Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

2011 ◽  
pp. 1738-1758
Author(s):  
Tillal Eldabi ◽  
Robert D. Macredie ◽  
Ray J. Paul
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Economic Evaluation ◽  
Simulation Model ◽  
Randomized Clinical Trial ◽  
Simulation Modeling ◽  
Randomized Clinical Trials ◽  
Healthcare Systems ◽  
Modeling Process

This chapter reports on the use of simulation in supporting decision-making about what data to collect in a randomized clinical trial (RCT). We show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” Healthcare systems pose many of the challenges, including difficulty in understanding the system being studied, uncertainty over which data to collect, and problems of communication between problem owners. In this chapter we show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” The experience of developing the simulation model leads us to suggest simple but extremely valuable lessons. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models.

Simulation Modeling as a Decision-Making Aid in Economic Evaluation for Randomized Clinical Trials

2008 ◽  
pp. 219-243 ◽  
Author(s):  
Tillal Eldabi ◽  
Robert D. Macredie ◽  
Ray J. Paul
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Economic Evaluation ◽  
Simulation Model ◽  
Randomized Clinical Trial ◽  
Simulation Modeling ◽  
Randomized Clinical Trials ◽  
Healthcare Systems ◽  
Modeling Process

This chapter reports on the use of simulation in supporting decision-making about what data to collect in a randomized clinical trial (RCT). We show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” Healthcare systems pose many of the challenges, including difficulty in understanding the system being studied, uncertainty over which data to collect, and problems of communication between problem owners. In this chapter we show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” The experience of developing the simulation model leads us to suggest simple but extremely valuable lessons. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models.

scholarly journals Guidelines on how to assess the validity of results presented in subgroup analysis of clinical trials

2002 ◽  
Vol 57 (2) ◽  
pp. 83-88 ◽  
Author(s):  
Edson Duarte Moreira ◽  
Ezra Susser
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Subgroup Analysis ◽  
Observational Studies ◽  
Randomized Clinical Trials ◽  
Usual Method ◽  
Validity Of Results ◽  
Results Of Treatment ◽  
Trial Participants

In observational studies, identification of associations within particular subgroups is the usual method of investigation. As an exploratory method, it is the bread and butter of epidemiological research. Nearly everything that has been learned in epidemiology has been derived from the analysis of subgroups. In a randomized clinical trial, the entire purpose is the comparison of the test subjects and the controls, and when there is particular interest in the results of treatment in a certain section of trial participants, a subgroup analysis is performed. These subgroups are examined to see if they are liable to a greater benefit or risk from treatment. Thus, analyzing patient subsets is a natural part of the process of improving therapeutic knowledge through clinical trials. Nevertheless, the reliability of subgroup analysis can often be poor because of problems of multiplicity and limitations in the numbers of patients studied. The naive interpretation of the results of such examinations is a cause of great confusion in the therapeutic literature. We emphasize the need for readers to be aware that inferences based on comparisons between subgroups in randomized clinical trials should be approached more cautiously than those based on the main comparison. That is, subgroup analysis results derived from a sound clinical trial are not necessarily valid; one must not jump to conclusions and accept the validity of subgroup analysis results without an appropriate judgment.

Extracorporeal Membrane Oxygenation and the Play the Winner Rule

PEDIATRICS ◽  
1985 ◽  
Vol 76 (4) ◽  
pp. 622-623
Author(s):  
NIGEL PANETH ◽  
SYLVAN WALLENSTEIN
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Extracorporeal Membrane Oxygenation ◽  
Randomized Clinical Trial ◽  
Conventional Treatment ◽  
Randomized Clinical Trials ◽  
Ethical Problem ◽  
Therapeutic Trial ◽  
Membrane Oxygenation ◽  
Treatment Comparisons

The therapeutic trial comparing extracorporeal membrane oxygenation with conventional treatment in neonatal respiratory failure reported by Bartlett et al (Pediatrics 1985;76:479-487) uses a method of comparing treatments unlikely to be familiar to most pediatricians. Known as the "randomized play the winner" method, it has thus far been little used in clinical research. Most clinical investigators consider the conventional randomized clinical trial to be the last word in treatment comparisons. But randomized clinical trials are costly, cumbersome, and to some observers less than ideal ethically. The ethical problem arises from the fact that during a "successful" randomized clinical trial (ie, one that demonstrates a significant advantage to one treatment) about half of the trial subjects will receive a treatment which, at the end of the trial, will be known to be inferior.

Why Cancer Patients Enter Randomized Clinical Trials: Exploring the Factors That Influence Their Decision

2004 ◽  
Vol 22 (21) ◽  
pp. 4312-4318 ◽  
Author(s):  
James R. Wright ◽  
Timothy J. Whelan ◽  
Susan Schiff ◽  
Sacha Dubois ◽  
Dauna Crooks ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Logistic Regression ◽  
Cancer Patients ◽  
Randomized Clinical Trials ◽  
Univariate Analysis ◽  
Multivariate Logistic Regression Model ◽  
Decision Making Process ◽  
Trial Entry

