UEUE4

Question 1

1. What tools can be used to assess the reporting and methodological quality of this study?sysetamtic reviews

Answer 1

PRISMA et AMSTAR2

Question 2

2. Please list the main items you need to check to assess the methodological quality of the systematic review and meta-analysis.

Answer 2

Research Question/Objectives: Is the research question clearly defined and relevant? Are the objectives clearly stated?

Inclusion and Exclusion Criteria: Are the criteria for selecting studies well-defined? Are they applied consistently across included studies?

Search Strategy: Is the search strategy comprehensive and systematic? Are all relevant databases and sources searched? Is there an effort to minimize publication bias?

Study Selection and Data Extraction: Is the process of study selection and data extraction well-described and reproducible? Are multiple reviewers involved in these processes to reduce bias?

Quality Assessment of Included Studies: Are the methods used to assess the quality of included studies clearly described? Are the quality assessment tools appropriate for the study designs included?

Data Synthesis: Is the approach to data synthesis clearly explained? Are statistical methods for combining results appropriate for the included studies?

Heterogeneity: Is heterogeneity among studies assessed? Are methods to explore and explain heterogeneity appropriate?

Publication Bias: Are efforts made to assess and minimize publication bias (e.g., funnel plot analysis, Egger’s test)?

Sensitivity Analysis: Is sensitivity analysis conducted to assess the robustness of results? Are different assumptions or methods tested?

Reporting Quality: Does the review follow reporting guidelines (e.g., PRISMA for systematic reviews, PRISMA-P for protocols)? Are all essential components of reporting included?

Conflict of Interest Declaration: Are potential conflicts of interest declared by the authors? Could these conflicts affect the interpretation of the results?

Discussion and Conclusion: Are the implications of the findings discussed appropriately? Are limitations acknowledged? Are conclusions supported by the data presented?

Assessing these elements helps determine the rigor and reliability of a systematic review and meta-analysis, ensuring that it’s conducted with methodological soundness and integrity.

Question 3

3. You search PROSPERO and did not retrieve anything. Did the authors register the protocol? How will this affect your interpretation of the results? Justify your answer

Answer 3

No
This reduces confidence in the results
We do not know whether the authors used selective reporting of results or analyses of the most favourable results.
Had the “time and effect measures” section in Prospero been completed, we would have been able to assess whether selective reporting of outcomes occurred. Furthermore, it is not known on what basis the subgroups were determined, this should have been pre-specified when registering the protocol in PROSPERO.

Question 4

8. How did the authors assess the risk of bias in the included studies? Do you agree with their approach? Is there any other tool you would recommend using?

Answer 4

Assessing bias in a meta-analysis involves evaluating various aspects to ensure the reliability and validity of the results. Two commonly used tools for this purpose are ROBIS (Risk of Bias in Systematic Reviews) and ROBINS-I (Risk Of Bias In Non-randomized Studies - of Interventions).

ROBIS (Risk of Bias in Systematic Reviews):

ROBIS is a tool designed specifically to assess the risk of bias in systematic reviews. It helps reviewers evaluate the process of conducting and reporting the review itself.
It involves three main phases:
Assessing relevance and identification of the review’s scope.
Identifying concerns regarding the review process.
Judging risk of bias in the review, considering multiple domains such as study eligibility criteria, identification and selection of studies, data collection, and synthesis of findings.
ROBINS-I (Risk Of Bias In Non-randomized Studies - of Interventions):

ROBINS-I is a tool used to assess bias in non-randomized studies of interventions included in a meta-analysis.
It evaluates bias in seven domains:
Confounding variables
Selection of participants
Classification of interventions
Deviations from intended interventions
Missing data
Measurement of outcomes
Selection of reported results
Both tools provide structured frameworks for evaluating bias in systematic reviews and individual studies, respectively, aiding researchers in critically assessing the quality of evidence included in a meta-analysis.

Assessing bias involves considering various factors, such as study design, methodology, reporting, and the potential impact of biases on the overall findings. Reviewers typically use these tools along with a detailed examination of the included studies’ characteristics to make informed judgments about the risk of bias across the meta-analysis.

Question 5

9. List the main sources of biases you should assess in the primary trials when performing a systematic review.

Answer 5

Randomisation, deviation from intervention, missing outcome, selection of reported results and measurement of the outcome. Publication bias. Outcome reporting bias.

