Analysis Of Real-life Data Research Articles

Clinical pharmacology facing the real-world setting: Pharmacovigilance, pharmacoepidemiology and the economic evaluation of drugs

Traditionally, clinical pharmacology has focused its activities on drug-organism interaction, from an individual or collective perspective. Drug efficacy assessment by performing randomized clinical trials and analysis of drug use in clinical practice by carrying out drug utilization studies have also been other areas of interest. From now on, Clinical pharmacology should move from the analysis of the drug-individual interaction to the analysis of the drug-individual-society interaction. It should also analyze the clinical and economic consequences of the use of drugs in the conditions of normal clinical practice, beyond clinical trials. The current exponential technological development that facilitates the analysis of real-life data offers us a golden opportunity to move to all these other areas of interest. This review describes the role that clinical pharmacology has played at the beginning and during the evolution of pharmacovigilance, pharmacoepidemiology and economic drug evaluations in Spain. In addition, the challenges that clinical pharmacology is going to face in the following years in these three areas are going to be outlined too.

Effectiveness and Safety of Remdesivir in Treating Hospitalised Patients with COVID-19: A Propensity Score Analysis of Real-Life Data from a Monocentric Observational Study in Times of Health Emergency.

Remdesivir is an antiviral agent, which was shown to be safe and effective in treating early COVID-19, but its favourable impact in hospitalised patients with non-critical disease is still under investigation. The present study aimed to assess the effectiveness and safety of remdesivir as a treatment for hospitalised patients with COVID-19 by a propensity score analysis of observational data. In this monocentric retrospective cohort study, the effectiveness and safety of a 5-day course of remdesivir (200 mg intravenously at Day 1, then 100 mg from Days 2-5) in association with the standard of care were assessed in comparison with the standard of care only. The primary endpoint was the proportion of recovery on Day 14. Of 3662 eligible inpatients who tested positive for the severe acute respiratory syndrome coronavirus 2 genome by nasopharyngeal swab at admission, 861 (24%) non-critical patients were included in a propensity score analysis and 281 (33%) were exposed to remdesivir. In total, 242/281 (86.1%) and 435/580 (75.0%) patients recovered in exposed and non-exposed, respectively, with a relative improvement of 11.1% (95% CI +5.8 to 16.5%; unadjusted odds ratio: 2.07, 95% CI 1.40-3.05, p = 0.0001; after adjustment by propensity score weighting, odds ratio: 1.92, 95% CI 1.30-2.83, p = 0.001). In treated patients, 1 (0.03%) anaphylactic reaction and 1 (0.03%) acute reaction during drug injection were reported, and 24 (8.5%) patients stopped the treatment due to adverse reactions. No significant differences were found with respect to the secondary efficacy endpoints (in-hospital all-cause death, need for intensive care treatments, clinical improvement score at Day 28) and safety endpoints (any and serious adverse reactions). A 5-day course of remdesivir in association with the standard of care effectively promoted recovery from COVID-19 among non-critical in-hospital patients and had an acceptable safety profile.

Multiview Regularized Discriminant Canonical Correlation Analysis: Sequential Extraction of Relevant Features From Multiblock Data.

One of the important issues associated with real-life high-dimensional data analysis is how to extract significant and relevant features from multiview data. The multiset canonical correlation analysis (MCCA) is a well-known statistical method for multiview data integration. It finds a linear subspace that maximizes the correlations among different views. However, the existing methods to find the multiset canonical variables are computationally very expensive, which restricts the application of the MCCA in real-life big data analysis. The covariance matrix of each high-dimensional view may also suffer from the singularity problem due to the limited number of samples. Moreover, the MCCA-based existing feature extraction algorithms are, in general, unsupervised in nature. In this regard, a new supervised feature extraction algorithm is proposed, which integrates multimodal multidimensional data sets by solving maximal correlation problem of the MCCA. A new block matrix representation is introduced to reduce the computational complexity for computing the canonical variables of the MCCA. The analytical formulation enables efficient computation of the multiset canonical variables under supervised ridge regression optimization technique. It deals with the "curse of dimensionality" problem associated with high-dimensional data and facilitates the sequential generation of relevant features with significantly lower computational cost. The effectiveness of the proposed multiblock data integration algorithm, along with a comparison with other existing methods, is demonstrated on several benchmark and real-life cancer data.

Adverse events related to alpha-blockers: analysis of real-life data from Eudra-Vigilance.

