A randomized study of how physicians interpret research funding disclosures.

BACKGROUND: The effects of clinical-trial funding on the interpretation of trial results are poorly understood. We examined how such support affects physicians' reactions to trials with a high, medium, or low level of methodologic rigor. METHODS: We presented 503 board-certified internists with abstracts that we designed describing clinical trials of three hypothetical drugs. The trials had high, medium, or low methodologic rigor, and each report included one of three support disclosures: funding from a pharmaceutical company, NIH funding, or none. For both factors studied (rigor and funding), one of the three possible variations was randomly selected for inclusion in the abstracts. Follow-up questions assessed the physicians' impressions of the trials' rigor, their confidence in the results, and their willingness to prescribe the drugs. RESULTS: The 269 respondents (53.5% response rate) perceived the level of study rigor accurately. Physicians reported that they would be less willing to prescribe drugs tested in low-rigor trials than those tested in medium-rigor trials (odds ratio, 0.64; 95% confidence interval [CI], 0.46 to 0.89; P=0.008) and would be more willing to prescribe drugs tested in high-rigor trials than those tested in medium-rigor trials (odds ratio, 3.07; 95% CI, 2.18 to 4.32; P<0.001). Disclosure of industry funding, as compared with no disclosure of funding, led physicians to downgrade the rigor of a trial (odds ratio, 0.63; 95% CI, 0.46 to 0.87; P=0.006), their confidence in the results (odds ratio, 0.71; 95% CI, 0.51 to 0.98; P=0.04), and their willingness to prescribe the hypothetical drugs (odds ratio, 0.68; 95% CI, 0.49 to 0.94; P=0.02). Physicians were half as willing to prescribe drugs studied in industry-funded trials as they were to prescribe drugs studied in NIH-funded trials (odds ratio, 0.52; 95% CI, 0.37 to 0.71; P<0.001). These effects were consistent across all levels of methodologic rigor. CONCLUSIONS: Physicians discriminate among trials of varying degrees of rigor, but industry sponsorship negatively influences their perception of methodologic quality and reduces their willingness to believe and act on trial findings, independently of the trial's quality. These effects may influence the translation of clinical research into practice.

No differences in cancer screening rates in patients with rheumatoid arthritis compared to the general population.

OBJECTIVE: Previous study findings have suggested that patients with chronic diseases such as rheumatoid arthritis (RA) do not receive optimal preventive medical services, including cancer screening tests. This study was undertaken to evaluate cancer screening rates in RA patients compared to non-RA control populations. METHODS: Using data from a large US commercial insurance plan, we examined rates of screening tests for cervical, breast, and colon cancer in patients with RA compared to control subjects without RA (non-RA controls) or control subjects with hypertension. Individuals were included in the RA cohort if they had at least 2 visits coded for a diagnosis of RA and had received at least 1 prescription for a disease-modifying antirheumatic drug during the study period. Multivariable Cox proportional hazards models were used to compare the rates of different cancer screening tests between RA patients and non-RA controls. RESULTS: RA patients (n = 13,314) and control subjects (non-RA and hypertension controls) (n = 212,324) were screened, on average, once every 3 years for cervical cancer and once every 2 years for breast cancer during the followup period (mean 2.3 years of followup). In the age-adjusted Cox regression model, women with RA were more likely to receive ≥ 1 Papanicolaou smear (hazard ratio [HR] 1.21, 95% confidence interval [95% CI] 1.17-1.24), ≥ 1 mammogram (HR 1.49, 95% CI 1.45-1.53), and ≥ 1 colonoscopy (HR 1.69, 95% CI 1.61-1.77) compared to female non-RA control subjects. Men with RA were also more likely to receive at least 1 colonoscopy (HR 1.52, 95% CI 1.40-1.64) than were male non-RA control subjects. These results were robust in multivariable analyses adjusted for age, number of physician visits, percentage of visits made to primary care physicians, and the Charlson Comorbidity Index. CONCLUSION: Patients with RA did not appear to be at risk for receiving fewer cancer screening tests when compared to individuals without RA. The majority of both RA patients and non-RA control subjects were screened regularly for cervical, breast, and colon cancer, in accordance with current recommendations.

Effects of adjusting for instrumental variables on bias and precision of effect estimates.

Recent theoretical studies have shown that conditioning on an instrumental variable (IV), a variable that is associated with exposure but not associated with outcome except through exposure, can increase both bias and variance of exposure effect estimates. Although these findings have obvious implications in cases of known IVs, their meaning remains unclear in the more common scenario where investigators are uncertain whether a measured covariate meets the criteria for an IV or rather a confounder. The authors present results from two simulation studies designed to provide insight into the problem of conditioning on potential IVs in routine epidemiologic practice. The simulations explored the effects of conditioning on IVs, near-IVs (predictors of exposure that are weakly associated with outcome), and confounders on the bias and variance of a binary exposure effect estimate. The results indicate that effect estimates which are conditional on a perfect IV or near-IV may have larger bias and variance than the unconditional estimate. However, in most scenarios considered, the increases in error due to conditioning were small compared with the total estimation error. In these cases, minimizing unmeasured confounding should be the priority when selecting variables for adjustment, even at the risk of conditioning on IVs.