Question 6

9. List the main sources of biases you should assess in the primary trials when performing a systematic review

Answer 6

When conducting a systematic review, assessing biases in primary trials is crucial to understanding the reliability and validity of the evidence. Here are the main sources of bias that should be evaluated in primary trials:

Selection Bias:

Random Sequence Generation: How were participants allocated to interventions? Was the allocation adequately randomized?
Allocation Concealment: Was the allocation adequately concealed from those assigning participants to interventions?
Performance Bias:

Blinding/Masking: Were participants, personnel, and outcome assessors blinded to the intervention?
Detection Bias:

Blinding of Outcome Assessment: Were outcome assessors blinded to the intervention received by participants?
Attrition Bias:

Incomplete Outcome Data: Were there missing outcome data? Was the handling of incomplete outcome data adequately addressed (e.g., intention-to-treat analysis)?
Reporting Bias:

Selective Reporting: Were all prespecified outcomes reported? Was there evidence of selective reporting of outcomes based on the results?
Publication Bias:

Was there potential for publication bias due to non-publication of studies with null or negative results?
Other Potential Biases:

Baseline Imbalances: Were there significant differences between groups at baseline that could affect outcomes?
Industry Sponsorship: Was the study sponsored by an entity that could introduce biases in design, conduct, or reporting?
Conflict of Interest: Were there conflicts of interest that could impact the study’s objectivity?
Assessing these biases helps evaluate the internal validity of primary trials and their potential impact on the overall findings when aggregated in a systematic review and meta-analysis. Evaluating these sources of bias aids in determining the strength and reliability of the evidence synthesized in the review.

Question 7

How to assess publication bias in a systematic review

Answer 7

the funnel plot was symmetrical (supplementary Figure 2), and Egger’s (P=.513) and Begg’s test (P=.392) also did not find any publication bias.

gger’s Test:

Egger’s test examines whether there is a systematic relationship between the effect size (or log of the effect size) and its precision (usually measured as the standard error) in a meta-analysis.
It’s a regression-based test that evaluates the asymmetry of the funnel plot (a graphical representation of the relationship between effect sizes and their precisions).
A significant p-value in Egger’s test suggests potential publication bias. If the plot shows a funnel-shaped asymmetry and the p-value is less than the chosen significance level (typically 0.05), it indicates the presence of publication bias.
Begg’s Test:

Begg’s test is a rank correlation test used to assess the correlation between effect sizes and their variances across studies in a meta-analysis.
Like Egger’s test, it aims to identify publication bias by examining the relationship between the effect size and study size (or variance).
A low p-value (typically below 0.05) suggests publication bias. If there’s a correlation between effect size and study size, it might indicate potential bias, particularly if smaller studies tend to report more positive or more negative results.
Interpreting these tests involves considering their p-values. A significant p-value indicates potential publication bias, but it’s important to note that these tests are not definitive proof of bias; they signal the need for further investigation or caution in interpreting the results.

Moreover, when interpreting these tests, it’s crucial to consider other factors that might influence the asymmetry or correlation observed in these tests, such as genuine heterogeneity between studies, methodological differences, or true small study effects.

Consulting with a statistician or a specialist in meta-analysis can provide a more comprehensive understanding and appropriate interpretation of these tests in the context of a specific meta-analysis.

Question 8

what is QUADAS 2

Answer 8

QUADAS-2 stands for “Quality Assessment of Diagnostic Accuracy Studies-2.” It’s a tool specifically designed for assessing the risk of bias and methodological quality in studies that evaluate diagnostic accuracy, such as tests, imaging techniques, or other methods used to diagnose diseases or conditions.

QUADAS-2 focuses on four key domains:

Patient Selection: Assesses the methods used to select participants in the study and determine whether they represent the intended population.

Index Test: Evaluates the method of the diagnostic test itself, including its conduct and interpretation.

Reference Standard: Looks at the suitability of the criteria used to establish the presence or absence of the condition being diagnosed.

Flow and Timing: Assesses potential sources of bias related to the timing of the index test and reference standard and whether all participants received both tests.

QUADAS-2 helps researchers and reviewers systematically evaluate the risk of bias in diagnostic accuracy studies by considering these domains. This evaluation aids in determining the reliability and applicability of the study’s findings.

Question 9

what does PRISMA and AMSTAR2 do

Answer 9

PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and AMSTAR2 (A MeaSurement Tool to Assess systematic Reviews) are both tools used in the field of evidence-based medicine to evaluate and improve the quality of systematic reviews and meta-analyses.

PRISMA provides a checklist for reporting systematic reviews and meta-analyses. It outlines items that should be included in these types of publications to ensure transparency and completeness. Researchers and authors use PRISMA guidelines to structure their reports, enhancing the clarity and usefulness of their findings.

AMSTAR2, on the other hand, is a tool used to assess the methodological quality of systematic reviews. It helps researchers critically appraise systematic reviews by evaluating various aspects such as the study design, data extraction, analysis, and the consideration of bias. AMSTAR2 aims to guide users in assessing the reliability and trustworthiness of systematic review studies.

Both PRISMA and AMSTAR2 play crucial roles in promoting transparency, rigor, and quality in the publication and assessment of systematic reviews and meta-analyses in the medical and scientific communities.

Question 10

How to know fi a study condcuetd a extensive search startegy

Answer 10

Assessing whether a systematic review conducted a good search strategy involves evaluating several key components:

Comprehensiveness of the Search Strategy: A good search strategy should cover multiple databases relevant to the topic. It’s essential to include not only commonly used databases like PubMed/MEDLINE but also others specific to the field, ensuring a wide scope of literature is considered.