Patients on alpha-blockers (ABs) treatment may have an increased risk of adverse events (AEs). Aim of our study was to compare real-life data on neuro-vascular and sexual AEs associated with ABs based on Eudra-Vigilance reported AEs. Eudra-Vigilance (EV) database is the system for managing and analyzing information on suspected adverse reactions to medicines which have been authorized or being studied in clinical trials in the European Economic Area (EEA). We recorded the number of sexual and neuro-vascular AEs for tamsulosin, alfuzosin, silodosin, prazosin and doxazosin per category and severity until July 30th, 2022. Pooled Relative Risk (PRR) was used to compare data between drugs. Overall the number of AEs were 2842 for Alfuzosin, 11,086 for tamsulosin, 792 for terazosin, 572 for prazosin and 4641 for doxazosin. Different percentages of AEs were obtained for each drug and in different age groups according to EV sub-groups (≤65, 65-85, ≥85). On PRR analysis, the risk of ejaculatory disorders for Silodosin (11%) is 18.5 times higher (PRR 18.5 95%CI; 10.7-31.8; P<0.05) when compared to alfuzosin and the risk of orthostatic hypotension is 2 times lower (PRR=1,84 95%CI 1.32-2.57; P<0.05). Real life data is consistent with registry studies regarding side effects related to alpha-blockers. Alfuzosin is safer in terms of ejaculatory disorders while silodosin and tamsulosin in terms of orthostatic hypotension. Clinicians should consider these data when prescribing ABs especially in younger and older patients.

Inference for covariate-adjusted time-dependent prognosticaccuracy measures.

Evaluating the prognostic performance of candidate markers for future disease onset or progression is one of the major goals in medical research. A marker's prognostic performance refers to how well it separates patients at the high or low risk of a future disease state. Often the discriminative performance of a marker is affected by the patient characteristics (covariates). Time-dependent receiver operating characteristic (ROC) curves that ignore the informativeness of the covariates will lead to biased estimates of the accuracy parameters. We propose a time-dependent ROC curve that accounts for the informativeness of the covariates in the case of censored data. We propose inverse probability weighted (IPW) estimators for estimating the proposed accuracy parameters. We investigate the performance of the IPW estimators through simulation studies and real-life data analysis.

Effectiveness of biologic therapies in children with palmoplantar plaque psoriasis: An analysis of real-life data from the BiPe cohorts.

Palmoplantar plaque psoriasis is a frequent clinical subtype of childhood psoriasis. This study evaluated the effectiveness of biologic therapies in children with palmoplantar plaque psoriasis using data from the two Biological treatments for Pediatric Psoriasis (BiPe) cohorts. Data for all 170 patients included in the BiPe cohorts were analyzed. Data on the effectiveness (PGA, PASI between baseline and 3 months of treatment) of biologic therapies were then compared between children with palmoplantar plaque psoriasis (n = 20) and those with generalized plaque psoriasis (n = 136). Clinical and demographic data were also analyzed. Children in the palmoplantar group were more likely to be male (p = .04), with an earlier age of psoriasis onset (p < .001), and more frequent nail involvement (p < .001). After 3 months of biologic treatment, mean PGA scores were higher in the palmoplantar group than in the generalized plaque psoriasis group (p = .004). In the palmoplantar group, continuation rates were higher for adalimumab than for etanercept or ustekinumab (p = .01). Primary inefficacy was a more frequent reason for stopping biologic therapies in the palmoplantar group (p = .01), and disease remission was less frequent (p = .05). Combined systemic and biologic therapies were more frequently used in palmoplantar plaque psoriasis (p < .001). This study demonstrated the treatment-resistant nature of palmoplantar plaque psoriasis and indicated that adalimumab could be the most effective biologic treatment. Larger studies are needed to allow therapeutic algorithms for palmoplantar plaque psoriasis to be proposed in pediatric psoriasis management guidelines.

