Presentation on theme: "Getting the Big Picture Clinical Trials Experiments that study the effectiveness of medical treatments on actual patients Need comparative experiments."— Presentation transcript:
Getting the Big Picture Clinical Trials Experiments that study the effectiveness of medical treatments on actual patients Need comparative experiments to see true effects of new treatments Balance future benefits against present risks. The trials pose risks for the subjects of the trial “Interests of the subject must always prevail over the interests of science and society” – 1964 Helsinki Declaration of the World Medical Association, most respected international standard. Medical treatments can be tested in clinical trial only when there is reason to hope that they will help the patients who are subjects in the trials.
Getting the Big Picture PHASE I TRIALS: Initial studies to determine the metabolism and pharmacologic actions of drugs in humans, the side effects associated with increasing doses, and to gain early evidence of effectiveness; may include healthy participants and/or patients. PHASE II TRIALS: Controlled clinical studies conducted to evaluate the effectiveness of the drug for a particular indication or indications in patients with the disease or condition under study and to determine the common short-term side effects and risks. PHASE III TRIALS: Expanded controlled and uncontrolled trials after preliminary evidence suggesting effectiveness of the drug has been obtained, and are intended to gather additional information to evaluate the overall benefit-risk relationship of the drug and provide and adequate basis for physician labeling. PHASE IV TRIALS: Post-marketing studies to delineate additional information including the drug's risks, benefits, and optimal use. The Four Phases of Drug Development In the procedure mandated by the Fodd and Drug Administration (FDA), Phase II clinical trials gauge efficacy, and Phase III trials confirm it.
PROTOCOL: A study plan on which all clinical trials are based. The plan is carefully designed to safeguard the health of the participants as well as answer specific research questions. A protocol describes what types of people may participate in the trial; the schedule of tests, procedures, medications, and dosages; and the length of the study. While in a clinical trial, participants following a protocol are seen regularly by the research staff to monitor their health and to determine the safety and effectiveness of their treatment. CLINICAL INVESTIGATOR: A medical researcher in charge of carrying out a clinical trial's protocol. INCLUSION/EXCLUSION CRITERIA: The medical or social standards determining whether a person may or may not be allowed to enter a clinical trial. These criteria are based on such factors as age, gender, the type and stage of a disease, previous treatment history, and other medical conditions. It is important to note that inclusion and exclusion criteria are not used to reject people personally, but rather to identify appropriate participants and keep them safe. Clinical Trial Process
Getting the Big Picture Case Report Form The patient data, demographic, safety and efficacy, is collected in what is known as the Case Report Forms (CRFs). Demographic Age, weight, sex, race, etc. Safety: Concomitant Medications, Adverse Events, Laboratory Data, Electrocardigram (ECGs), Medical History, Vital Signs and Physical Examinations Treatment Emergent Adverse Event :An unwanted effect caused by the administration of the drug. Efficacy : The maximum ability of a drug or treatment to produce a result regardless of dosage. A drug passes efficacy trials if it is effective at the dose tested and against the illness for which it is prescribed. Example Study: Eletriptan for the Acute Treatment of Migraine in Adolescents: Results of a Double- Blind, Placebo-Controlled Trial Efficacy Measurements: Headache Severity, Associated Symptoms – Nausea, Vomiting, Phonophobia and Photophobia Clinical Trial Process
Getting the Big Picture Clinical Trial Process - Statistician Statistician writes the Statistical Analysis Plan based on Protocol and data collected in the CRF. A Statistical Analysis Plan (SAP) is a critical link between the conduct of the clinical trial and the clinical study report. Regulatory bodies in Canada, Europe, Japan, and the United States(Food and Drug Administration – FDA) expect that the SAP will meet requirements in pre-specifying inferential analyses and other important statistical techniques. Additionally, pharmaceutical companies expect that the SAP will provide explicit guidance to be followed by the statistician and the SAS/Statistical programmer. That guidance will be in the form of clearly written text and mockup tables as well as a table of contents for all tables, figures, and listings. The SAP presents the statistician the opportunity to open lines of communication among the following personnel: SAS/Statistical programming, medical writing, senior management in the biostatistics department and regulatory. Medical writers creates documents that effectively and clearly describe research results, product use, and other medical information. SAP is reviewed and approved by the entire team.
