Some t-tests for N-of-1 trials with serial correlation

N-of-1 trials allow inference between two treatments given to a single individual. Most often, clinical investigators analyze an individual's N-of-1 trial data with usual t-tests or simple nonparametric methods. These simple methods do not account for serial correlation in repeated observations coming from the individual. Existing methods accounting for serial correlation require simulation, multiple N-of-1 trials, or both. Here, we develop t-tests that account for serial correlation in a single individual. The development includes effect size and precision calculations, both of which are useful for study planning. We then evaluate and compare their Type I and II errors and interval estimators to those of usual t-tests analogues via Monte Carlo simulation. The serial t-tests clearly outperform the usual t-tests commonly used in reporting N-of-1 results. Examples from N-of-1 clinical trials in fibromyalgia patients and from a behavioral health setting exhibit how accounting for serial correlation can change inferences. These t-tests are easily implemented and more appropriate than simple methods commonly used; however, caution is needed when analyzing only a few observations. Keywords: Autocorrelation; Cross-over studies; Repeated measures analysis; Single-case experimental design; Time-seriesComment: 23 pages, 6 figures, 6 table

NONLINEAR REGRESSION PARAMETERS AS OUTCOMES: SIMPLE VS. SOPHISTICATED ANALYSES

Sometimes a nonlinear regression parameter for an individual is the outcome of interest. But due to variability among individuals, the individuals’ regression parameters cannot be estimated with the same amount of precision. This problem of heterogeneous variance complicates the ultimate goal of estimating population-level regression parameters with two usual methods: (i) the simple arithmetic mean of individually estimated regression parameters and (ii) random coefficients regression (RCR). Weights are proposed for each method to account for the heterogeneity problem. The methods are illustrated with chick weights collected over time. Monte Carlo simulation allows comparison of statistical properties of the four estimators for small, moderate and large sample sizes. The arithmetic means tended to outperform the RCR estimators with respect to mean square error and bias; and their associated confidence intervals held nominal levels. Actual coverage of confidence intervals produced from RCR methods fell below nominal levels in some cases; however this discrepancy may be an algorithm error. Overall, the simpler arithmetic mean estimators tend to have either better or comparable statistical properties to those estimators from RCR methods

A BAYESIAN RANDOM COEFFICIENT NONLINEAR REGRESSION MODEL FOR A SPLIT-PLOT EXPERIMENT

In random coefficients regression, we are often interested in the mean of a certain para-meter particular to the experimental unit (EU). When the mean depends on some treatment regimen, we are then interested in comparing the means among the different treatments. When the EUs are repeatedly measured on a variable containing information about the EU parameter, a standard procedure is to estimate each EU parameter and treat the estimates as the response variables. This is especially true when the regression model for an EU is non-linear. Often, for designed experiments with a factorial treatment structure, the estimated EU parameters are then modeled with an appropriate linear (mixed) model. Here, we consider a split-plot experiment conducted to detect differences in the half-life of a compound between different treatment regimens of the compound, namely compound preparation and temperature (whole-plot factors) and initial compound amount (split-plot factor). Initially, we provide a standard (classical) analysis plan, and then present a Bayes random coefficients regression model to address the researcher’s questions of interest. We finally compare the results from the standard and Bayes analyses

ARE SPATIAL MODELS NEEDED WITH ADEQUATELY BLOCKED FIELD TRIALS?

The use of nearest neighbors and spatial models (SPAT) to analyze field trial data has become commonplace in recent years. These two types of analyses improve precision compared to ANOVA when trials are poorly blocked, but results are less clear in well-blocked trials. We examined data from wheat trials containing 60 cultivars, conducted at five locations, where each location was set up as an alpha lattice design. We compared the relative efficiency of detecting cultivar differences for spatial models and nearest neighbors analyses (NNA) to ANOVA, fit of the models, and correlations of ranked cultivars. Though the SPAT and NNA generally outperformed the ANOVA, the selection of desirable cultivars remained relatively unchanged when using a well-blocked design analyzed with an ANOVA

Discounting of money and sex: Effects of commodity and temporal position in stimulant-dependent men and women

