Biostatistical Research Consulting

A research collaborator to junior researchers, tenured faculty, and institutional investigators.

Table 1. Baseline Characteristics of Participants

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Characteristic	Intervention (n = 142)	Control (n = 138)
Age, mean (SD), y	58.4 (11.2)	57.9 (10.8)
Female, No. (%)	78 (54.9)	74 (53.6)
BMI, mean (SD)	29.1 (5.4)	28.7 (5.9)
Current smoker, No. (%)	31 (21.8)	29 (21.0)
Comorbidity, No. (%)
Hypertension	64 (45.1)	60 (43.5)
Diabetes	38 (26.8)	35 (25.4)

Abbreviation: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared). Synthetic data.

Figure 2. Odds of the Primary Outcome, by Subgroup

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Error bars indicate 95% CIs. Synthetic data.

Figure 3. Statistical Power by Sample Size per Group

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Two-sided α = .05; standardized effect size d. Synthetic scenario.

Figure 4. Kaplan-Meier Estimates of Event-Free Survival

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Tick marks indicate censored observations. Synthetic data.

Methods (excerpt)

Illustrative example

Statistical Analysis

Baseline characteristics were summarized as mean (SD) for continuous variables and No. (%) for categorical variables.

The primary outcome was analyzed with multivariable logistic regression adjusted for age, sex, and baseline comorbidity; results are reported as odds ratios with 95% CIs. Time to first event was estimated with the Kaplan-Meier method and compared with Cox proportional hazards models, with the proportional hazards assumption assessed by Schoenfeld residuals.

A sample of 280 participants provided 80% power to detect a standardized difference of 0.34 at a 2-sided α of .05. Missing covariate data were handled by multiple imputation.

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Sample size, power, and the statistics your IRB protocol and grant need.

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Senior statisticians design and run every analysis.

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Every number we report traces back to the data and the code that produced it.

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