design_consequences: Consequences of a Design: Power, Sign and Magnitude Errors, and Expected Precision

Description

Evaluates what a design of a given precision will actually deliver, under both of the package's lenses at once. The significance lens: the power of the two-sided test, the type_s_error (the probability that a statistically significant estimate has the wrong sign), and the exaggeration_ratio (Type M: the average factor by which significant estimates overstate the true effect), following the design analysis of Gelman and Carlin (2014). The precision lens, in the accuracy in parameter estimation (AIPE) tradition: the expected half-width and full width of the conf_level confidence interval the design will produce, the spread of that realized width, and, when a target width w is supplied, pct_ci_less_w, the probability that the realized interval is no wider than the target, the same quantities the ss_aipe_*_sensitivity() family estimates by Monte Carlo, here in closed form.

Usage

design_consequences(
  true_effect = NULL,
  se = NULL,
  sd = NULL,
  n_1 = NULL,
  n_2 = NULL,
  alpha_level = 0.05,
  df = NULL,
  conf_level = 0.95,
  w = NULL
)

Value

A data.frame (class dmar_tbl) with the significance-lens rows (power, type_s_error,

exaggeration_ratio), the precision-lens rows (expected_half_width, mean_ci_width,

median_ci_width, sd_ci_width, pct_ci_less_w,

target_width; the last two are NA when no w is supplied), and the design rows (true_effect, se,

df, alpha_level). The confidence level is recorded in the "conf_level" attribute. The schema is constant: rows that do not apply are NA, never dropped.

Arguments

true_effect: The assumed true (population) effect, on the scale of the estimate (a mean difference, a regression coefficient, a standardized mean difference). May be negative. May be NULL, in which case the significance-lens rows are returned as NA and only the precision lens (which does not involve the true effect) is informative.
se: The standard error of the estimate the design will produce, on the same scale as true_effect. Supply se directly, or supply sd with n_1 (and n_2) and let the function derive it.
sd, n_1, n_2: An alternative to se for the two most common cases: with sd and n_1 only, the one-sample (or paired-difference) design se = sd / sqrt(n_1) with df = n_1 - 1; with n_2 as well, the two-group design se = sd * sqrt(1/n_1 + 1/n_2) with df = n_1 + n_2 - 2 (sd is the common within-group standard deviation). A df supplied explicitly overrides the derived one.
alpha_level: Two-sided Type I error rate of the significance test. Defaults to 0.05.
df: Degrees of freedom of the reference t distribution. Defaults to Inf (the normal case) unless derived from n_1 / n_2.
conf_level: Confidence level of the interval evaluated by the precision lens. Defaults to 0.95.
w: Optional target full width for the confidence interval; when supplied, pct_ci_less_w reports the probability that the realized interval is no wider than w.

Author

Ken Kelley [email protected]

Details

Together they answer the two questions a chosen design should be interrogated with before data collection: if I run this study and filter it through a significance test, what will the published record look like? and how precisely will I estimate the effect regardless of significance? An underpowered design fails both: its significant estimates are exaggerated and possibly sign-reversed, and its confidence intervals are too wide to be informative.

Significance lens. Writing $\lambda = \theta / \mathrm{se}$ and $c$ for the two-sided critical value, the power and the Type S error follow from the two tails of the distribution of the test statistic: the noncentral t with noncentrality $\lambda$ when df is finite (the exact distribution of the t statistic when the standard error is estimated from the data, the same sampling model the precision lens uses), and the normal when df = Inf. The exaggeration ratio is the expected absolute estimate conditional on significance over the absolute true effect, computed exactly: from truncated normal moments when df = Inf, and otherwise by integrating those moments over the chi distribution of the estimated standard error, so no simulation error enters. Gelman and Carlin's retrodesign() instead evaluates a central t shifted by $\lambda$ (and simulates the exaggeration ratio under that model), an approximation that treats the standard error as known; the two agree as df grows and coincide at df = Inf, but at small df they differ: in the underpowered regime the design analysis is aimed at (power below about 0.7), the known-se approximation understates power and overstates the Type S and Type M errors, so their published finite-df values differ from the exact ones reported here. When true_effect = 0 the power equals alpha_level, the Type S error is 0.5, and the exaggeration ratio is undefined (NA).