Purpose Few interventions have been designed and tested to improve recruitment to clinical trials in oncology. The multiple factors influencing patients' decisions have made the prioritization of specific interventions challenging. The present study was undertaken to identify the independent predictors of a cancer patient's decision to enter a randomized clinical trial. Methods A list of factors from the medical literature was augmented with a series of focus groups involving cancer patients, physicians, and clinical research associates (CRAs). A series of questionnaires was developed with items based on these factors and were administered concurrently to 189 cancer patients, their physicians, and CRAs following the patient's decision regarding trial entry. Forward logistic regression modeling was performed using the items significantly correlated (by univariate analysis) with the decision to enter a clinical trial. Results A number of items were significantly correlated with the patient's decision. In the multivariate logistic regression model, the patient's perception of personal benefit was the most important, with an odds ratio (OR) of 3.08 (P < .05). CRA-related items involving supportive aspects of the decision-making process were also important. These included whether the CRA helped with the decision (OR = 1.71; P < .05), and whether the decision was hard for the patient to make (OR = 0.52; P < .05). Conclusion Strategies that better address the potential benefits of trial entry may result in improved accrual. Interventions or aids that focus on the supportive aspects of the decision-making process while respecting the need for information and patient autonomy may also lead to meaningful improvements in accrual.

scholarly journals Overall Survival As the Outcome for Randomized Clinical Trials With Effective Subsequent Therapies

2011 ◽  
Vol 29 (17) ◽  
pp. 2439-2442 ◽  
Author(s):  
Edward L. Korn ◽  
Boris Freidlin ◽  
Jeffrey S. Abrams
Keyword(s):  
Clinical Trial ◽  
Overall Survival ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Clinical Benefit ◽  
Randomized Clinical Trials ◽  
Experimental Treatment ◽  
Standard Of Care ◽  
Current Standard ◽  
End Point

We review how overall survival (OS) comparisons should be interpreted with increasing availability of effective therapies that can be given subsequently to the treatment assigned in a randomized clinical trial (RCT). We examine in detail how effective subsequent therapies influence OS comparisons under varying conditions in RCTs. A subsequent therapy given after tumor progression (or relapse) in an RCT that works better in the standard arm than the experimental arm will lead to a smaller OS difference (possibly no difference) than one would see if the subsequent therapy was not available. Subsequent treatments that are equally effective in the treatment arms would not be expected to affect the absolute OS benefit of the experimental treatment but will make the relative improvement in OS smaller. In trials in which control arm patients cross over to the experimental treatment after their condition worsens, a smaller OS difference could be observed than one would see without cross-overs. In particular, use of cross-over designs in the first definitive evaluation of a new agent in a given disease compromises the ability to assess clinical benefit. In disease settings in which there is not an intermediate end point that directly measures clinical benefit, OS should be the primary end point of an RCT. The observed difference in OS should be considered the measure of clinical benefit to the patients, regardless of subsequent therapies, provided that the subsequent therapies used in both treatment arms follow the current standard of care.

scholarly journals Public access to protocols of contemporary cancer randomized clinical trials

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Christopher Babu ◽  
Loren Mell ◽  
Nancy Lee ◽  
Kaveh Zakeri
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Randomized Clinical Trials ◽  
Public Access ◽  
Research Protocol ◽  
Study Results ◽  
Research Transparency

AbstractAccess to randomized clinical trial (RCT) protocols is necessary for the interpretation and reproducibility of the study results, but protocol availability has been lacking. We determined the prevalence of protocol availability for all published cancer RCTs in January 2020. We found that only 36.1% (48/133) of RCTs had an accessible protocol and only 11.3% of RCTs (15/133) had a publicly accessible protocol that was not behind a paywall. Only 18.0% (24/133) of RCTs were published in conjunction with the protocol on the journal website. In conclusion, few cancer RCTs have an accessible research protocol. Journals should require publication of RCT protocols along with manuscripts to improve research transparency.

scholarly journals The age of randomized clinical trials: three important aspects of randomized clinical trials in cardiovascular pharmacotherapy with examples from lipid, diabetes, and antithrombotic trials

2020 ◽  
Author(s):  
Heinz Drexel ◽  
Basil S Lewis ◽  
Giuseppe M C Rosano ◽  
Christoph H Saely ◽  
Gerda Tautermann ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Randomized Clinical Trials ◽  
Safety Monitoring ◽  
Review Article ◽  
Early Termination ◽  
Regulatory Aspects ◽  
Cardiovascular Pharmacotherapy ◽  
Insight Into

Abstract This review article aims to explain the important issues that data safety monitoring boards (DSMB) face when considering early termination of a trial and is specifically addressed to the needs of clinical and research cardiologists. We give an insight into the overall background and then focus on the three principal reasons for stopping trials, i.e. efficacy, futility, and harm. The statistical essentials are also addressed to familiarize clinicians with the key principles. The topic is further highlighted by numerous examples from lipid trials and antithrombotic trials. This is followed by an overview of regulatory aspects, including an insight into industry–investigator interactions. To conclude, we summarize the key elements that are the basis for a decision to stop a randomized clinical trial (RCT).

Randomized clinical trials in the evaluation of surgical innovation

1979 ◽  
Vol 51 (1) ◽  
pp. 5-11 ◽  
Author(s):  
Stephen J. Haines
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
Randomized Clinical Trials ◽  
Random Allocation ◽  
Surgical Innovation ◽  
Content Type ◽  
Observation Bias ◽  
Fine Print

✓ Randomized clinical trials are widely accepted as the standard for evaluation of therapeutic innovation in many fields of medicine. The three basic components of such trials (concurrent comparison, random allocation, and objective observation) are designed to control four forms of bias (chronology bias, susceptibility bias, compliance bias, and observation bias) that may interfere with the interpretation of the results of a study. Only 2% of the articles evaluating therapeutic maneuvers published in the Journal of Neurosurgery have attempted to use concurrent controls. Only one of 863 such articles met the criteria for a randomized clinical trial. Reasons for underutilization of such trials in neurosurgery are discussed and suggestions for their wider use are offered.

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