Problems in dealing with missing data and informative censoring in clinical trials

A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many

Incorporating medical interventions into carrier probability estimation for genetic counseling

BACKGROUND: Mendelian models for predicting who may carry an inherited deleterious mutation of known disease genes based on family history are used in a variety of clinical and research activities. People presenting for genetic counseling are increasingly reporting risk-reducing medical interventions in their family histories because, recently, a slew of prophylactic interventions have become available for certain diseases. For example, oophorectomy reduces risk of breast and ovarian cancers, and is now increasingly being offered to women with family histories of breast and ovarian cancer. Mendelian models should account for medical interventions because interventions modify mutation penetrances and thus affect the carrier probability estimate. METHODS: We extend Mendelian models to account for medical interventions by accounting for post-intervention disease history through an extra factor that can be estimated from published studies of the effects of interventions. We apply our methods to incorporate oophorectomy into the BRCAPRO model, which predicts a woman's risk of carrying mutations in BRCA1 and BRCA2 based on her family history of breast and ovarian cancer. This new BRCAPRO is available for clinical use. RESULTS: We show that accounting for interventions undergone by family members can seriously affect the mutation carrier probability estimate, especially if the family member has lived many years post-intervention. We show that interventions have more impact on the carrier probability as the benefits of intervention differ more between carriers and non-carriers. CONCLUSION: These findings imply that carrier probability estimates that do not account for medical interventions may be seriously misleading and could affect a clinician's recommendation about offering genetic testing. The BayesMendel software, which allows one to implement any Mendelian carrier probability model, has been extended to allow medical interventions, so future Mendelian models can easily account for interventions

Statistical design of personalized medicine interventions: The Clarification of Optimal Anticoagulation through Genetics (COAG) trial

<p>Abstract</p> <p>Background</p> <p>There is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.</p> <p>Methods</p> <p>The statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in <it>CYP2C9 </it>and <it>VKORC1</it>; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.</p> <p>Results</p> <p>We determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either <it>CYP2C9 </it>or <it>VKORC1 </it>and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.</p> <p>Conclusions</p> <p>In a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.</p> <p>Trial Registration</p> <p>clinicaltrials.gov: NCT00839657</p

Agreement, reliability and validity in 3 shoulder questionnaires in patients with rotator cuff disease

Background Self-report questionnaires play an important role as outcome measures in shoulder research. Having an estimate of the measurement error of these questionnaires is of importance when assessing follow-up results after treatment and when planning intervention studies. The aim of this study was to cross-culturally adapt the Norwegian version of the OSS and WORC questionnaire and examine and compare agreement, reliability and construct validity of the disease-specific shoulder questionnaire WORC with two commonly used shoulder questionnaires, SPADI and OSS, in patients with rotator cuff disease. Methods 74 patients with rotator cuff disease were recruited from the outpatient clinic of the Physical Medicine and Rehabilitation Department at Ullevaal University Hospital in Oslo, Norway. A test-retest design was used, and the questionnaires were filled out by the patients at the clinic, with a one week interval between test administrations. Agreement (repeatability coefficient), reliability (ICC) and construct validity were examined and compared for WORC, SPADI and OSS. Results Reliability analysis was restricted to the 55 patients (51 ± 10 yrs) who reported no change between test administrations according to scoring on a global scale. The agreement, reliability and construct validity was moderate for all three questionnaires with ICC ranging from 0.83 to 0.85, repeatability coefficient from 16.1 to 19.7 and Spearman rank correlations between total scores from r = 0.57 to 0.69. There was a lower degree of floor and ceiling effects in SPADI compared to WORC and OSS. Conclusion We conclude that the agreement and reliability of the three shoulder questionnaires examined, WORC index, SPADI and OSS are acceptable and that differences between scores were small. The Norwegian version of the questionnaires is acceptable for assessing Norwegian-speaking patients with rotator cuff disease. The moderate agreement and construct validity should be taken into consideration when assessing follow-up results after treatment and in the planning of prospective studies

Comparing methods to estimate treatment effects on a continuous outcome in multicentre randomized controlled trials: A simulation study

<p>Abstract</p> <p>Background</p> <p>Multicentre randomized controlled trials (RCTs) routinely use randomization and analysis stratified by centre to control for differences between centres and to improve precision. No consensus has been reached on how to best analyze correlated continuous outcomes in such settings. Our objective was to investigate the properties of commonly used statistical models at various levels of clustering in the context of multicentre RCTs.</p> <p>Methods</p> <p>Assuming no treatment by centre interaction, we compared six methods (ignoring centre effects, including centres as fixed effects, including centres as random effects, generalized estimating equation (GEE), and fixed- and random-effects centre-level analysis) to analyze continuous outcomes in multicentre RCTs using simulations over a wide spectrum of intraclass correlation (ICC) values, and varying numbers of centres and centre size. The performance of models was evaluated in terms of bias, precision, mean squared error of the point estimator of treatment effect, empirical coverage of the 95% confidence interval, and statistical power of the procedure.</p> <p>Results</p> <p>While all methods yielded unbiased estimates of treatment effect, ignoring centres led to inflation of standard error and loss of statistical power when within centre correlation was present. Mixed-effects model was most efficient and attained nominal coverage of 95% and 90% power in almost all scenarios. Fixed-effects model was less precise when the number of centres was large and treatment allocation was subject to chance imbalance within centre. GEE approach underestimated standard error of the treatment effect when the number of centres was small. The two centre-level models led to more variable point estimates and relatively low interval coverage or statistical power depending on whether or not heterogeneity of treatment contrasts was considered in the analysis.</p> <p>Conclusions</p> <p>All six models produced unbiased estimates of treatment effect in the context of multicentre trials. Adjusting for centre as a random intercept led to the most efficient treatment effect estimation across all simulations under the normality assumption, when there was no treatment by centre interaction.</p

