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| a | b/Analysis top20 vs bottom20.ipynb | ||
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| 1 | { |
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| 2 | "cells": [ |
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| 3 | { |
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| 4 | "cell_type": "code", |
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| 5 | "execution_count": null, |
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| 6 | "metadata": { |
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| 7 | "id": "8KSBPEA9iZc1" |
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| 8 | }, |
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| 9 | "outputs": [], |
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| 10 | "source": [ |
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| 11 | "\"\"\"\n", |
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| 12 | "Initialize environment and import necessary libraries for the detailed comparison\n", |
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| 13 | "of top 20% vs bottom 20% diverse head and neck cancer clinical trials.\n", |
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| 14 | "\"\"\"\n", |
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| 15 | "\n", |
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| 16 | "import numpy as np\n", |
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| 17 | "import pandas as pd\n", |
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| 18 | "\n", |
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| 19 | "import plotly\n", |
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| 20 | "import plotly.express as px" |
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| 21 | ] |
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| 22 | }, |
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| 23 | { |
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| 24 | "cell_type": "code", |
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| 25 | "execution_count": null, |
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| 26 | "metadata": { |
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| 27 | "id": "U1XH-hqSieep" |
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| 28 | }, |
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| 29 | "outputs": [], |
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| 30 | "source": [ |
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| 31 | "# Load preprocessed datasets of top 20% and bottom 20% diverse studies\n", |
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| 32 | "df_top = pd.read_csv(\"t20.csv\")\n", |
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| 33 | "df_bottom = pd.read_csv(\"b20.csv\")" |
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| 34 | ] |
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| 35 | }, |
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| 36 | { |
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| 37 | "cell_type": "code", |
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| 38 | "execution_count": null, |
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| 39 | "metadata": { |
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| 40 | "colab": { |
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| 41 | "base_uri": "https://localhost:8080/" |
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| 42 | }, |
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| 43 | "executionInfo": { |
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| 44 | "elapsed": 183, |
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| 45 | "status": "ok", |
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| 46 | "timestamp": 1711119148341, |
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| 47 | "user": { |
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| 48 | "displayName": "Ojasvi Vachharajani", |
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| 49 | "userId": "08925121883437033531" |
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| 50 | }, |
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| 51 | "user_tz": 420 |
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| 52 | }, |
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| 53 | "id": "i2oWqy-Fiehy", |
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| 54 | "outputId": "599a5439-bf8a-437b-b404-10b0393e84ee" |
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| 55 | }, |
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| 56 | "outputs": [], |
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| 57 | "source": [ |
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| 58 | "set(df_top.columns) == set(df_bottom.columns)" |
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| 59 | ] |
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| 60 | }, |
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| 61 | { |
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| 62 | "cell_type": "code", |
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| 63 | "execution_count": null, |
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| 64 | "metadata": { |
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| 65 | "id": "sMugRtp8pli-" |
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| 66 | }, |
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| 67 | "outputs": [], |
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| 68 | "source": [ |
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| 69 | "df_top[\"success_category\"] = \"top\"\n", |
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| 70 | "df_bottom[\"success_category\"] = \"bottom\"\n", |
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| 71 | "\n", |
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| 72 | "df_all = pd.concat([df_top, df_bottom])" |
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| 73 | ] |
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| 74 | }, |
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| 75 | { |
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| 76 | "cell_type": "code", |
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| 77 | "execution_count": null, |
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| 78 | "metadata": { |
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| 79 | "colab": { |
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| 80 | "base_uri": "https://localhost:8080/" |
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| 81 | }, |
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| 82 | "executionInfo": { |
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| 83 | "elapsed": 4, |
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| 84 | "status": "ok", |
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| 85 | "timestamp": 1711119288331, |
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| 86 | "user": { |
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| 87 | "displayName": "Ojasvi Vachharajani", |
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| 88 | "userId": "08925121883437033531" |
