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Levels of Confidence in Scientific Studies

The confidence in scientific studies is determined by the strength and quality of evidence, based on a hierarchy of study designs. Higher-quality studies provide greater confidence in the reliability and applicability of findings. It is important to have an understanding as to how confidence levels in scientific studies is determined so when evaluating a literature, one may have insight as to what degree of emphasis and trust one would place in a topic.

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Definitions and Terms Related to Pain

Levels of Confidence in Scientific Studies

Introduction

This document outlines key study types, their characteristics, and their impact on confidence levels, with examples relevant to pain management research. Confidence levels on this website is generally rated as High (robust, large-scale studies), Moderate (smaller or less rigorous studies), or Low (preliminary or non-human studies), however, there are degrees of “high”, including highest, higher and high.  These ratings are influenced by factors such as study design, sample size, and consistency of results.

Hierarchy of Levels of Confidence

Below is a refined hierarchy, aligned with standard evidence-based medicine principles, tailored for pain management. The hierarchy of evidence ranks study designs by their ability to minimize bias and provide reliable conclusions.

From highest to lowest confidence:

  1. Highest Confidence:
    • Cochrane Reviews (with or without Meta-Analyses): Rigorous systematic reviews, often including meta-analyses, conducted by the Cochrane Collaboration. Example: Cochrane review of topical NSAIDs for acute pain (high confidence) [7]. Limitations: Few exist for emerging treatments like low-dose naltrexone (LDN).
  1. Very High Confidence:
    • Meta-Analyses (Non-Cochrane): Statistical pooling of data from multiple studies, providing precise effect estimates. Example: Meta-analysis of omega-3s for osteoarthritis pain (high confidence) [8]. Limitations: Heterogeneity or low-quality studies reduce confidence.
  1. High Confidence:
    • Systematic Reviews (without Meta-Analyses): Qualitative synthesis of studies using strict criteria. Example: Systematic review of LDN for IBD (moderate to high confidence, limited by few RCTs) [9]. Limitations: Less precise than meta-analyses.
    • Randomized Controlled Trials (RCTs, Large/Well-Designed): Controlled, randomized studies with large samples and low bias. Example: Large RCT of gabapentin for neuropathic pain (high confidence) [10]. Limitations: Generalizability, cost.
  1. Moderate Confidence:
    • Small RCTs: RCTs with small samples (e.g., n<50) or short durations. Example: LDN RCT for fibromyalgia (n=31, moderate confidence) [11]. Limitations: Limited power, potential bias.
    • Non-Randomized Controlled Trials: Controlled studies without randomization. Example: Non-randomized trial of acupuncture for chronic pain (moderate confidence) [12]. Limitations: Higher bias risk.
  1. Low to Moderate Confidence:
    • Pilot Studies: Exploratory studies, often small and open-label, testing feasibility. Example: Pilot study of LDN for CRPS (low to moderate confidence) [13]. Limitations: Lack of controls, bias.
    • Observational Studies (Cohort, Case-Control, Cross-Sectional): Non-experimental studies examining associations. Example: Cohort study of cannabis for chronic pain (low to moderate confidence) [14]. Limitations: Confounding, bias.
  1. Low Confidence:
    • Case Reports/Series: Descriptive reports of few patients. Example: Case reports of LDN for diabetic neuropathy (low confidence) [13]. Limitations: Anecdotal, no controls.
    • Preclinical Studies: Animal or lab-based studies. Example: Preclinical LDN for acute pain transition (low confidence) [15]. Limitations: Limited human applicability.
  1. Very Low Confidence:
    • Expert Opinion: Based on clinical experience, not empirical data. Example: Expert recommendations for LDN dosing (very low confidence) [3]. Limitations: Subjective, untested.

 

Key Study Types and Confidence Levels (lowest to highest)

Scoping Reviews

  • A scoping review is a type of literature review that aims to map the breadth and depth of research on a particular topic, identifying key concepts, research types, and gaps in the existing literature. Unlike systematic reviews, which focus on answering specific, focused questions with a rigorous assessment of study quality, scoping reviews take a broader approach to explore a topic, often when the literature is vast or the research question is not yet well-defined.

Randomized Controlled Trials (RCTs)

  • Description: Participants are randomly assigned to treatment or control groups, minimizing bias. RCTs test efficacy under controlled conditions, often double-blinded to reduce placebo effects. Example: An RCT testing low-dose naltrexone (LDN) for fibromyalgia pain reduction [1].
  • Confidence Level: High when well-designed (large sample, long duration, low bias). Confidence decreases with small samples or short durations. Example: LDN RCTs for fibromyalgia show moderate confidence due to small sample sizes (n=31) [1].
  • Strengths: Controls bias, establishes causality.
  • Limitations: Small samples, limited generalizability, high cost.

