As the data collection methods have extreme influence over the validity of the research outcomes, it is considered as the crucial aspect of the studies
May 2025 | Source: News-Medical
The pharma and biotech industries are plagued by high levels of complexity, capital investment and risk. Development of new drugs or treatments requires large amounts of money (often 1 billion or more dollars) and takes a long time (typically many years, often a decade or more). Even with all the improvements in medical research, the success rates of new drug candidates are low – almost 90% fail in clinical trials. The stakes are so high that companies have no option other than to use the best available evidence to inform their decision making.[1]
Meta-analysis for pharma and meta-analysis for biotech are key methods in increasing the strength of the evidence and decreasing the risks associated with drug development. Pharma evidence synthesis via meta-analysis aggregates data across multiple different sources of evidence to provide a more accurate and actionable evaluation of a drug’s potential effectiveness and safety which can decrease the risk associated with R&D.
Meta-analysis is a statistical approach that combines outcomes from several independent studies examining a common topic or research question, with the goal of achieving a more accurate and dependable conclusion. The goal is to compile the available data from each of those studies to allow the researcher(s) the opportunity to make a more generalizable conclusion than what might have been determined in a single study. In analysis, regardless of the topic, to increase statistical power (strength), researchers appear to be less misleading by random error(s) or biases which may affect an individual trial,[2]
Meta-analysis works by:
Step | Description | Contribution (%) |
Locating Relevant Articles | Gather studies related to the research question | 15% |
Summarizing Key Data | Extract effect sizes, sample sizes, outcomes | 20% |
Combining Data Across Studies | Pool study estimates for overall effect size | 25% |
Calculating Overall Effect Size | Determine the overall treatment impact | 20% |
Adjusting for Variance | Account for bias and errors across studies | 10% |
Testing for Bias | Check for publication or other biases | 10% |
In the case of threats to the study based on bias/research interpretation, or chance/randomization, researchers see value in that it’s a way to lower risk in biotechnology R&D with respect to marrying/truths effects from a set of data points, across various clinical trial studies.[3]
Meta-analysis improves the evidence base for new treatments by combining the evidence from several studies to increase the sample size, and thus, the precision of the results. A meta-analysis helps establish trends across studies to reconcile inconsistencies across trials for more trustworthy conclusions. This method of analysis also ensures pharmaceutical, and biotech companies employ the most accurate evidence during research and development.[4]
The risk associated with development in pharma and biotech is exacerbated by the substantial failure rates in R&D. Meta-analysis can help manage some of this risk by:
No. of Drugs Launched | 8-13 Drugs | 4-6 Drugs | 2-3 Drugs | 1 Drug |
R&D Costs (Avg.) | $5.998B | $5.052B | $2.303B | $953M |
R&D Costs (Median) | $5.459B | $5.151B | $1.803B | $351M |
This table shows the average and median R&D costs based on the number of drugs launched. As the number of successful drugs increases, the R&D costs per drug generally become more efficient, as reflected in the lower costs for launching 8-13 drugs compared to just 1 drug.
Phase of R&D
| Meta-Analysis Contribution |
Preclinical Trials | Supports clinical success prediction by synthesizing animal model information.[4] Supports clinical success prediction by synthesizing animal model information. |
Clinical Trials (Phase 1-3): | Recognizes the likelihood of positive effects from new compounds by optimizing the clinical trial design. |
Post Market Surveillance | Analysing data for a wider population allows rare side effects to be identified.
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Several case studies demonstrate the power of meta-analysis in pharma and biotech R&D:
Statins for Cardiovascular Disease: A massive meta-analysis of clinical trials revealed that statins protect against heart attack and stroke. This evidence was an important factor for widely adopting statins as standard of care for cardiovascular disease.
Cancer Immunotherapy: Meta-analysis of immunotherapy research studies allowed for the best use of immune checkpoint inhibitors, and modifications were made to optimize treatment regimens and patient outcomes.[5]
These cases demonstrate that meta-analysis for pharma and meta-analysis are critical for understanding the efficacy and safety of drugs during regulatory decision-making and is also essential after drugs are on the market in patient treatments.
If done right, meta-analysis can provide accurate, credible, and valuable information. But a meta-analysis doesn’t happen in a single step, it requires competent pre-planning and execution of many activities.
Publication Bias: Suitable & appropriate models should assess available and relevant (including unpublished) studies to limit publication bias.
Data Inconsistency: Be cautious at the time of the analysis when combining studies that differ significantly in methodology or measures related to outcomes.[5]
In the pharmaceutical and biotechnology sector, meta-analysis is essential to combine data from multiple studies to build the evidential base to make better drug development decisions based on sound evidence. Meta-analysis builds evidence for the early identification of effective treatment approaches, improves trial efficiency, and provides supportive evidence for regulatory approval decisions all while mitigating R&D risks.
Pharmaceutical and biotech companies that utilize systematic review and meta-analysis in their R&D or clinical trials will be able to make better; data informed decisions, decrease discovery trial failures, and more quickly develop safe and effective treatments.[5] Meta-analysis helps to moderate risk, drives innovation, and enhances return on investment.
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