People’s outcomes should be assessed similarly

This is the eighteenth blog in a series of 36 blogs explaining 36 key concepts we need to be able to understand to think critically about treatment claims.

If an outcome is measured differently in two comparison groups, differences in that outcome may be due to how the outcome was measured rather than because of the treatment received by people in each group. For example, if outcome assessors believe that a particular treatment works and they know which patients have received that treatment, they may be more likely to observe better outcomes in those who have received the treatment.

Comparing the validity and robustness of different statistical methods for meta-analysis of rare event data

Tarang Sharma was lead-author of a recent article entitled “The Yusuf-Peto method was not a robust method for meta-analyses of rare events data from antidepressant trials”. In this blog, Tarang gives more details about meta-analysis methods of rare events and sparse data, and why these can lead to misleading results.

Evidence-based health practice: a fairytale or reality?

This blog, written by Leonard Goh, was the winner of Cochrane Malaysia and Penang Medical College’s recent evidence-based medicine blog writing competition. Leonard has written an insightful and informative piece to answer the question: ‘Evidence-based health practice: a fairytale or reality’.

People should not know which treatment they get

This is the seventeenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.

People in a treatment group may experience improvements (for example, less pain) because they believe they are receiving a better treatment, even if the treatment is not actually better (this is called a placebo effect), or because they behave differently (due to knowing which treatment they received, compared to how they otherwise would have behaved). If individuals know that they are receiving (they are not “blinded” to) a treatment that they believe is better, some or all of the apparent effects of the treatment may be due either to a placebo effect or because the recipients behaved differently.

Comparison groups should be treated equally

This is the sixteenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.

Apart from the treatments being compared, people in the treatment comparison groups should otherwise receive similar care. If, for example, people in one group receive more attention and care than people in the comparison group, differences in outcomes could be due to differences in the amount of attention each group received rather than due to the treatments that are being compared. One way of preventing this is to keep providers unaware (“blind”) of which people have been allocated to which treatment.

A thorough look into meta-analysis

This blog follows on from Ammar’s previous post on meta-analysis, and provides further details on the history, value and implementation of this approach.

People’s outcomes should be analyzed in their original groups

This is the fifteenth blog in a series of 34 blogs explaining 34 key concepts we need to be able to understand to think critically about treatment claims.

This blog explains how randomized allocation helps to ensure that the groups have similar characteristics. However, people sometimes do not receive or take the allocated treatments. The characteristics of such people often differ from those who do take the treatment as allocated. Therefore, excluding from the analysis people who did not receive the allocated treatment may mean that like is no longer being compared with like.