Non-Englishlanguagedatabases

Use of Keywords and Search Terms: The review should employ a well-defined set of keywords and search terms related to the research question. These terms should be comprehensive and inclusive of synonyms, alternate spellings, and variations to capture all relevant studies.

Inclusion of Grey Literature and Unpublished Studies: Beyond academic databases, a robust search strategy should encompass grey literature sources, conference proceedings, dissertations, and unpublished studies to minimize publication bias.

Transparent Reporting: The systematic review should transparently report the search strategy. This includes detailing the search terms used, the databases searched, any limitations or filters applied, and the date range covered.

Search Protocol Documentation: A well-documented search protocol ensures transparency in the process. It should describe the search strategy and criteria for study selection, reducing the likelihood of bias or arbitrary selection.

Consultation with Subject Experts or Librarians: Collaboration with subject experts or experienced librarians during the search strategy design can enhance its quality and comprehensiveness.

Assessment of Search Strategy Quality: Tools like PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) outline a checklist that includes items related to the search strategy. Reviewing adherence to these guidelines helps assess the adequacy of the search strategy.

Sensitivity Analysis: Some systematic reviews perform sensitivity analyses to assess the impact of the search strategy on the overall results. This can involve re-running analyses with different search strategies to check if it substantially affects the findings.

Documentation of Search Results: Clear documentation of the search results, including the number of studies identified, screened, assessed for eligibility, and included in the review, aids in understanding the thoroughness of the search strategy.

A well-conducted search strategy is crucial for the comprehensiveness and reliability of a systematic review. It ensures that relevant studies are included, minimizing bias and providing a more comprehensive view of the available evidence on the research topic.

Question 11

Where you shoudlnt limit a seach strategy

Answer 11

Do notlimit searchto:languagesource (journal, database, etc)publication status (full article, conference abstract, unpublished,etc)publication date, unlessjustifiable

Question 12

Specificty in a search strategy

Answer 12

The search strategy is precise enough to minimise the number of records retrieved that is relevant
To increase sesnityvity
Broaden your saech quetsion
use more synonyms
use truncation or wildcard to find plurals or alternate spellings
use adjaceny operator to specify that words dont neccessarrily have to appear side by side
explode all subject headings
use a combiantion of free text and subject headings

Question 13

Comprehensiveness

Answer 13

Search is sensitive enough to retrieve as many records as psossible that are relevent to your review
Inccrease specificty by
Narrowing the question
not truncateing or using wildcards
look for pecific phrases
use a subject heading saerch rather than a free text search
limit by language f+of article human or anumal publication type or year of publicatiomn

Question 14

Transparency of their search strategy

Answer 14

Sure, here’s a neater layout for the components of search documentation:

Search Documentation should include:

Sources searched: All sources, including databases and non-database sources (grey literature, conference proceedings, etc.).

Search strategy used: Comprehensive details of the search terms, keywords, Boolean operators, and any filters or truncations applied.

Platform or provider: Specify the platforms or providers utilized for the search (e.g., Ovid, Dialog, PubMed, etc.).

Time periods of the search: Clearly define the timeframe covered by the search (e.g., 1966 to the current date or a specific range).

Limits used: Any limitations applied during the search, such as language restrictions or human studies only.

Number of hits for each database: Record the number of results retrieved from each individual database to demonstrate the yield of the search in each source.

Ensuring comprehensive and clear documentation of these components enhances the transparency and reproducibility of the systematic review’s search strategy, aiding in the assessment of its quality and thoroughness.

Question 15

An adaptive enrichment design

Answer 15

This concept refers to an adaptive enrichment design in clinical trials, specifically involving the selective inclusion of a subgroup based on certain characteristics or markers during the trial’s progression.

Here’s a breakdown:

Adaptive Enrichment Design: This refers to a trial design where the selection or enrichment of a subgroup occurs during the trial based on certain criteria or characteristics. It allows for adaptation based on interim analysis.

Selecting a Subgroup Adaptively: This means that during the trial, based on certain interim analyses or data evaluations, specific subgroups (such as a clinical subgroup or a genomic subpopulation) are chosen for continued inclusion or exclusion.

Planned Interim Analysis: This involves pre-planned moments during the trial when data is analyzed to make decisions about how the trial should proceed. These analyses guide whether a particular subgroup should be included or excluded from the trial going forward.

Strong Control of Type I Error Probability: This is about maintaining a stringent control over the possibility of falsely concluding that there is a treatment effect when, in reality, there isn’t one. This control is important to ensure the reliability and validity of the trial’s results.

Growing Literature in the Field: Implies that there is existing research or a body of work that supports or discusses this approach within the specific field or area of study.

Objective - Terminate Early for Non-Responding Subgroups: The primary goal of this adaptive enrichment design is to identify non-responding subgroups early in the trial and potentially exclude them from further participation. This approach aims to focus resources and efforts on the subgroups more likely to respond positively to the treatment.

Overall, this approach aims to streamline clinical trials by identifying and focusing on specific subgroups that are more likely to respond to the treatment, thereby potentially improving efficiency and efficacy in therapeutic interventions.

