Jesse A. Berlin

SPIRIT 2013 Statement: defining standard protocol items for clinical trials.

The protocol of a clinical trial serves as the foundation for study planning, conduct, reporting, and appraisal. However, trial protocols and existing protocol guidelines vary greatly in content and quality. This article describes the systematic development and scope of SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2013, a guideline for the minimum content of a clinical trial protocol.The 33-item SPIRIT checklist applies to protocols for all clinical trials and focuses on content rather than format. The checklist recommends a full description of what is planned; it does not prescribe how to design or conduct a trial. By providing guidance for key content, the SPIRIT recommendations aim to facilitate the drafting of high-quality protocols. Adherence to SPIRIT would also enhance the transparency and completeness of trial protocols for the benefit of investigators, trial participants, patients, sponsors, funders, research ethics committees or institutional review boards, peer reviewers, journals, trial registries, policymakers, regulators, and other key stakeholders.

SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials.

High quality protocols facilitate proper conduct, reporting, and external review of clinical trials. However, the completeness of trial protocols is often inadequate. To help improve the content and quality of protocols, an international group of stakeholders developed the SPIRIT 2013 Statement (Standard Protocol Items: Recommendations for Interventional Trials). The SPIRIT Statement provides guidance in the form of a checklist of recommended items to include in a clinical trial protocol. This SPIRIT 2013 Explanation and Elaboration paper provides important information to promote full understanding of the checklist recommendations. For each checklist item, we provide a rationale and detailed description; a model example from an actual protocol; and relevant references supporting its importance. We strongly recommend that this explanatory paper be used in conjunction with the SPIRIT Statement. A website of resources is also available (www.spirit-statement.org). The SPIRIT 2013 Explanation and Elaboration paper, together with the Statement, should help with the drafting of trial protocols. Complete documentation of key trial elements can facilitate transparency and protocol review for the benefit of all stakeholders.

Selective Serotonin Reuptake Inhibitors and Myocardial Infarction

Background—Depression is an independent risk factor for myocardial infarction (MI). Selective serotonin reuptake inhibitors (SSRIs) may reduce this risk through attenuation of serotonin-mediated platelet activation in addition to treatment of depression itself. Methods and Results—A case-control study of first MI in smokers 30 to 65 years of age was conducted among all 68 hospitals in an 8-county area during a 28-month period. Cases were patients hospitalized with a first MI. Approximately 4 community control subjects per case were randomly selected from the same geographic area using random digit dialing. Detailed information regarding use of antidepressant medication as well as other clinical and demographic data were obtained by telephone interview. A total of 653 cases of first MI and 2990 control subjects participated. After adjustment, using multivariable logistic regression, for age, sex, race, education, exercise, quantity smoked per day, body mass index, aspirin use, family history of MI, number of physician encounters, and history of coronary disease, diabetes, hypertension, or hypercholesterolemia, the odds ratio for MI among current SSRI users compared with nonusers was 0.35 (95% CI 0.18, 0.68;P <0.01). Non-SSRI antidepressant users had a nonsignificant reduction in MI risk with wide confidence intervals (adjusted odds ratio 0.48, CI 0.17, 1.32;P =0.15). However, analysis of this group was limited by the small number of exposed subjects. Conclusions—The use of SSRIs may confer a protective effect against MI. This could be attributable to the inhibitory effect SSRIs have on serotonin-mediated platelet activation or possibly amelioration of other factors associated with increased risk for MI in depression.

Adverse Event Detection in Drug Development: Recommendations and Obligations Beyond Phase 3

Premarketing studies of drugs, although large enough to demonstrate efficacy and detect common adverse events, cannot reliably detect an increased incidence of rare adverse events or events with significant latency. For most drugs, only about 500 to 3000 participants are studied, for relatively short durations, before a drug is marketed. Systems for assessment of postmarketing adverse events include spontaneous reports, computerized claims or medical record databases, and formal postmarketing studies. We briefly review the strengths and limitations of each. Postmarketing surveillance is essential for developing a full understanding of the balance between benefits and adverse effects. More work is needed in analysis of data from spontaneous reports of adverse effects and automated databases, design of ad hoc studies, and design of economically feasible large randomized studies.

