjust for the baseline differences between the groups and therefore lead to biased estimates of the treatment effect. In the real life example, due to the differences at baseline between the treatment and control group, the different methods lead to different estimates of the treatment effect.

conclusionRegarding the analysis of RCT data, it is advised to use longitudinal analysis of covariance or a repeated measures analysis without the treatment variable, but with the interaction between treatment and time in the model.

Indexed as

Analysis of changesLongitudinal of covarianceRandomised controlled trialsRegression to the meanRepeated measures

Identifiers

PMID29696162
PMCPMC5898524

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.