Question 16

why do enrichment

Answer 16

Enrichment strategies in clinical trials aim to improve the likelihood of detecting treatment effects by focusing on specific patient populations that are more likely to respond to a given intervention. They involve both prognostic and predictive enrichment approaches but have different goals and implications.

Decreasing Heterogeneity of Patients: Enrichment, particularly when using prognostic or predictive factors, helps identify subgroups of patients who share common characteristics or biomarkers. By targeting these specific groups, it reduces the heterogeneity within the trial population. This can make it easier to detect treatment effects since the enrolled patients are more likely to respond due to shared characteristics.

Prognostic Enrichment: Prognostic enrichment focuses on identifying patients who are more likely to have a certain outcome or disease progression regardless of the treatment given. It aims to enroll patients who are expected to have a higher event rate, thus potentially making it easier to observe treatment effects.

Predictive Enrichment: On the other hand, predictive enrichment identifies patients who are more likely to respond to a specific treatment. It focuses on biomarkers or characteristics that indicate a patient’s likelihood of responding favorably to the intervention, allowing for a more targeted approach to treatment.

Debatable Aspects of Prognostic Enrichment: One debatable aspect is whether prognostic enrichment truly leads to a meaningful improvement in trial outcomes. While it may increase the likelihood of observing an event or disease progression, it might not necessarily enhance the ability to detect treatment effects. Additionally, there can be concerns about excluding certain patient groups who might still benefit from the treatment, even if they have a lower predicted event rate.

Stratified Medicine - Predictive Enrichment: Stratified medicine, often more focused on predictive enrichment, aims to tailor treatments based on predictive markers or characteristics rather than merely predicting the course of a disease. This approach targets patients who are more likely to respond positively to a specific treatment, thereby maximizing the treatment’s efficacy.

In summary, while prognostic enrichment might aid in identifying patient populations with higher event rates, its impact on detecting treatment effects and potential exclusions of certain patient groups remain debatable, especially when compared to predictive enrichment strategies that focus on identifying responders to specific treatments.

Question 17

Types of adaptive enrichment designs

Answer 17

Adaptive enrichment designs in clinical trials involve altering the trial’s enrollment criteria or treatment allocation based on accumulating data. These designs aim to maximize the likelihood of observing treatment effects by focusing on subgroups more likely to benefit from the intervention. Here are some types of adaptive enrichment designs:

Marker-Stratified Design: This involves enrolling patients based on specific biomarkers or characteristics. It may adaptively allocate treatments to different subgroups based on these markers.

Predictive Enrichment Design: Focuses on identifying patients likely to respond to a specific treatment. It adapts enrollment or treatment allocation based on predictive markers.

Prognostic Enrichment Design: Identifies patients more likely to have disease progression or certain outcomes regardless of treatment. It adapts enrollment based on prognostic factors.

Biomarker-Driven Design: Incorporates biomarker information collected during the trial to guide treatment allocation or subgroup identification.

Adaptive Randomization Design: Adjusts randomization ratios based on interim data to allocate more patients to the treatment arms showing more promise.

Adaptive Dose-Finding Design: Adjusts dosage or treatment regimen based on observed responses or toxicities during the trial.

Drop-the-Losers Design: Allows for the early dropping of treatment arms that show no efficacy, focusing resources on more promising arms or subgroups.

Adaptive Sample Size Re-Estimation: Allows for the adjustment of the sample size during the trial based on observed effect sizes or event rates.

Each adaptive enrichment design aims to increase the likelihood of detecting treatment effects by focusing on specific patient characteristics or adjusting the trial based on accumulating data. These designs can enhance trial efficiency, improve patient outcomes, and potentially accelerate the development of effective treatments.

Question 18

key characteirstics of a systrematic revuew

Answer 18

Absolutely, those are indeed the key characteristics of a systematic review:

Clearly Stated Objectives with Pre-defined Criteria: Systematic reviews begin with well-defined objectives and criteria for study selection, outlining what aspects of the topic will be covered and the specific characteristics studies must meet to be included.

Explicit, Reproducible Methodology: Systematic reviews follow a methodological plan that’s transparent and replicable. This includes detailing the search strategy, inclusion/exclusion criteria, data extraction methods, and analysis techniques.

Systematic Search for Eligible Studies: A comprehensive and systematic search is conducted across multiple databases and sources to identify all relevant studies that meet the predetermined criteria. This aims to minimize bias by ensuring all relevant literature is considered.

Assessment of Validity of Included Studies: The included studies’ quality, risk of bias, and reliability are assessed using established tools or criteria. This step is crucial for evaluating the overall strength of evidence and considering the reliability of the findings.

Systematic Presentation and Synthesis of Studies: Data extracted from the included studies are systematically organized and synthesized. This can involve quantitative meta-analysis or qualitative synthesis, depending on the nature of the data. The goal is to provide a comprehensive summary and draw meaningful conclusions.

By adhering to these key characteristics, systematic reviews aim to provide a rigorous, comprehensive, and unbiased summary of existing evidence on a particular topic, which can guide decision-making in healthcare, policy, or further research.

