Control Groups

To date, the reliability of induction of a depressive-like state using chronic stress models is confronted by many methodological limitations. We believe that the modifications to the stress paradigm in mice proposed herein allow some of these limitations to be overcome. Here, we discuss a variant of the standard stress paradigm, which results in anhedonia. This anhedonic state was defined by a decrease in sucrose preference that was not exhibited by all animals. As such, we propose the use of non-anhedonic, stressed mice as an internal control in experimental mouse models of depression. The application of an internal control for the effects of stress, along with optimized behavioural testing, can enable the analysis of biological correlates of stress-induced anhedonia versus the consequences of stress alone in a chronic-stress depression model. This is illustrated, for instance, by distinct physiological and molecular profiles in anhedonic and non-anhedonic groups subjected to stress. These results argue for the use of a subgroup of individuals who are negative for the induction of a depressive phenotype during experimental paradigms of depression as an internal control, for more refined modeling of this disorder in animals.

BackgroundIn a recent individual patient data meta-analysis, acupuncture was found to be superior to both sham and non-sham controls in patients with chronic pain. In this paper we identify variations in types of sham and non-sham controls used and analyze their impact on the effect size of acupuncture.MethodsBased on literature searches of acupuncture trials involving patients with headache and migraine, osteoarthritis, and back, neck and shoulder pain, 29 trials met inclusion criteria, 20 involving sham controls (n = 5,230) and 18 non-sham controls (n = 14,597). For sham controls, we analysed non-needle sham, penetrating sham needles and non-penetrating sham needles. For non-sham controls, we analysed non-specified routine care and protocol-guided care. Using meta-regression we explored impact of choice of control on effect of acupuncture.FindingsAcupuncture was significantly superior to all categories of control group. For trials that used penetrating needles for sham control, acupuncture had smaller effect sizes than for trials with non-penetrating sham or sham control without needles. The difference in effect size was -0.45 (95% C.I. -0.78, -0.12; p = 0.007), or -0.19 (95% C.I. -0.39, 0.01; p = 0.058) after exclusion of outlying studies showing very large effects of acupuncture. In trials with non-sham controls, larger effect sizes associated with acupuncture vs. non-specified routine care than vs. protocol-guided care. Although the difference in effect size was large (0.26), it was not significant with a wide confidence interval (95% C.I. -0.05, 0.57, p = 0.1).ConclusionAcupuncture is significantly superior to control irrespective of the subtype of control. While the choice of control should be driven by the study question, our findings can help inform study design in acupuncture, particularly with respect to sample size. Penetrating needles appear to have important physiologic activity. We recommend that this type of sham be avoided.

PurposeImportant considerations for exercise trials in cancer patients are contamination and differential drop-out among the control group members that might jeopardize the internal validity. This systematic review provides an overview of different control groups design characteristics of exercise-oncology trials and explores the association with contamination and drop-out rates.MethodsRandomized controlled exercise-oncology trials from two Cochrane reviews were included. Additionally, a computer-aided search using Medline (Pubmed), Embase and CINAHL was conducted after completion date of the Cochrane reviews. Eligible studies were classified according to three control group design characteristics: the exercise instruction given to controls before start of the study (exercise allowed or not); and the intervention the control group was offered during (any (e.g., education sessions or telephone contacts) or none) or after (any (e.g., cross-over or exercise instruction) or none) the intervention period. Contamination (yes or no) and excess drop-out rates (i.e., drop-out rate of the control group minus the drop-out rate exercise group) were described according to the three design characteristics of the control group and according to the combinations of these three characteristics; so we additionally made subgroups based on combinations of type and timing of instructions received.Results40 exercise-oncology trials were included based on pre-specified eligibility criteria. The lowest contamination (7.1% of studies) and low drop-out rates (excess drop-out rate -4.7±9.2) were found in control groups offered an intervention after the intervention period. When control groups were offered an intervention both during and after the intervention period, contamination (0%) and excess drop-out rates (-10.0±12.8%) were even lower.ConclusionsControl groups receiving an intervention during and after the study intervention period have lower contamination and drop-out rates. The present findings can be considered when designing future exercise-oncology trials.

BackgroundIn observational studies estimating the association between treatment and time-to-event outcomes, time-related biases can substantially impact results. Immortal time bias is one of such biases, and two types are known: misclassified immortal time bias and excluded immortal time bias. These biases often arise from incorrect time-zero definition, especially with non-user controls. This study aims to illustrate immortal time bias in non-user controls using formulas, simulations, and real-world data.MethodsFor our simulations, we considered two scenarios: one with no confounding and no treatment effect, and the other with time-dependent confounding. We compare three different settings of time-zero for treatment and control groups. Method 1: Both groups were followed from cohort entry date (CED). Method 2: The treatment group was followed from treatment initiation date (TID), while the non-user group was followed from CED. Method 3: The treatment group was followed from TID, and non-users were matched to treatment patients, followed from the corresponding TID of their matched patient.ResultsOur simulation showed that both method 1 and method 2 can exhibit large biases in the estimated treatment effect due to immortal time bias. The magnitude of the bias is greater for method 1 than for method 2. On the other hand, method 3 showed almost no bias. Even in the presence of time-dependent confounding, method 3 did not introduce bias.ConclusionTo reduce time-related biases, it is crucial for researchers to carefully define an appropriate time-zero, especially when using a non-user control group.