Neoadjuvant chemoradiation followed by surgery versus surgery alone for patients with adenocarcinoma or squamous cell carcinoma of the esophagus (CROSS)

textabstractBackground. A surgical resection is currently the preferred treatment for esophageal cancer if the tumor is considered to be resectable without evidence of distant metastases (cT1-3 N0-1 M0). A high percentage of irradical resections is reported in studies using neoadjuvant chemotherapy followed by surgery versus surgery alone and in trials in which patients are treated with surgery alone. Improvement of locoregional control by using neoadjuvant chemoradiotherapy might therefore improve the prognosis in these patients. We previously reported that after neoadjuvant chemoradiotherapy with weekly administrations of Carboplatin and Paclitaxel combined with concurrent radiotherapy nearly always a complete R0-resection could be performed. The concept that this neoadjuvant chemoradiotherapy regimen improves overall survival has, however, to be proven in a randomized phase III trial. Methods/design. The CROSS trial is a multicenter, randomized phase III, clinical trial. The study compares neoadjuvant chemoradiotherapy followed by surgery with surgery alone in patients with potentially curable esophageal cancer, with inclusion of 175 patients per arm. The objectives of the CROSS trial are to compare median survival rates and quality of life (before, during and after treatment), pathological responses, progression free survival, the number of R0 resections, treatment toxicity and costs between patients treated with neoadjuvant chemoradiotherapy followed by surgery with surgery alone for surgically resectable esophageal adenocarcinoma or squamous cell carcinoma. Over a 5 week period concurrent chemoradiotherapy will be applied on an outpatient basis. Paclitaxel (50 mg/m2) and Carboplatin (Area-Under-Curve = 2) are administered by i.v. infusion on days 1, 8, 15, 22, and 29. External beam radiation with a total dose of 41.4 Gy is given in 23 fractions of 1.8 Gy, 5 fractions a week. After completion of the protocol, patients will be followed up every 3 months for the first year, every 6 months for the second year, and then at the end of each year until 5 years after treatment. Quality of life questionnaires will be filled out during the first year of follow-up. Discussion. This study will contribute to the evidence on any benefits of neoadjuvant treatment in esophageal cancer patients using a promising chemoradiotherapy regimen. Trial registration. ISRCTN80832026

A randomised controlled trial linking mental health inpatients to community smoking cessation supports: A study protocol

<p>Abstract</p> <p>Background</p> <p>Mental health inpatients smoke at higher rates than the general population and are disproportionately affected by tobacco dependence. Despite the advent of smoke free policies within mental health hospitals, limited systems are in place to support a cessation attempt post hospitalisation, and international evidence suggests that most smokers return to pre-admission smoking levels following discharge. This protocol describes a randomised controlled trial that will test the feasibility, acceptability and efficacy of linking inpatient smoking care with ongoing community cessation support for smokers with a mental illness.</p> <p>Methods/Design</p> <p>This study will be conducted as a randomised controlled trial. 200 smokers with an acute mental illness will be recruited from a large inpatient mental health facility. Participants will complete a baseline survey and will be randomised to either a multimodal smoking cessation intervention or provided with hospital smoking care only. Randomisation will be stratified by diagnosis (psychotic, non-psychotic). Intervention participants will be provided with a brief motivational interview in the inpatient setting and options of ongoing smoking cessation support post discharge: nicotine replacement therapy (NRT); referral to Quitline; smoking cessation groups; and fortnightly telephone support. Outcome data, including cigarettes smoked per day, quit attempts, and self-reported 7-day point prevalence abstinence (validated by exhaled carbon monoxide), will be collected via blind interview at one week, two months, four months and six months post discharge. Process information will also be collected, including the use of cessation supports and cost of the intervention.</p> <p>Discussion</p> <p>This study will provide comprehensive data on the potential of an integrated, multimodal smoking cessation intervention for persons with an acute mental illness, linking inpatient with community cessation support.</p> <p>Trial Registration</p> <p>Australian and New Zealand Clinical Trials Registry ANZTCN: <a href="http://www.anzctr.org.au/ACTRN12609000465257.aspx">ACTRN12609000465257</a></p

Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods

We gratefully acknowledge Rachel Schaperow, MedStar Health Research Institute, for editing the manuscript.Disclaimer: The opinions expressed in this paper are those of the authors and do not necessarily reflect the views of the Indian Health Service.Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS). Studying these conditions simultaneously in longitudinal studies is challenging, because the morbidity and mortality associated with these diseases result in missing data, and these data are likely not missing at random. When such data are merely excluded, study findings may be compromised. In this article, a subset of 2264 participants with complete renal function data from Strong Heart Exams 1 (1989–1991), 2 (1993–1995), and 3 (1998–1999) was used to examine the performance of five methods used to impute missing data: listwise deletion, mean of serial measures, adjacent value, multiple imputation, and pattern-mixture. Three missing at random models and one non-missing at random model were used to compare the performance of the imputation techniques on randomly and non-randomly missing data. The pattern-mixture method was found to perform best for imputing renal function data that were not missing at random. Determining whether data are missing at random or not can help in choosing the imputation method that will provide the most accurate results.Yeshttp://www.plosone.org/static/editorial#pee