Meta-analysis as Evidence: Building a Better Pyramid

In following the practice of evidence-based medicine, when faced with a question about prevention or treatment the clinician should seek out the best evidence that addresses the question. If quality of evidence is considered a pyramid, what category should be placed at the peak? One dogma argues that it is the best-conducted randomized clinical trial (RCT) comprising patients similar to those seen by the clinician, reasoning that a well-done RCT mimics pure experimental conditions better than any other study design, hence minimizing the likelihood of confounding. A counterargument is that the best evidence is a systematic review with meta-analysis, because this approach can integrate all of the relevant evidence and provide a more reliable answer than a single study, however well conducted. The notion that a synthesis that includes mathematically combiningacompletebodyofevidenceprovidesthehighestlevel of evidence is attractive. However, as with most of evidencebased medicine, the principles are rational, consistent, and appealing, but in practice are fraught with practical challenges, ambiguities, and nuances. Moreover, a busy clinician faces tension betweensearchingforandassessingthebest-qualityprimaryevidence vs accepting the efficiency of using easily obtained but potentially inferior information as a shortcut to an answer. As a general principle, generating, summarizing, and understanding the best available evidence are essential for establishing the benefits and safety of interventions. Metaanalysis has become a valuable tool toward these ends. There has been a proliferation of guidelines by professional societies and others, aimed at ensuring that the best preventive interventions or treatment options are provided to the appropriate patients at the appropriate time; these guidelines often incorporate meta-analyses as a key evidence support for their recommendations. However, limitations of meta-analysis as a study design preclude consistently placing this evidence at the top of the pyramid, and a number of issues need to be resolved before that can happen. These are the problems that researchers, guideline developers, journal editors, and critical readers of the literature struggle with, and understanding the limitations of metaanalytic evidence is crucial for each of these stakeholders.1 One useful way to view these challenges is to divide them into 2 categories: heterogeneity and methodological dilemmas. Heterogeneity (variation in true effect sizes and in factors that might influence those effect sizes) is inherent in metaanalysis, not a problem to be solved. It includes clinical components (eg, diversity in patient populations or interventions) and statistical components (eg, random differences). There are statistical approaches to try to quantify some elements of heterogeneity, including the Q statistic (a measure of total within-study variance), the I2 statistic (the ratio of variability of results among studies to total observed variation), and τ2 (a measure of between-studies variance). Heterogeneity can be investigated and sometimes managed, but not eliminated as an issue. In some instances, helpful insights can be gained when the heterogeneity of findings of component studies can be related to characteristics of those studies (eg, disease severity, outcome definition, duration of follow-up, duration of treatment, or dose of a drug). For example, treatments might show different effects in patients with severe disease than in those with mild to moderate disease. Studies might also give different answers because of flaws in the design or conduct, such as excessive loss to follow-up.2 Modelingtounderstandheterogeneitycanbehelpful.However, relationships that would allow drawing conclusions about effects in different populations, or effects for an ideal study, do not always emerge from the analyses of heterogeneity. Study factors are often confounded with each other; for example, if high-dose studies were conducted in more severely ill populations and low-dose studies in less ill populations, it would be difficult if not impossible to separate the effects of dose from the effects of severity of illness. Most meta-analyses use the aggregate data as presented in the report of the primary study, but when individual patient–level data are not available important relationships can be missed and spurious relationships can be found.3,4 Moreover, inherent heterogeneity presents a challenge in practical clinical interpretation, even when the heterogeneity is assessed and its effects understood. For a single RCT, interpretation of the average effect size may exaggerate the benefit that most patients are likely to achieve5 and often does not provide the information needed for a clinician to understand how to apply the findings to a particular patient. With a meta-analysis, in which 5, 10, or 40 RCTs are combined, the interpretation of the average effect size for application to an individual patient is likely to be even more obscured. Because of the innate uncertainty generated by combining RCT data from disparate sources (which can include differences in patients, interventions, and assessed outcomes), the ability to make causal inferences can be limited; JAMA considers metaanalysis to represent an observational design, with measures that should be interpreted as associations rather than causal effects. Related article page 623 Opinion

Placebo response changes depending on the neuropathic pain syndrome: results of a systematic review and meta-analysis.

OBJECTIVE To compare placebo responses in neuropathic pain syndromes. DESIGN Systematic literature review and meta-analysis. SETTING AND PATIENTS Randomized placebo-controlled trials assessing pain intensity or pain relief in any neuropathic pain syndrome published since 1995 with ≥5days follow-up. INTERVENTIONS Placebo response. OUTCOME MEASURES Pain intensity and responder rates (proportion reporting ≥50% pain relief). Meta-regression models were built. RESULTS Ninety-four studies (N=5,317) were included in the pain intensity analysis; 47 studies (N=3,087) were included in the responder analysis. After controlling for potential confounders (e.g., subject characteristics, study design characteristics), the placebo response was found to be large and varied with the pain syndrome. Compared with diabetic neuropathic/polyneuropathic pain (DPN), the placebo response for a decline in pain intensity and responder rate was smaller in trials that assessed central pain and postherpetic neuralgia (PHN) and larger in trials that assessed HIV pain. The model-predicted mean decrease (95% confidence interval [CI]) from baseline in pain intensity (0-10 scale) was as follows: DPN, 1.45 (1.35 to 1.55); PHN, 1.16 (1.03 to 1.29); central pain, 0.44 (-0.41 to 1.30); HIV pain, 1.82 (1.51 to 2.12). The predicted responder rates (95% CI) were as follows: DPN, 20% (14.6 to 25.8); PHN, 11.5% (8.4 to 14.5); central pain, 7.2% (2.1 to 12.3); HIV pain, 42.8% (34.9 to 50.7). The type of treatment in the active arm also influenced the placebo response. CONCLUSIONS Placebo response is influenced by the pain syndrome evaluated. These differences should be considered when evaluating novel compounds for the treatment of neuropathic pain conditions.