Question 19

steps in a systematci review - detailed walkthrough

Answer 19

Formulating Research Questions and Objectives: Clearly define the research questions and objectives that the systematic review aims to address. These questions guide the entire process.

Developing Inclusion and Exclusion Criteria: Create specific criteria for selecting studies. This includes defining the population, interventions/exposures, comparators, outcomes of interest (known as PICO or PICOS criteria), study types, and publication date ranges.

Protocol Development: Develop a detailed protocol outlining the methods to be used in the review. This includes search strategies, data extraction processes, assessment of study quality, and methods for data synthesis.

Systematic Literature Search: Conduct a comprehensive search across multiple databases (such as PubMed, Scopus, Web of Science) using predefined search terms and strategies to identify all relevant studies. Additional sources like grey literature and reference lists of included studies may also be explored.

Study Selection: Independently screen the search results against the predefined inclusion and exclusion criteria. This involves an initial screening of titles and abstracts followed by a full-text review of potentially relevant articles to select studies that meet the criteria.

Data Extraction: Systematically extract relevant data from the selected studies using a standardized form. This typically includes information on study characteristics, methods, participants, interventions, outcomes, and results.

Quality Assessment: Evaluate the quality, risk of bias, and reliability of the included studies. This can be done using various tools or checklists specific to different study designs.

Data Synthesis: Analyze and synthesize the extracted data. This could involve quantitative synthesis (meta-analysis) or qualitative synthesis (narrative synthesis) depending on the nature of the data and the research questions.

Interpretation and Reporting: Interpret the findings in the context of the research questions, and report the results following established reporting guidelines (such as PRISMA - Preferred Reporting Items for Systematic Reviews and Meta-Analyses).

Discussion and Implications: Discuss the implications of the findings, including limitations and areas for future research.

Throughout these steps, it’s essential to document the process meticulously to ensure transparency and reproducibility of the systematic review. This structured approach aims to minimize bias and provide a comprehensive and reliable summary of the available evidence on a specific topic.

Question 20

Types of studies in syetmatci reviews

Answer 20

Randomized Controlled Trials (RCTs): These are considered the gold standard for evaluating interventions. They involve random allocation of participants into treatment and control groups and typically minimize bias. Parallel RCTs are commonly included in systematic reviews due to their robust design.

Non-Randomized Studies: These include observational studies (cohort studies, case-control studies) that lack randomization in assigning interventions or exposures. They might be prone to selection bias, confounding variables, and other biases, which should be carefully considered during the review process.

Cross-over Studies: These trials involve participants receiving multiple interventions in a sequential order. They might face issues related to carryover effects and the necessity for washout periods to minimize these effects.

Multi-Site Studies: Trials conducted across multiple sites might introduce variability due to differences in study settings, participant characteristics, or methodologies. Statistical adjustments might be necessary to account for these variations.

Cluster Randomized Trials: These trials randomize groups (clusters) rather than individuals. Analytical methods need to consider clustering effects when combining data from these studies in a systematic review.

Single-Arm Studies: Trials where all participants receive the same intervention without a control group might lack a comparative element, requiring careful interpretation of results.

Diagnostic Test Accuracy Studies: These assess the accuracy of diagnostic tests and have their own set of methodological considerations, such as spectrum bias or verification bias, which should be addressed in the review.

Qualitative Studies: Some systematic reviews include qualitative research to synthesize findings from qualitative studies, interviews, focus groups, etc. These studies explore experiences, attitudes, and perceptions and require specific analysis methods like thematic analysis.

Each study type brings its own set of strengths, weaknesses, and biases. Systematic reviews aim to carefully consider these nuances when synthesizing evidence across various studies, using appropriate statistical techniques and methods to address heterogeneity and potential biases.

Question 21

data collection and managment for aystematic review

Answer 21

Data Collection:

a. Search Strategy: Execute a comprehensive and systematic search across multiple databases, using predefined search terms and strategies.

b. Screening Process: Screen the search results by initially reviewing titles and abstracts to identify potentially relevant articles. Then conduct a full-text review of selected articles to determine final inclusion.

c. Data Extraction: Develop standardized forms to extract relevant data from included studies. Extract information on study characteristics, methods, participants, interventions, outcomes, and results.

d. Quality Assessment: Assess the quality, risk of bias, and reliability of included studies using appropriate tools or criteria. This helps in evaluating the strength of evidence.

Data Analysis:

a. Quantitative Synthesis (Meta-Analysis):

Effect Size Calculation: Calculate effect sizes from individual studies, which might involve odds ratios, risk ratios, mean differences, etc., depending on the type of data.

Pooling Data: Combine data across studies using statistical techniques (such as meta-analysis) to obtain an overall estimate of effect size. This requires handling heterogeneity among studies.

Forest Plots: Visual representation of the effect sizes and confidence intervals of individual studies and the overall estimate.