Creating a developmental framework for evaluating RRI implementation in research organisations

The evaluation of Responsible Research and Innovation (RRI) actions presents important challenges for the key stakeholders engaged in the process of RRI implementation, such as policy makers, programme managers, and researchers. While there is a considerable body of literature on the conceptualization of responsibility in research and a plethora of attempts to practice RRI, there is a need for increased attention to the monitoring and evaluation of case studies of RRI implementations in research organisations, in particular regarding their structural change effects. This paper aims to discuss a contextualised developmental framework for evaluating RRI implementation in research organisations, with a specific focus on achieving structural change through tailor-made action plans. The framework, developed through RRI evaluation work in the field of biosciences, adopts a systemic and process-oriented perspective, encompassing participatory, anticipatory, reflexive, and responsive dimensions. Concrete empirical examples from bioscience organizations are provided to illustrate how the framework relates to specific conditions, experiences, and solutions, demonstrating how conceptual insights have emerged from real-life practices and data analysis. While the framework was initially customized for the specific contexts of six bioscience research organizations, it holds potential for broader relevance and applicability in addressing challenges related to RRI design, implementation, and evaluation.

A Novel Extended Power-Lomax Distribution for Modeling Real-Life Data: Properties and Inference

One of the important features of generalized distribution is its ability and flexibility to model real-life data in several applied fields such as medicine, engineering, and survival analysis, among others. In this paper, a flexible four-parameter Lomax extension called the alpha-power power-Lomax (APPLx) distribution is introduced. The APPLx distribution is analytically tractable, and it can be used quite effectively for real-life data analysis. Key mathematical properties of the APPLx distribution including mode, moments, stress-strength reliability, quantile and generating functions, and order statistics are presented. The APPEx parameters are estimated by using eight classical estimation methods. Extensive simulation studies are provided to explore the performance of the proposed estimation methods and to provide a guideline for practitioners and engineers to choose the best estimation method. Three real-life datasets from applied fields are fitted to assess empirically the flexibility of the APPLx distribution. The APPLx distribution shows greater flexibility as compared to the McDonal–Lomax, Fréchet Topp–Leone Lomax, transmuted Weibull–Lomax, Kumaraswamy–Lomax, beta exponentiated-Lomax, Weibull–Lomax, Burr-X Lomax, Lomax–Weibull, odd exponentiated half-logistic Lomax, and alpha-power Lomax distributions.

Machine-Learning-Based Combined Path Loss and Shadowing Model in LoRaWAN for Energy Efficiency Enhancement

Many practical Internet of Things (IoT) applications require deploying End Nodes (ENs) in hard-to-access places where replacing batteries is difficult or impossible. As a result, the ENs demand high energy efficiency. Long Range Wide Area Network (LoRaWAN) is an IoT protocol that aims to achieve low energy consumption. However, the energy consumption in LoRaWAN is related to transmission power, which can be set mainly based on path loss and shadow fading modeling and link budget analysis. Hence, appropriately setting this transmission power parameter saves energy and guarantees reliable communication links. Traditional path loss and shadow fading modeling and transmission power setting do not consider the variations caused by different environmental effects. In this work, we show via real-life data analysis that path loss and shadow fading depend on environmental variables. We propose Machine Learning models to calculate the empirical path loss and shadow fading, which is used to set the transmission power to save ENs’ energy. Our models include the effects of distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and Signal to Noise Ratio. Specifically, the models are based on Multiple Linear Regression, Support Vector Regression, Random Forests, and Artificial Neural Networks, exhibiting a Root Mean Square Error (RMSE) up to 1.566 dB and R up to 0.94. For energy saving, the developed models serve to set the transmission power and Spreading Factor based on the Adaptative Data Rate (ADR) algorithm principles, which reduces the link margin saving energy up to 43% compared with the traditional ADR protocol.

85 - Major Adverse Cardiovascular Events (MACE) related to phosphodiesterase 5 inhibitors (PDE5I): Analysis of real-life data from Eudra-Vigilance (EV) database

85 - Major Adverse Cardiovascular Events (MACE) related to phosphodiesterase 5 inhibitors (PDE5I): Analysis of real-life data from Eudra-Vigilance (EV) database

Pharmacovigilance signals from active surveillance of mRNA platform vaccines (tozinameran and elasomeran)

