RESEARCH

Here, we aim to illuminate your path through the exciting world of academic research. Whether you're crafting your first research question or preparing to publish your findings, our resources are designed to support and inspire you every step of the way.

First step: developing a research question

Developing a research question is a critical first step in guiding the direction of your study. Begin by focusing on the primary question you wish to explore, then use your literature review to narrow it down, ensuring it addresses a specific knowledge gap in the field. The PICOT criteria—Population, Intervention, Comparator, Outcome, and Time Frame—can help refine your question by specifying the group of interest, the intervention under study, the comparison group, the expected outcomes, and the timeframe over which the outcomes will be measured or observed. Additionally, applying the FINER criteria ensures that your research question is Feasible, given your resources and time; Interesting to both you and the wider academic community; Novel, contributing new knowledge or insights; Ethical, respecting the dignity and rights of participants; and Relevant, addressing important issues that can inform policy or practice. By meticulously crafting your research question to meet these criteria, you set a strong foundation for a meaningful and impactful study.

Literature Review

When developing a research question, it's essential to thoroughly evaluate the current literature on your topic to identify existing knowledge gaps. Begin by considering which databases to utilize, such as PubMed, Scopus, and Cochrane, and determine the keywords and concepts central to your research question. For each concept, identify the preferred indexing terms, such as MeSH for PubMed and Emtree for Embase, to refine your search. Once you've created your search strategy, save it to apply across different databases. Next, filter your search results based on predetermined inclusion and exclusion criteria to narrow down the most relevant studies. As you review these articles, pay close attention to their primary findings, study limitations, and suggestions for future research directions, as these sections often highlight the knowledge gaps your study aims to address. This meticulous approach ensures that your research question is both relevant and grounded in the current state of scientific inquiry.

Components of a research proposal

In crafting a comprehensive research proposal, start with a detailed background and literature review to pinpoint the knowledge gap and underline the significance of your study. Clearly articulate your study's objective and hypothesis, specifying the knowledge gap it aims to fill, anticipated results, and the reasoning behind those expectations. The methodology section should detail the study design, target population, and sample size, alongside methods for data collection—be it through national databases, chart reviews, prospective studies, or basic scientific techniques. Discuss the data analysis and statistical methods to be employed, and outline measures for protecting patient information where necessary. Address expected findings in light of the literature review, considering potential limitations or challenges and how they might be mitigated. Finally, outline contingency plans for data acquisition if primary sources are unavailable and discuss any limitations of your chosen methods, alongside strategies to address these issues, ensuring a well-rounded approach to your research inquiry.

TYPES OF STUDY DESIGNS

Algorithm for Study Designs

Meta-analysis versus systematic review

A meta-analysis and a systematic review are both rigorous approaches to synthesizing research findings, but they differ significantly in methodology and outcome. A systematic review comprehensively collects and critically evaluates all relevant studies on a specific topic to summarize the findings qualitatively. It aims to provide a high-level overview of the research evidence, identifying patterns, strengths, and gaps without statistically combining results. On the other hand, a meta-analysis goes a step further by quantitatively combining data from multiple studies that meet inclusion criteria, using statistical techniques to calculate an overall effect size. This process allows for a more precise estimate of the effect of an intervention or the association between variables across studies. While a systematic review offers a qualitative summary of existing evidence, a meta-analysis provides a quantitative estimate, making it a powerful tool for deriving more definitive conclusions about research questions. Both methodologies, however, require a high degree of rigor in their execution to ensure the reliability and validity of their conclusions.

Public Databases for Data Collection

National Surgical Quality Improvement Program (NSQIP)

The 2020 Participant Use Data File (PUF) from NSQIP includes 902,968 cases from 706 participating sites. Additionally, there are fourteen other separate NSQIP PUFs available, comprising a comprehensive database of over 8.7 million cases.

For detailed information, refer to the NSQIP PUF User Guide.