Getting the Big Picture Clinical Trial Analysis Process - SAP – Table of Contents 5. STATISTICAL ANALYSIS 5.1 General 5.2 Interim Analyses 5.3 Methods for Handling Missing Data 5.4 Statistical Analytical Issues 6. EVALUATION OF DEMOGRAPHICS AND BASELINE CHARACTERISTICS 6.1 Demographics and Baseline Characteristics 6.2 Medical History and Prior Medical Therapy 9. EVALUATION OF EFFICACY PARAMETERS 9.1 Analysis of Primary, Secondary, Efficacy Endpoints 9.2 Method for Analysis of Efficacy Endpoints 10. EVALUATION OF SAFETY PARAMETERS 10.1 Adverse Events 10.2 Clinical Laboratory Evaluation 10.3 Concomitant Medications 10.4 Vital Signs and Physical Examinations 1. STUDY SYNOPSIS 2. STUDY OBJECTIVES 2.1 Primary Objective 2.2 Secondary Objectives 2.3 Assessment of Objectives 3. STUDY DESIGN 3.1 General Design and Plan 3.2 Sample Size 3.3 Randomization and Blinding 3.4 Study Assessments 4. STUDY POPULATIONS 4.1 Subject Disposition 4.2 Definition of Populations for Analysis 4.3 Intent-to-treat (ITT) 4.4 Major Protocol Deviations
Getting the Big Picture The SAP and the annotated CRF are the two documents most often used by the statistical programmer to complete the programming assignments required for the trial. The SAP provides the statistical methods to be used- how subject data will be summarized Data-handling rules for reporting purposes-when and how to impute missing/partial dates, what combination of values qualifies a subject for efficacy analysis and so on. A complete table of contents with all Tables/Figures/Listings (TFL's ) to be programmed. Mocks (or shells) of all unique TLF's to be programmed-representations of what each TLF will look like but without the actual numbers. Clinical Trial Process – SAS/Statistical Programmer
The role of a clinical data manager is to track and commit to a database the data gathered from clinical trials of new drugs. They must ensure the completeness, accuracy and consistency of the data so that it meets the standards of quality expected for reporting to regulatory bodies. As soon as data arrives in from the investigator sites the data manager will start work on validating the data. Data manager reviews CRF and writes a series of criteria for checking the validity of the data. The data checks (often called edit checks) are run continually as soon as the data starts coming in from the investigator sites. If the data manager finds a problem with the data, he will first check the CRF to see if the data was entered properly. Then he will contact the investigator site in order to check if there was a mistake on their end. This process begins as soon as data is available from the investigator sites. All database updates/changes are tracked and recorded by the data manager. Clinical Trial Process – Data Manager Getting the Big Picture
Clinical Trial Process Concurrently, the SAS/statistical programmer begins work on creating the derived datasets from the raw datasets. At the same time, the programmer starts creating the data listings. The FDA requires a full listing of the data as part of the submission. Once the derived datasets are completed the programmer will start work on the analysis tables. Once completed the programmer and a second programmer will begin the process of validating the programs used to produce the analysis tables. The programmer will cross-check the tables against the listings. The second programmer will write separate programs to validate results. The whole procedure is done and documented in accordance with the Standard Operating Procedures (SOPs) and the Software Development Life Cycle (SDLC).
Getting the Big Picture Clinical Trial Process Throughout the process, the statistician meets with the data manager and programmer to discuss any issues with the data or the creating of the analysis tables. During the process, there is a meeting to access which patients do not belong in the Intent-To- Treat population. INTENT TO TREAT (ITT) - Patients that have a valid baseline assessment and at least one valid post baseline assessment. Once all data has been received from the investigator sites, the database is locked, the randomization codes are broken and the final version of tables/figures/listings are created. Everything (TLFs, QC documentation) is sent to QA for final approval. Finally, the statistician works on completing the clinical study report from the final results
Getting the Big Picture Eletriptan for the Acute Treatment of Migraine in Adolescents: Results of a Double-Blind, Placebo-Controlled Trial Background.— Eletriptan is a potent 5-HT1B/1D agonist with proven efficacy in the acute treatment of migraine in adults. Objective.—To evaluate the efficacy and tolerability of eletriptan 40 mg versus placebo in adolescent patients (aged 12-17). Methods.—A multicenter, double-blind, parallel-group, placebo-controlled trial was conducted comparing 40 mg of oral eletriptan with placebo for the treatment of migraine in adolescent patients. The primary efficacy endpoint was 2-hour headache response, and a number of secondary endpoints were also evaluated. Results.—Of 274 patients who treated a migraine attack, 267 were evaluated for efficacy (n = 138 eletriptan; n=129 placebo) at 2 hours post-dose. There was no significant difference in 2-hour headache response for eletriptan 40 mg versus placebo (57% vs 57%), and no significant improvements were observed for any of the outcomes at 1 or 2 hours post-dose.
