SECONDARY STUDY DESIGNS

Secondary study designs are study designs that use pre-existing data. Rather than collect data by conducting a research project, a researcher analyzes, interprets, and draws conclusions from past research studies. There are two types of secondary study designs: non-systematic reviews, and systematic reviews. Systematic reviews are then further categorized into qualitative systematic reviews, and meta-analyses.

A non-systematic review is a summary of other published studies. The reviewer interprets the studies and draws conclusions from them, rather than using external criteria. It is also known as a literature review, or a narrative review.

Non-systematic reviews are well suited for general topics or broad coverage of a field as they usually cover a wide range of issues within a given topic. A non-systematic review can be identified by the general title and the absence of the words such as study of, clinical trial, or effects of.

Non-systematic reviews are typically written by the expert of a certain field, rather than by experts in research methodology. Because of this, a biased selection of the resources cited is bound to occur. This is known as selection bias. Another common bias in nonsystematic reviews is failure to include research that conflicts with the beliefs or opinions of the expert.

The term “systematic review” is a general term describing a look back at multiple published reports on a single topic. Its purpose is to answer one or more focused questions relevant to the topic.

Systematic reviews are actually a type of scientific investigation. What sets them apart from clinical trials is that the “subjects” are a cluster of previous published studies that meet strict criteria.

Investigators comb through bibliographic databases and then eliminate studies that do not meet certain criteria, like sufficient sample size or appropriate randomization. The use of a search strategy is what makes a review “systematic,” rather than just a nonsystematic (literature) review.

There are two types of systematic reviews: qualitative systematic reviews, and metaanalyses.

In a qualitative systematic review, the primary studies are reviewed and summarized without attempts at statistical analysis. Here, the authors use an explicit and systematic method to search for, critically appraise and combine the conclusions from individual studies on the topic of interest.

What sets apart a qualitative systematic review from a meta-analysis is that it doesn‖t view data from a numerical perspective. Rather, it looks at the conclusions derived, and summarizes them into one larger study.

Selection is based on predefined inclusion and exclusion criteria, quality is assessed, and data are abstracted in a standardized format. This means that the published result contains no numerical data; it contains conclusions derived from said data.

By explicitly stating how the evidence was found, how it was appraised or validated, and which studies were excluded (and why), qualitative systematic reviews eliminate many of the biases inherent in non-systematic reviews.

A meta-analysis is often a component of a systematic review. Meta-analyses combine the results of several studies about a topic as if they were from one large study.

Meta-analyses may be included in qualitative systematic reviews, since their numerical data can be abstracted. The studies included in a meta-analysis are found using the same rigorous search methodology as that used for systematic reviews.

In a sense, meta-analysis is a data-oriented, statistically grounded research study about research studies. Like other research studies, meta-analyses use relative risk, odds ratios, confidence intervals, and data that are used in reports of clinical trial research.

A pilot study is a sort of trial study. Its purpose is to determine whether the proposed experiment for a research problem is appropriate, ethical, and effective. It utilizes conditions approximating those of the larger, proposed study but with a smaller number of subjects over a much shorter period of time.

The pilot can provide information about the difficulties surrounding the recruitment of participants and appropriateness and ethicality of data collection procedures. In addition, the pilot can be used to assess the efficacy of the proposed research instruments.The pilot can also obtain preliminary estimates of many statistics useful in informing sample size calculations for future investigations. In certain circumstances, the pilot can even produce preliminary answers to certain research questions.

If successful, the results of a pilot study can be helpful in convincing potential stakeholders and other researchers that the proposed research is feasible and is likely to yield results.

If the investigator concludes that the study is defective or too time-costly, they may consider modifications to render the study appropriate. Examples of such modifications include expanding the scope of the problem, adding additional study sites, and altering the study design.

Study Design Randomized Control Trial Crosssectional Cohort Case-Control

Study Population Highly selected population, highly controlled environment

Diverse population observed in a range of setting

Primary Use Demonstrating efficacy of an intervention

Screening hypotheses; prevalence studies

Assessing association between multiple exposures and outcomes over time

Assessing associations between exposures and rare outcome

Analysis Straightforward

Internal Validity

External Validity

High

Sophisticated multivariate techniques may be required to account for confounding

Low

Low-Moderate

High