Failing to control for temporal auto-correlation increases type 1 error rates.

<p>Simulated data sets with known levels of temporal auto-correlation between residuals (spanning the range observed in published data sets) were generated using R version 2.12.1 (The R Foundation for Statistical Computing; <a href="http://www.R-project.org" target="_blank">http://www.R-project.org</a>). Auto-correlation is highest between consecutive days and reduces as the duration between data points increases (auto-regressive error structure). The simulated data sets were designed so that any difference in treatment groups was due to chance: if the linear mixed effects model performs correctly it should, by definition, return a <i>p</i>-value of ≤0.05, 5% of the time. The red line represents the proportion of false positive results (where a statistically significant difference between treatment groups is wrongly concluded) from linear mixed effects models. The blue line shows the proportion of false positive results if an auto-regressive error structure is included in the model. Values are averaged from a minimum of 10,000 simulated data sets and the black lines show the 95% confidence intervals around the mean.</p

The majority of publications in parasitology do not control for temporal auto-correlation.

<p>Bars show the result of a literature search for papers using mixed effects models to analyse time-course data sets in seven parasitology journals from January 2009 to August 2011. Of 76 papers examined, 19 explicitly controlled for temporal auto-correlation (blue), but no controls were mentioned in 56 (red).</p

The extent of auto-correlation in experimental rodent malaria infections.

<p>Estimates of temporal auto-correlation in key parasite and host traits observed during daily sampling of infections initiated with controlled <i>Plasmodium chabaudi</i> parasite doses in mice matched for strain, age, and sex. (A) Data for days 5–15 post infection taken from <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002590#ppat.1002590-Reece1" target="_blank">[16]</a>, <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002590#ppat.1002590-Pollitt1" target="_blank">[17]</a>. Colours represent different wild-type parasite genotypes (green = AS, red = AJ, yellow = ER, blue = DK, purple = CW, orange = CR). (B) Data for days 3–18 post infection taken from <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002590#ppat.1002590-Mideo2" target="_blank">[18]</a> in mice with depleted levels of CD4+ T cells (light bars) or unmanipulated immune responses (dark bars) for genotypes AS (green) and DK (blue). These estimates demonstrate how levels of auto-correlation can be both high (up to 87% correlation between residuals on consecutive days) and variable between traits. The implications of this will depend on the analysis performed, but auto-correlation at such high levels has the potential to dramatically increase type 1 error rates (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1002590#ppat-1002590-g002" target="_blank">Figure 2</a>).</p

Lemaitre et al. dryad - Data

Dataframe used for main analysis in the paper

Pedigree

Pedigree, calculated as described in the paper

Female Rut Data

Female Rut data from field observations. Please see ReadMe file

Male Rut Data

Male rut data, from observations. Please see ReadMe file

Appendix A. Results of linear mixed-effects models of within-individual changes in mass.

Results of linear mixed-effects models of within-individual changes in mass

Supplementary Table 1 from The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis

List of studies assess for eligibility in full, with reasons for exclusio

International journal of forest engineering

R script used to calculate hazard ratio