Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

Choice of the primary analysis in longitudinal clinical trials

Missing data, and the bias they can cause, are an almost ever‐present concern in clinical trials. The last observation carried forward (LOCF) approach has been frequently utilized to handle missing data in clinical trials, and is often specified in conjunction with analysis of variance (LOCF ANOVA) for the primary analysis. Considerable advances in statistical methodology, and in our ability to implement these methods, have been made in recent years. Likelihood‐based, mixed‐effects model approaches implemented under the missing at random (MAR) framework are now easy to implement, and are commonly used to analyse clinical trial data. Furthermore, such approaches are more robust to the biases from missing data, and provide better control of Type I and Type II errors than LOCF ANOVA. Empirical research and analytic proof have demonstrated that the behaviour of LOCF is uncertain, and in many situations it has not been conservative. Using LOCF as a composite measure of safety, tolerability and efficacy can lead to erroneous conclusions regarding the effectiveness of a drug. This approach also violates the fundamental basis of statistics as it involves testing an outcome that is not a physical parameter of the population, but rather a quantity that can be influenced by investigator behaviour, trial design, etc. Practice should shift away from using LOCF ANOVA as the primary analysis and focus on likelihood‐based, mixed‐effects model approaches developed under the MAR framework, with missing not at random methods used to assess robustness of the primary analysis. Copyright © 2004 John Wiley & Sons, Ltd.     

Setting operational goals for corporate social responsibility

In this article the author argues that more formality in social responsibility planning efforts is a concept which is extremely pertinent in today's conditions. Rewards will accrue to the organization that can move beyond aesthetic objectives and achieve operationality in social responsibility goal setting. This requires, basically, the application of techniques that have proved to be successful in MBO programs. By translating broadly stated social objectives into goals that are clear, concise, time related and cost related, management will inject an increased measure of integrity into its planning process, improve social responsibility programs, assure future successes in the social performance arena, facilitate the appraisal of management personnel, and enhance the organization's credibility with its multiple publics. Positive benefits will flow to the organization, its immediate publics, and to the society at large as a consequence of operational planning methods as described herein.     

Process evaluation of an environmental walking and healthy eating pilot in small rural worksites

Small Steps are Easier Together (SS) was a pilot environmental intervention in small rural worksites in Upstate New York in collaboration with Extension educators. Worksite leaders teamed with co-workers to select and implement environmental changes to increase walking steps over individual baseline and to choose healthy eating options over 10 weeks. Participants were 226 primarily white, women employees in 5 sites. A mixed methods process evaluation, conducted to identify determinants of intervention effectiveness and to explain differences in outcomes across worksites, included surveys, self-reports of walking and eating, interviews, focus groups, and an intervention log. The evaluation assessed reach, characteristics of recruited participants, dose delivered, dose received, and context and compared sites on walking and eating outcomes. Emergent elements of participant-reported dose received included: active leadership, visible environmental changes, critical mass of participants, public display of accomplishments, accountability to co-workers, and group decision making. Participants at sites with high reach and dose were significantly more likely than sites with low reach and dose to achieve intervention goals. Although this small pilot needs replication, these findings describe how these evaluation methods can be applied and analyzed in an environmental intervention and provide information on trends in the data.     

Differing Relationship Outcomes When Sex Happens Before,On, or After First Dates

While recent studies have suggested that the timing of sexual initiation within a couple's romantic relationship has important associations with later relationship success, few studies have examined how such timing is associated with relationship quality among unmarried couples. Using a sample of 10,932 individuals in unmarried, romantic relationships, we examined how four sexual-timing patterns (i.e., having sex prior to dating, initiating sex on the first date or shortly after, having sex after a few weeks of dating, and sexual abstinence) were associated with relationship satisfaction, stability, and communication in dating relationships. Results suggested that waiting to initiate sexual intimacy in unmarried relationships was generally associated with positive outcomes. This effect was strongly moderated by relationship length, with individuals who reported early sexual initiation reporting increasingly lower outcomes in relationships of longer than two years.     

