Neurodevelopmental disorders are different from adult acquired disorders involving traumatic brain injury because they involve no period of normal development. Any IQ score in a neurodevelopmental disorder is confounded with the condition. The IQ score can never be separated from the effects of the condition.

Early on in intelligence testing, IQ was seen as a latent variable. Intelligence, as measured by ‘g’ was believed to have a causal power. It was believed to be independent of test conditions and other methodological issues. However, ‘g’ or intelligence varies among time and place.

ANCOVA was devised to minimize pre-existing group differences (e.g. differences in SES). However, it is not possible to treat IQ as a covariate in neurocognitive research  since it is an attribute of the disorders (e.g. ADHD). A covariate should be used when the assignment to the independent variable is done randomly (1), the covariate is related to the outcome measure (2) and the covariate is unrelated to the independent variable (3). It can be used if the researcher is trying to find out the direct effect of the independent variable on the outcome variable and the covariate is spuriously related to either variable or when it mediates the relationship between the independent and the outcome variable.

The ANCOVA has several assumptions:

1. Homogeneity of regression
   The within-group regression of the covariate and the dependent variable are not different.
2. Normally distributed residuals
   The residuals should be normally distributed.
3. Equal variance
   The variance in each group should be equal.

The presence of a relationship between the covariate and the dependent variable does not imply that the covariate mediates or moderates the relationship between the group measure and the dependent variable.

There is no consensus on the definition of IQ. IQ can be used as a covariate for acquired brain damage if the preinjury IQ scores are available or when preinjury IQ proxies are available.

There are three important questions when assessing the relationship between variables:

1. Statistical significance
2. Effect size
3. Practical significance

The practical significance (i.e. clinical significance) can be assessed by considering factors such as clinical benefit (1), cost (2) and side effects (3). In order to assess the practical significance, the strength of the association (i.e. ‘r’) (1), magnitude of the difference between the difference between treatment and comparison (i.e. ‘d’) (2) and measures of risk potency (3) can be used.  Risk potency can be assessed using the odds ratio (1), risk ratio (2), relative risk reduction (3), risk difference (4) and number needed to treat (5).

The p-value refers to the probability that the found outcome or more extreme is found, given that the null hypothesis is true. It is possible to have statistical significance by chance and outcomes with lower p-values are sometimes interpreted as having stronger effect sizes. A non-significant result also does not tell us anything about the truth of the null hypothesis.

There are several different effect size measures:

1. The ‘r’ family
   This is expressing effect size in terms of strength of association (e.g. correlation).
2. The ‘d’ family
   This is an effect size that can be used when the independent variable is binary and the dependent variable is ordered. It can be computed by subtracting the mean of the second group from the mean of the first group and divide it by the pooled standard deviation of both groups. The effect size ranges from minus to plus infinity.
3. Measures of risk potency
   This is an effect size that can be used when both the dependent and independent variables are binary. The odds ratio and risk ratio vary from 0 to infinity and 1 indicates no effect. Risk relative reduction and risk difference vary between -1 and 1 with 0 indicating no effect. Number needed to treat (NNT) ranges from 1 to plus infinity with very large values indicating no treatment effect.
4. AUC
   This is an effect size that can be used when the independent variable is binary but the dependent variable is either binary or ordered. It ranges from 0% to 100% with 50% indicating no effect.

This standard is relative and it should be noted that ‘larger than typical’ should be used rather than ‘large’. However, it might be best to find information about typical effect sizes in the context of a particular research field.

The disadvantages to the ‘d’ and the ‘r’ as measures of clinical significance are that they are relatively abstract (1), they were not intended as measures of clinical significance (2) and they are not readily interpretable in terms of how much individuals are affected by treatment (3).

There is no consensus on the externally provided standards for the clinical significance of treatments. Clinical significance could be defined as a change to normal

Randomized controlled trials without sufficient statistical power may not adequately test the underlying hypotheses. This makes it useless and, thus, unethical. However, underpowered research continues to be conducted.

