how to tell if treatment groups were similar at baseline

by Prof. Amelie Lind DDS Published 3 years ago Updated 2 years ago

Statistical hypothesis testing was used to compare the treatment groups in baseline characteristics, including sex distribution, age, birth rank, health problems, and ethnicity, plus education level of the parents. The researchers reported that the treatment groups were comparable in baseline characteristics.

Full Answer

How to compare the treatment groups in baseline characteristics?

Your question mirrors that of people who run randomized clinical trials & want to test for differences in covariates at baseline. The groups were randomly assigned initially. In this example, the Treated group would have been a randomly chosen from an initial population of 200,000 (75% to treated, 25% to control).

Why is it important for the treatment groups to be similar?

Dec 22, 2011 · Table Table1 1 shows simulated data from a randomised trial, with two groups (A and B) of size 30 drawn from the same population, so that there is no systematic baseline difference between the groups. There is a baseline measurement, with standard deviation 2.0, and a final measurement, equal to the baseline plus a random variable with mean 0.0, standard …

Is it possible to test for differences at baseline?

Jul 17, 1999 · In a controlled trial randomisation ensures that allocation of patients to treatments is left purely to chance. The characteristics of patients that may influence outcome are distributed between treatment groups so that any difference in outcome can be assumed to be due to the intervention. However, imbalance between groups in baseline ...

How are the characteristics of patients distributed between treatment groups?

Planned comparisons: C1: baseline vs. other two combined; C2: control vs. single treatment would appear to make good sense here. On the other …

Which test is used to compare the two groups at baseline?

paired t test

For each group, we can compare baseline with final measurement using a paired t test.Dec 22, 2011

Why should groups be similar at baseline?

If randomisation is successful and the groups are similar at baseline, the investigator can be more confident that observed differences in outcomes between the groups are related to the intervention rather than confounding factors.

Why is it important to have similar baseline characteristics?

Baseline characteristics also allow the success of randomisation to be assessed. In studies where important baseline factors appear well balanced, it is likely that any differences in outcome between the intervention and control groups are a real effect of treatment (one component of internal validity).Jul 21, 2003

How do you describe baseline characteristics?

Baseline characteristics or data are demographic, medical and other information, particularly prognostic variables. Researchers collect this information about each participant at the beginning of a study, before they have received the treatments that are going to be compared in a treatment comparison.

What does baseline comparison mean?

The Baseline Comparison Report displays a side-by-side comparison of items and relationships in two baselines. This report is like the Baseline Compared to Current report, but instead compares two specific states of selected items at a certain point in time.Jan 13, 2022

Did baseline differences between intervention groups suggest a problem with the randomization process?

Whether: the allocation sequence was random; the allocation sequence was adequately concealed; baseline differences between intervention groups suggest a problem with the randomization process.

What does intention to treat mean in research?

Intention-to-treat analysis is a method for analyzing results in a prospective randomized study where all participants who are randomized are included in the statistical analysis and analyzed according to the group they were originally assigned, regardless of what treatment (if any) they received.

Why is baseline data important in healthcare?

The purpose of the baseline information is to assess the effect of the program and to compare what happens before and after the program has been implemented. Without baseline data, it's difficult to estimate any changes or to demonstrate progress, so it's best to capture baseline whenever possible.

What are baseline variables?

A baseline covariate is usually defined as a qualitative factor or a quantitative variable measured or observed before a subject starts taking study medication (usually before randomisation) and expected to influence the primary variable to be analysed.

What is the baseline in clinical trials?

An initial measurement of a condition that is taken at an early time point and used for comparison over time to look for changes. For example, the size of a tumor will be measured before treatment (baseline) and then afterwards to see if the treatment had an effect.

What is baseline data in clinical trials?

Lead Author(s): Information gathered at the beginning of a study from which variations found in the study are measured. A person's health status before he or she begins a clinical trial.

What is baseline age?

