Meta-analysis for one group

The previous article, explained how to enter data for performing meta-analysis of the studies reporting comparison data. Therefore this article explains the data entry methodology, for performing meta-analysis on the outcomes of studies having a single group. For this purpose, the single group of patients who were administered drug treatment will be taken into consideration.

One group and matched group designs in Meta-analysis

In matched group design, the unit of analysis is the pair of participants who have been matched on the basis of a particular attribute as deemed fit by the researcher. One member from each pair is assigned to different groups, thus each pair serves as its own control. This tends to reduce error and increase statistical significance of results. Here, the degree of correlation has particular importance, because it influences the degree of impact between the matched participants (White & Sabarwal 2014).

One group (pre-post) design includes administration of the treatment to the single group of patients. Also, the evaluations are carried out by measuring the patient condition before and after the treatment (Marsden & Torgerson 20152). In the present Obsessive-Compulsive Disorder (OCD) case study, the patients were administered drug treatment and their pre-post treatment scores were measured over CY-BOCS scale.

One group (pre-post) data entry

The figure below shows the effect size data directory for ‘One group (Pre-post) and matched groups’. The encircled format is the desired format for present demonstration. Select any of the formats as per their preference and available data.

Figure 1: Effect size data directory for 'One group (Pre-post) and matched groups' for meta-analysis in CMA
Figure 1: Effect size data directory for ‘One group (Pre-post) and matched groups’ for meta-analysis in CMA

The figure below shows the interface of the spreadsheet pertaining to the ‘Mean difference, Standard Deviation of difference and sample size’ format in ‘One group (pre-post)’ directory. However this format offers no option for customizing the field names and the columns have already been named as per the data to be entered. Also, the effect measures to be calculated are same as in the previous article as the outcome is continuous.

Figure 2 : Interface for the selected data entry format for meta-analysis in CMA
Figure 2 : Interface for the selected data entry format for meta-analysis in CMA

Note: The different calculations to be performed for obtaining the data as per the requirement of the format have been shown below.

  • Mean difference: It is obtained by carrying out simple subtraction of post mean from the pre mean.
  • Standard deviation of difference: Here the standard deviation of the difference scores has to be calculated. It could be obtained by using the following formula:

Here also ‘Pre or Post Corr’ field refers to the correlation values between the pre and post scores within group. The correlation values were not readily available, hence imputed (0.5), following the same logic as in the previous article.

The figure below shows the completed spreadsheet, with calculated effect size. The mean difference values have been kept as negative, as the post scores were lower than the pre scores. The effect direction was determined automatically by the sign of ‘Mean difference’.

Figure 3 : Single group spreadsheet with calculated effected sizes for meta-analysis in CMA
Figure 3 : Single group spreadsheet with calculated effected sizes for meta-analysis in CMA

Data analysis procedures

Furthermore, proceed with the analysis for the single group studies. The next article will discuss data analysis procedures, wherein the results for both the case studies will be analyzed and discussed.

Computing meta analysis with different effect size with CMAAnalysis and interpretation of results using meta analysis

Yashika Kapoor

Research analyst at Project Guru
Yashika has completed her bachelors in life sciences and holds a masters in forensic sciences. Being a major in forensic biology, she is trained in techniques of DNA extraction and sequencing. She also has hands on experience of dealing with sensitive evidences and case files. She aims at developing her knowledge base through fact based learning. With a penchant for reading, and writing, she likes to keep her facts concrete. She is a confident person and aims at achieving perfection in every task assigned to her. She aims at securing a place in her professional life which allows her to explore different areas relevant to her field of work. Along with academics, she is a creative soul. Food, art and craft are some of her other passions.
Yashika Kapoor


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