Arguments in favour of using underpowered research are that meta-analyses may save the small studies by providing a means of combining the results with those of similar studies (1) and that confidence intervals could be used to estimate treatment effects (2).

However, underpowered trials can only be properly used in interventions of rare diseases (1) and in the early-phase of the development of drugs and devices (2). The interventions for rare diseases need to specify that they are planning to use their results in a meta-analysis of similar interventions. However, in both cases, the participants must be informed that their participation may only indirectly contribute to future health care benefits.

The number of participants (1), the expected variability (2) and the chosen probability of a type-I error (3) are used to calculate statistical power. There is no consensus regarding of how small an effect size must be to be clinically significant and thus must be recognized and in cases like this, the expected effect size can be used to calculate the number of participants for any given study. Empirical definitions of clinically meaningful effects (1) and data from earlier trials (2) must be used if it exists or if it doesn’t, the moderate effect sizes described by Cohen (3) should be used in the sample size calculation.

Studies that contain too few participants to detect a positive effect via hypothesis testing will also include unacceptably wide confidence intervals, which cannot properly be used to estimate the effect size of the treatment. Furthermore, problems with synthesizing the results may prevent the calculation of valid treatment effects in a meta-analysis. The ideal conditions for meta-analysis (e.g. comparable research methods among the primary trials) are not often met, which leads different meta-analyses to have different results. Lastly, underpowered trials are more likely to produce negative results and thus less likely to be published, this reduces the probability of it being used in a meta-analysis.

It is necessary to inform the participants of an underpowered study of their limited value to the participants because of ethical concerns. However, this information is often not given to participants because investigators do not do an a priori power analysis (1), investigators do not enrol enough participants in a timely fashion (2) or investigators fear it will reduce enrolment (3).

Only prospectively designed meta-analyses can justify the risks to participants in individually underpowered trials because they provide sufficient assurance that a study’s results will eventually contribute to valuable or important knowledge. Plans for large, comparative trials of experimental intervention can justify the conduct of small studies in earlier phases of drug or device development.

Empirically supported treatments refer to clearly specified psychological treatments shown to be efficacious in controlled research with a delineated population. Treatment efficacy must be demonstrated in controlled research in which it is reasonable to conclude that benefits observed are due to the effects of the treatment and not to chance or confounds. This is best demonstrated in randomized clinical trials. Replication is critical in this case.

A treatment which has been found efficacious in at least two studies by independent research teams is an efficacious treatment. A treatment which only has one study supporting the efficacy or if all the research has been conducted by one team is possibly efficacious treatment.

The efficacy research has to be done with proper methods. There are different types of group design of efficacy research:

1. Comparison with no treatment
   This comparison demonstrates whether a treatment actually works.
2. Comparisons with other treatments or with placebo
   This comparison demonstrates whether a treatment effect is due to the treatment or due to another effect (e.g. placebo; receiving attention).
3. Comparisons with other rival interventions
   This comparison demonstrates whether a treatment effect is due to competing mechanisms (i.e. the mechanism of another treatment) or due to a unique mechanism. It can also compare relative benefits of competing interventions.

The more stringent the comparison is, the more value researchers should attach to the results. Treatments that are efficacious and specific are treatments which control for other factors that could explain the treatment effect (e.g. therapeutic alliance). This increases confidence in the theoretical explanatory model of the treatment.

Problems with comparisons with other rival interventions are interpretation (i.e. interpreting the treatment as being efficacious while it is a downgraded version of a well-established treatment) (1) and low statistical power (2).

In order to demonstrate that a treatment is equivalent to a well-established treatment, it should meet the following criteria:

1. Investigators have a sample size of 25-30 per condition.
2. The unproven treatment is not significantly inferior to the established efficacious treatments on tests of significance .
3. The pattern of data indicates no trends for the established efficacious treatment to be superior.

A psychological treatment plus placebo condition which is better than a placebo condition may not be due to the psychological intervention alone but due to the interaction between the placebo and the psychological treatment.

Efficacy needs to be described for a specific population or problem rather than in general. Factors that may determine the population (e.g. socioeconomic status) need to be described. The tools used in efficacy research need to have properly demonstrated reliability and validity. It is best to use multiple methods of assessment. Measures of life quality and functioning, rather than just symptoms need to be included as well, to properly evaluate a treatment.

It is important to know whether a treatment has an enduring effect and whether treatments

There are several characteristics of therapy with children and adolescents:

1. Dysfunction is difficult to assess
   The problematic behaviour may represent short-lived problems or perturbations in development rather than signs of lasting clinical impairment.
2. Identifying cases is problematic
   Youth rarely refer themselves to treatment which makes that externalizing problems are overrepresented in treatment.
3. Dependence on adults
   The dependence of children on adults makes them vulnerable to multiple influences over which they have little control (e.g. living circumstances; parental mental health). Therefore, the family context needs to be addressed in treatment as well.
4. Social environment and treatment
   The social environment plays a very important role for children which makes that taking treatment alone (i.e. without a peer, parent or sibling) is often not possible.
5. Methodological challenges
   It is not clear whether self-report is an appropriate measure for young children and other methods may be flawed when used with youth (e.g. standardized assessment methods).
6. Heterogeneity of samples
   The studies that are conducted typically have very heterogeneous samples which makes interpretation of the results difficult.

Many emotional and behavioural problems that are treated in therapy are often evident in less extreme forms in early development. Treatments have shown beneficial effects in the treatment of children.

Most therapy studies focus on non-referred cases (1), provide relatively brief treatments conducted in group format (2), evaluate treatment in relation to symptom reduction and neglects impairment or adaptive functioning (3), do not evaluate clinical significance of symptom changes (4) and do not conduct a follow-up (5).

Diverse differences among different age groups (e.g. language skills) indicate that treatment with similar general features must differ in numerous specific details when applied in different developmental periods. This leads to a classification dilemma (i.e. what cut-off to use).

A study needs to meet the following criteria to be a good study:

1. Replicable treatment processes.
2. Uniform therapist training and therapists adhering to the planned procedures.
3. Random assignment.
4. Use of clinical samples.
5. Multimethod outcome assessment.
6. Tests of clinical significance.
7. Test of treatment effect on real world, functional outcomes.
8. Assessment of long-term outcomes.

It is likely that dysfunctional anxiety becomes a self-perpetuating cycle of elevated biological response to stress, debilitating cognitions and avoidance of stressful circumstances. CBT appears to be effective for child anxiety.

Depressed children are seen as subject to schemas and cognitive distortions that cast everyday experience in an unduly negative light and as lacking important skills needed to generate supportive social relationships and regulate emotion through daily activity.

Coping skills training (CST) appears to be effective in the treatment of depression for children. It includes structured homework assignments as well as peer or therapist modelling. The mediators and differential effectiveness relative to alternative, simpler treatments still need to be tested.

Cognitive processes refer to a broad class of constructs that

The network model to psychopathology states that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This holds that symptoms are not passive indicators of a latent, common cause, of the disorder but agents in a causal system.

The causality hypothesis states that when causal relations among symptoms are strong, the onset of one symptom will lead to the onset of others. The connectivity hypothesis states that a strongly interconnected symptom network is vulnerable to a contagion effect of spreading activation through the network. Widespread symptom activation as a result of an external stressor can lead to symptoms persisting when the initiating stressor is removed. The centrality hypothesis states that highly central symptoms have greater potential to spread symptom activation throughout the network than do symptoms on the periphery. The comorbidity hypothesis states that symptoms can occur in multiple disorders and that some symptoms can thus bridge different disorders.

A mental disorder is characterized by both the state and the structure of the network (i.e. a mental disorder is characterized by a state of harmful equilibrium). The boundary between health and disorder will vary as a function of network structure. In weakly connected networks, activation varies dimensionally. However, in strongly connected networks, activation within the system rapidly leads to a state of psychopathology (i.e. more discrete rather than continuous).

The momentary perspective states that symptoms are aggregates of moment-to-moment experiences. According to this perspective, these experiences constitute the true building blocks of psychopathology. This highlights the importance of understanding the chronometry of experiences, symptoms and disorders.

The assumptions of the network model currently do not always align with how disorders are believed to operate.

There is a conditional positive manifold for most disorders. This states that symptoms of a positive disorder tend to be positively interconnected, even after controlling for shared variance among symptoms. This suggests meaningful clustering of symptoms in the syndromes we call mental disorders. Connectivity tends to be consistent across time and demographic groups. However, differences have been observed across countries.

Greater connectivity (i.e. network density) may confer risk for psychopathology. This is based on the fact that there appears to be greater connectivity between symptoms in people with more severe mental disorders. It is also possible that greater connectivity leads to disorder persistence. However, there is no consensus regarding these topics. There is some evidence that connectivity of negative mood state networks is associated with psychopathology but minimal evidence that broader networks of momentary experience exhibit such associations.

Node strength refers to the summed absolute strength of a node’s direct link. Non-DSM symptoms often exhibit elevated centrality (e.g. feeling disliked in depression) and some DSM-nodes are weakly connected to the network. It is not clear whether the symptoms which the DSM identifies as especially important are more central to less important DSM-symptoms. The DSM most likely has not captured all symptoms of a disorder and has not

Single-case experimental designs (SCED) are useful methods in clinical research to investigate individual client progress. It could also be used to determine whether an intervention works. In single-case experimental designs, a single participant is repeatedly assessed on one or multiple symptoms during various phases (e.g. baseline). The statistical analysis of this data is difficult.

Advantages of the single-case experimental designs are that it can be used to test novel interventions before RCTs are conducted (1), it may be the only way to investigate treatment outcomes in heterogeneous groups (2) and it offers the possibility to systematically document the knowledge of researchers and clinicians, preventing loss of information (3).

The most common single-case experimental design is the AB design. It consists of two phases (i.e. baseline and treatment). It is similar to an interrupted time series. In order to obtain an adequate analysis of the differences, the overall pattern in the time series should be modelled adequately (1) and there should be adequate modelling of potential correlations between residuals (2).

A common assumption of adequate models is that there is a linear function for both the baseline and the treatment phase. Both of these linear functions should be described by an intercept and a slope. Modelling of potential correlations between residuals means that there should be adequate modelling after the overall pattern has been accounted for. The correlation between residuals of the observations is the autocorrelation. It implies that the residuals are not independent. If residuals are correlated, the correlations are likely to decrease with increasing separation between timepoints.

The tests on the intercepts and the slopes of the linear functions will be unreliable if the correlations between the residuals are not modelled adequately (e.g. incorrectly assuming that the residuals are uncorrelated). If positively correlated residuals are assumed to be uncorrelated, the chances of finding significant results will be too high.

Modelling the overall pattern by the intercept and the slope for each phase (i.e. each timepoint) does not yield a direct test of intercept and slope differences. Therefore, it is useful to re-parameterize the model in terms of an intercept and slope for the baseline phase and baseline-treatment differences in the intercepts and slopes. This takes the following formula:

 Y(i) denotes the outcome variable score at time point i. Phase(i) denotes the phase in which time point i is contained. Time_in_phase denotes time points within each phase. E(i) denotes the residual at time point i.

The parameter b0 is interpreted as the baseline intercept. B1 is interpreted as the treatment-baseline difference in intercepts. B2 is interpreted as the baseline slope and b3 is interpreted as the treatment-baseline differences in slopes. These parameters can also be interpreted as effect sizes.

The b0 and b1 refer to symptom scores when time_in_phase is zero. Therefore, when coding the variables, time_in_phase zero should denote the start of each phase. However, when time_in_phase zero