Research on delay discounting has contributed to the understanding of numerous addiction-related phenomena. For example, studies have shown that substance dependent individuals discount their addictive substances (e.g., cocaine) more rapidly than they do other commodities (e.g., money). Recent research has shown that substance dependent individuals discount delayed sex more rapidly than delayed money, and their discounting rates for delayed sex were higher than those of non-addicted individuals. The particular reason that delay discounting rates for sex are higher than those for money, however, are unclear. Do individuals discount delayed sex rapidly because immediate sex is particularly appealing or because delayed sex does not retain its value? Moreover, do the same factors influence men and women’s choices? The current study examined delay discounting in four conditions (money now versus money later; sex now versus sex later; money now, versus sex later; sex now versus money later) in cocaine dependent men and women. The procedures used isolated the role of the immediate versus delayed commodity. For men, the higher rates of delay discounting for sex were because delayed sex did not retain its value, whereas both the immediate and delayed commodity influenced the female participants’ decisions

Contingency management effects on delay discounting among patients receiving smoking cessation treatment

Antecedentes: la evidencia sugiere que el descuento por demora puede cambiar tras recibir intervenciones eficaces. No obstante, estudios previos que evaluaron el efecto del manejo de contingencias (MC) sobre el descuento por demora son escasos y presentan resultados mixtos. Se evaluó si el MC combinado con tratamiento cognitivo-conductual (TCC) para dejar de fumar se asoció con cambios en el descuento por demora al final del tratamiento y a los seis de seguimiento comparado con TCC. Método: Ciento dieciséis fumadores fueron asignados aleatoriamente a MC+TCC (n = 69) o a TCC solo (n = 47). Completaron la tarea de descuento por demora en la línea base, al final del tratamiento y a los seis meses de seguimiento. Evaluamos el efecto del MC en el descuento por demora con métodos paramétricos y no paramétricos. Resultados: Los análisis entre-grupos mostraron que ninguno de los tratamientos modificó el descuento por demora al final del tratamiento y a los seis meses de seguimiento. No obstante, algunos análisis intra-grupos mostraron que la condición de MC + TCC evidenció cierta reducción. Conclusiones: una intervención de MC no se asocia robustamente con cambios en el descuento por demora. Futuros estudios han de abordar qué tratamientos pueden modificarlo

Employing external facilitation to implement cognitive behavioral therapy in VA clinics: a pilot study

<p>Abstract</p> <p>Background</p> <p>Although for more than a decade healthcare systems have attempted to provide evidence-based mental health treatments, the availability and use of psychotherapies remains low. A significant need exists to identify simple but effective implementation strategies to adopt complex practices within complex systems of care. Emerging evidence suggests that facilitation may be an effective integrative implementation strategy for adoption of complex practices. The current pilot examined the use of external facilitation for adoption of cognitive behavioral therapy (CBT) in 20 Department of Veteran Affairs (VA) clinics.</p> <p>Methods</p> <p>The 20 clinics were paired on facility characteristics, and 23 clinicians from these were trained in CBT. A clinic in each pair was randomly selected to receive external facilitation. Quantitative methods were used to examine the extent of CBT implementation in 10 clinics that received external facilitation compared with 10 clinics that did not, and to better understand the relationship between individual providers' characteristics and attitudes and their CBT use. Costs of external facilitation were assessed by tracking the time spent by the facilitator and therapists in activities related to implementing CBT. Qualitative methods were used to explore contextual and other factors thought to influence implementation.</p> <p>Results</p> <p>Examination of change scores showed that facilitated therapists averaged an increase of 19% [95% CI: (2, 36)] in self-reported CBT use from baseline, while control therapists averaged a 4% [95% CI: (-14, 21)] increase. Therapists in the facilitated condition who were not providing CBT at baseline showed the greatest increase (35%) compared to a control therapist who was not providing CBT at baseline (10%) or to therapists in either condition who were providing CBT at baseline (average 3%). Increased CBT use was unrelated to prior CBT training. Barriers to CBT implementation were therapists' lack of control over their clinic schedule and poor communication with clinical leaders.</p> <p>Conclusions</p> <p>These findings suggest that facilitation may help clinicians make complex practice changes such as implementing an evidence-based psychotherapy. Furthermore, the substantial increase in CBT usage among the facilitation group was achieved at a modest cost.</p

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates