Clinical Trials Assignment Help
Clinical trials or clinical research is an area of study that investigates proposed medical treatments, assess the relative benefits of therapies, and developing working treatment combinations. For example, in the treatment of prostate cancer, clinical research may answer questions like should the patient undergo radiation, radical prostatectomy, or watchfully wait? Do patients receiving a new pain-relieving therapy suffer a greater incidence of adverse effects than those who are on standard therapy? Earlier on before experimental trials were in use, clinicians attempted to answer the questions above by studying the experiences of individual patients and the population at large. In exceptional cases, they applied their clinical knowledge, judgments, and reasoning. However, the topic of variability among patients and its sources were not adequately addressed.
According to Piantadosi, 2005, the twentieth and the twenty-first century has seen significant developments in the fields of statistics, theoretical sciences, and formal studies. Statistical methods were introduced into clinical trials to account for sources of variability among patients. These stat processes strive to answer why patients respond differently to a treatment. In times of uncertainty, clinical researchers can use statistics to draw accurate and reasonable inferences from information collected for sound decision making. The only challenge is they have to master various statistical concepts to prevent biases and numerous errors in medical research.
Statistical reasoning involves:
- Developing a framework and objective of the investigation
- Data and theory should be placed on an equal scientific footing
- Producing data through experimentation
- Systematic and random effect estimates
- Using formal methods to combine data and theory
Scheaffer, Marks, and Carter (1986) states that:
Statistics is quite a unique discipline. Its reasoning is needed at every stage of virtually all research investigations such as planning, choosing a sample, managing the data, and interpreting results.
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Our clinical research homework helpers say that both statistical and clinical reasoning are essential to the progress of medicine. Researchers must combine theory with empirical evidence and generalize it from few to many. Medical approaches are usually drawn from established hypotheses and biology, while practical knowledge is generated from observations and data. On the other hand, statistical theory is often derived from probabilistic and mathematical models. All these concepts can be complicated for students to learn. That is why we recommend that you avail of our statistics assignment help service for instant assistance. Our knowledgeable professionals are known to produce perfect papers within the agreed period.
Constituents of a clinical trial
Clinical research is like an experiment for testing medical treatments on patients. An experiment can be defined as a series of observations made under controlled conditions. It is the clinical investigator who decides on the control factors that contribute to variability. These factors include the methods to be used for analysis, selection of sample or subjects, application of the treatment, and evaluation of results. The experimental nature of a clinical trial is what makes it distinct.
This involves coming up with a structure or process to isolate the factors of interest. A researcher designs a trial to control variability as a result of factors other than the treatment he is interested in. However, a controlled laboratory situation cannot comprehensively study the inherently larger variability in human research.
Researchers prefer to use the term “clinical trial” over “clinical experiment.” This is because the latter connotes disrespect to the value of human life.
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Application of clinical trials
- Develop and test interventions
Clinical research is applied in nearly all the areas of public health and medicine. Its concepts are used to develop and test interventions. Most countries use clinical trials to check the efficacy and safety of new drugs and vaccines in the market. In the US, manufacturers of high-risk or new medical devices are required by the FDA to provide detailed information demonstrating clinical effectiveness and safety.
Furthermore, clinical trials are primarily used to prove the safety and effectiveness of diagnostic procedures, medical therapies, and preventive measures.
- Confirming findings from earlier studies
Sometimes, studies may contradict biological theories or produce surprising results. Confirmatory research may be needed to highlight flaws in the design, analysis and reporting mistakes, methodological areas, or any other problem with the study. However, medical practice doesn’t change its course based on a single research.
Clinical trials are deemed to be quite expensive and time-consuming. They are labor-intensive and need the cooperative efforts of patients, physicians, data managers, nurses, statisticians, methodologists, etc. Additionally, recruiting patients is also cumbersome. Many clinical types of research are usually carried out during a window of opportunity. This is a period when they are most feasible and have a significant impact on clinical practice. The window of opportunity for comparative trials is usually relatively early in the development of a new therapy. Formally testing the effectiveness of the procedure can, however, become impossible if the treatment is widely accepted based on anecdotal experience.
In the medical field, some advances have been made without controlled clinical trials like statistical design and analysis. For example, insulin, vitamins, some vaccines, and some antibiotics. The following are the requirements for non-experimental comparative studies to provide convincing and valid evidence:
- Natural occurrence of treatment of interest
- The subjects being studied must give valid observations for the biological question
- The history of the disease in the absence or presence of standard therapy must be known
- Evidence of effectiveness must be in line with biological knowledge
- The effect of the treatment should overshadow random error and bias
We provide clinical trials homework help on a wide range of topics, including:
- Historical remarks, some diseases, and discoveries
- Steps(phases)in drug development
- Scope of clinical trials: new drugs, generics, devices, psychiatric therapy, alternative medicine.
- Role of statistics
Planning a Clinical Trial – Statistican’s Inputs
- Principles of design of experiments (replication, local control, randomization)
- Power and sample size
- Bias reduction (blinding)
- Commonly used designs
Statistical Analysis Plan (SAP) of Clinical Trial
- Trial objectives, hypotheses, choice of techniques, nature of endpoints
- End point Binary: A Randomizated Evaluation of First-Dollar Coverage for Post-MI Secondary Preventive Therapies (Post-MI FREEE)
- End point – Normal: accuracy study of SoftTouch (a non-invasive device for measurement of peripheral blood biomarkers)
- End point – count data: Pediatric Asthma Alert Intervention for Minority Children With Asthma (PAAL)
- End point – non-Normal: TBTC Study 27/28 PK: Moxifloxacin Pharmacokinetics During TB Treatment
Illustrative Statistical Analysis of Clinical Trial Data
- One sample problem – reduction in blood pressure
- Two sample problem – anorexia
- K-sample problem – drowsiness due to antihistamines
- Cochrane’s Q test – allergic response
- Analysis of Time-concentration data in pharmacokinetic study
Pharmacokinetics (PK) and Bioavailability
- Basic concepts of PK
- PK analysis of time-concentration data (bioavailability assessment)
- Oral administration
- Estimation of Cmax, Tmax, AUC, Ke, Ka
- Intravenous administration
- Dose-response modeling
- Types of dose-response relationships
- Michaelis-Menton model for saturating relationship
- Power model: A model that includes three shapes
- Types of dose-response relationships
Inference for Pharmocokinetic (PK) data
- Normality testing of PK parameters (AUC, Cmax)
- Transformations for achieving normality (AUC, Cmax)
- Parametric (AUC, Cmax) and Non-parametric tests (Tmax)
- Bootstrap confidence interval for t1/2
Analysis of Dose-Response Data
- Estimation of median effective dose
- Testing of dose proportionality in power model
Bioequivalence Studies-Parallel Design
- Statistical equality vs. clinical equivalence
- Testing bioequivalence (AUC)
- CI approach (AUC)
- Testing bioequivalence (Cmax)
- CI approach (Cmax)
Bioequivalence Studies 2 x 2 (Crossover Design)
- What is crossover design?
- Analysis of illustrative data using two-sample tests
- Test for carry over effect
- Test for period effect
- Test for treatment difference
- Testing equivalence using CI
- Parallel vs. crossover design
- R fundamentals associated with clinical trials
- A simple simulated clinical trial
- Statistical models for treatment comparisons
- Incorporating covariates
- Time-to-event data structure
- Statistical models for survival data
- Right-censored data analysis
- Interval-censored data analysis
Analysis of Data from Longitudinal Clinical Trials
- Trial designs and data structure
- Statistical models and analysis
Analysis of Bioequivalence Clinical Trials
- Data from bioequivalence clinical trials
- Bioequivalence clinical trial endpoints
- Statistical methods to analyze bioequivalence