Two severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) messenger RNA (mRNA) vaccines, tozinameran/BNT162b2 (Comirnaty®, Pfizer-BioNTech) and elasomeran/mRNA-1273 (Spikevax®, Moderna), were approved by the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) at the end of 2020, less than a year after the start of the coronavirus disease 2019 (COVID-19) pandemic. In France, the health authorities have requested an intensive vaccination campaign, accompanied by a reinforced and active pharmacovigilance surveillance. This surveillance and analysis of real-life data, based on spontaneous reports received by the French Network of Regional PharmacoVigilance Centers (RFCRPV), has enabled to identify numerous pharmacovigilance signals. Some of them, such as myocarditis and heavy menstrual bleeding, have been confirmed as adverse effects of these vaccines. We propose a descriptive review of the main pharmacovigilance signals identified by the RFCRPV concerning vaccines from the mRNA platform. Most pharmacovigilance signals were common to both mRNA vaccines: myocarditis, menstrual disorders, acquired haemophilia, Parsonage-Turner syndrome, rhizomelic pseudo-polyarthritis and hearing disorders. Other signals were more specific, such as arterial hypertension with tozinameran or delayed reaction site injection with elasomeran. This non-exhaustive review illustrates the experience of RFCRPV in identifying and monitoring pharmacovigilance signals related to mRNA vaccines in France during the COVID-19 pandemics, and the crucial role of pharmacological and clinical expertise in this area. It also highlights the predominant contribution of spontaneous reporting in the generation of pharmacovigilance signals, particularly for serious and rare adverse events not detected before marketing.

A nested error regression model with high-dimensional parameter for small area estimation

Abstract In this paper, we propose a flexible nested error regression small area model with high-dimensional parameter that incorporates heterogeneity in regression coefficients and variance components. We develop a new robust small area-specific estimating equations method that allows appropriate pooling of a large number of areas in estimating small area-specific model parameters. We propose a parametric bootstrap and jackknife method to estimate not only the mean squared errors but also other commonly used uncertainty measures such as standard errors and coefficients of variation. We conduct both model-based and design-based simulation experiments and real-life data analysis to evaluate the proposed methodology.

Upfront Advanced Radiotherapy and New Drugs for NSCLC Patients with Synchronous Brain Metastases: Is the Juice Worth the Squeeze? A Real-World Analysis from Lombardy, Italy

Healthcare administrative databases represent a valuable source for real-life data analysis. The primary aim of this study is to compare effectiveness and cost profile in non-small-cell lung cancer (NSCLC) patients harboring synchronous brain metastases (BMs) who received non-chemo first-line systemic therapy with or without advanced radiotherapy (aRT). Diagnostic ICD-9-CM codes were used for identifying all patients with a new diagnosis of lung cancer between 2012 and 2019. Among these, patients who had started a first-line systemic treatment with either TKIs or pembrolizumab, alone or in combination with intensity-modulated or stereotactic RT, were selected. Clinical outcomes investigated included overall survival (OS), progression-free survival (PFS), and time-to-treatment failure (TTF). The cost outcome was defined as the average per capita cumulative healthcare direct costs of the treatment, including all inpatient and outpatient costs. The final cohort included 177 patients, of whom 58 were treated with systemic treatment plus aRT (STRT) and 119 with systemic treatment alone. The addition of aRT to systemic treatment was associated with a significantly better OS (p = 0.020) and PFS (p = 0.041) than systemic therapy alone. The ICER (incremental cost-effectiveness ratio) value indicated an average cost of €3792 for each month of survival after STRT treatment and confirmed clinical effectiveness but higher healthcare costs. This real-world study suggests that upfront aRT for NCLSC patients with synchronous BMs represents a valid treatment strategy, boosting the efficacy of novel and emerging drug classes with sustainable costs for the health service. The present real-world study reports that the use of upfront advanced radiotherapyaRT and new-generation systemic agents, such as TKIs and pembrolizumab, may have higher oncological control and an improved cost-effectiveness profile than the use of new-generation systemic agents alone in NCLSC patients with synchronous brain metastases. Acquired evidence can also be used to inform policymakers that adding advanced radiotherapy results is a sustainable cost for the health service. Since approximately 50% of patients do not meet RCT inclusion criteria, a significant proportion of them is receiving treatment that is not evidence-informed; therefore, these results warrant further studies to identify the best radiotherapy timing and possible dose escalation approaches to improving treatment efficacy in patient subgroups not typically represented in randomized controlled trials.

Farlie–Gumbel–Morgenstern Bivariate Moment Exponential Distribution and Its Inferences Based on Concomitants of Order Statistics

In this research, we design the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, a bivariate analogue of the moment exponential distribution, using the Farlie–Gumbel–Morgenstern approach. With the analysis of real-life data, the competitiveness of the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution in comparison with the other Farlie–Gumbel–Morgenstern distributions is discussed. Based on the Farlie–Gumbel–Morgenstern bivariate moment exponential distribution, we develop the distribution theory of concomitants of order statistics and derive the best linear unbiased estimator of the parameter associated with the variable of primary interest (study variable). Evaluations are also conducted regarding the efficiency comparison of the best linear unbiased estimator relative to the respective unbiased estimator. Additionally, empirical illustrations of the best linear unbiased estimator with respect to the unbiased estimator are performed.