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| 89 | }, |
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| 90 | "user_tz": 420 |
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| 91 | }, |
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| 92 | "id": "he-X6Ez4qCzq", |
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| 93 | "outputId": "b7f15feb-b680-4ca4-d754-2fd769fc0629" |
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| 94 | }, |
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| 95 | "outputs": [], |
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| 96 | "source": [ |
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| 97 | "df_all.columns" |
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| 98 | ] |
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| 99 | }, |
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| 100 | { |
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| 101 | "cell_type": "code", |
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| 102 | "execution_count": null, |
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| 103 | "metadata": { |
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| 104 | "colab": { |
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| 105 | "base_uri": "https://localhost:8080/", |
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| 106 | "height": 542 |
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| 107 | }, |
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| 108 | "executionInfo": { |
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| 109 | "elapsed": 2, |
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| 110 | "status": "ok", |
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| 111 | "timestamp": 1711119330500, |
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| 112 | "user": { |
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| 113 | "displayName": "Ojasvi Vachharajani", |
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| 114 | "userId": "08925121883437033531" |
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| 115 | }, |
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| 116 | "user_tz": 420 |
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| 117 | }, |
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| 118 | "id": "w4ohZwM8t3kU", |
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| 119 | "outputId": "033642de-9d3c-417b-f713-4ac567a3833a" |
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| 120 | }, |
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| 121 | "outputs": [], |
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| 122 | "source": [ |
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| 123 | "px.box(df_all, x=\"success_category\", y=\"num_participants\").update_layout(width=700)" |
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| 124 | ] |
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| 125 | }, |
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| 126 | { |
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| 127 | "cell_type": "code", |
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| 128 | "execution_count": null, |
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| 129 | "metadata": { |
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| 130 | "colab": { |
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| 131 | "base_uri": "https://localhost:8080/", |
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| 132 | "height": 542 |
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| 133 | }, |
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| 134 | "executionInfo": { |
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| 135 | "elapsed": 221, |
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| 136 | "status": "ok", |
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| 137 | "timestamp": 1711119560043, |
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| 138 | "user": { |
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| 139 | "displayName": "Ojasvi Vachharajani", |
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| 140 | "userId": "08925121883437033531" |
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| 141 | }, |
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| 142 | "user_tz": 420 |
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| 143 | }, |
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| 144 | "id": "fvVRp9sxt3nd", |
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| 145 | "outputId": "4b43d996-9082-4da0-e42b-d37264c8f0ef" |
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| 146 | }, |
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| 147 | "outputs": [], |
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| 148 | "source": [ |
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| 149 | "df_num_participants = pd.concat(\n", |
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| 150 | " [\n", |
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| 151 | " df_all[[\"success_category\", \"num_male_participants\"]].assign(sex=\"male\").rename(columns={\"num_male_participants\": \"num_participants\"}),\n", |
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| 152 | " df_all[[\"success_category\", \"num_female_participants\"]].assign(sex=\"female\").rename(columns={\"num_female_participants\": \"num_participants\"}),\n", |
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| 153 | " ]\n", |
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| 154 | ")\n", |
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| 155 | "\n", |
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| 156 | "px.box(df_num_participants, x=\"success_category\", y=\"num_participants\", color=\"sex\").update_layout(width=700)" |
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| 157 | ] |
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| 158 | } |
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| 159 | ], |
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| 160 | "metadata": { |
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| 161 | "colab": { |
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| 162 | "authorship_tag": "ABX9TyMDSF+xer7CPEwEYBcCijkb", |
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| 163 | "provenance": [] |
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| 164 | }, |
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| 165 | "kernelspec": { |
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| 166 | "display_name": "Python 3", |
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| 167 | "name": "python3" |
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| 168 | }, |
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| 169 | "language_info": { |
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| 170 | "name": "python" |
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| 171 | } |
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| 172 | }, |
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| 173 | "nbformat": 4, |
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| 174 | "nbformat_minor": 0 |
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| 175 | } |