Systematic Reviews

  • Description: Comprehensive analyses of multiple studies on a specific topic, using predefined criteria (e.g., PRISMA guidelines) to select and evaluate studies. They synthesize findings qualitatively, summarizing trends without statistical pooling. Example: A systematic review of LDN for inflammatory bowel disease (IBD) [2].
  • Confidence Level: High when including robust RCTs, but lower than meta-analyses due to lack of quantitative precision. Confidence depends on included study quality. Example: LDN reviews for IBD provide moderate confidence due to limited RCTs [2].
  • Strengths: Broad overview, identifies research gaps, reduces bias.
  • Limitations: Subjective synthesis, study heterogeneity, no pooled effect size.

Meta-Analyses

  • Description: Statistical analyses combining quantitative data from multiple studies (often from systematic reviews) to produce a pooled effect estimate (e.g., risk ratio). They use models (e.g., fixed or random effects) to account for variability. Example: A meta-analysis of omega-3 supplementation for osteoarthritis pain [3].
  • Confidence Level: Higher than systematic reviews due to increased statistical power and precision. Confidence is reduced by heterogeneity (e.g., differing doses) or low-quality studies. Example: Omega-3 meta-analyses show moderate to high confidence for pain relief [3].
  • Strengths: Quantitative precision, resolves conflicting results, large effective sample size.
  • Limitations: Risk of bias from poor-quality studies, heterogeneity, publication bias.

Cochrane Reviews

  • Description: Systematic reviews conducted by the Cochrane Collaboration, following rigorous, standardized protocols, often including meta-analyses. They focus on healthcare interventions and are considered the gold standard. Example: Cochrane review of topical NSAIDs for acute pain [4].
  • Confidence Level: Highest confidence due to transparency, strict methodology, and frequent inclusion of meta-analyses. Limited by availability (few for emerging treatments like LDN). Example: Topical NSAID reviews provide high confidence for acute pain efficacy [4].
  • Strengths: Rigorous, reproducible, minimizes bias.
  • Limitations: Limited scope, time-intensive, may exclude newer studies.

Other Study Types

  • Observational Studies: Include cohort (follow over time), case-control (compare cases to controls), or cross-sectional (snapshot) designs. Provide lower confidence due to bias and confounding. Example: Observational study of acupuncture for chronic pain [5].
  • Case Reports/Series: Descriptive reports of individual or small groups of patients. Offer low confidence but generate hypotheses. Example: Case reports of LDN for diabetic neuropathy [6].
  • Preclinical Studies: Animal or laboratory studies. Lowest confidence for human applicability but inform mechanisms. Example: Preclinical LDN studies for acute pain transition [7].

Factors Influencing Confidence

  • Sample Size: Larger samples increase confidence by reducing variability. Small studies (e.g., n<50) lower confidence [1].
  • Study Duration: Longer studies provide more reliable outcomes, especially for chronic conditions.
  • Consistency: Agreement across studies increases confidence. Conflicting results reduce it.
  • Bias: Low risk of bias (e.g., randomization, blinding) enhances confidence. High bias (e.g., open-label studies) reduces it.
  • Publication Bias: Selective reporting of positive results lowers confidence in meta-analyses.
  • Applicability: Studies matching the target population (e.g., chronic pain patients) increase confidence.

 

 

References

  1. Younger J, et al. (2013). Arthritis & Rheumatism, 65(2):529–538. doi: 10.1002/art.37734.
  2. Smith JP, et al. (2011). American Journal of Gastroenterology, 106(7):1208–1209. doi: 10.1038/ajg.2011.186.
  3. Wen Deng et al. (2023). Journal of Orthopedic Research, 41(4):879–889. doi: 10.1002/jor.25412.
  4. Derry S, et al. (2017). Cochrane Database of Systematic Reviews, 4:CD007402. doi: 10.1002/14651858.CD007402.pub3.
  5. Vickers AJ, et al. (2018). Journal of Pain, 19(6):611–621. doi: 10.1016/j.jpain.2017.11.005.
  6. Chopra P, et al. (2019). Pain Management, 9(4):379–388. doi: 10.2217/pmt-2019-0012.
  7. Parkitny L, et al. (2018). Pain Medicine, 19(4):843–854. doi: 10.1093/pm/pnx156.
  8. Pinho-Ribeiro FA, et al. (2017). Trends in Immunology, 38(8):573–583. doi: 10.1016/j.it.2017.04.005.

 

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