Major adverse cardiovascular events (MACE) related to phosphodiesterase 5 inhibitors (PDE5i): Analysis of real-life data from Eudra-Vigilance (EV) database

Major adverse cardiovascular events (MACE) related to phosphodiesterase 5 inhibitors (PDE5i): Analysis of real-life data from Eudra-Vigilance (EV) database

Wearable cardioverter defibrillator after cardiac surgery: Analysis of real-life data from patients at transient risk of sudden cardiac death.

Especially in the first 3months after cardiac surgery, patients are at transient risk of sudden cardiac death (SCD). To close the gap between hospital discharge and the final implantable cardioverter-defibrillator (ICD) decision, guidelines recommend temporarily using a wearable cardioverter-defibrillator (WCD) to protect these patients from SCD. We investigated real-life data on the safety, effectiveness, and compliance of the WCD in this population. Data for analysis were collected via the Zoll Patient Management Network (ZPM) from patients who underwent cardiac surgery and who were discharged with a WCD between 2018 and 2021 at the Cardiac Surgery Center of the University of Erlangen in Germany. The majority of the 55 patients were male (90.9%) and underwent a coronary artery bypass graft (80.0%). The number of patients with left ventricular ejection fraction (LVEF) >35% increased from 9.1% at the beginning of WCD use to 58.2% at the end of WCD use. Six ventricular tachycardia (VT) episodes occurred in four patients. The WCD appropriately defibrillated two patients with VT episodes. There were no inadequate shocks and no fatalities during the observation time. WCD wearing compliance was high, with a median wear time of 23.3h/day. This retrospective analysis in a single cardiac surgery center confirms prior data on the safety and effectiveness of the WCD in patients in post-surgery care in a real-life setting. The WCD successfully protected patients from SCD during life-threatening VT episodes. WCD wearing compliance was high.

Total macular volume as a potential biomarker in the assessment of anti-VEGF response in patients with diabetic macular edema: real-life data analysis

Aim: To evaluate the functional and anatomic efficacy of intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy in patients with diabetic macular edema (DME) and investigate the association between central macular thickness (CMT) and total macular volume (TMV) in real-life settings.Material and Method: In this retrospective, observational, longitudinal study 38 eyes of 23 consecutive patients with center-involving DME were included. A loading phase of three monthly intravitreal anti-VEGF injections was initiated, followed by anti-VEGF injections if needed as per clinicians’ discretion. Results: Mean Early Treatment Diabetic Retinopathy Study (ETDRS) letters gained was 3.2 letters at month 12. The reduction in the mean of CMT and TMV were 60 μm and 1.33 mm3 respectively at the end of 12 months. Best-corrected visual acuity (BCVA) was negatively correlated with CMT (r=-0.573, p &amp;lt; 0.01) and TMV (r=-0.533, p &amp;lt; 0.01) initially. There was a statistically significant positive correlation between the CMT and the TMV initially (r=0.765, p &amp;lt; 0.01) and month 12 (r=0.937, p &amp;lt; 0.01). Baseline TMV was found to be more predictive of treatment response at the 9th month than baseline CMT. Conclusion: It is demonstrated that TMV may be a suitable biomarker in the assessment of treatment response of the macular region when regarded as a complete three-dimensional macular unit instead of central vertical thickness only. Although the present study contributes to a better understanding of managing DME in real-life settings, further prospective, and controlled investigations are needed.

Cardiovascular adverse events-related to GnRH agonists and GnRH antagonists: analysis of real-life data from Eudra-Vigilance and Food and Drug Administration databases entries.