Centers for Medicare and Medicaid Services (CMS)

The Centers for Medicare and Medicaid Services (CMS) partners with the Research Data Assistance Center (ResDAC) to facilitate data access requests. ResDAC's website provides comprehensive information on obtaining Medicare and/or Medicaid data for research purposes, including both restricted and limited datasets as well as public use files (PUF)/non-identifiable files.

National Inpatinet Sample (NIS)

The National (Nationwide) Inpatient Sample (NIS) is the largest publicly accessible all-payer inpatient care database in the United States, featuring data on more than seven million hospital stays.

To ensure proper use of the NIS, follow the NIS Checklist.

National Cancer Database (NCD)

For researchers interested in the National Cancer Database, application instructions for accessing the Participant Use File (PUF) are available here.

Data Collection: Inclusion and Exclusion Criteria

When designing a research study, it is essential to establish clear inclusion and exclusion criteria. These criteria determine who can and cannot participate in the study, ensuring the collection of relevant and reliable data.

Clear Scientific or Clinical Rationale

Inclusion and exclusion criteria should be based on a clear scientific or clinical rationale. This ensures that the study population is appropriately defined and that the results will be meaningful and applicable to the research question. Criteria should be supported by existing scientific literature, clinical guidelines, or preliminary data.

NIH Policy on Sex as a Biological Variable

The National Institutes of Health (NIH) has established a policy on factoring sex as a biological variable in research designs. This policy emphasizes the importance of considering sex in all aspects of research involving vertebrate animals and human studies.

Gender or Race

Consideration of gender and race in research studies is critical to address potential differences in health outcomes. Including diverse populations helps to ensure that the study findings are generalizable and applicable to a broader audience. When setting criteria, researchers should aim for inclusivity while being mindful of any scientifically valid reasons for focusing on a specific gender or racial group.

NIH Policy Statement

“NIH expects that sex as a biological variable will be factored into research designs, analyses, and reporting in vertebrate animal and human studies. Strong justification from the scientific literature, preliminary data, or other relevant considerations must be provided for applications proposing to study only one sex.”

Researchers must provide a compelling justification if they propose to study only one sex. This justification should be based on:

Scientific Literature: Evidence from previous studies indicating significant sex-specific differences or the irrelevance of sex in the context of the research.
Preliminary Data: Initial data suggesting that including both sexes would not impact the research outcomes or that studying one sex is necessary to address specific scientific questions.
Other Relevant Considerations: Practical or ethical reasons, such as limited access to one sex in the study population or specific health conditions affecting only one sex.

By adhering to these guidelines, researchers can ensure that their studies are robust, inclusive, and aligned with best practices in scientific research.

When establishing inclusion and exclusion criteria for data collection, it's essential to consider various factors to ensure the study population is appropriately defined and the results are relevant. Age range is a fundamental criterion, allowing researchers to target specific populations accurately. Developmental considerations, such as pre vs. post-pubertal and pre vs. post-menopausal stages, must be factored in due to their significant physiological impacts. Studies should clearly differentiate between pediatric and adult populations, given their distinct developmental and physiological differences. Lifestyle factors, including weight/BMI, diet, exercise, smoking, alcohol, and recreational drug use, are crucial as they can significantly influence health outcomes and potentially bias the dataset. Researchers should evaluate whether extremes of these factors are necessary for addressing the research question, such as the impact of pre-operative BMI or the use of cannabis for postoperative pain management.

Medical comorbidities, medications, and lab abnormalities also play a critical role in defining inclusion and exclusion criteria. Certain comorbidities and medications, including over-the-counter drugs and contraceptives, may bias the dataset or be central to the research question. Abnormal lab results should be considered for their potential impact on participant eligibility or as secondary research questions. In randomized controlled trials (RCTs) and prospective studies, ensuring patient safety is paramount. Researchers must design studies that minimize risk to participants while adhering to ethical standards and regulatory guidelines. By thoroughly considering these criteria, studies can achieve robust, reliable, and generalizable results.