Methods to assess measurement error in questionnaires of sedentary behavior

Sedentary behavior has already been associated with mortality, cardiovascular disease, and cancer. Questionnaires are an affordable tool for measuring sedentary behavior in large epidemiological studies. Here, we introduce and evaluate two statistical methods for quantifying measurement error in questionnaires. Accurate estimates are needed for assessing questionnaire quality. The two methods would be applied to validation studies that measure a sedentary behavior by both questionnaire and accelerometer on multiple days. The first method fits a reduced model by assuming the accelerometer is without error, while the second method fits a more complete model that allows both measures to have error. Because accelerometers tend to be highly accurate, we show that ignoring the accelerometer's measurement error, can result in more accurate estimates of measurement error in some scenarios. In this article, we derive asymptotic approximations for the mean-squared error of the estimated parameters from both methods, evaluate their dependence on study design and behavior characteristics, and offer an R package so investigators can make an informed choice between the two methods. We demonstrate the difference between the two methods in a recent validation study comparing previous day recalls to an accelerometer-based ActivPal.     

Back to basics: explaining sample size in outcome trials,are statisticians doing a thorough job?

Time to event outcome trials in clinical research are typically large, expensive and high‐profile affairs. Such trials are commonplace in oncology and cardiovascular therapeutic areas but are also seen in other areas such as respiratory in indications like chronic obstructive pulmonary disease. Their progress is closely monitored and results are often eagerly awaited. Once available, the top line result is often big news, at least within the therapeutic area in which it was conducted, and the data are subsequently fully scrutinized in a series of high‐profile publications. In such circumstances, the statistician has a vital role to play in the design, conduct, analysis and reporting of the trial. In particular, in drug development it is incumbent on the statistician to ensure at the outset that the sizing of the trial is fully appreciated by their medical, and other non‐statistical, drug development team colleagues and that the risk of delivering a statistically significant but clinically unpersuasive result is minimized. The statistician also has a key role in advising the team when, early in the life of an outcomes trial, a lower than anticipated event rate appears to be emerging. This paper highlights some of the important features relating to outcome trial sample sizing and makes a number of simple recommendations aimed at ensuring a better, common understanding of the interplay between sample size and power and the final result required to provide a statistically positive and clinically persuasive outcome. Copyright © 2009 John Wiley & Sons, Ltd.     

Search (f)or Work: Der Übergang vom Studium in den Beruf

Previous research shows that college graduates in the humanities face a notably difficult college-to-work transition compared to graduates from other disciplines. Their transition to work not only takes longer; graduates in the humanities are also more likely to wind up in atypical employment. This paper asks, what factors explain the difficulties in the college-to-work transition for graduates in the humanities. Taking an interdisciplinary perspective, graduates in the humanities are compared to two reference groups: law and economics as well as engineering. Using data from the HIS Absolventenpanel 1997, separate piecewise constant hazard regression models were estimated to identify the determinants of the college-to-work transition for each discipline. Drawing on Spence’s theory of signaling and Rosenbaum’s tournament theory, we find that tournaments and signals structure the college-to-work transition differently in each discipline. While selection processes are weak in the humanities, graduates need to acquire additional occupationally useful skills before or during their studies in order to make a smoother transition into the labor market.     

Sustainable Caregiving? Demands Upon and Resources of Female Carers of Adults with Intellectual Disability

Female carers of adults with an intellectual disability are increasing in our communities as those with intellectual disability live into middle and old age. Twenty-seven carers between the ages of 45 and 85 years responded to questionnaires about demands, resources, and their health. Almost all were in the clinical range on the measure of psychological functioning. Demands contributed significantly to poorer functioning; however, other measures did not. The women in this study appeared to underreport the level of difficulties they experienced. The caregiving role undertaken by these women does not appear to be sustainable under their current circumstances.