GnRH agonists and GnRH antagonists are two of the mainstays of hormonal therapy (HT) for prostate cancer (PCa). These drugs are at increased risk of cardiovascular (CV) adverse events (AEs). Aim of our study was to compare real-life data on AEs associated with GnRH agonists and GnRH antagonists based on Eudra-Vigilance (EV) and Food and Drug Administration (FDA) reported AEs. EV and FDA databases were queried and the number of CV adverse events (AEs) for degarelix, buserelin, goserelin, leuprorelin, triptorelin until September 2021 were recorded. Specific CV AEs were recorded and data were analyzed per age and severity. pooled relative risk (PRR) was used to compare data between drugs. CV events were reported in 315/5128 (6%) for Degarelix, in 55/628 for Buserelin (9%), in 843/12,145 (7%) for Goserelin, in 3395/71,160 (5%) for Leuprorelin and in 214/4969 (5%) for Triptorelin. In terms of specific CV disorders, Degarelix presented lower risk of hypertension (PRR 0.60 (95% CI 0.37-0.98), p = 0.04), of myocardial infarction (PRR 0.05 (95% CI 0.01-0.39), p < 0.01) and thrombosis (PRR 0.14 (0.02-1.07), p = 0.06) when compared to GnRH agonists. Overall, younger patients (<65 years) presented a very low risk of CV AEs. Side effects were classified as serious in 90-96% of the cases. Fatal AEs were 5-20% over the CV AEs and 0.2-1% over the total AEs. Real-life data are consistent with registry studies regarding side effects related to HT. Real-life data suggest GnRH agonists are associated with higher CV AEs when compared to GnRH antagonists. Clinicians should consider these data when prescribing HT especially in patients with CV comorbidities.

How Did the Updated 2019 European Society of Cardiology/European Atherosclerosis Society Risk Categorization for Patients with Diabetes Affect the Risk Perception and Lipid Goals? A Simulated Analysis of Real-life Data from EPHESUS Study.

The recent 2019 European Society of Cardiology/European Atherosclerosis Society practice guidelines introduced a new risk categorization for patients with diabetes. We aimed to compare the implications of the 2016 and 2019 European Society of Cardiology/European Atherosclerosis Society guidelines with regard to the lipid-lowering treatment use, low-density lipoprotein cholesterol goal attainment rates, and the estimated proportion of patients who would be at goal in an ideal setting. Patients with diabetes were classified into 4 risk categories according to 2019 European Society of Cardiology/European Atherosclerosis Society dyslipidemia guidelines from the database of EPHESUS (cross-sectional, observational, countrywide registry of cardiology outpatient clinics) study. The use of lipid-lowering treatment and low-density lipoprotein cholesterol goal attainment rates were then compared according to previous and new guidelines. This analysis included a total of 873 diabetic adults. Half of the study population (53.8%) were on lipid-lowering treatment and almost one-fifth (19.1%) were on high-intensity statins. While low-density lipoprotein cholesterol goal was achieved in 19.5% and 7.5% of patients, 87.4% and 69.6% would be on target if their lipid-lowering treatment was intensified according to 2016 and 2019 European Society of Cardiology/European Atherosclerosis Society lipid guidelines, respectively. The new target <55 mg/dL could only be achieved in 2.2% and 8.1% of very high-risk primary prevention and secondary prevention patients, respectively. The control of dyslipidemia was extremely poor among patients with diabetes. The use of lipid-lowering treatment was not at the desired level, and high-intensity lipid-lowering treatment use was even lower. Our simulation model showed that the high-dose statin plus ezetimibe therapy would improve goal attainment; however, it would not be possible to get goals with this treatment in more than one-third of the patients.

Gamma Dağılımının Parametrelerinin Tahmini için Metasezgisel Yöntemlerin Değerlendirilmesi ve Karşılaştırılması

Parameter estimation of three parameter (3-p) Gamma distribution is very important as it is one of the most popular distributions used to model skewed data. Maximum Likelihood (ML) method based on finding estimators that maximize the likelihood function, is a well-known parameter estimation method. It is rather difficult to maximize the likelihood function formed for the parameter estimation of the 3-p Gamma distribution. In this study, five well known metaheuristic methods, Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Artificial Bee Colony (ABC), are suggested to obtain ML estimates of the parameters for the 3-p Gamma distribution. Monte-Carlo simulations are performed to examine efficiencies of the metaheuristic methods for the parameter estimation problem of the 3-p Gamma distribution. Also, differences between solution qualities and computation time of the algorithms are investigated by statistical tests. Moreover, one of the multi-criteria decision-making methods, Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), is preferred for ranking the metaheuristic algorithms according to their performance in parameter estimation. Results show that Differential Evolution is superior to the others for this problem in consideration of all the criteria of solution quality, computation time, simplicity, and robustness of the metaheuristic algorithms. In addition, an analysis of real-life data is presented to demonstrate the implementation of the suggested metaheuristic methods.