Software for Data Analysis

Types of Statistics Used in Systematic Meta-analysis

Each of these statistical methods plays a crucial role in analyzing data within a systematic meta-analysis, providing insights into relationships, differences, and effects across studies.

T-test

A t-test is used to compare the means of two groups. It determines if there is a statistically significant difference between the groups’ average values. Commonly used in comparing treatment effects in clinical trials.

Chi Square Test

The chi-square test assesses the association between categorical variables. It helps determine if the observed frequencies in a contingency table differ significantly from the expected frequencies.

Regression Analysis

Regression analysis examines the relationship between dependent and independent variables. It helps in understanding how changes in predictor variables influence the outcome variable. Types include linear regression, logistic regression, and multiple regression.

Odds Ratio

Odds ratio (OR) measures the odds of an event occurring in one group compared to another. It is often used in case-control studies to determine the strength of the association between exposure and outcome.

Relative Risk

Relative risk (RR) compares the risk of a certain event occurring in two groups. It is commonly used in cohort studies to assess the likelihood of an event occurring in an exposed group versus a non-exposed group.

ANOVA (Analysis of Variance)

ANOVA tests for significant differences between the means of three or more groups. It helps in determining if at least one group mean is different from the others, often used in comparing multiple treatment groups.

Designing Figures, Graphs, and Tables

The software commonly used for statistical analysis can also generate figures, graphs, and tables. Some of these versatile tools include Excel, SPSS, Stats, SAS, and Matlab. Additionally, other specialized software may enhance your data visualization:

GraphPad Prism
Biorender
Adobe Illustrator
Tableau

Many of these programs offer free or discounted versions for students, or you may be able to access them through your institution.

For comparisons, links, and pricing information, visit Edanz Learning.

Composing a Manuscript

Composing a manuscript for submission involves several key steps to ensure it meets the standards of your target journal. Start by crafting a clear and concise abstract that summarizes the main findings and significance of your research. Follow this with an introduction that provides background information and states your research question or hypothesis. The methods section should detail the procedures and techniques used in your study, while the results section presents your findings with appropriate figures and tables. In the discussion, interpret your results, explain their implications, and compare them with existing literature. Conclude with a summary of your findings and suggestions for future research. Ensure your manuscript adheres to the journal's formatting guidelines, and include all necessary supplementary materials, such as references, acknowledgments, and ethical approval statements. Proofread carefully to eliminate errors and enhance readability before submission

Tips and Resources:

11 steps to structuring a science paper editors will take seriously: https://www.elsevier.com/connect/11-steps-to-structuring-a-science-paper-editors-will-take-seriously
How to write a first-class paper: https://www.nature.com/articles/d41586-018-02404-4

Tips for Manuscript Submission

When submitting your manuscript, it's crucial to comply with the journal's instructions for authors. Ensure all components are in the proper format to avoid automatic rejection. Utilize the expertise of your Principal Investigator (PI), corresponding author, or faculty mentor, especially when requesting specific reviewers.

Potential Outcomes

Potential Outcomes after submission

Accepted
Accepted with Minor Revision
Accepted with Major Revision
Rejected with Opportunity to Resubmit
Rejected without Opportunity to Resubmit

Don’t get discouraged if your manuscript is not accepted on the first try—it often takes submissions to multiple journals to get published!

Additional Resources: NIH Introduction to the Principles and Practice of Clinical Research

The Introduction to the Principles and Practice of Clinical Research (IPPCR) course provides comprehensive training on how to effectively and safely conduct clinical research. This free course covers a wide range of topics, including:

Biostatistical and epidemiologic methods
Study design
Protocol preparation
Patient monitoring
Quality assurance
Ethical and legal issues

The IPPCR course is offered online, featuring recorded lectures and an online discussion board where participants can submit questions to faculty. The course consists of approximately 40 lectures, each ranging from 15 to 90 minutes.

For more information and to enroll, visit the IPPCR course page.

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