CONSORT Statement: Quantitative Research Methods

• Week 5 focuses on quantitative research methods and critical appraisal.

Purpose of the CONSORT Statement

• CONSORT (Consolidated Standards of Reporting Trials) provides a set of guidelines that improves the quality, transparency, and reporting of randomized controlled trials (RCTs).
• It ensures research reporting is complete, enabling studies to be evaluated, replicated, and included in systematic reviews and meta-analyses.
• It ensures RCTs are reported comprehensively and in a standardized way.
• Clear and reproducible results enhance study credibility.
• It helps in reducing bias, and improves how the findings are interpreted.
• The statement improves the comparability of studies in systematic reviews.
• An RCT study is clearly identified in the title and abstract.
• Background information and study purposes are included in the introduction.
• The methods section describes:
  • Trial design
  • Participants
  • Interventions
  • Outcomes
  • Randomization
  • Blinding
• Findings regarding attrition and dropouts are covered in the results, which also includes participant flow.
• The results include:
  • Recruitment specifics
  • Characteristics at baseline
  • Outcome data with effect size
• Summarizing the interpretation of results, study limitations, as well as generalizability takes place during the discussion process.
• Other information:
  • Funding sources
  • Conflicts of interest
• It helps evaluat RCTs for evidence-based nursing practice, fosters replication of studies allowing for more research, and promotes critical appraisal skills among nurses.

Study Findings and Treatment Effect

• Study findings should clearly report the effect of the intervention, including differences in means, confidence intervals, and standard deviations.
• The effect size quantifies the magnitude of the influence exerted by the treatment.
• The statistical significance (p-value) determines if the results are a consequence of chance.
• Understanding treatment effect: the impact of an intervention on a given outcome.
• Common measures of treatment effect include:
  • Absolute Risk Reduction (ARR): Difference in risk between control and treatment groups.
  • Relative Risk (RR): Likelihood of an event in the intervention group compared to the control.
  • Number Needed to Treat (NNT): Number of patients needed to be treated to prevent one adverse outcome, and NNT = 10 means 10 patients need treatment to prevent one event.
• Evaluating study results leads to determining if the treatment is clinically significant.
• It supports nurses in understanding research results and using them for patient care.
• The mean difference is the difference between group averages, for example reduction in blood pressure by 5 mmHg
• Relative Risk (RR) is the Likelihood of event occurring in the treatment group vs. control reduction, for example RR = 0.75 (25% risk reduction)

Role of the p-Value in Hypothesis Testing

• The p-value quantifies the probability that observed results occurred by chance rather than through intervention.
• The common threshold to look for is: p < 0.05.
• p < 0.05 implies statistical significance, meaning it's unlikely due to chance.
• p > 0.05 implies there's no statistical significance, and it could be due to chance.
• p < 0.01 means there is strong evidence against the null hypothesis.
• Role of the p-value in Hypothesis testing:
  • Null Hypothesis (Ho): Treatment has no effect.
  • Alternative Hypothesis (H1): The treatment has an effect.
  • If p < 0.05, reject Ho (there is an effect).
  • If p > 0.05, fail to reject Ho (no evidence of effect).
• Doesn't measure clinical significance, and a small p-value does not mean a large treatment effect.
• Large studies may detect tiny, non-relevant differences, as it depend on sample size.
• It helps in deciding if something is statistically or clinically significant, and makes sure results are not due to chance before applying them to patient care.

Importance of Blinding and Allocation Concealment

• Blinding stops bias by keeping participants or researchers from knowing which assignments belong to everyone.
• Types of blinding:
  • Single-blind: Participants are unaware of their group assignment.
  • Double-blind: Both participants and researchers are unaware.
  • Triple-blind: Participants, researchers, and data analysts are unaware.
• Blinding is important because:
  • It reduces experimenter bias.
  • It prevents placebo effect.
  • It enhances validity of results.
• Allocation concealment makes sure that the randomization sequence is hidden until participants are assigned.
• It is a technique that prevents selection bias, and keeps researchers from guessing assignments.
• Blinding reduces bias after participant assignment, while allocation concealment prevents it before assignment.
• Allocation concealment protects against selection bias, while blinding protects against the placebo effect, and researcher bias.
• Blinding happens after randomization, whereas allocation concealment occurs before randomization.
• It's critical for ensuring unbiased results in trials testing nursing interventions.
• They are used during medication trials, behavioral interventions, and clinical procedures.

Evaluating a Quantitative Study

• The critical elements to evaluate includes:
  • Research Methodology
  • Statistical Analysis
  • Results Interpretation
  • Study Validity and Reliability
• Research methodology evaluates:
  • Study design (Experimental vs. Non-Experimental)
  • Sample size & selection (Adequate and representative?)
  • Data collection methods (Surveys, lab tests, observations?)
  • Control of variables (Blinding, randomization, allocation concealment?)
• Statistical analysis looks at:
  • p-Value (Were findings statistically significant?)
  • Confidence Intervals (CI) (Does it indicate precision?)
  • Effect Size (Is the treatment effect meaningful?)
• Results interpretation considers whether:
  • Results are clinically significant?
  • Conclusions are supported by data?
  • Limitations and biases are acknowledged?
• Study validity and reliability assesses:
  • Internal validity (Were confounding variables controlled?)
  • External validity (Can results be generalized?)
  • Reliability (Were measures consistent and reproducible?)
• These help nurses critically analyze studies, before incorporating findings into clinical practice, and it also ensures safe and effective patient care decisions.
• Example of how things are measured:
  • Study Design = Randomized Controlled Trial (RCT)
  • Blinding Used = Double-blind study
  • Sample Size = 500 participants
  • p-Value = p = 0.03 (statistically significant)
  • Effect Size = Large (NNT = 5)
  • Limitations = Small follow-up period, high dropout rate

Key Takeaways

• CONSORT statement aims to standardize the reporting of RCTs, which improves transparency and comparability.
• Study findings and treatment effect include effect sizes, relative risk, and significance.
• The p-value quantifies chance in hypothesis testing (p < 0.05).
• Allocation concealment and Blinding keeps bias away before and after the assignment of participants.
• Evaluating a Quantitative Study requires the assessment of study design, methodology, statistics, and conclusions.