Characteristics of clinical trials to support approval of orphan vs nonorphan drugs for cancer.

CONTEXT: The Orphan Drug Act incentivizes medication development for rare diseases, offering substantial financial benefits to the manufacturer. Orphan products constitute most new drug approvals in oncology, but safety and efficacy questions have emerged about some of these agents. OBJECTIVES: To define characteristics of orphan cancer drugs and their pivotal clinical trials and to compare these with nonorphan drugs. DESIGN AND SETTING: We identified all new orphan and nonorphan drugs approved between 2004 and 2010 to treat cancer. We then collected data on key development variables from publicly available information on the US Food and Drug Administration's Web site and in the Code of Federal Regulations. MAIN OUTCOME MEASURES: We assessed clinical testing dates, approved indications, and regulatory characteristics (regular vs accelerated review, advisory committee review, postmarketing commitments). We then compared design features (randomization, blinding, primary end point) of pivotal trials supporting approval of orphan and nonorphan drugs and rates of adverse safety outcomes (deaths not attributed to disease progression, serious adverse events, dropouts) in pivotal trials. RESULTS: Fifteen orphan and 12 nonorphan drugs were approved between January 1, 2004, and December 31, 2010. Pivotal trials of orphan drugs had smaller participant numbers (median, 96 [interquartile range {IQR}, 66-152] vs 290 [IQR, 185-394] patients exposed to the drug; P < .001) and were less likely to be randomized (30% vs 80%; P = .007). Orphan and nonorphan pivotal trials varied in their blinding (P = .04), with orphan trials less likely to be double-blind (4% vs 33%). Primary study outcomes also varied (P = .04), with orphan trials more likely to assess disease response (68% vs 27%) rather than overall survival (8% vs 27%). More treated patients had serious adverse events in trials of orphan drugs vs trials of nonorphan drugs (48% vs 36%; odds ratio, 1.72; 95% confidence interval, 1.02-2.92; P = .04). CONCLUSION: Compared with pivotal trials used to approve nonorphan cancer drugs, pivotal trials for recently approved orphan drugs for cancer were more likely to be smaller and to use nonrandomized, unblinded trial designs and surrogate end points to assess efficacy.

Water and hygiene interventions to reduce diarrhoea in rural Afghanistan: a randomized controlled study.

A randomized controlled trial of four interventions was conducted using tubewells (n=2,486), liquid sodium hypochlorite ('Clorin') distributed with an improved water vessel (n=2,305), hygiene promotion (n=1,877), and a combination of the three (n=2,040) to create an evidence-base for water policy in Afghanistan. A fifth group served as a control (n=2,377). Interventions were randomized across 32 villages in Wardak province. Outcomes were measured through two household surveys separated by one year and twice-weekly household surveillance conducted over 16 months. The households receiving all three interventions showed reduction in diarrhoea compared with the control group, through both longitudinal surveillance data (IRR [95% CI]=0.61 [0.47-0.81]) and cross-sectional survey data (AOR [95% CI]=0.53 [0.30-0.93]). This reduction was significant when all household members were included, but did not reach significance when only children under five were considered. These results suggest multi-barrier methods are necessary where there are many opportunities for water contamination. Surveillance data suggested a greater impact of interventions on reducing diarrhoeal diseases than data from the surveys. Higher economic status as measured through household assets was associated with lower rates of diarrhoea and greater intervention uptake, excepting Clorin. Use of soap was also associated with lower prevalence of diarrhoea.

Comparing treatments via the propensity score: stratification or modeling?

In observational studies of treatments or interventions, propensity score (PS) adjustment is often useful for controlling bias in estimation of treatment effects. Regression on PS is used most often and can be highly efficient, but it can lead to biased results when model assumptions are violated. The validity of stratification on PS depends on fewer model assumptions, but this approach is less efficient than regression adjustment when the regression assumptions hold. To investigate these issues, we compare stratification and regression adjustments in a Monte Carlo simulation study. We consider two stratification approaches: equal frequency strata and an approach that attempts to choose strata that minimize the mean squared error (MSE) of the treatment effect estimate. The regression approach that we consider is a Generalized Additive Model (GAM) that estimates treatment effect controlling for a potentially nonlinear association between PS and outcome. We find that under a wide range of plausible data generating distributions the GAM approach outperforms stratification in treatment effect estimation with respect to bias, variance, and thereby MSE. We illustrate each approach in an analysis of insurance plan choice and its relation to satisfaction with asthma care.

Empirical power and sample size calculations for cluster-randomized and cluster-randomized crossover studies.

