Consequences of missing data

Either I've been missing something or nothing has been going on. —Karen Elizabeth Gordon

Missing data are everywhere. Subjects may decline to participate in a survey or fail to answer sensitive questions. A cell culture might fail due to contamination. Instrument failures or mishandling of a sample may lead to missing observations. But why are missing data a problem?

▲ Nature Methods Points of Significance column: Consequences of missing data. (read)

This month, we begin a series of articles about practical and statistical aspects of missing data. We'll see that missing data can increase variability and introduce bias and we will ask whether anything can be done to mitigate these consequences. It turns out that, in the case where we know nothing about the missing subjects, no mitigation is possible. We must accept higher variability and, if we have a suspicion that the missing subjects aren’t completely random, possible bias as well.

Tanujit Dey, T., Lipsitz, S.R., Fitzmaurice, G., Krzywinski, M. & Altman, N. (2026) Points of significance: Consequences of missing data. Nat. Methods 23 (in print).

Double Robustness

It is not certain that everything is uncertain. —Blaise Pascal

We have already explored how we can mitigate bias caused by confounding variables in observational studies using propensity score (PS) matching (PSM) and propensity score weighting (PSW). However, any statistical model is only as good as its assumptions and, if it is specified incorrectly, it can itself produce biased estimates of the treatment effect.

▲ Nature Methods Points of Significance column: Double Robustness. (read)

This month, we explore double robustness, a powerful statistical concept that provides a valuable “safety net” against the risk of an incorrect model. It offers two opportunities, instead of just one, to obtain a valid estimate of the treatment effect — making it possible to draw credible causal inferences from observational data without having to depend on a single set of modeling assumptions.

Kurz, C.F., Krzywinski, M. & Altman, N. (2026) Points of significance: Double Robustness. Nat. Methods 23:868–869.

Nature Biotechnology cover

My cover design on the 7 April 2026 Nature Biotechnology issue shows the dendrogram that represents a cluster of uniquely expressed (or downregulated) genes in human naive stem cells induced from such cells. Within each dendrogram block, the genomic barcode sequence (sampled from Supplementary Table 1) is depicted with a Code 39 barcode. The highlighted barcode is one of those used for cell isolation.

Ishiguro S. et al. A multi-kingdom genetic barcoding system for precise clone isolation (2026) Nature Biotechnology 44:616–629.

▲ My Nature Biotechnology phylogenetic tree cover (volume 44, issue 4, 7 April 2026). (more)

Browse my gallery of cover designs.

▲ A catalogue of my journal and magazine cover designs. (more)

Happy 2026 π Day—
Art for the 5%

Celebrate π Day (March 14th) and enjoy the art — but only if you're part of the 5%.

Go ahead, see what you can't see.

▲ 2026 π DAY | Art for the 5%. Shown in the style of Ishihara color test plates, the art is visible only to those with colour blindness. (details)

Ishihara's Tests for Colour Deficiency

Authentic and accurate images of Ishihara's test plates photographed (and lovingly color-corrected) from the 38-plate Ishihara's Tests for Colour Deficiency.

I also provide the position, size, and color of each circle on each test plate.

▲ ISHIHARA'S TEST PLATE 6 | This plate is part of the set of transformation plates. If you see 5, you're ok. If you see 2, you're not. (details)

▲ ISHIHARA'S TEST PLATE 18 | This plate is part of the set of mysterious hidden plates. If you don't see anything, you're ok. If you see 5, you're not. (details)

Symmetric alternatives to the ordinary least squares regression

What immortal hand or eye, could frame thy fearful symmetry? — William Blake, "The Tyger"

This month, we look at symmetric regression, which, unlike simple linear regression, it is reversible — remaining unaltered when the variables are swapped.

Simple linear regression can summarize the linear relationship between two variables `X` and `Y` — for example, when `Y` is considered the response (dependent) and `X` the predictor (independent) variable.

However, there are times when we are not interested (or able) to distinguish between dependent and independent variables — either because they have the same importance or the same role. This is where symmetric regression can help.

▲ Nature Methods Points of Significance column: Symmetric alternatives to the ordinary least squares regression. Geometry of quantities minimized in OLS and symmetric regression. OLS minimizes `\Sigma e_y^2` in `Y` ~ `X` and `\Sigma e_x^2` `X` ~ `Y`. Pythagorean regression minimizes AB (magenta). Geometric means regression (GMR) minimizes area of ABP (orange). Orthogonal regression (OR) minimizes HP (blue). (read)

Luca Greco, George Luta, Martin Krzywinski & Naomi Altman (2025) Points of significance: Symmetric alternatives to the ordinary least squares regression. Nat. Methods 22:1610–1612.