Enterprise AI Analysis
Psychosocial interventions indicate prolonged survival in cancer patients in a systematic review, meta-analysis, and multiverse meta-analysis of randomized controlled trials
Our AI-powered deep dive reveals significant findings from this research, offering actionable insights for your enterprise strategy.
Executive Impact Summary
Our AI's synthesis of the key findings, translated into quantifiable enterprise value:
0.80
Hazard Ratio (HR)
Lower HR indicates better survival for intervention group. A 0.80 HR signifies a 20% reduction in risk of death for the intervention group compared to control.
3.9 Months
Median Survival Benefit
An average extension of 3.9 months in median survival time for patients receiving psychosocial interventions.
Confirmed
Overall Survival Effect by Multiverse Analysis
Robustness of findings across varied analytical specifications, enhancing confidence in the overall survival benefit.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overall Effect and Heterogeneity
Psychosocial interventions, provided alongside medical treatment, demonstrated a small, positive and robust overall effect on survival in cancer patients, with an HR of 0.80, 95% CI [0.71, 0.90] across 32 RCTs comprising 5,704 participants. Heterogeneity was moderate to substantial with an I² = 48% and a wide 95% PI (HR 0.49–1.29). Median survival time benefit was estimated at 3.9 months, 95% CI [−0.7, 8.5], based on data from 16 trials.
Multiverse meta-analysis confirmed the presence of a general overall survival effect and indicated that previously conflicting meta-analytic conclusions primarily stemmed from differences in effect size metrics and analytic decisions.
0.80
Overall Hazard Ratio (HR) for Survival
This value indicates a 20% reduction in the hazard of death for patients receiving psychosocial interventions compared to controls, demonstrating a statistically significant and clinically meaningful survival benefit.
Multiverse Meta-Analysis Approach
The specification curve and multiverse-analysis approach provided insights into the causes of heterogeneity in the results of past meta-analyses and specifically enabled evaluation of the generalizability and robustness of findings across all different meta-analytic specifications. This advanced method accounted for varied data and model universes, calculating overall effects across numerous combinations to establish a more coherent understanding.
Enterprise Process Flow: Multiverse Meta-Analysis
Define Data Universe (WHICH Factors)
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Define Model Universe (HOW Factors)
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Calculate All Overall Effects
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Generate Specification Curves
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Apply Parametric Bootstrap
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Infer Robustness & Generalizability
Comparable Survival Benefits & Cost-Effectiveness
Psychosocial interventions demonstrate survival benefits comparable to established medical cancer treatments, but without the adverse side effects and at significantly lower costs. This highlights the potential for a paradigm shift in comprehensive cancer care.
| Treatment Type | Survival Benefit (HR/RR/OR) |
|---|---|
| Psychosocial Interventions | HR 0.80 (20% reduction in risk of death) |
| Chemotherapy (Pancreatic Cancer) | HR 0.71 |
| Chemotherapy (Metastatic Breast Cancer) | HR 0.87 |
| Hormone Therapy (Breast Cancer) | HR 0.79 |
Cost-Effectiveness of Psychosocial Interventions
Compared to the substantial financial burden of medical treatments like chemotherapy ($62,302 to $104,197 per patient) and radiotherapy ($8,100 per course), psychosocial interventions offer significant savings while providing comparable benefits.
For example, six weekly 120-minute sessions of mindfulness-based stress reduction cost around $666 per patient, and cognitive behavioral group therapy costs approximately $406 per patient. These interventions are low-cost, low-risk, and scalable, improving patient well-being and survival without the high financial and physiological burden of traditional medical approaches.
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Your AI Implementation Roadmap
A clear path to integrating AI for measurable impact within your organization, inspired by robust meta-analytic methodologies.
Phase 1: Discovery & Strategy
Identify key pain points and opportunities, define clear objectives, and develop a tailored AI strategy based on our advanced research insights.
Phase 2: Pilot & Optimization
Implement a targeted AI pilot program, collect data, and iterate on solutions to optimize performance and demonstrate tangible ROI.
Phase 3: Scaling & Integration
Scale successful AI initiatives across your enterprise, integrate solutions into existing workflows, and establish continuous monitoring for long-term value.
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