Meta-Analysis

The only recent meta-analyses offering results applicable to the scope of the present study, was Gulpers et al. (2016). In the updated meta-analysis here, there will be points of

comparison with Gulpers et al. The first such comparison point is about methods. One of the analyses by Gulpers et al. considered anxiety as a predictor of progression from MCI to dementia. As noted in the previous paragraph at point F, progression studies with MCI at baseline, overlook critical, temporal, confounding effects. Therefore, such associations are of no greater value that cross-sectional correlations. So, this category of the meta-analyses will not be pursued, here. The principal remaining analyses by Gulpers et al. were for progressions from cognitively healthy to MCI and cognitively healthy to dementia. Each provided useful information for comparison the present review. The general systematic review described above (Section 2.2.1) considered categories of MCI and dementia as more specific prognostic outcome variables. However, the updated meta-analysis here, will consider only the broader outcome categories of MCI and dementia because the small number of studies accepted into the meta-analysis does not permit such categorisation into even smaller sub-analyses. Similarly, the small number of studies accepted by Gulpers et al. did not permit this further categorisation of meta-analysis.

Meta-analysis of association between anxiety and cognitive decline, for individual

domains (such as attention or memory), were not possible for the data available. Gulpers et al. (2016) were unable to pool results within such categories of cognition, for any suitable

combination of studies. For the updated meta-analyses reported below, the same conclusion applies. The heterogeneity of results and methods has not facilitated meta-analysis for cognitive decline.

Since the census date (January 2015) for the meta-analysis by Gulpers et al. (2016), relevant new studies have been published. These contribute to the meta-analyses to follow.

2.2.2.2 Study selection.

Of the 37 articles rated as suitable for the literature review, 30 were excluded from meta- analysis for one or more of the following reasons:

1) Results were only for cognitive decline (which has been excluded from this meta- analysis because the heterogeneity of methods was not able to be accommodated); 2) The baseline sample included participants with cognitive impairment;

3) The results were unadjusted for key confounds (particularly depression); or 4) Results were presented in the article in a form that was unsuitable for inclusion in

the meta-analysis (e.g., important parameters were missing) and suitable information could not be obtained directly from the authors.

Among these exclusions were studies presenting results in the form of hazard ratios (HR) rather than odds ratios (OR) or relative risk (RR). Meta-analysis requires conversion of ratios to one type. Here, the type chosen was RR, while noting conversion from OR to RR is straight forward. However, conversion of HR to RR is not valid. Firstly, Stare and Maucort- Boulch (2016) explained many studies have used RR and HR interchangeably, but there appears to be no derivation to support this assumption of equivalence.

Explanation of differences between RR and HR are available from Kaewkungwal (2018) and Stare and Maucort-Boulch (2016). Put simply, RR is a function of the cumulative events over the observation period, while HR is a function of the rate of such events within the observation period. HR could be understood also as the slope of the survival curve. Stare and Maucort demonstrated approximate equivalence (between RR and HR) would apply only in the special, combined conditions of: identical time frames; small hazard ratios; and, small probabilities. Further, given that time is treated differently for each of RR and HR, inclusion of time-varying predictors and outcome variables in regression models, would seem, in my opinion, to add confusion to any proposition of equivalence.

If RR and HR are not equivalent, the next logical question is whether one can be

transformed to the other. However, I searched the literature and found no published derivation of a transformation between RR and HR. The lead author of Stare and Maucort-Boulch

(2016), advised me that it was unlikely an equation would be available for conversion from HR to RR (Janez Stare, personal communication, May 29, 2019).The Australian National University Statistical Consulting Unit expressed the same view (Marijke Welvaert, personal communication, May 30, 2019).

Thus, there is no suitable transformation available, and I have chosen not to introduce an unknown degree of error by simply declaring equivalence, without theoretical foundation.

2.2.2.3 Data extraction.

In addition to data extracted for the wider literature review described above, data extracted for the meta-analysis was: The odds ratios, relative risks, p values, the covariates, baseline cognitive status, inclusion and exclusion criteria, impairment definition or cognitive scale, anxiety scale or diagnosis criteria, follow-up criteria and metrics, loss to follow-up description and analysis, and conclusion of the study.

2.2.2.4 Assessment of methodological quality.

The method for assessing quality of the studies was adapted from methods recommended by Altman (2001) and Hayden, Côté, and Bombardier (2006), and partially modelled on these methods as deployed by Gulpers et al. (2016). The resulting summary framework for quality assessment is described at Table 2.1. This framework included additional criteria for the key limitations identified above (Section 2.2.1). Each of the 25 items at Table 2.1 was rated between zero and one and aggregated for each study then converted to a mean with value lying between zero and one.

Table 2.1

Framework for Quality Assessment

Category Item # Item

Study sample 1 Selection explained

2 Inclusion & exclusion criteria described

Category Item # Item 4 Diagnostic criteria described 5 Relevant characteristics described 6 Representative of the general population

Length of study 7 Study length suitable, relative to temporal confounding from prodromal effects

Follow-up 8 Follow-up at regular intervals

9 Number of follow-ups

10 Follow-ups included re-measurement anxiety 11 Reasons for loss to follow-up

12 Analysis of loss to follow-up, examining differences in characteristics

Outcome 13 Defined

14 Objective unbiased

15 Measured for all participants, or a high proportion Prognostic outcome 16 Defined

17 Measured precisely

18 Valid method

19 Measured for all participants, or a high proportion 20 All results described

Predictor variables 21 Defined

22 Appropriate category for the study (e.g., “trait anxiety”) Analysis 23 Appropriate analysis method

24 Adjusted for key confounds

2.2.2.5 Statistical analysis.

The software for this meta-analysis was Stata, version 15.0 (StataCorp, College Station, Texas USA), using the metan command for meta-analysis with random effects (Borenstein, Hedges, & Rothstein, 2009; DerSimonian & Laird, 1986). This software was used to calculate pooled RR with 95% CI. Reported OR were converted to RR. Only fully adjusted results were included in the analysis, except where noted in the tables.

2.3 Results