Subgroup Analysis and Sensitivity Analysis: Conduct subgroup analyses based on different variables (e.g., age, gender) or sensitivity analyses to test the robustness of results.

b. Qualitative Synthesis (Narrative Synthesis):

Thematic Analysis: Identify themes or patterns across qualitative studies to synthesize findings. This involves coding, categorizing, and synthesizing qualitative data.

Framework Synthesis: Use a predefined framework to organize and synthesize findings from qualitative studies.

Interpretation and Reporting:

a. Interpret Findings: Interpret the results in light of the research questions, considering strengths, weaknesses, and limitations of included studies.

b. Report Writing: Prepare a structured report following established reporting guidelines (e.g., PRISMA) that includes an introduction, methods, results, discussion, and conclusions.

Throughout these steps, transparency, rigor, and reproducibility are critical. Detailed documentation of the methods and decisions made during data collection and analysis ensures the systematic review’s reliability and credibility.

Question 22

Prisma detailed

Answer 22

PRISMA, which stands for Preferred Reporting Items for Systematic Reviews and Meta-Analyses, is a guideline for reporting systematic reviews and meta-analyses of studies in healthcare and other fields. It provides a structured approach for authors to transparently report their methods and findings, ensuring clarity, completeness, and reproducibility of systematic reviews. PRISMA includes a checklist and flow diagram to guide authors in reporting key components of their review process. Following PRISMA guidelines enhances the quality and reliability of systematic reviews.

Planning and Protocol:

Title and Abstract: The systematic review’s title and abstract should clearly and accurately represent the review’s objectives, methods, and findings.

Introduction: Provide context for the review, explaining the rationale, objectives, and research questions.

Methods:

Protocol Registration: Ideally, systematic review protocols should be registered in a database such as PROSPERO.

Search Strategy: Describe the comprehensive search strategy used to identify relevant studies across various databases, including search terms, date ranges, and inclusion/exclusion criteria.

Study Selection: Detail the process of study selection, including the screening process, criteria for inclusion/exclusion, and any disagreements resolved by discussion or arbitration.

Data Extraction: Explain the method used for data extraction, including the data items collected and any tools or forms used.

Quality Assessment: Describe how the quality, risk of bias, or reliability of included studies was assessed and how it influenced the analysis.

Data Synthesis: Explain the method used to synthesize data, whether through meta-analysis, qualitative synthesis, or both. Discuss how heterogeneity was addressed.

Results:

Study Flow Diagram: Present a flow diagram depicting the study selection process, showing the number of records identified, screened, assessed for eligibility, and included in the review.

Characteristics of Included Studies: Summarize the key characteristics of the included studies (e.g., study design, participants, interventions, outcomes).

Results of Individual Studies: Present the results of each study included in the review, possibly in tables or figures.

Synthesis of Results: If applicable, present the overall results of the synthesis, such as pooled effect sizes in meta-analysis or synthesized themes in qualitative synthesis.

Discussion:

Summary of Evidence: Summarize the main findings, considering the strengths, limitations, and implications of the review’s findings.

Limitations: Discuss limitations encountered during the review, including biases, methodological shortcomings, or heterogeneity.

Conclusion: Provide a concise summary of the implications of the findings and suggestions for future research.

Adhering to PRISMA guidelines ensures a systematic and comprehensive presentation of the systematic review process, from the initial search to the final interpretation of findings, enhancing the review’s transparency and reliability.

Question 23

When can/should you do a meta-analysis?

Answer 23

You can consider conducting a meta-analysis when certain conditions are met:

Multiple Studies Estimating Treatment Effect: Meta-analysis becomes feasible when there are several individual studies that have assessed the same or similar interventions and outcomes.

Consistency in Characteristics Across Studies: While some variability is expected, minimal heterogeneity in participant characteristics, interventions, and study designs across the selected studies is ideal. This helps ensure that the studies are comparable.

Consistent Outcome Measurement: The outcome of interest should have been measured in a similar manner across the studies. This consistency allows for meaningful pooling and comparison of results.

Availability of Data from Each Study: Access to the necessary data from each study is crucial for conducting a meta-analysis. Without access to the primary data, conducting a meta-analysis may not be feasible.

Homogeneity of Studies (or Addressable Heterogeneity): While some level of heterogeneity among studies is expected, it should be manageable or addressable. Statistical techniques can help assess and manage heterogeneity to ensure the validity of the meta-analysis.

Remember, a meta-analysis combines data from multiple studies to generate a more precise estimate of treatment effect. However, conducting a meta-analysis should be approached cautiously and with consideration of the quality, design, and characteristics of the included studies. If these conditions are met, a meta-analysis can provide valuable insights by synthesizing evidence from multiple sources.