In recent years, the number of studies using a cluster-randomized design has grown dramatically. In addition, the cluster-randomized crossover design has been touted as a methodological advance that can increase efficiency of cluster-randomized studies in certain situations. While the cluster-randomized crossover trial has become a popular tool, standards of design, analysis, reporting and implementation have not been established for this emergent design. We address one particular aspect of cluster-randomized and cluster-randomized crossover trial design: estimating statistical power. We present a general framework for estimating power via simulation in cluster-randomized studies with or without one or more crossover periods. We have implemented this framework in the clusterPower software package for R, freely available online from the Comprehensive R Archive Network. Our simulation framework is easy to implement and users may customize the methods used for data analysis. We give four examples of using the software in practice. The clusterPower package could play an important role in the design of future cluster-randomized and cluster-randomized crossover studies. This work is the first to establish a universal method for calculating power for both cluster-randomized and cluster-randomized clinical trials. More research is needed to develop standardized and recommended methodology for cluster-randomized crossover studies.

The prevalence and cost of unapproved uses of top-selling orphan drugs.

INTRODUCTION: The Orphan Drug Act encourages drug development for rare conditions. However, some orphan drugs become top sellers for unclear reasons. We sought to evaluate the extent and cost of approved and unapproved uses of orphan drugs with the highest unit sales. METHODS: We assessed prescription patterns for four top-selling orphan drugs: lidocaine patch (Lidoderm) approved for post-herpetic neuralgia, modafinil (Provigil) approved for narcolepsy, cinacalcet (Sensipar) approved for hypercalcemia of parathyroid carcinoma, and imatinib (Gleevec) approved for chronic myelogenous leukemia and gastrointestinal stromal tumor. We pooled patient-specific diagnosis and prescription data from two large US state pharmaceutical benefit programs for the elderly. We analyzed the number of new and total patients using each drug and patterns of reimbursement for approved and unapproved uses. For lidocaine patch, we subcategorized approved prescriptions into two subtypes of unapproved uses: neuropathic pain, for which some evidence of efficacy exists, and non-neuropathic pain. RESULTS: We found that prescriptions for lidocaine patch, modafinil, and cinacalcet associated with non-orphan diagnoses rose at substantially higher rates (average monthly increases in number of patients of 14.6, 1.45, and 1.58) than prescriptions associated with their orphan diagnoses (3.12, 0.24, and 0.03, respectively (p<0.001 for all)). By contrast, for imatinib, approved uses increased significantly over off-label (0.97 vs. 0.47 patients, p<0.001). Spending on off-label uses was highest for lidocaine patch and modafinil (>75%). Increases in lidocaine patch use for non-neuropathic pain far exceeded neuropathic pain (10.2 vs. 3.6 patients, p<0.001). DISCUSSION: In our sample, three of four top-selling orphan drugs were used more commonly for non-orphan indications. These orphan drugs treated common clinical symptoms (pain and fatigue) or laboratory abnormalities. We should continue to monitor orphan drug use after approval to identify products that come to be widely used for non-FDA approved indications, particularly those without adequate evidence of efficacy.

Antidepressants and breast and ovarian cancer risk: a review of the literature and researchers' financial associations with industry.

BACKGROUND: Antidepressant (AD) use has been purported to increase the risk of breast and ovarian cancer, although both epidemiological and pre-clinical studies have reported mixed results. Previous studies in a variety of biomedical fields have found that financial ties to drug companies are associated with favorable study conclusions. METHODS AND FINDINGS: We searched English-language articles in MEDLINE, PsychINFO, the Science Citations Index and the Cochrane Central Register of Controlled Clinical Trials (through November 2010). A total of 61 articles that assessed the relationship between breast and ovarian cancer and AD use and articles that examined the effect of ADs on cell growth were included. Multi-modal screening techniques were used to investigate researchers' financial ties with industry. A random effects meta-analysis was used to pool the findings from the epidemiological literature. Thirty-three percent (20/61) of the studies reported a positive association between ADs and cancer. Sixty-seven percent (41/61) of the studies reported no association or antiproliferative effect. The pooled odds ratio for the association between AD use and breast/ovarian cancer in the epidemiologic studies was 1.11 (95% CI, 1.03-1.20). Researchers with industry affiliations were significantly less likely than researchers without those ties to conclude that ADs increase the risk of breast or ovarian cancer. (0/15 [0%] vs 20/46 [43.5%] (Fisher's Exact test P = 0.0012). CONCLUSIONS: Both the pre-clinical and clinical data are mixed in terms of showing an association between AD use and breast and ovarian cancer. The possibility that ADs may exhibit a bi-phasic effect, whereby short-term use and/or low dose antidepressants may increase the risk of breast and ovarian cancer, warrants further investigation. Industry affiliations were significantly associated with negative conclusions regarding cancer risk. The findings have implications in light of the 2009 USPSTF guidelines for breast cancer screening and for the informed consent process.