Initial metformin or sulphonylurea exposure and cancer occurrence among patients with type 2 diabetes mellitus

This was a retrospective cohort study of type 2 diabetes patients, to evaluate the association between initial metformin or sulphonylurea treatment and cancer incidence.

An international survey indicated that unpublished systematic reviews exist

OBJECTIVE To determine the frequency of unpublished systematic reviews (SRs) and explore factors contributing to their occurrence. STUDY DESIGN AND SETTING First or corresponding authors from a sample of SRs published in 2005 were asked to participate in a 26-item survey administered through the Internet, facsimile, and postal mail. Outcomes included median and range of published and unpublished SRs, and barriers, facilitators, and reasons for not publishing SRs. Descriptive analyses were performed. RESULTS 55.7% (348 of 625) of those invited participated, half of which were from Europe and 22.7% were from the United States. Participants reported 1,405 published (median: 2.0, range: 1-150) and 199 unpublished (median: 2.0, range: 1-33) SRs. Lack of time and lack of funding and organizational support were barriers, whereas time availability and self-motivation were facilitators to publishing reviews. For most recent unpublished SRs (n=52), the reasons for not publishing included lack of time (12 of 52, 23.0%), the manuscript being rejected (10 of 52, 19.0%), and operational issues (six of 52, 11.5%). CONCLUSION Unpublished SRs do exist. Lack of time, funding, and organizational support were consistent reasons for not publishing SRs. Statistical significance of SR results was not reported as being a major barrier or reason for not publishing. Further research on unpublished SRs is warranted.

Effects of L-carnitine on dialysis-related hypotension and muscle cramps: a meta-analysis.

BACKGROUND L-Carnitine is an endogenous compound thought to be helpful in treating patients with dialysis-related hypotension and muscle cramps; however, sufficient evidence for these indications is lacking. STUDY DESIGN Systematic review and meta-analysis. SETTING & POPULATION Adult patients with end-stage renal disease receiving long-term hemodialysis. SELECTION CRITERIA FOR STUDIES All published English-language reports of randomized placebo-controlled trials of L-carnitine supplementation in adult long-term hemodialysis patients. INTERVENTION Supplemental L-carnitine (or placebo) for at least 8 weeks. OUTCOME Random-effects pooled odds ratio for intradialytic cramping or hypotension in L-carnitine-treated participants. RESULTS Of 317 potentially relevant articles, 7 (total enrollment of 193 patients) met criteria for inclusion. Four articles reported results for both hypotension and cramps, 1 had results for only hypotension, and 2 reported results for only cramps. Using data from all 6 relevant trials, the pooled odds ratio for cramping after L-carnitine supplementation was 0.30 (95% confidence interval, 0.09 to 1.00; P = 0.05). Analysis of the 5 studies examining the response of intradialytic hypotension to l-carnitine supplementation yielded a pooled odds ratio of 0.28 (95% confidence interval, 0.04 to 2.23; P = 0.2). LIMITATIONS The small number of available studies yielded limited statistical power. In addition, there was considerable interstudy heterogeneity. CONCLUSIONS Although suggestive in the case of muscle cramping, the available evidence does not confirm a beneficial effect of L-carnitine supplementation on dialysis-related muscle cramping or intradialytic hypotension. Additional study in the form of large rigorous randomized trials is needed in both cases.

Comparison of methods for correcting population stratification in a genome-wide association study of rheumatoid arthritis: principal-component analysis versus multidimensional scaling

Population stratification (PS) represents a major challenge in genome-wide association studies. Using the Genetic Analysis Workshop 16 Problem 1 data, which include samples of rheumatoid arthritis patients and healthy controls, we compared two methods that can be used to evaluate population structure and correct PS in genome-wide association studies: the principal-component analysis method and the multidimensional-scaling method. While both methods identified similar population structures in this dataset, principal-component analysis performed slightly better than the multidimensional-scaling method in correcting for PS in genome-wide association analysis of this dataset.