1) A variable that is constant over the course of the study (e.g., sex, race) or that changes for everyone at the same rate (e.g., age). When the variables change at a uniform rate, these measures are taken at some point just prior to treatment, like the pre-treatment visit.Mar 11, 2018

Most recent answer

Thank you all greatly for your detailed responses. I truly appreciate the help, we have determined that due to the nature of the study that simple two-way ANOVA is most appropriate.

All Answers (9)

You might consider MANOVA for the multiple dependent variables in your treatment versus control groups, or if you have enough observations within each group, you could consider growth curve modeling.

What group do high risk patients end up in?

If the severe, high-risk patients are normally situated in low number rooms (closer to the nurses’ station), more severe cases will end up in group A, inflating the risk for the outcome (death) in this group.

What are the drawbacks of randomization?

Despite its advantages, there are two major drawbacks to random group allocation 1 Randomization can fail, especially with very small sample sizes because the study arms can become out of balance simply due to chance (3). Importantly, this is not a problem with randomization per se, but rather a problem with inadequate sample size. 2 Though ideal for testing treatments, for some research questions a randomized controlled trial is inappropriate due to ethical or practical concerns. For example, it would be unethical to randomize individuals to a ‘smoking’ vs. ‘no smoking’ group to test whether smoking causes cancer.

Why is randomization important?

Randomization ensures that both groups have a similar prognosis for the outcome before the start of treatment and that any differences will be chance differences. This thus best approximates the counterfactual ideal, as described above.

What are some examples of outcomes in randomised trials?

Examples of some outcomes include blood pressure, pain, physiological responses, range of motion, etc.

What is the purpose of covariance analysis?

An analysis of covariance adjusts each subject’s score for their baseline score and is unaffected by chance baseline differences and regression to the mean. This method is easily applied to data at two time points, and assumes linearity between baseline and follow up scores where data are continuous.

Abstract

In randomised trials, rather than comparing randomised groups directly some researchers carry out a significance test comparing a baseline with a final measurement separately in each group.

Background

When we randomise trial participants into two or more groups, we do this so that they will be comparable in every respect except the intervention which they then receive. The essence of a randomised trial is to compare the outcomes of groups of individuals that start off the same.

Discussion

Using separate paired tests against baseline and interpreting only one being significant as indicating a difference between treatments is a frequent practice. It is conceptually wrong, statistically invalid, and consequently highly misleading.

Conclusions

We think that randomised groups should be compared directly by two-sample methods and that separate tests against baseline are highly misleading. We also think that trialists should produce estimates with confidence intervals rather than significance tests [ 2, 3 ].

Rights and permissions

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

About this article

Bland, J.M., Altman, D.G. Comparisons against baseline within randomised groups are often used and can be highly misleading. Trials 12, 264 (2011). https://doi.org/10.1186/1745-6215-12-264

INTRODUCTION

Throughout the design and implementation of the channeling demonstration, emphasis has been placed on the importance of random assignment of eligible applicants into treatment and control groups.

I. SCREEN DATA AND RANDOMIZATION

The source and nature of the screen data on which this analysis is based are discussed below, and sample sizes are indicated. This is followed by a brief description of the randomization procedures.

II. ASSESSMENT OF EQUIVALENCE OF TREATMENT AND CONTROL GROUPS

To assess whether the treatment and control groups created by the randomization procedures were equivalent at the time of randomization, variables describing the characteristics of the sample members were constructed from the screen data.

III. SUMMARY AND IMPLICATIONS FOR FUTURE ANALYSES

The overriding conclusion from all of the comparisons made between treatment and control groups is that the randomization procedure has resulted in groups that are very similar on observable characteristics.

APPENDIX A. ESTIMATION METHODOLOGY

While simple differences in grand means for the treatment and control groups could be used to estimate treatment/control differences on any variable, the potential differences across sites in these variables and in the ratio of treatments to controls could lead to distorted estimates.

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