Precision lens. The realized interval half-width is $t_{1-\alpha^*/2,\,\mathit{df}} \cdot \widehat{\mathrm{se}}$ with $\alpha^* = 1 - \mathtt{conf\_level}$, and $\widehat{\mathrm{se}} = \mathrm{se}\sqrt{W/\mathit{df}}$ with $W \sim \chi^2_{\mathit{df}}$, so the width's mean, median, and standard deviation have closed chi-distribution forms and $$P(\mathrm{width} \le w) \;=\; P\!\left(W \le \mathit{df}\left[\frac{w} {2\,t\,\mathrm{se}}\right]^{2}\right).$$ These are the population versions of the mean_ci_width, median_ci_width, sd_ci_width, and pct_ci_less_w terms that the ss_aipe_*_sensitivity() functions estimate by Monte Carlo. With df = Inf the standard error is treated as known, the width is deterministic, and pct_ci_less_w is a step: 1 when the fixed width is at most w and 0 otherwise.

The function complements the planners rather than replacing them: ss_power_*() chooses a sample size for detection, ss_aipe_*() chooses one for precision, and design_consequences() interrogates whatever design came out (or the design a completed study used). The two lenses are the power and accuracy in parameter estimation approaches to sample size planning reviewed by Maxwell, Kelley, and Rausch (2008).

References

Gelman, A., & Carlin, J. (2014). Beyond power calculations: Assessing Type S (sign) and Type M (magnitude) errors. Perspectives on Psychological Science, 9(6), 641--651. tools:::Rd_expr_doi("10.1177/1745691614551642") (Their accompanying retrodesign() function evaluates a location-shifted central t, the known-se approximation, and obtains the exaggeration ratio by simulation; the finite-df case here uses the exact noncentral t and exact moments instead, so the two differ at small df. See Details.)

Kelley, K., & Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8(3), 305--321. tools:::Rd_expr_doi("10.1037/1082-989X.8.3.305")

Kelley, K., Maxwell, S. E., & Rausch, J. R. (2003). Obtaining power or obtaining precision: Delineating methods of sample size planning. Evaluation and the Health Professions, 26(3), 258--287. tools:::Rd_expr_doi("10.1177/0163278703255242")

Maxwell, S. E., Delaney, H. D., & Kelley, K. (2027). Designing experiments and analyzing data: A model comparison perspective (4th ed.). Routledge.

Maxwell, S. E., Kelley, K., & Rausch, J. R. (2008). Sample size planning for statistical power and accuracy in parameter estimation. Annual Review of Psychology, 59, 537--563. tools:::Rd_expr_doi("10.1146/annurev.psych.59.103006.093735")

Examples

Run this code

# ---- Both lenses on one underpowered design --------------------------
# True effect 0.1 measured with standard error 0.3 (say, d = .1 with
# about 22 per group, 45 in total): power is 6 percent, a significant
# result has a 17 percent chance of the wrong sign and overstates the
# truth seven-fold, and the 95 percent CI is about 1.2 wide, twelve
# times the effect. Bad for detection, bad for precision.
design_consequences(true_effect = 0.1, se = 0.3)

# ---- The same effect, precisely measured -----------------------------
design_consequences(true_effect = 0.1, se = 0.03)

# ---- From a planned two-group design (sd and per-group n) ------------
# d-type effect 0.4, common sd 1, 60 per group; finite df flows through
# both lenses.
design_consequences(true_effect = 0.4, sd = 1, n_1 = 60, n_2 = 60)

# ---- Will the interval beat a target width? --------------------------
# Same design, asking for the probability the realized 95 percent CI is
# no wider than 0.7 (the closed-form pct_ci_less_w that the
# ss_aipe_smd_sensitivity() simulation estimates).
design_consequences(true_effect = 0.4, sd = 1, n_1 = 60, n_2 = 60,
                    w = 0.7)

# ---- Precision lens alone (no effect assumption needed) --------------
design_consequences(true_effect = NULL, sd = 1, n_1 = 60, n_2 = 60,
                    w = 0.7)

# ---- After an AIPE plan: check the detection side --------------------
# Plan for a full width of 0.5 on the SMD at delta = .4, then ask what
# that design does under the significance filter.
n_plan <- ss_aipe_smd(delta = 0.4, width = 0.5)$value[1]
design_consequences(true_effect = 0.4, sd = 1,
                    n_1 = n_plan, n_2 = n_plan, w = 0.5)

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