Question 24

interpreting data

Answer 24

Absolutely, here’s a more organized breakdown of analyzing data within a systematic review:

Stage 1: Summary Statistics for Each Study

For Dichotomous Data:

Risk Ratio (Relative Risk)
Odds Ratio
Risk Difference
For Continuous Data:

Difference Between Means (When Measured on the Same Scale)
Standardized Mean Difference (When Measured on Different Scales)
For Counts and Rates:

Rate Ratio (Especially for Rare Events)
Stage 2: Weighting the Studies

Weighting each study provides more information and accounts for variance.
Studies with more participants or events have lower variance and greater influence.
Weight is inversely proportional to the variance, where wider confidence intervals result in less weight given to that study.
Stage 3: Displaying Results Graphically

Forest Plots are commonly used to visually represent statistical results and assess heterogeneity among studies.
They offer a snapshot of effect sizes and confidence intervals for each study.
Stage 4: Interpretation

Evaluate the consistency of results across studies.
Assess the degree of heterogeneity or homogeneity among study outcomes.
Validate results against biological plausibility and clinical understanding.
Formulate conclusions that accurately reflect the findings without overemphasizing inconclusive results.
Consider the applicability of the findings to clinical practice, answering the “So what?” question.
Focus on interpreting effect estimates and their confidence intervals, avoiding reliance solely on “statistically significant” or “statistically non-significant” distinctions.
This structured approach to data analysis within a systematic review ensures a comprehensive and nuanced interpretation of the collected evidence, emphasizing effect estimates, variability, and clinical relevance.

Question 25

Interpreting forewts plot

Answer 25

The diamond represents the treatment effect based on pooled results from meta-analysis

The point estimate is represented by the position on the axis of the vertical height of the diamond

The confidence interval is represented by the horizontal width of the diamond
This is the % of weight
given to each study
in the pooled analysis
Each study is given a blob representing the treatment effect (observed effect)

The size of the blob is proportional to the weight given to that study

The horizontal line is the confidence interval - an arrow means the CI is off the scale

The wider the confidence interval, the less likely
The vertical line in the
middle is the line of no effect

The horizontal line crosses
the line of no effect when
there is no difference
between the treatment and
control
The horizontal line at the bottom is the scale measuring the treatment effect

Question 26

Standard mean difference

Answer 26

The standard mean difference (SMD) is a measure of the difference in means between two groups, typically used in meta-analysis or research comparisons. It’s calculated by dividing the difference in means by the pooled standard deviation. SMD helps express the effect size in standard deviation units, allowing for comparison between studies with different scales or units.

Question 27

weighting studies

Answer 27

Byobtaining aweighted average*Randomization ispreservedand larger (more precise)studieshave larger weightinthe analysis35

nversevariancemethodany type ofdata,both fixed andrandomeffectsin factthisisthemaximumlikelihood estimator

Question 28

chi squared tests

Answer 28

The chi-squared test (χ² test) is a statistical test used to examine the association between categorical variables. In the context of a systematic review or meta-analysis, the chi-squared test might be employed in various ways:

Assessing Homogeneity/Heterogeneity: In meta-analysis, the chi-squared test can be utilized within a statistical measure called the Cochran’s Q test. This test helps evaluate whether there is significant heterogeneity among the effect sizes of individual studies included in the meta-analysis. If the Q statistic’s p-value is below a certain significance level (usually 0.05), it suggests that there is heterogeneity beyond what would be expected by chance.

The Chi2 test measures the amount of variation in a set of trials, and tells us if it is more than would be expected by chance alone

Bigger values of c2 suggest heterogeneity of intervention effects

Rule of thumb: if c2 bigger than df (1 less than number of studies in forest plot) then evidence of heterogeneity

Care must be taken in the interpretation of the Chi2test, since it has low power in the (common) situation of a meta-analysis when studies have small sample size or are few in number

Question 29

Statistics 2 - p value significance

Answer 29

P = less than 0.05 - strong likelihood of statistical heterogeneity
P = between 0.05 and less than 0.1 - likely to be heterogeneous
P = greater than 0.1 – not heterogeneous

Lack of statistical significance does not mean there is no heterogeneity

So, a more conservative p-value of 0.1 is often used to suggest statistical significance rather than the usual 0.05

Question 30

I-square statistic

Answer 30

The I-square (I²) statistic is a measure of heterogeneity in meta-analyses. It quantifies the proportion of total variation across studies due to heterogeneity rather than chance. In simpler terms, it assesses the degree of inconsistency or variability between the results of different studies included in a meta-analysis.

Here’s what it signifies:

Low I² (around 0-25%): Indicates low heterogeneity. It suggests that most of the variability in effect sizes between studies is due to chance, and the studies are relatively consistent in their findings.

Moderate I² (around 25-75%): Suggests moderate heterogeneity. This range indicates a substantial level of variability among the studies’ results beyond what might be expected by chance alone.

High I² (around 75-100%): Indicates high heterogeneity. It suggests considerable inconsistency among study findings that is unlikely to be due to chance, indicating the need for further exploration of the sources of heterogeneity.

The I² statistic complements other measures of heterogeneity and helps researchers determine the appropriateness of combining study results in a meta-analysis. If significant heterogeneity exists, it might influence the interpretation of the pooled effect estimate. In such cases, researchers might conduct subgroup analyses or sensitivity analyses to explore potential reasons behind the heterogeneity, such as differences in study populations, interventions, or methodologies.