Getting the Big Picture Results: By contrast, there was a significant advantage for eletriptan 40 mg in reducing headache recurrence within 24 hours post-dose (11% vs 25%, P =.028) Post hoc analyses showed statistically significant differences for sustained headache response rates (52% vs 39%; P =.04) and sustained pain-free response rates (22% vs 10%;P=.013). Conclusions.—The high placebo response rates reported here for 1- and 2-hour outcomes are in accordance with other studies of triptans in adolescent patients. The evaluation of treatment effect in adolescent migraine might benefit from use of more stringent outcome measures, such as headache recurrence, sustained headache response, and sustained pain-free response at 24 hours post-dose. Eletriptan for the Acute Treatment of Migraine in Adolescents: Results of a Double-Blind, Placebo-Controlled Trial
Getting the Big Picture F.D.A. to Restrict Avandia, Citing Heart Risk, NY Times, September 23 rd, 2010 In a highly unusual coordinated announcement, drug regulators in Europe and the United States said Thursday that Avandia, the controversial diabetes medicine, would no longer be widely available. The drug’s sales will be suspended entirely in Europe, while patients in the United States will be allowed access to the medicine only if they and their doctors attest that they have tried every other diabetes medicine and that patients have been made aware of the drug’s substantial risks to the heart. The Food and Drug Administration’s decision shows that the Obama administration is taking a harder line on drug safety issues, even in the face of scientific uncertainty. Dr. Margaret Hamburg, the agency’s commissioner, said that passions within the agency had run high on the Avandia decision. “As F.D.A. commissioner, my job would be infinitely easier if we had consensus and full scientific clarity,” she said. Dr. Steven Nissen, a Cleveland Clinic cardiologist whose studies highlighted Avandia’s heart attack risks, said that the decision brought an end to “one of the worst drug safety tragedies in our lifetime,” adding that it was “essential to fully investigate what went wrong with the regulatory process to prevent this type of tragedy from endangering patients in the future.”
Getting the Big Picture F.D.A. to Restrict Avandia, Citing Heart Risk, NY Times, September 23 rd, 2010 One study estimated that from 1999 to 2009, more than 47,000 people taking Avandia needlessly suffered a heart attack, stroke or heart failure, or died. Because of Avandia, the F.D.A. announced in 2008 that it would no longer approve medicines simply because they help diabetics control blood sugar levels — the standard for more than 80 years. Instead, the F.D.A. now insists that drugmakers conduct trials lasting at least two years to show that their medicines do not hurt the heart and that they improve the quality or length of diabetics’ lives, far tougher tests. Avandia’s risks became known only after Dr. Nissen analyzed data from clinical trials that GlaxoSmithKline, the maker of the drug, had been forced to post on its Web site as a result of a legal settlement. Such public postings are increasingly the norm, which means that drugmakers can no longer easily hide or control scientific information about their medicines. The agency’s decision to order restrictions on Avandia’s sales also demonstrates that the F.D.A. — given new powers over drugmakers and drug distribution in a 2007 law — intends to use those powers. The agency has now ordered that dozens of drugs be sold only with special restrictions.
Getting the Big Picture Study Pharmaceutical industry sponsorship and research outcome and quality: systematic review Objective: To investigate whether funding of drug studies by the pharmaceutical industry is associated with outcomes that are favourable to the funder and whether the methods of trials funded by pharmaceutical companies differ from the methods in trials with other sources of support. Methods: Medline (January 1966 to December 2002) and Embase (January 1980 to December 2002) searches were supplemented with material identified in the references and in the authors’ personal files. Data were independently abstracted by three of the authors and disagreements were resolved by consensus. Results: 30 studies were included. Research funded by drug companies was less likely to be published than research funded by other sources. Studies sponsored by pharmaceutical companies were more likely to have outcomes favouring the sponsor than were studies with other sponsors (odds ratio 4.05; 95% confidence interval 2.98 to 5.51; 18 comparisons). None of the 13 studies that analysed methods reported that studies funded by industry was of poorer quality. Conclusion: Systematic bias favours products which are made by the company funding the research. Explanations include the selection of an inappropriate comparator to the product being investigated and publication bias.
Getting the Big Picture Association between suicide attempts and selective serotonin reuptake inhibitors: systematic review of randomised controlled trials Objective: To establish whether an association exists between use of selective serotonin reuptake inhibitors (SSRIs - class of compounds typically used as antidepressants) and suicide attempts. Design: Systematic review of randomised controlled trials. Data sources Medline and the Cochrane Collaboration’s register of controlled trials (November 2004) for trials produced by the Cochrane depression, anxiety, and neurosis group. Selection of studies: Studies had to be randomised controlled trials comparing an SSRI with either placebo or an active non-SSRI control. We included clinical trials that evaluated SSRIs for any clinical condition. We excluded abstracts, crossover trials, and all trials whose follow up was less than one week.
Getting the Big Picture Results: Seven hundred and two trials met our inclusion criteria. A significant increase in the odds of suicide attempts (odds ratio 2.28, 95% confidence 1.14 to 4.55) was observed for patients receiving SSRIs compared with placebo. An increase in the odds ratio of suicide attempts was also observed in comparing SSRIs with therapeutic interventions other than tricyclic antidepressants (1.94, 1.06 to 3.57, 239). In the pooled analysis of SSRIs versus tricyclic antidepressants, we did not detect a difference in the odds ratio of suicide attempts (0.88, 0.54 to 1.42). Discussion: Our systematic review, which included a total of 87,650 patients, documented an association between suicide attempts and the use of SSRIs. We also observed several major methodological limitations in the published trials. A more accurate estimation of risks of suicide could be garnered from investigators fully disclosing all events. Association between suicide attempts and selective serotonin reuptake inhibitors: systematic review of randomised controlled trials