Recently, I faced a question on the statistical analysis method for an animal study involving testing of lipid lowering agents. The study was designed along the following lines. Animals were randomized to receive one of the study agents. Prior to initiation of treatment, lipid levels (LDL, HDL and total cholesterol) were measured (baseline). Study drugs were administered daily for xx days and the lipid levels were again measured at end of study (post treatment).

This kind of study design is often labelled as a pretest – posttest design and is quite common in the medical field for comparing different treatments.

In many clinical studies of cholesterol lowering agents, the percent change from baseline is analyzed for between group differences. Hence, I suggested using percent change from baseline as the outcome measure in an ANOVA as the statistical analysis method for the above animal experiment. On further scanning of the literature, I however noted that in spite of the widespread prevalence of the pretest – posttest experimental design, there is a lack of consensus on the best method for the data analysis.

The possible choices for the outcome measure to use in the analysis of data from a pretest – posttest study could be the post-treatment values (PV) or the change between baseline and post treatment values, referred to in the literature as change scores or gain scores (DIFF) or any measure of relative change between baseline and post treatment values. Percentage change from baseline (PC) mentioned above is an example of a measure of relative change. Some of the other measures of relative change include symmetrized percent change (SPC) and log ratio of post-treatment to baseline (LR). Furthermore, the methods for statistical analysis include parametric and non-parametric versions of ANOVA or ANCOVA on any of the above outcome measure. So indeed there exist a lot of possibilities for the analysis of data from a pretest – posttest study!

Various simulation studies have provided us with pointers to guide the choice of the outcome measure and analysis method. The current thinking seems to be that an ANCOVA on PV has higher power than a simple ANOVA on PC, especially in the situation where little correlation exists between baseline and post treatment values. However, SPC with wither the ANOVA or ANCOVA seems to be a good option in the case of additive or multiplicative correlation between baseline and post treatment values.

The simulation studies have tried to mimic various real scenarios. Vickers has studied in detail the situation where the outcome is continuous and there is an additive correlation between post treatment and baseline values. SPC as an outcome measure was not studied (Vickers A. J. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study. BMC Medical Research Methodology (2001) 1:6). Meanwhile, Berry & Ayers consider count data, include SPC as an outcome measure and consider parametric and non-parametric analysis methods in the presence of additive and multiplicative correlation between baseline and post treatment values (Berry D. A. & Ayers G. D. Symmetrized Percent Change for Treatment Comparisons. The American Statistician (2006) 60:1, 27-31).

At the time of design of the statistical analysis plan, there is bound to be little information available on the extent and type of correlation that may exist between the baseline and post treatment values. Hence there is a need to conduct extensive review and/or simulations, taking into account also the type of data and correlation structures encountered in different therapeutic areas, to identify (therapeutic area specific!) best practices for the choice of the outcome measure and analysis method for the pretest – posttest design.