𝐼^2 is not an absolute measure for heterogeneity
thus we cannot use it to check whether or not we have much heterogeneity in our data
it should be interpreted as the percentage of the total variation that is due to heterogeneity
Uncertainty in the value I2is substantial when the number of studies small

Question 31

The Q STATSITC AND tHE tAU STAT

Answer 31

In systematic reviews and meta-analyses, the Q-statistic and T-statistic are important measures used to assess heterogeneity among effect sizes from individual studies:

Q-Statistic (Cochran’s
Q):

The Q-statistic assesses the degree of heterogeneity among effect sizes from different studies included in a meta-analysis.

It’s calculated by summing the squared differences between each study’s effect size and the overall effect size, weighted by the inverse variance of each study.

Q-statistic with a low p-value (usually less than 0.05) suggests that there is more variability in effect sizes than would be expected due to random sampling error alone, indicating significant heterogeneity.

-Statistic (Tau-Squared):
is a measure of the true variance of effect sizes among studies. It represents the amount of variability or heterogeneity that cannot be attributed to chance alone.

It quantifies the between-study variance or dispersion of true effects, providing an estimate of the amount of true heterogeneity in effect sizes across studies.

Larger values of T stat indicate higher between-study variability and thus more substantial heterogeneity among effect sizes.

Both statistics are crucial in assessing heterogeneity in meta-analyses. The
Q-statistic identifies whether there is statistically significant heterogeneity, while the Tau stat provides an estimate of the magnitude of the true between-study variance or heterogeneity. These measures help researchers determine the appropriateness of using fixed-effects or random-effects models in combining effect sizes and interpreting the overall findings of the meta-analysis.

Question 32

predicitopn intervals

Answer 32

Prediction intervals are the most reliable choices of absolutes measures
They inform us on how much the effects are varying
e.g. if the summary mean is 0.50 and Tau-statistic is 0.40 then the true effects are varying from 0.10 to 0.90
The range of the interval represents the range in which we expect that the effect size of a future study will fall in
if the prediction interval contains the line of no effect then future studies might change the direction of the summary effect
Prediction intervals rely on the normality assumption
we should be cautious when we have a small sample size (i.e. #studies<4)
Prediction intervals are indeed valuable in meta-analysis as they provide insights into the range within which the true effect sizes of future studies are likely to fall. Here are some key points about prediction intervals:

Reliability of Absolute Measures: Prediction intervals offer a more comprehensive understanding of the variability in effect sizes across studies. They consider not just the point estimate (such as the summary mean effect size) but also the variability or dispersion of the true effects.

Informing Variability of Effects: Prediction intervals indicate the extent of variation among true effect sizes. For instance, if the summary mean is 0.50 with a
�
2
τ
2
statistic of 0.40, the prediction interval might suggest that future studies’ effect sizes could range from 0.10 to 0.90.

Implications for Future Studies: When the prediction interval includes the line of no effect (usually 0 in many contexts), it suggests that future studies might produce effect sizes that change the direction of the summary effect. This emphasizes the uncertainty in predicting the outcomes of future studies.

Assumptions and Cautionary Notes: Prediction intervals rely on certain assumptions, including the normality assumption. Also, caution should be exercised when dealing with small sample sizes (e.g., fewer than four studies) as prediction intervals might be less reliable due to limited data.

Overall, prediction intervals provide a broader perspective by considering the range of potential effect sizes in future studies, acknowledging the variability and uncertainty inherent in meta-analytic results. They guide researchers in understanding the potential range of effect sizes and the implications for the interpretation of the overall findings.

Question 33

Aspects of methodological and clinical heterogeneity

Answer 33

Types of studies
quality of allocation concealment
Types of participants
children or adults (age range)
Types of interventions
differences in frequency or intensity of treatment, dose
Types of outcomes
when or how outcome is measured
A consistent treatment effect across different situations reinforces our belief that it’s a real effect

… but if the results are too different, belief in the answer is threatened
…. an easy thing to criticize post-hoc if people don’t agree with the result
ones where we believe the difference between studies is likely to make a big difference to the treatment effect
especially if it might make the effect go the other way, or
where the meta-analysis might drown out a real effect in one group
where the result does not make clinical sense
Certain factors may produce misleading results of statistical analysis
One way to assess the impact that differences in participant characteristics have on pooled results
Differences across studies may lead to inaccurate measure of treatment effect
Example: participants with mild vs. severe level of disease, young vs. old
Careful with interpretation
Subsequent studies often fail to confirm findings of subgroup results

Question 34

sensitivity analysis

Answer 34

Investigates influence, bias, and robustness
Variations in statistical methods, methodological quality, and degree of bias in each study can effect pooled result of meta-analysis

Are the findings influenced by choice of statistical model…?
Is bias in study methods (allocation concealment, blinding) affecting the outcome?
Are the findings robust to different assumptions (intention to treat, missing data)?

Question 35

dont do a meat metanalysis

Answer 35

It may be inappropriate to perform meta-analysis when:

High level of heterogeneity
Studies or outcome data are missing from the review either because results unavailable or because e.g. data are skewed