Making Sense of Data: The Key Elements of a Statistical Analysis Plan (SAP)

Statistical Analysis Plan (SAP)
Statistical Analysis Plan (SAP)

Clinical trials are a pillar of evidence-based medicine and one of its foundations include sound statistical analysis. Although study design is crucial for ensuring sponsors and contract research organizations (CROs) collect the necessary data related to the research question, statistics is what will allow researchers to summarize and interpret the results in a meaningful way. With the multiplicity of clinical trials and other experimental studies in recent decades, one of the key challenges scientists have identified is the issue of reproducibility. Statistics functions as an objective link between groups of previous findings to provide a basis for valid clinical applications and conclusions.

For this reason, clinical trial protocols are accompanied by an external Statistical Analysis Plan (SAP), which is critical for ensuring that data analysis is conducted accurately and efficiently. Although creating this document can be complex, requiring careful planning, we present the three key elements of an SAP in this post to help any clinical trial plan get on track even faster!

What is a Statistical Analysis Plan (SAP)?

A Statistical Analysis Plan (SAP) is a document separate from the clinical trial protocol that outlines the statistical methods that will be used to analyze the data collected in a research study. It serves as a blueprint for data analysis and ensures that the analysis is conducted in a systematic and transparent manner to be later analyzed or reproduced by other researchers. More specifically, it can provide instructions for data monitoring, interim statistical analysis, an integrated statistical analysis strategy, or a plan for statistical analysis of a clinical trial.

Compared to its accompanying protocol, SAPs are typically far more comprehensive documents, with in-depth technical details on the intended clinical trial analysis. A well-written SAP includes detailed descriptions of the statistical methods that will be used, as well as the data variables or endpoints that will be analyzed. It also includes information on the software that will be used for analysis, the assumptions that will be made, and the criteria for determining statistical significance.

Key Elements of an SAP

1 | Study Design and Data Collection

First, every SAP should provide a detailed description of the study design, including the following details:

  • Experimental design label or description: Longitudinal, observational, cohort, etc.
  • Comparison type: non-inferiority, superiority, equivalence
  • Type(s) of control: Placebo, no treatment, active drug, different dose, historical, etc.
  • Blinding label or description: Single-blinded, double-blinded, open-label, etc.
  • Method of treatment assignment: Randomization with stratification, minimization, adaptive assignment designs, etc.
  • Order and length of study periods: Screening, baseline, active treatment, and follow-up (this is often represented as a flowchart to clearly visualize the trial design)

An SAP will also include information on the population being studied, as well as any sampling strategies which may be utilized. Researchers must provide a precise description of which population will be included in the final analysis (e.g., Intention-To-Treat (ITT), as randomized, safety population, etc.). This section may involve copying over inclusion/exclusion criteria straight from the protocol or a basic description of relevant diagnostic or disease criteria (e.g. having a history of atopic dermatitis for at least the last 12 months). Other information that can be taken directly from the protocol are the study objectives, primary and secondary endpoints, sample size justification and power analysis, as well as the study schedule of assessments.

2 | Statistical Methods

The second key element of an SAP is including detailed descriptions of the statistical methods which will be used to analyze the data, including but not limited to the following:

  • Characterizing the null and alternative hypotheses
  • The chosen level of statistical significance (e.g., an alpha level of 0.05 is most common)
  • How missing data and outliers will be accounted for
  • Descriptions of covariates to be included
  • Procedures for handling noncompliance, withdrawals, and protocol deviations
  • Assessing treatment interactions, multiple comparisons, and subgroup analysis
  • Any interim or sequential analyses which may be applicable
  • Procedures for terminating a trial
  • Specifications of computer systems and statistical software packages (e.g., SPSS, SAS) to be used to analyze the data

Depending on whether the chosen measurement scales are nominal (e.g., if a disease is present or not), ordinal (e.g., the disease stages of cancer severity), or interval (e.g., blood pressure), different statistical methods must be used to accurately summarize the data. This section will also reveal the type of statistical tests to be conducted. For example, if certain assumptions can be made about the shape of distribution (e.g. normal) of the chosen population and this distribution’s form or parameters (e.g. means and standard deviations), parametric tests will be used; if these assumptions are not possible or applicable, nonparametric procedures will be used.

3 | Reporting and Interpretation of Results

The third element of SAPs should clearly define the criteria for determining statistical significance and provide a detailed explanation of the results, including how they will be reported and interpreted. This includes information on the format of the results (e.g., tables, graphs), the statistical significance of the findings, and any limitations of the analysis. Descriptive or summary statistics (e.g., non-missing sample size, mean, standard deviation, etc.) should also be displayed for continuous and categorical data. All data, in general, must be listed by site, treatment, subject, and visit if applicable, all separated between each treatment group and annotated with the total population size and any missing observations. The SAP should also include information on how the results will be presented in the final clinical trial report.

SAP: A Critical Tool in Clinical Research

A Statistical Analysis Plan (SAP) is a critical tool for supporting evidence-based clinical practices in medicine and scientific research. It provides a blueprint for data analysis that not only allows for a transparent assessment of the study’s results, but it provides a systematic approach for other researchers to replicate these results. A well-written SAP includes detailed descriptions of the study design and data collection methods, the statistical methods that will be used, and the reporting and interpretation of results.

By following the key elements outlined in this post, any sponsor’s or CRO’s researchers can ensure that their SAP is comprehensive and effective in guiding data analysis. Although the process of developing an SAP may be time-consuming and complex, it is a critical step in the research process that can lead to more accurate and impactful clinical trial findings.

Vial, The CRO for Biotech

As a leading full-service CRO powered by technology, our team at Vial believes in leveraging innovative digital technology to streamline data collection and simplify data analysis further for our clients. Trusted by leading sponsors, our specialized teams are paving the way for modernized clinical research by leveraging digital innovation to deliver shorter study timelines, quality, affordable services, and a clinical trial experience that puts you first. Contact a team member today to discover how we can help!

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